FOUNDATIONS OF PRICING AND INVESTMENT IN ELECTRICITY TRANSMISSION

Transcription

1 FOUNDATIONS OF PRICING AND INVESTMENT IN ELECTRICITY TRANSMISSION A thess submtted to the Unversty of Manchester Insttute of Scence and Technology for the degree of Master of Phlosophy Juan C. Araneda Manchester Centre for Electrcal Energy Department of Electrcal Engneerng and Electroncs March 2002

2 Appendx C Smulatons on the IEEE 24-Bus Network Declaraton No porton of the work referred to n ths thess has been submtted n support of an applcaton for another degree or qualfcaton of ths or any other unversty, or other nsttuton of learnng. The deas and work developed by the author represent hs own thnkng and not necessary represent the poston of the company he works for.

3 Appendx C Smulatons on the IEEE 24-Bus Network Acknowledgements I wsh to thank frst and foremost to Mr. Gullermo Espnosa, Dens Pelleter and José Antono Valdés from HQI Transelec Chle S.A. for ther support to ths research and sponsorshp. I also want to say thanks to Mr. Claude Tardf from Hydro-Québec. I wsh to thank my supervsor Professor Goran Strbac for hs gudance, valuable dscussons and frendshp throughout ths research. I wsh to say thanks to Dr. Joseph Mutale and Stuart Neld whose prevous works on transmsson optmal nvestments contrbuted to the fulflment of ths research. I also wsh to thank to Juan Carlos Ausn for hs valuable co-operaton durng the fnal smulatons stage. I am very grateful to my wfe Rosa for her love, partnershp and understandng durng all the tme I spent workng on ths research.

4 Appendx C Smulatons on the IEEE 24-Bus Network v The Author Juan C. Araneda receved hs Degree n Electrcal Engneerng from Federco Santa María Techncal Unversty, Valparaíso, Chle, n He was awarded wth the maxmum dstnctons ncludng the Federco Santa María Award and the Chlectra V Regón Medal as the best electrcal engneer graduated n He also awarded the frst prze n the IEEE Student Paper Contest 1983 of the IEEE Chlean Branch. He has 18 years of workng experence n the Chlean deregulated energy market coverng the generaton, transmsson and dstrbuton areas. From 1984 to 1989 he worked for Chlqunta, an electrcty dstrbuton company operatng n the Ffth Regon of Chle, where he was a commercal analyst and a plannng engneer. From 1989 to 1994 he worked for Colbún, a generaton company operatng n the Chlean deregulated energy market, where he was a plannng engneer, Head of the Operatonal Studes Department and Head of the Plannng Department. From 1994 to date he works for HQI Transelec Chle S.A., the man electrcty transmsson company n the Chlean Central Interconnected System, where he has been Head of the Plannng Department and Head of the Commercal Evaluaton Department. Currently he holds a poston as Head of Strategc Plannng n Transelec. He has also partcpated as member of thess commssons for ndustral and electrcal engneerng students at Federco Santa María Techncal Unversty, Catholc Unversty of Chle and Unversty of Santago of Chle.

5 Appendx C Smulatons on the IEEE 24-Bus Network v Abstract Transmsson prcng has become a central ssue n the dscussons regardng the redesgn of deregulated electrcty markets. In that frame, open access to the transmsson system s one of the fundamental topcs to allow competton among agents n the energy market. Although transmsson systems costs represent close to 10% of the energy market prce, they have a sgnfcant mpact on relatve compettveness among partcpants n the energy market as well as on short and long term economc effcency of the whole electrcty ndustry. Ths research analyses how to deal wth transmsson costs, coverng short and long term ssues n electrcty transmsson prcng and ther lnk wth the energy market. Transmsson short run margnal cost (SRMC) schemes are studed and partcularly, n relaton to fnancal and physcal transmsson rghts. Varants of those schemes are currently n use n the Unted States and a smlar scheme based on frm access rghts (FAR) has been proposed n the New Electrcty Tradng Arrangements (NETA) for England and Wales. Ths research concludes that transmsson rghts schemes work well as a complement of the energy market but they do not and cannot resolve the problem of cost allocaton of the exstent transmsson assets and nvestments. The reasons are smple: SRMC do not have a drect relatonshp wth transmsson nvestment costs and transmsson busness s a natural monopoly. Therefore an effcent transmsson access prcng methodology s requred to allow the recovery of transmsson nvestment costs. For that reason, transmsson prcng based on the concept of economcally adapted network (EAN) s examned and recommended. Prces derved from the EAN have the advantage to be n tune wth the maxmum revenue allowed to the owner of transmsson assets and facltate the optmal allocaton of transmsson costs among users. Fundamental features of the EAN scheme have been llustrated on a number of examples ncludng IEEE 24 bus Relablty Test System.

6 Appendx C Smulatons on the IEEE 24-Bus Network v Table of Contents Page Declaraton Acknowledgements The Author Abstract Table of Contents v v v CHAPTER 1 Man ssues n transmsson prcng Overvew Role of transmsson prcng Open access and energy market Scope and objectves of ths research Man contrbutons of ths research Thess structure 10 CHAPTER 2 Methods and experences n transmsson prcng Objectves of transmsson prcng Electrcty transmsson as a busness Short and long run costs of transmsson Methods for transmsson prcng Postage-stamp method LRMC method SRMC method Energy market desgn and transmsson prcng Energy market desgn Pool-based energy markets Blateral energy markets Energy market prcng Energy market and system operaton 28

7 Appendx C Smulatons on the IEEE 24-Bus Network v Page 2.6 Internatonal experences 29 CHAPTER 3 Theoretcal framework for analyss of transmsson Introducton Theoretcal framework Short term and energy market effcency Long term and network development Economcally adapted network (EAN) an example Energy market and transmsson prcng Energy market balance usng nodal SRMC prcng Energy market balance usng SMP Impact of transmsson n the energy market Other transmsson prcng ssues Economes of scale n transmsson Securty of servce requrements 64 CHAPTER 4 Transmsson rghts, SRMC surplus and nvestments Man concepts Applcatons of transmsson rghts n the US Transmsson rghts and NETA Frm Access Rghts ssues Short term ssues n FAR Long term ssues n FAR Transmsson prcng based on SRMC Tests on a 3-bus network Formulaton of the problem Results 80

8 Appendx C Smulatons on the IEEE 24-Bus Network v Page Tests on the IEEE 24-bus network Formulaton of the problem Results 87 CHAPTER 5 Use of the concept of economcally adapted network for transmsson prcng Man ssues Transmsson prcng based on an EAN Allocaton of transmsson costs Formulaton of the method Tests on a 3-bus network Tests on the IEEE 24-bus network Case studes on the IEEE 24-bus network Network cost recovery Robust and weak networks Impact of securty n network desgn Implementaton n a real system: England & Wales and Chle cases Implementaton n England & Wales Implementaton n Chle 111 CHAPTER 6 Concluson Man conclusons Achevements and contrbutons of ths research Recommendatons for future research 117

9 Appendx C Smulatons on the IEEE 24-Bus Network x Page References 119 Appendx A Nodal SRMC on a Transmsson Network 123 Appendx B Smulatons on a 3-Bus Network 131 Appendx C Smulatons on the IEEE 24-Bus Network 143

10 CHAPTER 1 Man ssues n transmsson prcng Summary Ths chapter descrbes the role of the electrcty transmsson network n the new deregulated schemes n practse over the world and the man challenges regardng the search for an effcent method for transmsson prcng. Open and non dscrmnatory access to the transmsson network capacty s analysed as a pllar of competton n the energy market. The objectves, scope and man contrbutons of ths research are addressed. An outlne of the thess structure s also gven. 1.1 Overvew Under the new electrcty deregulated market schemes n practse over the world, transmsson prcng has been a focus of research and dscussons over the past years. Ths has been drven manly by the mportance that open access to the transmsson system capacty has on the overall economc effcency and compettveness n the energy market. Although transmsson costs represent only lke 10% of the energy market costs and no more than 4% of the fnal customers bll, transmsson capacty constrants and transmsson lne outages can have a sgnfcant mpact on the locatonal costs of electrcty. Therefore, transmsson system capacty affects the relatve compettveness of generators and customers connected to the electrcty network. Hence the mportance to develop an effcent prcng scheme for electrcty transmsson n tune wth the energy market prcng scheme and able to provde effcent sgnals n the short and long term.

11 Appendx C Smulatons on the IEEE 24-Bus Network 2 Experences n transmsson prcng over the world are dverse regardng how to front the man ssues. The reasons for that dversty are closely related to the economc prncples and regulatory belefs that drve the desgn of the energy market and the adopton of a prcng scheme as part of the new deregulated electrcty ndustry. 1.2 Role of transmsson prcng Network prcng s one of the most crtcal ssues n assurng the successful operaton of a market based electrcty ndustry (Mutale, J., 2000). Prcng of network servces has become an mportant subject because of the role the networks play n facltatng competton n the generaton and retal segments of the ndustry. Owners of transmsson facltes must provde open and non dscrmnatory access to the avalable transport capacty of the transmsson network and cost reflexve prces should be charged to the users. The economc theory of electrcty transmsson prcng says that the frst-best prce of electrcty at each node on a network equals the margnal cost of provdng electrcty at that node (Green, R., 1998). The electrcty must be generated and delvered to that node consderng transmsson constrants and electrcal losses. If transmsson constrants are bndng, t means the power flow through a lne s at the lmt of ts secure transmsson capacty, then cheap but dstant generaton must be replaced wth more expensve local generaton n order to lmt the power flow. In the constraned area the optmal prce of electrcty rses to the margnal cost of the local generaton. Therefore a set of nodal prces arses n the short term operaton of the electrcty system and sends sgnals regardng the value of electrcty at any tme and locaton on the transmsson network. In the long term nodal prces and the prce dfferentals between nodes arse as powerful sgnals to drve nvestments to upgrade the capacty of the transmsson network. Although the basc economc prncples are well known, the desgn of an effcent prcng scheme for electrcty transmsson s not a straght forward task. Real networks characterstcs and energy market mperfectons mpede that the economc theory of

12 Appendx C Smulatons on the IEEE 24-Bus Network 3 perfect competton works well to prce the use of the network. Nevertheless the applcaton of the man prncples can help to formulate effectve schemes for transmsson prcng. Internatonal experences n electrcty transmsson regulaton show a wde varety of prcng schemes, coverng methods based on short run margnal costs (SRMC) at dfferent locatons on the network, lke transmsson rghts schemes n usage n several systems n the Unted States, long run margnal costs of transmsson (LRMC) and the determnaton of a reference network or economcally adapted network (EAN), and fnally the smple postage stamp methods. From the regulatory perspectve, transmsson prcng has a fundamental role n the desgn of a compettve energy market. The man ssues to consder n the defnton of a transmsson prcng scheme are presented below. Cost allocaton of the exstent network The exstent assets of the transmsson network are sunk costs, therefore these costs must be charged to the users of the network (generators and consumers) n a way that does not dstort the short term sgnals provded n the energy market. In that sense the short term sgnals for the compettve generaton despatch and supply must not be affected by transmsson charges. It means that transmsson prces desgned to recover the costs of the exstent network must be fxed costs that act lke postage stamp charges, for nstance. The applcaton of stamped charges does not mean that those charges must be flat and calculated n a smple dstrbutve way. The allocaton of costs of the exstent transmsson assets s a relevant topc and one of the focus of ths research. On other hand, the allocaton of costs must be performed by the regulator due to the re-dstrbutve nature of the task. Nobody n the energy market would lke to pay a transmsson charge bgger than ts compettor and therefore the payment of those charges must be a regulatory oblgaton for all partcpants n the energy market.

13 Appendx C Smulatons on the IEEE 24-Bus Network 4 Maxmum revenue allowed (prce control) The electrcty transmsson busness s a natural monopoly, then the allowed revenues for transmsson networks must be regulated by means of some knd of prce control. Thus a relevant topc s the regulatory defnton of the total revenue for every transmsson asset owner or the defnton of the maxmum revenue attrbutable to every one of the assets n the transmsson system. Another way to deal wth ths ssue s the determnaton of a reference network or economcally adapted network that allows the calculaton of optmal transmsson capactes for everyone of the elements n the network, and therefore to determne the nvestment cost of such an deal network. One mportant aspect n the regulatory defnton of the maxmum revenues allowed s the perodcty to perform such prce control, for example every four or fve years. Durng the prce control perod some mechansm to approve upgrades n the transmsson network when relevant changes n generaton or demand occur must be mplemented. Addtonally, a way to update the regulated revenues n the prce control perod s the settng of prce ndexes together wth the ntal settng of the maxmum revenues. Those prce ndexes must be cost reflectve of the man cost components affectng every specfc asset and therefore, they can be defned for dfferent knds of transmsson assets (transmsson lnes, power transformers, reactve power compensaton equpment, etc.) and also for operaton and mantenance costs. A far and long term defnton of the prce control by the regulator wll ncentve transmsson owners to perform renforcements and new nvestments n the network. Drvng nvestments A fundamental pece of regulaton s network development when t probes to be economcally convenent from the system pont of vew. In that sense, partcpants n the energy market can perform an ex-ante estmaton of the mpact that a network renforcement wll have for them f the rght prces are n place. Thereby wllngness

14 Appendx C Smulatons on the IEEE 24-Bus Network 5 to pay the nvestment cost of new transmsson assets can be dentfed by partcpants f they have the rght prcng sgnals. Transmsson nvestments are facltated when only one or few users collect the benefts of network development. On the other sde, when many users capture the benefts of addtonal transmsson capacty t s very dffcult to acheve a collectve agreement among users, and then the regulatory hand s requred. Among transmsson nvestments wth many users havng benefts are those renforcements that mprove the qualty and securty of servce of the system. Market drven nvestments can be a realty n a world where co-operaton becomes as mportant as competton. Another mportant aspect s the tmng requred to construct new transmsson facltes. Usually a long duraton perod of at least 2 or 3 years s requred to construct a new transmsson lne or substaton and then all knd of agreements about nvestments costs and allocaton among users must be sgned by the partes before the decson to start constructng s made. Short and long term effcency The nteracton between short and long run costs of the network and the energy market prcng scheme must be consdered. For nstance, transmsson losses can be consdered as part of the energy market and then to defne prces that contans a loss component or they can be ncluded as part of the access market. It means that a consstent and stable scheme of energy and access polces and prcng must be desgned for the long term. Tme of use sgnals A relevant ssue regardng the nteracton between the energy and access market prcng scheme s the consderaton of tme of use sgnals n the calculaton of transmsson prces. Alternatvely, they must be left only as short run sgnals n the energy market. Maxmum demands for transmsson do not follow the same temporal pattern of demand. Moreover, the power flow transported through a transmsson lne depends on the combnaton of generaton njectons and demand

15 Appendx C Smulatons on the IEEE 24-Bus Network 6 wthdraws at both sdes of the lne. Therefore, some knd of tme of use sgnal attrbutable to the maxmum usage of every lne n the network s a valuable pece of an economcally effcent transmsson prcng scheme. Locaton specfc sgnals Another ssue of nteracton among energy and access market prcng s the consderaton of locaton specfc sgnals n the calculaton of transmsson prces. Alternatvely, they must be left only as short run sgnals n the energy market. Locaton specfc transmsson prces take nto account the mpact of an user at dfferent locatons n the network. Ths s another valuable pece of an economcally effcent transmsson prcng scheme. 1.3 Open access and energy market Open and non dscrmnatory access to the transmsson system s one of the pllar to facltate competton n the energy market. Transmsson owners must provde open access to the transmsson network and t means open access to nject power by generators and to wthdraw power by consumers takng nto account transmsson constrants accordng to the co-ordnaton of a system operator. Prces n the energy market can be defned n two ways dependng on the consderaton or not of the transmsson network. One way s a one node prcng system where the transmsson network s gnored but some compensaton mechansms must be n practse to solve transmsson constrants through changes on the orgnal despatch. The other way s a mult-nodal prcng system where a locatonal representaton of the transmsson network s consdered that can be ether zonal or nodal. Energy market prcng schemes are analysed n secton Most of the experences n open access prcng move around two man methodologes: value-based methods or methods drven by generatons costs and cost-based methods or methods drven by transmsson nvestment costs. Those methods are descrbed n more detal n secton 2.4. Bascally value-based methods determne the value of transmsson

16 Appendx C Smulatons on the IEEE 24-Bus Network 7 as the dfference of the energy market prces between two nodes n the network. Prces n a compettve energy market must always reflect short run margnal costs (SRMC) and therefore, the value of transmsson s equal to the SRMC dfference between two nodes. However t s a well known fact that prcng the use of the network wth SRMC produces a revenue surplus that s not necessarly matched wth the transmsson nvestment costs of the network. So dependng on the network transmsson capacty, the SRMC surplus can be lower or hgher than the transmsson nvestment costs, as t s modelled and analysed n depth n Chapter 3. Thus f the energy market prces are defned on a nodal bass, a SRMC revenue surplus wll arse. The queston here s what to do wth the SRMC surplus: to pass t straght to the transmsson owner or to allocate t among the users of the network? On the other hand, f the energy market prces are defned on a one-node bass, then a well founded cost-based method must be used to prce the use of the transmsson network. The use of a value-based prcng scheme n transmsson means that a compettve access market s created to dscover the market value of transmsson. On the other hand, the use of a cost-based prcng scheme n transmsson means the defnton of a regulated framework for access prcng that must be tuned wth the scheme n use for the compettve energy market. Therefore a compatble transmsson prcng scheme must be tuned wth the energy market scheme to work together and send opportune and rght short and long term sgnals to the market agents regardng the use of the transmsson network. 1.4 Scope and objectves of ths research The scope of ths research has been focused on the analyss of the foundatons of transmsson access prcng n deregulated electrcty markets and the study of the lnk between short term effcency and long term development of the transmsson network. Dfferent realtes regardng poltcal, organsatonal, topologcal, envronmental and

17 Appendx C Smulatons on the IEEE 24-Bus Network 8 even cultural ssues have determned dfferent regulatory schemes n applcaton n dfferent countres. However a common rule s the complementary characterstc of both energy market and access prcng scheme. Understandng the foundatons regardng the lnk between short and long term ssues n electrcty transmsson provde valuable nformaton about the scope and lmtatons of dfferent prcng schemes and serve as a gude for future developments n the area and practcal mplementaton n countres where deregulaton s stll under study. The objectves of ths research can be defned as follows: To revew the man nternatonal experences n electrcty transmsson prcng and analyse ts relatonshp wth the organsaton and prcng scheme n deregulated energy markets. To look for a lnk among short term and long term settlements n transmsson prcng, to determne effcent optons to prce the use (present) and development (future) of transmsson networks n a compettve energy market. To develop tools to smulate transmsson prcng schemes, partcularly short run margnal costs (SRMC), long run margnal costs (LRMC) and optmal transmsson prcng derved on an economcally adapted network (EAN). Then smulate, compare and evaluate those transmsson prcng schemes usng the tools developed. To obtan relevant conclusons regardng the advantages and lmtatons of the man prcng schemes for transmsson prcng. 1.5 Man contrbutons of ths research Ths research contrbutes to a better understandng of the man transmsson prcng schemes, revealng ther advantages, dsadvantages and lmtatons. One mportant contrbuton of ths work s the development of three dfferent models that provde a framework to analyse and evaluate dfferent prcng schemes for transmsson and the energy market. These models are as follows:

18 Appendx C Smulatons on the IEEE 24-Bus Network 9 A two bus network wth lnear producton margnal costs and a contnuous duraton demand curve, mplemented from the analytcal formulaton. A three bus meshed network wth three demand perods and four generators, mplemented usng the Solver tool n MS Excel. A mult-node and mult-perod power system model developed n C language, mplemented from a prevous modellng development at UMIST (Neld, S., 2000). The man contrbutons of ths research can be summarsed as follows: Presentaton of a jont analyss of transmsson open access schemes and ts nteracton wth the energy market to facltate the selecton of an approprate method to prce the use of transmsson networks. Development of a unfed methodology to analyse both transmsson access and energy market prcng to facltate the analyss and tests of dfferent prcng strateges. Analyss of the lnk between short and long term ssues n electrcty transmsson, more specfcally, focus on the allocaton of costs of the exstng network and the development of nvestments to ncrease the capacty of the network. Detecton of a relevant lmtaton of short run margnal costs (SRMC) to prce the usage of the transmsson network. Partcularly n meshed transmsson networks SRMC revenues follow Krchhoff Voltage Law (KVL) but nvestments do not. Therefore there s not a perfect match between transmsson SRMC revenues and nvestments n the optmal network, on a lne per lne bass. Of course, the same lmtaton s applcable to LRMC n the long term. Desgn and mplementaton of C-wrtten routnes to calculate SRMC and LRMC n a mult-node and mult-perod computer programme that determnes the economcally adapted network of a power system.

19 Appendx C Smulatons on the IEEE 24-Bus Network Thess structure Ths thess s consttuted by sx chapters and three appendxes whose contents are summarsed below. Chapter 1: Man ssues n transmsson prcng Ths chapter presents an overvew of the role of electrcty transmsson prcng n the new deregulated schemes n practse over the world and the man challenges regardng the search for an effcent method for transmsson prcng. The objectves, scope and man contrbutons of ths research are addressed. Chapter 2: Methods and experences n transmsson prcng Ths chapter descrbes the man objectves of a transmsson prcng scheme and the man methodologes n applcaton n deregulated energy markets. The relatonshp among energy market organsaton, ts prcng schemes, and transmsson prcng are analysed n depth. Dfferent transmsson prcng experences on deregulated energy markets around the world are addressed and analysed. Chapter 3: Theoretcal framework for analyss of transmsson The relatonshp among short term operaton and long term development of the transmsson network s analysed n ths chapter. The man ssues n electrcty transmsson prcng are derved through a two bus example. Energy prcng methods are smulated together wth transmsson prcng to check how revenues and costs are allocated among partcpants n the energy market. Chapter 4: Transmsson rghts, SRMC surplus and nvestments Transmsson rghts experences are dscussed and ther applcaton n England and Wales as frm access rghts s revewed n detal. A prcng method based on the SRMC surplus s tested on a three bus network and on the IEEE 24 bus Relablty Test System.

20 Appendx C Smulatons on the IEEE 24-Bus Network 11 Chapter 5: Use of the concept of economcally adapted network for transmsson prcng A prcng method that derves transmsson charges from the economcally adapted network (EAN) s desgned and tested n ths chapter. Tests are performed on a three bus network and on the IEEE 24 bus Relablty Test System. Chapter 6: Concluson Ths chapter summarses the man conclusons, achevements and contrbutons derved from ths research, and recommends areas for future research. Appendx A: Nodal SRMC on a Transmsson Network The calculaton of nodal short run margnal costs (SRMC) s derved n ths Appendx ncludng two calculaton methods: usng generalsed generaton dstrbuton factors (GGDF) and usng a securty constraned optmal power flow (SCOPF) representaton. Appendx B: Smulatons on a 3-Bus Network Ths Appendx shows the results of the man transmsson prcng methods on a 3-bus network. Appendx C: Smulatons on the IEEE 24-Bus Network Ths Appendx shows the results of the man transmsson prcng methods on the IEEE 24-bus Relablty Test System.

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22 CHAPTER 2 Methods and experences n transmsson prcng Summary Ths chapter descrbes the objectves of an electrcty transmsson prcng scheme and the man methodologes n use on deregulated energy markets. The relatonshp among the organsaton of the energy market and ts prcng scheme, and transmsson prcng are analysed n depth. An overvew of some relevant nternatonal experences n transmsson prcng are ncluded. 2.1 Objectves of transmsson prcng There have been many dscussons on how to address access prcng and what knd of scheme fts better wth a compettve energy market from both short and long term perspectves (Green, R., 1997). Accordng to those dscussons the objectves of an effcent prcng scheme for electrcty transmsson can be summarsed as follows: To provde short term sgnals regardng the transport costs mposed by partcpants n the energy market. To send locaton sgnals for nvestments n generaton and demand. To sgnal the need for nvestments n the transmsson network. To allow the recovery of the effcent costs of the exstent transmsson assets and the nvestment cost of new transmsson assets. To be smple and transparent n determnng the transmsson prces.

23 Appendx C Smulatons on the IEEE 24-Bus Network 14 From the regulatory perspectve those ssues must be covered by a methodology that allows the use of the transmsson capacty n an open and non dscrmnatory way and avodng any knd of market power by partcpants that dstort the goals of a compettve market. Among short term regulatory objectves one relevant s the way the prcng method s gong to deal wth transmsson losses. Sometmes losses are consdered as part of the energy market ssues but ther short term mpact s closely related wth the transportaton of electrcty usng the transmsson network. From that perspectve losses are better dealt as part of the access market. Among long term regulatory objectves one mportant ssue s the way a decson makng process for transmsson nvestments wll operate. Transmsson nvestments can be market drven or centrally co-ordnated by the regulator (Hogan, W., 1999). It s perfectly possble to rely more on market forces, partly f not completely, to drve transmsson expanson of the network. However, there are transmsson nvestments lke those destned to mprove securty of servce to a large number of consumers that are very dffcult to mplement wthout regulatory support. 2.2 Electrcty transmsson as a busness Electrcty transmsson s a new busness as a result of the electrcty deregulaton process that started n the 1980 decade. From then on several new transmsson companes have been created around the world to focus on the bulk transmsson of electrcty and, n same cases, those companes operate the power system too. In other cases there s an ndependent system operator n charge of the co-ordnaton of generaton despatch and network operaton. Among the man electrcty transmsson companes operatng n deregulated markets are Red Eléctrca de España (Span, 1985), Natonal Grd Company (England and Wales, 1989), Statnett (Norway, 1990), Transener (Argentna, 1992), Transelec (Chle, 1993), Transpower (New Zealand, 1993), ISA (Colomba, 1994) and Etecen and Etesur (Perú, 1995).

24 Appendx C Smulatons on the IEEE 24-Bus Network 15 The man functons of electrcty transmsson are: To lnk generators and consumers Transmsson networks provde electrcty transportaton from generators to consumers both located at dfferent geographcal locatons on the network. Generaton facltes are located close to the prmary sources of energy, for nstance hydroelectrc power plants are located besdes rvers wth apprecable nflows and heght dfferentals, coal-fred thermal plants are located close to coal mnes or harbours wth facltes to dsembark the coal and sea water for coolng, and combned-cycle gas turbnes are located close to gas ppelnes cty-gates. Consumers are geographcally dspersed dependng on the economc actvty they perform, for nstance resdental and commercal customers are located n ctes and towns, and ndustral consumers are located n places where they optmse transportaton costs of the dfferent producton factors. To provde economes of scope The nterconnecton of generatng power plants of dfferent characterstcs (fuel type and margnal cost, capacty, techncal lmts, etc.) va the transmsson network allows the mnmsaton of overall producton costs, co-ordnaton of mantenance schedules and sharng operatonal reserves of capacty, followng the demand curve pattern. Ancllary servces can be provded by power unts located far from the load centres and a market for such servces s feasble to develop thanks to the transmsson network. To provde securty of supply The nterconnecton of several generators through the transmsson network provdes securty of supply to consumers. Generatng unts and transmsson facltes (lnes, transformers, breakers, reactve compensaton equpment, etc.) do not have a 100% avalablty. Generatng unts have forced outages due to falures or problems n the producton process that mean the mmedate dsconnecton of the unt from the

25 Appendx C Smulatons on the IEEE 24-Bus Network 16 network to avod major damages on t. Transmsson facltes are subject to forced outages that mean the mmedate openng of the lne or the equpment that faled. The nterconnecton of generatng unts through transmsson facltes mnmses the mpact of forced outages on consumers, ncreasng the avalablty of the power system. Determnstc securty crtera such as N 1 have been settled n power systems for the provson of securty of supply to consumers. To make possble the tradng of electrcty Today a compettve tradng of electrcty n the energy market s a realty thanks to the exstence of transmsson networks. Interconnectng electrcty producers and consumers mean the perfect way to meet offer and demand to dscover prces n a compettve energy market. In that sense electrcty can be seen lke a commodty of partcular characterstcs. Electrcty cannot be stored and must be consumed at the same tme t s produced, and also, ts way from generators to consumers s not a smple straght path because of the physcal nteractons n the network (Krchhoff laws). Nevertheless the basc mcroeconomc prncples of compettve markets can be appled to create competton n the generaton and retal areas. Therefore, generators and consumers capture the benefts provded by transmsson networks and hence they have to pay for the use of the network to the transmsson assets owners. The man characterstcs of the transmsson busness from the owners pont of vew are: It s captal ntensve Transmsson nvestments are captal ntensve and non contnuous n tme. The constructon of new transmsson lnes, substatons or the addton of new power transformers are not a daly task. They are the result of a relevant growth n demand or the connecton of new power plants. If no one of those facts happen, transmsson nvestments could requre years to occur dependng on the yearly rate of demand growth. Transmsson assets are technologcally complex, hghly dedcated and

26 Appendx C Smulatons on the IEEE 24-Bus Network 17 some of them are rreversble (transmsson lnes and substatons). Therefore only electrcty companes wth bg fnancal shoulders can partcpate n ths busness. It has long lfe assets Most transmsson assets have a long lfe expectancy. Transmsson lnes and substatons typcally have an economc lfe of 30 years or more. On the other sde, capactor banks and some hgh tech assets lke protectve, control and telecommuncaton equpment have a shorter lfe rangng between 5 and 10 years because of technologcal changes. It has lumpness of nvestments Transmsson capacty of lnes (due to standard szes of wres and mnmum wre sectons by voltage lmtatons) and transformaton equpment have standard szes, then t s not possble to dmenson a transmsson asset to match exactly to transmsson demand requrements. It means some natural over-capacty of transmsson assets as a result of the transmsson network plannng and constructon process. Investments requre long tmes of constructon Envronmental and rghts of way permsson add an mportant extra-tme to the schedule for constructng new transmsson assets that mply long tmes of constructon, even longer than tmes nvolved n the constructon of new generaton facltes lke a combned-cycle gas turbne (estmated n two and half years). Constructon schedules are usually longer and more dffcult when the constructon of new transmsson assets nterfere wth the operaton of the exstent network, or n case of upgradng of the exstent transmsson capacty, and some facltes must be dsconnected to make possble the works. It has economes of scale Transmsson networks have mportant economes of scale, meanng that the costs per MW transported are lower as hgher are the MW transported. It mples that the

27 Appendx C Smulatons on the IEEE 24-Bus Network 18 margnal cost of expanson of the transmsson network s decreasng whle hgher s the network capacty. Ths ssue s especally relevant n power systems wth hgh demand growth rates (5 to 8% per year) because of the economes acheved when the transmsson network s dmensoned on a long term bass, for nstance coverng a ten years perod. It has natural monopoly characterstcs The presence of dedcated assets, rreversble nvestments and economes of scale means a perfect ste for a monopolstc behavour. The reason a monopoly exsts s that other frms fnd t unproftable or mpossble to enter the market (Ncholson, W., 1998). Barrers to entry are therefore the source of all monopoly power. The natural monopoly characterstc of the transmsson busness means that t must be regulated to mtgate any knd of market power comng from transmsson asset owners. Hence wres busness lke electrcty transmsson and dstrbuton are regulated and therefore, regulators have to address an economcally effcent prcng method to determne prces for those busness. Regulaton must prevent network companes from overchargng users of the network and must montor the qualty of servce provded. Thus, the regulator acts on behalf of network users to ensure open and nondscrmnatory access to the transmsson network as well as to promote the development of a compettve energy market. 2.3 Short and long run costs of transmsson The total cost functon (TTC) for a certan asset of the transmsson network, lke a transmsson lne or a substaton, can be wrtten on a yearly bass as follows: TTC( P) P b 2 = c a P d (2-1) where c represents the fxed annual admnstraton, operaton and mantenance costs of the transmsson asset.

28 Appendx C Smulatons on the IEEE 24-Bus Network 19 a P b represents the annuty of the asset nvestment cost modelled as a non-lnear functon of the power flow P, where the exponent 0<b<1 ndcates the presence of economes of scale n transmsson nvestments. d P 2 represents the annual cost of the transmsson losses. The average cost of transmsson (TAC) can be determned from equaton (2-1): TTC c b 1 TAC( P) = = a P d P (2-2) P P The margnal cost of transmsson (TMC) can also be determned from equaton (2-1): TTC TMC( P) = P = a b P b 1 2 d P (2-3) Fgure 2-1 shows graphcally the curves of the total cost (TTC), average cost (TAC) and margnal cost (TMC). By defnton TTC(P)= TAC(P) P and on the other sde TMC(P) P s always lower than TTC(P) when P<P 2. It means that the use of the margnal cost of transmsson, as seen from the purely cost pont of vew, as source of network revenues cannot cover the total costs of transmsson and some addtonal charge must be added to cover the total costs. As we wll see further ths stuaton changes when the margnal revenues are calculated based on the value of transmsson for the dfferent agents n the energy market. Fgure 2-1 Transmsson total, average and margnal costs

29 Appendx C Smulatons on the IEEE 24-Bus Network 20 It s customary n economcs to make a dstncton between the short run and the long run. Although no very precse temporal defnton can be provded for those terms, the general purpose of the dstncton s to dfferentate between a short perod durng whch economc agents have only lmted flexblty n ther actons and a longer perod that provdes greater freedom (Ncholson, W., 1998). Partcularly n the short run the capacty s consdered fxed. Therefore, short run average and margnal costs of transmsson can be defned based on equatons (2-1) and (2-2), consderng a fxed transmsson capacty P max. Thus n the short run, the margnal cost of transmsson s equal to the margnal cost of losses. In the long run capacty can be consdered a varable and the long run average and margnal costs of transmsson can be defned based on equatons (2-1) and (2-2), consderng a varable transmsson capacty P. Techncally, the long run total cost curve are sad to be an envelope of ther respectve short run curves, as shown n Fgure Methods for transmsson prcng A revew of the man methods to prce transmsson network servces around the world reveals that they can be classfed n two categores: cost-based methods or methods drven by transmsson nvestment costs and value-based methods or methods drven by generatons costs. Among cost-based methods we can fnd the followng methods: Contract-path MW-mle Postage-stamp Investment cost related network prcng (ICRP) Area of Influence Tracng methods

30 Appendx C Smulatons on the IEEE 24-Bus Network 21 Among value-based methods we fnd the well known short-run margnal cost (SRMC) method and the theoretcal long-run margnal cost (LRMC) method. Contract-path and MW-mle methods were developed at the end of the 80 s and used extensvely manly n the US for calculaton of wheelng charges. They have been wdely descrbed n lterature (Green, R., 1997). ICRP method was developed by the Natonal Grd Company (NGC) and t s currently used for calculaton of the Transmsson Network Use of System Charges (TNUoS) n England and Wales. The method s based on a transportaton model to determne the optmal capacty of the network (Mutale, J., 2000). The Area of Influence method was developed n Chle at the begnnng of the 90 s and t s currently n use n Chle and Bolva. It requres the calculaton of a pro-rata to allocate the cost of the transmsson assets ncluded n the area of nfluence among the users that share the same common area (Rudnck, H. et al 1999). Tracng methods to allocate transmsson system costs over generators and demand have been extensvely studed from the academc pont of vew but they are not n practcal use (Krschen, D. et al 1997, Strbac, G. et al 1998, Balek, J., 1998). Nevertheless, an optonal tracng method usng generalsed generaton dstrbuton factors (GGDF) has been used n Chle to calculate the pro-rata among users that share the same common Area of Influence (Rudnck, H., et al 1999). The most wdely used methods for transmsson prcng n deregulated markets are the postage stamp and the SRMC methods. Addtonally some prcng methods can be derved departng from the LRMC method. Therefore they are revewed n more detal below.

31 Appendx C Smulatons on the IEEE 24-Bus Network Postage-stamp methods Ths method bascally allocate the total transmsson network cost among users based on the peak demand (MW) or the yearly energy consumpton (MWh). Transmsson network costs can be postage stamped to generaton or demand or both. Postage stamp methods can be locatonal or not locatonal. Typcally the sub-transmsson and dstrbuton prcng methods are a locatonal postage stamp prcng, where network costs are allocated to every locatonal demand user dependng on the transmsson facltes that are used to supply electrcty towards a specfc geographc area. On the trunk transmsson system the allocaton of costs s typcally postage stamped n meshed networks where t s very dffcult to forecast the behavour of transmsson flows LRMC method Transmsson long-run margnal cost (LRMC) s the nvestment and operaton cost of transportng one addtonal MW across the network when transmsson capacty can be altered. Transmsson costs are usually determned usng a reference network or economcally adapted network (EAN). The determnaton of the EAN on a power system requres a complete set of data regardng producton costs of generaton and nvestment costs of transmsson, plus long term assessments about future generaton costs, locaton of new plants, demand forecastng and ts geographcal dstrbuton. Therefore the use of LRMC can be performed n systems where the regulatory authorty carres out a close followng up of the energy market behavour. Addtonally the regulator needs some consultaton mechansms to obtan the co-operaton from the agents n the energy market regardng the defnton of future scenaros and realstc nvestment optons SRMC method Transmsson short-run margnal cost (SRMC) s the generaton cost of transportng one addtonal MW across the network when transmsson capacty s fxed. The SRMC

32 Appendx C Smulatons on the IEEE 24-Bus Network 23 methods are based on locaton specfc generaton costs and therefore transmsson nvestment costs are not consdered. The SRMC methods are also referred as locatonal margnal prcng (LMP) or spot prcng. The reason derves from the fact that n deregulated energy markets the agents bd for prces that not necessarly correspond to generaton producton costs. However f the energy market behaves n a compettve way fnally the prces wll correspond to SRMC. Typcal approaches to determne LMP n real networks nclude: Use of centrally admnstered securty constraned optmal power flows (SCOPF) algorthms to derve LMP from bds n the energy market. Let the market to dscover the locatonal value of electrcty va auctons where transmsson access rghts are sold. One of the best known transmsson SRMC-based method s transmsson rghts whch have been developed as Fxed Transmsson Rghts (FTR) or Transmsson Congeston Contracts (TCC) n the US. In England and Wales, Frm Access Rghts (FAR) are currently under development as part of the New Electrcty Tradng Arrangements (NETA). These methods are descrbed n more detal n secton 3.4. Another transmsson SRMC-based method to work as an opton to transmsson rghts are the flowgate rghts, recently developed to deal wth the externaltes due to loop flows n a network (Chao, H.P. et al 2000). 2.5 Energy market desgn and transmsson prcng Competton among supplers of any commodty requres easy access to customers. In case of electrcty competton t requres that access to the transmsson system by generators and consumers be managed n a non-dscrmnatory and equtable manner (Sngh, H. et al 1998). Ths concept s well known as transmsson open access. However, two basc characterstcs of transmsson networks must be properly handed to acheve an effectve transmsson open access: transmsson congeston and losses.

33 Appendx C Smulatons on the IEEE 24-Bus Network 24 Congeston s a consequence of network constrants charactersng a fnte network capacty that lmts the smultaneous delvery of power from an assocated set of power transactons. Losses n transmsson networks corresponds to Ohmc and Corona losses that produce a dfference between the total supply and demand for power n the system. Both transmsson congeston and transmsson losses can result n an overall ncrease n the total power cost delvery. These ncrease n cost can be much greater n case of congeston than n case of losses. Relable operaton s a central requrement and constrant for any electrcty system. Gven the strong and complex nteractons n electrc networks, current technology wth a free-flowng transmsson network dctates the need for a system operator that coordnates use of the transmsson system (Hogan, W., 1998). Control of transmsson usage means control of despatch, whch s the prncpal or only means of adjustng the use of the network. Hence, open access to the transmsson network means open access to the despatch as well. Ths s the essental co-ordnaton functon provded by the system operator. In the analyss of electrcty markets, therefore, a key focus s the desgn of the nteracton between transmsson and despatch, both procedures and prcng, to support a compettve energy market Energy market desgn There are two approaches to deal wth energy market costs and constrants (.e. transmsson congeston costs). The frst approach s based on a nodal prcng framework and forms the bass of the pool model. The second approach s based on free market competton and t s called blateral model Pool-based energy markets The pool model s motvated by the need to accommodate the specal characterstcs of electrc power transmsson networks wthn the electrcty tradng process (Sngh, H. et al 1998). The locatonal aspects of the pool model are based on the theory of nodal spot

34 Appendx C Smulatons on the IEEE 24-Bus Network 25 prcng (Schweppe, F. et al 1988). Ths model reles on the actons of a central pool operator for recevng prce and quantty offers from generators, selectng the most effcent sources of supply to satsfy prevalng constrants and makng fnancal transactons that nvolve payments from consumers and payments to supplers. The prces that govern these payments are based on the bds submtted by despatched generators and an adjustment made by the pool operator to reflect the locatonal value of supplers n terms of ther contrbuton to system losses and constrants. In general, these adjusted prces called nodal spot prces or locatonal margnal prces, are hgher at consumers locatons than at generaton sources locatons. These locatonal prce dfferentals result n a net ncome or surplus for the pool operator. In some mplementatons of ths model, the surplus s used to pay-off holders of fnancal nstruments called frm transmsson rghts (FTR) or transmsson congeston contracts (TCC), already descrbed n secton In other mplementatons, the surplus s used to reduce the access charges used to recover the fxed costs of the transmsson network (.e. Chle). Another essental feature of the pool model s that all transactons made by partcpants n the energy market must be wth the pool operator and not blaterally arranged among partcpants Blateral energy markets The blateral model s motvated by the concept that free market competton s the best way to acheve competton n an electrcty market. Ths model has also been charactersed as one of that best acheves the goal of provdng customers drect access to a suppler of choce (Sngh, H. et al 1998). In ths model supplers and customers ndependently arrange power transactons wth each other accordng to ther own fnancal terms. Economc effcency s promoted by customers choosng the least expensve generaton optons. Ths model mght be an obvous choce f a commodty other than electrcty were beng traded. The specal characterstcs of electrc power networks ntroduce two problems that must be addressed n ths model. The frst problem relates to the presence of transmsson constrants whch requres that there exst some form of co-ordnaton to mantan system securty and make the most

35 Appendx C Smulatons on the IEEE 24-Bus Network 26 effcent use of the constraned transmsson system s capacty. The second problem relates to the treatment of transmsson system losses. In addton, other ancllary servces must be provded to secure the transfer of power from supplers to consumers wth the securty and qualty standards requred Energy market prcng The man aspect to consder when a scheme of energy market prces are defned s the ncluson or not of the mpact of the transmsson network characterstcs and ther constrants over the energy prces at every locaton n the system or, so called, locatonspecfc energy prces. Typcal optons to defne electrcty prces n deregulated energy markets are presented below. One node prcng It conssts n the calculaton of a unque energy prce or system margnal prce (SMP) for the whole system at every tme perod (.e. t was the prcng system used n England and Wales before NETA). The calculatons do not take nto account the transmsson network topology and constrants, thus a one-node power system s consdered to match the total supply and demand on every tme perod (.e. half hour). System operators have to manage transmsson congeston mechansms to deal wth transmsson constrants durng the day-ahead bddng process and also n real tme to determne the changes on the despatch. Zonal prcng A way to ncorporate a basc representaton of the transmsson network conssts n the defnton of zones that cover sets of nodes where congeston s nfrequent and possbly dffcult to predct, and then every zone can be prced nternally on an SMP bass (.e. Calforna, Norway). Congeston between zones s defned to be frequent wth large mpacts. Congeston management and prcng schemes between zones (nter-zonal) and wthn a zone (ntra-zonal) are requred n ths case.

36 Appendx C Smulatons on the IEEE 24-Bus Network 27 Nodal prcng Representng the whole topology and constrants of the transmsson network and calculatng nodal prces that result from the despatch are major tasks, usually afforded by pool system operators (.e. PJM, Chle). Nodal prces defne the true and full opportunty cost of electrcty n the short run (Hogan, W., 1998). At every node each generator and each consumer sees a sngle prce for the perod (.e. half hour), and prces vary over the perod to reflect changes on the supply and demand condtons. All the complextes of the transmsson network are ncluded n the economc despatch and calculaton of the locatonal SRMC prces. A whole vew of prcng optons n the energy market, ts organsaton, and the most sutable opton for transmsson prcng s presented n Fgure 2-2. Some remarkable nternatonal experences are ncluded there as a reference. Energy ONE NODE ZONAL NODAL Market Energy Transmsson Energy Transmsson Energy Transmsson POOL SMP LRMC Zonal LMP Fnancal FTR Nodal LMP Fnancal FTR Cong. Mgt. Post-stamp Post-stamp England &Wales (old) Norway PJM, N.York, N.England Colomba and N.Zealand Chle, Perú, Bolva (SRMC Tolls) BILATERAL SMP for LRMC Zonal LMP for Physcal FTR Nodal LMP for Physcal FTR unbalances unbalances Post-stamp unbalances Post-stamp Cong. Mgt. Cong. Mgt. Span Calforna Nobody's land England & Wales (NETA) Fgure 2-2 Energy market organsaton and prcng optons In summary, from a regulatory pont of vew a choce must be made among an energy market desgn structured as a pool model or blateral model, and the knd of prcng scheme, ether one node, zonal or nodal. To complete the pcture, a consstent

37 Appendx C Smulatons on the IEEE 24-Bus Network 28 transmsson prcng scheme must be added to cover the transmsson nvestment costs that were not covered by the energy market prcng scheme Energy market and system operaton System operaton can be performed by an ndependent system operator (ISO) or by a transmsson company that owns the network assets and also operates the power system (transmsson owner and system operator, also known as TO/SO). The ISO are commonly found n the US (.e. PJM Interconnecton, New York Power Pool) and n some South Amercan deregulated systems (CDEC n Chle, CAMMESA n Argentna, COES n Perú). Transmsson companes actng as TO/SO are found n Europe and Australasa (NGC n England and Wales, REE n Span and Transpower n New Zealand). Sometmes the energy market operaton s performed by another knd of ndependent nsttuton too (.e. Power Exchange n Calforna, Market Operator n Span). Another new nsttuton created to deal wth system operaton and co-ordnaton of transmsson actvtes among transmsson owners are the Regonal Transmsson Organzatons (RTO), defned by the recent Federal Energy Regulatory Commsson (FERC) Order No. 2000, n the US. A whole vew of the alternatves for system operaton, lnked to the energy market organsaton and ts prcng scheme, s presented n Fgure 2-3. Some remarkable nternatonal experences are ncluded n that fgure.

38 Appendx C Smulatons on the IEEE 24-Bus Network 29 ONE NODE ZONAL NODAL Energy Market ISO TO/SO ISO TO/SO ISO TO/SO POOL England & Wales Norway Chle New Zealand (NGC, prvate) (Statnett, state) (CDEC) (Transpower, st.) OLD PJM Colomba (PJM Interc.) (ISA, state) New York (NYPool) BILATERAL Span Calforna England & Wales (REE, state) (Cal.ISO) (NGC, prvate) NEW Fgure 2-3 Energy market organsaton and system operaton 2.6 Internatonal experences Many countres around the world have transformed ther vertcally ntegrated electrcty companes and have unbundled them nto generaton, transmsson and dstrbuton companes. Prvate partcpaton n the electrcty busness has been another common factor ntroduced n most of the cases, leavng governments only the regulatory and supervsory role. Ths new order has facltated the exchange of regulatory experences manly on energy market models and some smlar schemes can be dentfed. New deregulated schemes have also served as an ntegrated framework to allow nternatonal nvestors to partcpate n dfferent countres as part of the globalsaton process. Hence t s usual to see some well known nternatonal electrcty companes buyng exstent assets from local companes or nvestng n some emergng deregulated markets. Prvate nvestment has ncentves n presence of good rsk ratng n focus countres, compettve rates of return, smplfed regulatory frameworks, transparent tarff processes and effcent allocaton of resources respondng to economc sgnals va prces. Although generaton and dstrbuton have acheved a certan consensus regardng the use of prcng schemes, transmsson prcng has not. Therefore a wde varety of partcular schemes based on the man methods revewed n secton 2.4 can be found n

39 Appendx C Smulatons on the IEEE 24-Bus Network 30 deregulated markets around the world. Poltcal and economcal belefs joned to cultural ssues and advsory nfluence are the factors playng a leadng role n the desgn of a partcular prcng scheme for electrcty transmsson. Chle was a poneerng country n deregulaton and prvatsaton of the electrcty sector. In September 1982 the Chlean Government dctated a new Electrcty Law, DFL-1 of Mnstry of Mnng, that ntroduced the concepts of unbundlng the actvtes of generaton-transmsson and dstrbuton, open access to the transmsson system and margnal cost prcng on transactons among generatng companes. Followng, the electrcty supply ndustry n England and Wales was radcally restructured n 1990 to allow competton ntally n the generaton sector of the ndustry and ultmately n the retal sector as well (Green, R., 1997). In March 2001 a New Electrcty Tradng Arrangements (NETA) were ntroduced ntally n the energy market and ultmately n a new access market, transformng the pool-based organsaton nto a blateral model (Ofgem, 2001). After the frst step gven by Chle and England and Wales, n the 90 s new deregulated schemes were mplemented n the electrcty sector of the followng countres around the world: Latn Amerca: Argentna, Perú, Bolva, Colomba and Brazl North Amerca: USA (PJM, Calforna, New York and New England) and Canada (Alberta) Europe: Nordpool (Norway, Sweden, Fnland and Denmark), Span and Germany Australasa: New Zealand and Australa Everyone has developed ts own transmsson prcng scheme and a revew of relevant ssues are descrbed n specfc lterature (Green, R. et al 1997).

40 CHAPTER 3 Theoretcal framework for analyss of transmsson Summary In ths chapter the theoretcal framework to analyse the transmsson busness s developed, partcularly the relatonshp among short term operaton and long term development of the transmsson network. The determnaton of the optmal transmsson capacty of the network and the concept of an economcally adapted network are dscussed and analysed va an example. Energy prcng methods are smulated together wth transmsson prcng to determne how the revenues and costs are allocated among partcpants n the energy market. 3.1 Introducton The presence of the electrcty transmsson network means a constrant from the energy market pont of vew. Transmsson capacty and electrcty losses n the network affect the free transportaton of electrcty from generators to consumers. Moreover transmsson capacty s the key element that determnes the economc balance between short term operatonal effcency and long term optmal development of the network. A weak transmsson network wth demands for transportaton over ts capacty means hgh operaton costs of generaton due to the need for despatchng more expensve generaton at nodes where demand cannot be suppled wth cheaper generaton because of transmsson constrants. In that stuaton local markets are created and the energy market effcency s affected due to potental market power exercsed by some agents to ther own beneft. On the other sde a strong transmsson network wth a capacty hgher than maxmum demands for transportaton means a reduced amount of transmsson constrants, a cheaper despatch of generaton plants and an energy market free for compettve tradng. However nvestment costs could be very expensve for the

41 Appendx C Smulatons on the IEEE 24-Bus Network 32 users. Therefore there s an economc trade off between operaton costs of generaton and nvestment costs of transmsson. 3.2 Theoretcal framework A two-bus network wth a contnuous demand curve and prce responsve energy markets at both nodes wll be analysed to dentfy the relevant short and long term ssues n electrcty transmsson. Tradtonal models to analyse the relatonshp between optmal transmsson capacty and transmsson prcng do not consder the mportance that settng the rght prces have on market response. Hence there s a common belef that transmsson plannng and nvestments can be carred out by centrally co-ordnated nsttutons only. Certanly there are stuatons where market forces cannot respond to prce sgnals and a regulated framework must support nvestments that are socally desrable for the whole system. However most of those stuatons occur because the rght prces are not determned and agents work n a more compettve than co-operatve manner. Nevertheless market drven nvestments can be feasble f the rght prces for transmsson are set n the energy market or n the access market. For nstance nodal margnal prces permt partcpants n the energy market to receve a powerful sgnal n the short term regardng the spot value of electrcty at dfferent locatons on the network. On the other sde the use of transmsson rghts n the access market facltates the task of sendng powerful sgnals to partcpants n the energy market regardng the value of transmsson on dfferent paths n the transmsson network. 3.3 Short term and energy market effcency The network s shown n Fgure 3-1 and t consders two dentcal crcuts that connect nodes j and k. Every crcut has a transmsson capacty equal to F. There s a generator at both nodes and t s assumed that margnal producton cost of G j s lower than cost of G k and the demand at node j d j s lower than the demand at node k d k.

42 Appendx C Smulatons on the IEEE 24-Bus Network 33 Electrcty demand s represented by a yearly load duraton curve d(t), shown n Fgure 3-2, wth a maxmum demand D 1 and a mnmum demand D 0, and a nodal dstrbuton α j and α k. The addtonal smplfyng assumptons are consdered: transmsson losses, reactve power, voltage and dynamc stablty ssues are not ncluded n the model. total generaton capactes at both nodes exceed the maxmum load D 1. generaton reserve requrements are not consdered. Nj Nk gj gk Gj ----> f ----> <---- Gk dj -F < f < F dk Fgure 3-1 Two bus network d(t) D 1 d j (t) = α j d(t) and d k (t) = α k d(t) D 0 α j α k = 1 T= 8,760 hours T t Fgure 3-2 Load duraton curve and nodal dstrbuton The producton costs of the generators are represented by quadratc functons as follows:

43 Appendx C Smulatons on the IEEE 24-Bus Network 34 C(g j ) = c 0j c 1j g j c 2j g j 2 C(g k ) = c 0k c 1k g k c 2k g k 2 g j < G jm (3-1) g k < G km (3-2) The margnal costs of these functons are shown graphcally n Fgure 3-3. C (g j ) C (g k ) c 1k c 1j G jm g j G km g k Fgure 3-3 Producton margnal costs of generators In the short term the transmsson capacty F s constant. So the problem to fnd the optmal despatch of generators, and then to obtan the power flow f from node j to node k over a perod of tme T equal to a year, can be formulated through the mnmsaton of the total yearly operaton costs (OC). Usng the theory of spot prcng (Schweppe, F. et al., 1988) the formulaton follows: T Mnmse : OC( g j, g k ) = ( c( g j ) c( g k )) dt (3-3) 0 s.t. : 0 g / µ (3-4) j G jm k j 0 g / µ (3-5) k G km f F /τ (3-6) d g j g = 0 /λ (3-7) k Constrants (3-3) and (3-4) represent the ndvdual lmts of generaton of generators G j and G k. It s assumed that the transmsson network s operated wth an N-1 crtera n order to provde securty of servce to the users n case of an unexpected outage affectng one of the crcuts of the lne. Therefore the flow f must not overcome

44 Appendx C Smulatons on the IEEE 24-Bus Network 35 transmsson capacty F (equaton 3-6). Fnally equaton (3-7) represents the energy balance constrant: total generaton equals to total demand. Nearby everyone of the constrants equatons (3-4) to (3-7) a Lagrange multpler has been assocated. So we can rewrte the optmsaton problem as a Lagrangan: dt F f G g G g g g d g c g c Z km k k jm j j k j T k j )} ( ) ( ) ( ) ( ) ( ) ( { 0 = τ µ µ λ (3-8) The frst order condtons are: 0 ; 0 = = j g k Z g Z (3-9) Then: 0 ) ( j = j j j g f g g c τ µ λ (3-10) 0 ) ( k = k k k g f g g c τ µ λ (3-11) And the nodal short run margnal costs (SRMC) can be dentfed as: k j ) ( and ) ( µ λ µ λ = = k k k j j j g g c g g c (3-12) Nodal SRMC can also be wrtten as a functon of the Lagrange multplers assocated to the transmsson capacty constrant τ and the system demand constrant λ, usually known as system lambda : k k j j g f g f = = τ λ λ τ λ λ and (3-13) and transmsson SRMC s: ) ( k j j k g f g f = τ λ λ (3-14) but flow f can be expressed as: ; = 1 = j k k j j k g g f α α α α (3-15)

45 Appendx C Smulatons on the IEEE 24-Bus Network 36 f f Then = α k and = α j (3-16) g g j k and λ k λ j = τ (3-17) Equaton (3-17) shows the close relatonshp between transmsson capacty constrants and the nodal SRMC dfference between both sdes of a transmsson lne. Transmsson congeston means a non zero value of τ and therefore SRMC at nodes j and k are dfferent, n the absence of transmsson losses. Wthout congeston n the network, nodal SRMC are the same everywhere and they are equal to the system lambda λ. Therefore n the short term the market s the best way to dscover the actual value of the transmsson system for energy market partcpants, f the rght SRMC prces are calculated. However SRMC prces cannot assure that transmsson nvestment costs are really covered wth the money obtaned from short term balances among generators and customers. As t was shown n the short term formulaton (equaton 3-3), transmsson capacty F was absent because t was a constant, and therefore the lnk between SRMC and transmsson nvestments must be explored through a long term formulaton of the optmsaton problem. 3.4 Long term and network development Complementng equaton (3-3), n the long term the transmsson capacty F s a varable and ts optmal value can be determned. It s assumed that capactes of the generators are fxed and only transmsson capacty s a relevant varable. Thereby the long term problem can be formulated through the mnmsaton of the total yearly operaton costs and the annuty of the transmsson nvestment cost I(F). Fxed operaton and mantenance costs of transmsson are assumed to be ncluded n the I(F) functon. The long term formulaton of the operaton plus nvestment costs (OIC) follows: Mnmse : OIC( g j, g T, F) = ( c( g ) c( g )) dt I( F) (3-18) k j k 0

46 Appendx C Smulatons on the IEEE 24-Bus Network 37 s.t. : 0 g / µ (3-19) j G jm k j 0 g / µ (3-20) k G km f F /τ (3-21) d g j g = 0 /λ (3-22) k The frst order condtons for generaton are: Z g Z = 0 ; = 0 j g k (3-23) Then, f f λ j = λ τ ; λk = λ τ (3-24) g g j k and λ k λ j = τ (3-25) The frst order condton related to transmsson capacty F s: Z = 0 F It means: (3-26) - T 0 I ( F) τ dt = 0 (3-27) F and then, T 0 I( F) ( λ k λ j ) dt = (3-28) F Equaton (3-28) defnes the rule to determne the optmal transmsson capacty between two nodes. At the optmum, the margnal cost of nvestment to add one addtonal MW of transmsson capacty between two nodes must be equal to the operaton margnal cost savngs between those nodes, over a certan perod of tme. The optmal balance between generaton operaton costs and transmsson nvestment costs n the long term leads to the concept of a reference network or economcally

47 Appendx C Smulatons on the IEEE 24-Bus Network 38 adapted network (EAN). The EAN s defned as the transmsson network that mnmses the total operaton plus nvestment costs over a certan perod of tme. Ths concept s an useful reference from the regulatory pont of vew and can be used for prcng purposes due to the specal relatonshps that happen n the optmal network. 3.5 Economcally adapted network (EAN) an example Determnng the transmsson network that mnmses the total generaton operatonal cost plus the transmsson nvestment costs over a perod of tme means the calculaton of the optmal transmsson capacty on every path n the network. In the two nodes network shown n Fgure 3-1 t s assumed that the optmal transmsson capacty s hgher than the mnmum demand at node k and lower than the maxmum demand at the same node (α k D 0 < F < α k D 1 ). Thus the graphs of g j (t), g k (t) and f(t) are shown n Fgure 3-4. g j (t) α j D 1 F F (1α j / α k ) g k (t) α k D 1 - F D 0 f(t) 0 T 0 T t 0 T 0 T t F α k D 0 0 T 0 T t Fgure 3-4 Graphs of g j (t), g k (t) and f(t)

48 Appendx C Smulatons on the IEEE 24-Bus Network 39 Durng perod [T 0, T], total demand d(t) s suppled by generator G j only because t has a producton cost lower than G k. Durng perod [0, T 0 ], demand at node k cannot be suppled by generator G j because the flow f has reached the value of the lne transmsson capacty F. Therefore the more expensve generator G k must be despatched to supply the demand at node k on ths perod. The optmal capacty F can be determned evaluatng equaton (3-28), replacng the values of margnal costs at both nodes and the annuty of transmsson nvestment. The margnal costs can expressed as follows: λ 2 λ g (3-29) j = c1 j c2 j g j and k = c1 k 2 c2k T T0 Then : ( λ k λ j ) dt = ( c1 k c1 j ) T0 2 ( c2k g k c2 j g j ) dt (3-30) wth: T k T F = ( D1 ) (3-31) ( D D ) α 1 0 k Transmsson nvestment costs have typcally a non lnear curve related to the capacty F, denotng economes of scale. It means that nvestment costs per MW transported are reduced whle more MW are transported by a transmsson lne or power transformer. The mpact of economes of scale n transmsson s dscussed n secton For the purposes of ths analyss, a lnear relatonshp between transmsson nvestment cost and capacty wll be consdered: I( F) = a l F (3-32) where a s the annutsed margnal cost of nvestment plus fxed operaton and mantenance costs ( /MW-km-year) and l s the length of the lne (km). I( F) Then = a l (3-33) F Replacng at both sdes of equaton (3-28) we obtan: T0 ( 1k 1 j 0 2k k 2 j j ) 0 a l = c c ) T 2 ( c g c g dt (3-34)

49 Appendx C Smulatons on the IEEE 24-Bus Network 40 Solvng analytcally the ntegral at the rght sde of equaton (3-34), we obtan the followng second degree equaton that permts the calculaton of F opt : b 1 ( F = b F) ( b b ) (3-35) where: k a l ( D1 D0 ) b1 = α (3-36) T b b = α (3-37) 2 k D 1 = ( α α D (3-38) 3 c1 k c1 j c2k k c2 j j ) 1 b 4 α j = c2k c2 j (2 ) (3-39) α k The equaton that calculates F opt can be wrtten as: 2 b F b b b ) F ( b b b ) 0 (3-40) 4 ( = and the optmal transmsson capacty s: F opt 2 b3 b2 b4 ± ( b3 b2 b4 ) 4 b4 ( b2 b3 b1 ) = (3-41) 2 b 4 In the partcular case of constant margnal costs at both nodes (Mutale, J., 2000), total demand concentrated at node k and mnmum demand equal to zero, then b 4 s equal to zero and the equaton (3-40) s reduced to a frst degree equaton: c2 k = c2 j = 0 ; α k = 1 and D0 = 0 (3-42) For that partcular case the optmal transmsson capacty s: F opt a l = D1 (1 ) (3-43) ( c c ) T 1k 1 j A very mportant ssue that lnks short and long term at the optmal capacty pont s the market value of transmsson for the partcpants n the energy market (generators and consumers at nodes j and k ). Ths value corresponds to the revenue captured by the transmsson lne between nodes j and k when the power flow transported from j to k s valorsed wth the nodal SRMC at both sdes.

50 Appendx C Smulatons on the IEEE 24-Bus Network 41 Then, the SRMC transmsson revenue s calculated as follows: SRMC tr = T 0 ( λ ( t) λ ( t)) f ( t) dt (3-44) k j Nodal SRMC at nodes j and k are shown n Fgure 3-5. λ j (t) λ k (t) λ k1 λ k2 λ j1 λ j2 λ j3 λ k3 λ k4 0 T 0 T 0 T 0 T Fgure 3-5 Nodal SRMC at nodes j and k wth: λ = c c ( α D ) (3-45) j 1 1 j 2 2 j j 1 F α j λ j 2 = c1 j 2 c2 j F (1 ) (3-46) α j3 = c1 j 2 c2 j D0 k λ (3-47) λ = c c ( α D ) (3-48) k1 1k 2 2k k 1 F λ k 2 = c 1k (3-49) λ = λ and λ = λ (3-50) k3 j 2 k 4 j3 Therefore transmsson SRMC s:

51 Appendx C Smulatons on the IEEE 24-Bus Network 42 λ k -λ j (t) λ k1 -λ j1 λ k2 -λ j2 0 T 0 T Fgure 3-6 Transmsson SRMC Transmsson SRMC corresponds to the nodal dfference λ k -λ j, and ts curve s shown n Fgure 3-6. It can be notced that transmsson SRMC s zero n perod [T 0, T] when the transmsson capacty s not bndng. In perod [0, T 0 ] the transmsson capacty of the lne s bndng and a non zero SRMC value s obtaned. Valuaton of equaton (3-44) from ths curve and consderng flow f(t), shown n Fgure 3-4, determnes the followng expresson for the SRMC transmsson revenue: SRMC tr = ( b F T (3-51) 3 b4 F) 0 where b 3 and b 4 are defned by equatons (3-38) and (3-39) respectvely and T 0 s defned by equaton (3-31). Equaton (3-51) determnes a thrd order relatonshp between transmsson SRMC and capacty F, because T 0 s lnearly related to F. Moreover, for the optmal transmsson capacty, equaton (3-35) ncludes the same frst multpler contaned n equaton (3-51). Therefore we can re-order equaton (3-35) as follows: ( b opt b1 b4 F ) = (3-52) ( b F ) 3 opt 2 Replacng equaton (3-52) n equaton (3-51): b1 T SRMC tr = ( b F 2 0 opt F ) opt (3-53) Substtutng the values of b 1, b 2 and T 0 n equaton (3-53): opt opt SRMC tr = a l F = I( F ) = LRMCtr (3-54)

52 Appendx C Smulatons on the IEEE 24-Bus Network 43 Equaton (3-54) means an mportant concluson: for the optmal network transmsson SRMC revenue s equal to transmsson LRMC and equal to the transmsson nvestment cost. It must be notced that ths result s vald only when a lnear relatonshp between transmsson nvestment cost and capacty s consdered. For llustratve purposes, a numercal example that shows the man short and long term ssues s developed. The parameters for the system shown n Fgure 3-1 are: Maxmum and mnmum demand: D 1 =1000 MW, D 0 =400 MW Nodal demand dstrbuton: α j =20%, α k =80% Producton cost of G j : c 1j =20 /MWh, c 2j =0.005 /MWh 2 Producton cost of G k : c 1k =40 /MWh, c 2k =0.025 /MWh 2 Generaton lmts: G jm =G km =1000 MW Annutsed nvestment factor: a= 60 /MW-km-year Length of the lne: l= 1,000 km. The optmal transmsson capacty F opt, calculated by equaton (3-41), s equal to 607 MW and T 0 s 3,528 hours. The second soluton of equaton (3-40) s dscarded because t resulted numercally hgher than maxmum demand. A senstvty analyss to show the mpact of the man cost varables (nvestment and producton) on the optmal transmsson capacty s presented n Fgure 3-7.

53 Appendx C Smulatons on the IEEE 24-Bus Network 44 a /MW-km F MW Optmal Capacty MW Transmsson Capacty vs. Investment Factor a /MW-km-yr c 1k /MWh F MW 900 Transmsson Capacty vs. Producton cost of Gk Optmal Capacty MW c1k /MWh Fgure 3-7 Senstvty analyss on optmal transmsson capacty (base case s remarked wth an arrow) From Fgure 3-7 t can be notced that there s a trade off between the optmal transmsson capacty and the transmsson nvestment cost. Whle lower the nvestment factor s, a hgher optmal transmsson capacty s obtaned, keepng the same SRMC between the extremes of the lne. On the other sde, there s a straght relatonshp between optmal transmsson capacty and the SRMC dfference at both extremes of the lne, keepng a constant nvestment factor. Whle hgher the SRMC dfference between both extremes of a transmsson lne, hgher the optmal transmsson capacty. The yearly duraton curves of the total demand, transmsson and despatch of G j and G k, for a transmsson capacty of 607 MW, are shown n Fgure 3-8. The curves follow the same patterns already shown n Fgures 3-2 and 3-4.

54 Appendx C Smulatons on the IEEE 24-Bus Network 45 Total Demand Transmsson MW 500 MW t hr t hr MW Despatch Gj t hr MW Despatch Gk t hr Fgure 3-8 Duraton curves of demand, transmsson and generators despatch Evaluatng equaton (3-44) wth the optmal transmsson capacty and the SRMC lmts provded by equatons (3-45) to (3-49), so the SRMC transmsson revenue s equal to 36.4 Mllons. The annuty of the nvestment cost for the optmal transmsson capacty, calculated from equaton (3-32), s equal to 36.4 Mllons too. It was an expected result accordng to equaton (3-54). The economcally adapted network s optmally congested because generaton despatch costs and transmsson nvestment costs are optmally balanced. Usually the despatch cost wthout takng nto account transmsson constrants s called Mert-order generaton cost (MOG), and the addtonal cost of generaton due to transmsson constrants s called Out-of-mert generaton cost (OMG) or Uplft. MOG and OMG costs can be calculated as follows:

55 Appendx C Smulatons on the IEEE 24-Bus Network 46 T MOG cos t = c ( d( t)) dt (3-55) 0 T0 j OMG cost = ( c ( g ( t)) c ( g ( t))) dt (3-56) 0 k k j k Calculated values for operatng cost, transmsson nvestment cost, total cost, mertorder generaton cost and out-of-mert generaton cost for several values of transmsson capactes are presented n Table 3-1. Table 3-1 Operatng, nvestment and total cost for several transmsson capactes Capacty Operatng Investm. Total cost MOG OMG (MW) cost (M ) cost (M ) (M ) cost (M ) cost (M ) The mnmum total cost s obtaned for the optmal capacty equal to 607 MW, as t s shown n Fgure 3-9.

56 Appendx C Smulatons on the IEEE 24-Bus Network Operaton and Investment Costs Optmal Capacty 607 MW 200 Cost (Mllon ) Transmsson Capacty (MW) Investment Operaton Total Cost Fgure 3-9 Operaton, nvestment and total cost for several transmsson capactes Transmsson nvestment cost always ncreases when a hgher value of transmsson capacty s consdered. Nevertheless the users of the network do not perceve that transmsson s so mportant when hgher values of capactes are consdered. The value of transmsson for users of the network s hgher whle less capacty s avalable for them. The value of a good s strctly related to ts scarcty value and whle more scarce s transmsson more valued t s n the energy market. In Fgure 3-9 t s shown that for lower values of transmsson capacty, operaton cost s bgger and therefore some users must afford ths bg cost. The measure of the value of transmsson for the energy market partcpants s the SRMC transmsson revenue, also known as transmsson congeston cost. From the transmsson prcng pont of vew the concept of value of transmsson s drectly related to the SRMC transmsson revenue and therefore t s assocated to short term generaton costs. On the other sde the concept of cost of transmsson s related to transmsson nvestment costs and therefore wth long term transmsson costs that the transmsson assets owners must recover f they want to contnue nvestng for

57 Appendx C Smulatons on the IEEE 24-Bus Network 48 developng the network. The relatonshp between the value and cost of transmsson s presented n Fgure 3-10, where the SRMC transmsson revenue (transmsson congeston cost) and transmsson nvestment cost (assumed lnear) are shown. 80 Transmsson Congeston and Investment Costs TCC Ln.Inv Cost (Mllon ) Transmsson Capacty (MW) Fgure 3-10 Value vs. cost of transmsson The relatonshp between out-of-mert generaton cost and transmsson congeston cost s shown n Fgure Transmsson Congeston and OMG Costs TCC OMG Cost (Mllon ) Transmsson Capacty (MW) Fgure 3-11 OMG cost vs. transmsson congeston cost

58 Appendx C Smulatons on the IEEE 24-Bus Network 49 A more detaled analyss of the transmsson SRMC revenue reveals that the value of transmsson ncreases whle transmsson capacty decreases from ts optmal value. It means that a prcng method based on SRMC, lke transmsson rghts, wll brng more money than necessary to just cover the transmsson nvestment cost of the network when capactes are under the optmal value. Reducng the value of transmsson capacty there s a value from whch the value of transmsson begns to decrease towards zero, for a capacty equals to zero. Ths stuaton s produced by the way the transmsson SRMC s defned, as a product of the transmsson SRMC prce by the power flow through the lne. At the other sde, for transmsson capactes over the optmal value, transmsson SRMC decreases faster towards zero for slght overcapactes. The mpact of over-capacty, measured as the percentage n excess above optmal transmsson capacty, on the SRMC transmsson revenue s shown n Fgure % 90% SRMC Transmsson Revenue vs Transmsson Overcapacty SRMC rev. / Investment (%) 80% 70% 60% 50% 40% 30% 20% 10% 0% 0% 5% 10% 15% 20% 25% 30% 35% Overcapacty (%) Fgure 3-12 Impact of over-capactes on SRMC transmsson revenue Therefore, from the energy market pont of vew, transmsson have a null value for over-capactes above 32% n ths example. However the benefts of reduced operatng costs and economes of scope have already been captured by the energy market partcpants but not recognsed by the transmsson SRMC. Ths concluson s very mportant when a new transmsson prcng method s under desgn by a regulatory

59 Appendx C Smulatons on the IEEE 24-Bus Network 50 authorty because an SRMC-based prcng method could be a very bad choce dependng on the robustness or weakness of the network capacty under study. Partcularly n developng power systems t s usual that transmsson capacty s hgher than present demands for transportaton and then SRMC revenues cannot cover the total nvestment costs of the network. Addtonally, by securty of servce reasons t s usual that transmsson networks have some back up capacty whch wll not be pad by the SRMC revenue ether. In summary, an SRMC-based prcng method for transmsson does not have any relatonshp wth transmsson nvestment costs, except for the economcally adapted network when nvestment costs are assumed lnear. 3.6 Energy market and transmsson prcng The applcaton of the SRMC (nodal) and SMP (one node) prcng methods, presented n secton 2.5.2, n the energy market of the network shown n Fgure 3-1 s analysed. The economc despatch s the same for both methods and t corresponds to the optmal soluton of the short term problem (equaton 3-3). Table 3-2 Operatonal analyss of the network, consderng a transmsson capacty equal to the optmal (607 MW) Optmal despatch (MWh) Generaton [0, To] [To, T] Total Gj 2, , ,791.0 Gk Total G 3, , ,132.0 Demand [0, To] [To, T] Total Dj ,226.4 Dk 2, , ,905.6 Total D 3, , ,132.0 Transmsson [0, To] [To, T] Total Tjk 2, , ,564.6 Table 3-3 shows the total cost of operaton and transmsson nvestment of the network. The optmal annuty of transmsson nvestment cost s equal to 36.4 Mllons.

60 Appendx C Smulatons on the IEEE 24-Bus Network 51 Table 3-3 Total operatonal and network costs consderng a transmsson capacty equal to the optmal (607 MW) Operatonal Costs (Thousand ) [0, To] [To, T] Total Gj 66,018 69, ,681 Gk 14, ,738 Total Op.Costs 80,756 69, ,419 MOG cost 75,753 69, ,416 OMG cost 5, ,003 Transmsson Investments (Thousand ) Annuty Investment Cost 36,402 Total Costs (Thousand /yr) 186, Energy market balance usng nodal SRMC prcng The applcaton of SRMC prcng means that every transacton by generators and consumers s valued at the respectve node at any tme wth the nodal SRMC. For the optmal transmsson capacty the balance follows n Table 3-4. Table 3-4 Energy market balance wth SRMC prcng SRMC Revenues (Thousand ) Generaton [0, To] [To, T] Total Gj 76,823 78, ,541 Gk 15, ,836 Total G 92,660 78, ,378 Demand [0, To] [To, T] Total Dj -17,268-15,744-33,011 Dk -111,793-62, ,768 Total D -129,061-78, ,779 D-G -36, ,402 Transmsson [0, To] [To, T] Total Tjk 36, ,402

61 Appendx C Smulatons on the IEEE 24-Bus Network 52 Usng SRMC prcng demand pays more than generaton and the SRMC surplus s the transmsson SRMC revenue. The transmsson SRMC revenue s equal to the annuty of the nvestment cost for the optmal capacty (compare fgures n Table 3-3 and 3-4). From the regulatory pont of vew t s mportant to be careful wth the applcaton of SRMC prcng because the exstence of an SRMC surplus does not necessary mean that t must be allocated to the transmsson assets owners. Lookng at Fgure 3-10 t s evdent that there could be a perverse ncentve for owners to cause congeston to ncrease the transmsson SRMC revenue over the value of the assets f the transmsson busness s consdered a monopoly. Ths effect can be mtgated f the SRMC surplus has a neutral effect for the transmsson owners (TO). For nstance f the SRMC surplus s allocated to the energy market partcpants va fnancal transmsson rghts (lke n the US PJM or New York Pool) or f t s allocated to the TO as part of the transmsson revenue but annually re-lqudated aganst the maxmum revenue allowed to transmsson (lke n Chle). Nodal SRMC were calculated usng equatons (3-45) to (3-50) and Table 3-5 shows the average values at every perod and for the whole perod [0, T]. Table 3-5 Average nodal SRMC ( /MWh) SRMC [0, To] [To, T] [0, T] Node j Node k Lne j-k Energy market balance usng SMP The applcaton of a system margnal prcng (SMP) means that every transacton by generators and consumers are valued at any tme wth a unque prce equal to SMP, wth ndependence of the locaton of generators and consumers on the network. Ths prcng system was n use n England and Wales from 1990 to 2001, when t was replaced by the New Electrcty Tradng Arrangements (NETA).

62 Appendx C Smulatons on the IEEE 24-Bus Network 53 Usng SMP requres the prevous determnaton of the unconstraned optmal generaton schedule wthout consderaton of the transmsson constrants. The unconstraned despatch s performed only by generator G j to supply the total demand, therefore the SMP corresponds to the margnal cost of generator G j. Fgure 3-13 shows the SMP curve and Table 3-6 presents the average values at every perod and for the whole perod [0, T]. λ(t) λ 1 λ 2 λ 3 0 T 0 T t Fgure 3-13 System margnal prce (SMP) wth: λ (3-57) 1 = c1 j 2 c2 j D1 α λ = = (3-58) j 2 c1 j 2 c2 j F (1 ) λ j 2 α k λ = c c D = λ (3-59) 3 1 j 2 2 j 0 j3 Table 3-6 Average SMP ( /MWh) [0, To] [To, T] [0, T] SMP Afterward t s necessary to determne the constraned optmal generaton schedule n order to perform the despatch consderng the network constrants. The dfference between the constraned and unconstraned operatonal costs s equal to the out-of-mert generaton cost (OMG) or uplft.

63 Appendx C Smulatons on the IEEE 24-Bus Network 54 Table 3-7 presents the unconstraned and constraned despatch and the operatonal costs. The constraned optmal despatch s of course equal to the despatch shown n Table 3-2 and the operatonal costs shown n Table 3-3. Table 3-7 Despatch and operatonal costs wth SMP Despatch Unconstraned (MWh) Generaton [0, To] [To, T] Total Gj 3, , ,132.0 Gk Total G 3, , ,132.0 Constraned (MWh) Generaton [0, To] [To, T] Total Gj 2, , ,791.0 Gk Total G 3, , ,132.0 Operatonal Costs Unconstraned (Thousand ) [0, To] [To, T] Total Gj 75,753 69, ,416 Gk Total Op.Costs 75,753 69, ,416 Constraned (Thousand ) [0, To] [To, T] Total Gj 66,018 69, ,681 Gk 14, ,738 Total Op.Costs 80,756 69, ,419 Uplft 5, ,003 As a result of the transmsson constrants, generator G j s prevented from fully accessng the energy market and t must be compensated by the loss of proft. Therefore a constraned off payment s allocated to G j and t s equal to the prce margn between the SMP and the operaton cost C j multpled by the avoded despatch (unconstraned despatch mnus constraned despatch).

64 Appendx C Smulatons on the IEEE 24-Bus Network 55 un co Constraned off payment to G j = ( SMP C ) ( G G ) (3-60) To j j j un co un co = (λ j ( t) ( g g ) c j ( g ) c j j j j ( g j )) dt (3-61) 0 On the other sde, generator G k s called upon and a constraned on payment must be allocated to G k. That payment s equal to the addtonal operaton cost C k ncurred by generator G k over the SMP, multpled by the addtonal despatch (constraned despatch mnus unconstraned despatch). co un Constraned on payment to G k = ( C SMP) ( G G ) (3-62) = To 0 k k k co un co un ( c ( g ) c ( g ) λ ( t) ( g g )) dt (3-63) k k k k j k k Payments to generators usng SMP are adjusted usng equatons (3-61) and (3-62). The uplft s equal to the sum of the constraned off and on payments and t s pad by the demand n proporton to ts sze. Table 3-8 shows the SMP revenues. Table 3-8 SMP revenues SMP Revenues (Thousand ) Generaton [0, To] [To, T] Total Gj 79,519 78, ,237 Gk 9, ,955 Total G 89,474 78, ,192 Constraned payments Gen.Total Gj OFF ,457 Gk ON 4,783 14,738 Total G 5, ,195 Demand [0, To] [To, T] Total Dj -17,895-15,744-33,638 Dk -71,579-62, ,554 Total D -89,474-78, ,192 Constraned charges Dem.Total Dj 20% -1,001-34,639 Dk 80% -4, ,556 Total D -5, ,195

65 Appendx C Smulatons on the IEEE 24-Bus Network Impact of transmsson n the energy market As an mportant dfference wth SRMC prcng, n ths case SMP revenues of generators are equal to payments by demand. Partcularly n the example demand D k pays much more wth SRMC prcng n comparson wth SMP. On the other sde generator G k receves hgher revenues wth SRMC due to the hgher market value of energy at node k when the network s constraned. Therefore, usng SMP t can be notced that the allocaton of the uplft among demands D j and D k s questonable because a proportonal dstrbuton not necessary reflect an effcent cost allocaton. Comparng SRMC and SMP revenues t can be nferred that manly demand D k holds the cost of transmsson although ths concluson cannot be generalsed because t depends on how the energy market prces at nodes j and k react under constraned condtons. In the example, the energy market prces at nodes j and k are determned by the producton costs of generators G j and G k and the offer-demand balance at every node. Fgure 3-14 shows a senstvty analyss performed by changng transmsson capacty down and up from the optmal capacty (607 MW) to observe the mpact on nodal prces Energy Market Prce vs. Transmsson Capacty 38.7 Node j Nodal SRMC ( /MWh) Node k Transmsson Capacty (MW) Fgure 3-14 Impact of transmsson capacty on the energy market prces

66 Appendx C Smulatons on the IEEE 24-Bus Network 57 For lower values of the transmsson capacty the mpact on the energy market prces at nodes j and k s hgher due to the need to despatch generator G k to supply the demand at node k n order to respect the transmsson constrant. That hgher dfference of prces between both nodes means hgher transmsson congeston costs (as seen n Fgure 3-10). At the extreme, for a null transmsson capacty the energy markets at nodes j and k are economcally decoupled. On the other sde, for a transmsson capacty equal to the maxmum demand at node k (800 MW), there s no transmsson constrant and the energy market prces are equal to the SMP (27.0 /MWh) at both nodes. For the optmal transmsson capacty, locatonal margnal prces are equal to LRMC and they cover the optmal nvestment cost of the network. Thus, an optmal strategy to determne transmsson prces can be derved from the economcally adapted network. A set of transmsson prces that contan the full nteracton among optmal transmsson nvestment and the energy market behavour correspond to the dfference between nodal prces and SMP. Fgure 3-15 shows nodal prces and SMP for the optmal transmsson capacty (607 MW) and from then a set of annual transmsson prces s determned for node k equal to 6.5 /MWh and for node j equal to 0.4 /MWh. Nodal Prce ( /MWh) 33.5 node k : 6.5 /MWh Node j Node k SMP= node j : /MWh 607 MW Fgure 3-15 Transmsson prces and energy market prces Transmsson prces showed n Fgure 3-15 mean that demand at node k must pay an annual charge of 6.5 /MWh and generaton s pad the same prce. At node j demand must pay a charge of 0.4 /MWh (so demand s pad 0.4 /MWh) and generaton s

67 Appendx C Smulatons on the IEEE 24-Bus Network 58 pad the same prce (so generaton must pay 0.4 /MWh). Therefore, n ths example generator G j and demand D k must afford the cost of the transmsson network. A lnk between short and long term ssues n electrcty transmsson prcng can be derved from the prevous example. Fgure 3-16 shows a two node network that lnks the energy markets A and B. A set of generators G a and G b supply the demand D a and D b at nodes A and B respectvely. In the short term the transmsson capacty F s fxed and the power flow f can be consdered as a demand (D) at market A and as an offer (O) at node B. Then nodal prces (P a and P b ) are determned by the ntersecton of the offer and demand curves at every market. Ga Gb f Market A Market B Da -F < f < F Db Pa D Nodal prces at nodes A and B Pb D O O Pb Pa MW MW Fgure 3-16 Energy market and transmsson prces n the short term In the long term the transmsson capacty F can be consdered a varable and dependng on ts value the energy market equlbrum at nodes A and B s changed. Fgure 3-17 shows the mpact of two dfferent values of the transmsson capacty F on nodal prces P a and P b. If we assume that the capacty of the exstent network s F 1 and the economcally adapted network has an optmal capacty equal to F 2, then a set of

68 Appendx C Smulatons on the IEEE 24-Bus Network 59 transmsson prces that combne nvestment cost and energy market elements can be determned. Ga Gb f Market A Market B Da -F < f < F Db Pa D Nodal prces at nodes A and B F1 > F2 Pb D O O Pb MW Pa MW Fgure 3-17 Energy market and transmsson prces n the long term In spte of the economc advantages of ths theoretcal method for transmsson prcng, t fals n meshed networks due to addtonal physcal constrant created by the Krchhoff Voltage Law (KVL) as t wll be explaned n Chapter 4. Anyway the analyss of the economc concepts provde a sold base for desgnng an effcent prcng method that lnks the energy market and the access to the network. In concluson, SMP requres a transmsson prcng method that covers the full cost of the transmsson network. On the other sde, SRMC has two possble optons: frst, to add on top a transmsson prcng method to cover the dfference between the nvestment cost and the SRMC surplus or second, to send back the SRMC surplus to the energy market partcpants (for nstance va FTR) and to set a transmsson prcng method that covers the full cost of the transmsson network. Fgure 3-18 presents graphcally the typcal stuaton n the majorty of power systems around the world, wth

69 Appendx C Smulatons on the IEEE 24-Bus Network 60 over-capacty n the network. The applcaton of SRMC produces a surplus that s not enough to cover the nvestment costs of the network (for example n the Chlean system the SRMC surplus covers close to 20% of the nvestment costs of the exstent network). Investment Cost of the Optmal Transmsson Network SRMC surplus Fgure 3-18 Investment cost and SRMC surplus The queston s fndng an adequate mx between energy market prcng and transmsson access prcng. Among the methods that present the best match wth the prcng objectves the optons are: SRMC/LRMC: A value based method lke SRMC works very well n the energy market but t s not related to transmsson nvestments. Thus the addtonal nformaton regardng nvestments provded by LRMC could be the base for a strong prcng method. These methods wll be analysed n Chapter 4. Optmal transmsson prces derved from the EAN: A cost based method derved from the economcally adapted network (EAN) has the advantage to depart from the optmal nvestment cost of the network and the challenge conssts n fndng the best way to allocate the costs among generators and consumers. Ths method wll be analysed n Chapter 5. The postage stamp method has not been consdered n the analyss to come because t s a second best regulatory soluton that brngs smplcty to the calculaton of transmsson access charges but t does not have an economc foundaton.

70 Appendx C Smulatons on the IEEE 24-Bus Network Other transmsson prcng ssues Economes of scale n transmsson The economes of scale were mentoned n secton 2.2 among the man characterstcs of the transmsson busness from the owners pont of vew. In developng countres the transmsson system s stll under development and therefore the determnaton of the economcally adapted network must take nto account a long term soluton that mnmses the total nvestment plus operaton cost, consderng realstc scenaros concernng the development of the energy market. Transmsson costs have mportant economes of scale and they are reflected n the nvestment cost of transmsson lnes, transformers and substatons, meanng that the transmsson cost per MW s lower for hgher volumes of MW transported. Table 3-9 presents the nvestment costs of double crcut transmsson lnes based on Chlean fgures. Investment costs nclude the constructon of the transmsson lne, rghts of way and the assocated equpment n substatons. Table 3-9 Investment costs for transmsson lnes Lne characterstcs Investment costs Modelsaton Modelsaton Voltage Length Capacty Lne Substaton Total Lnear Non Lnear Error (%) kv km MW TUS$/km TUS$/km TUS$/km TUS$/km TUS$/km Lnear Non Lnear % -11% % 8% % 9% % 5% % -4% % -6% In Table 3-9 the nvestment costs per klometer I(F) are modelled by a lnear curve and a non lnear curve (exponental) as a functon of the transmsson capacty F, whose parameters were obtaned by a regresson analyss. Lnear curve: I( F) / km = F (Thousand US$/km) Non lnear curve: I ( F) / km F = (Thousand US$/km)

71 Appendx C Smulatons on the IEEE 24-Bus Network 62 Fgure 3-19 shows a better adjustment between the total transmsson nvestment cost curve and the non lnear curve that reflects the economes of scale characterstc (capacty exponent lower than 1). The error s also lower wth the non lnear model compared to the lnear model, as ndcated n Table Transmsson Investment Costs Investment (TUS$/km) Total Lnear Non Lnear Capacty (MW) Fgure 3-19 Modellng of the transmsson nvestment costs In summary, a lnear modellng of the transmsson nvestment costs does not exactly reflect a real characterstc of the transmsson network. Nevertheless, a lnear model provdes a reasonable foundaton to buld the transmsson prcng theory. In that sense the economes of scale are not a relevant ssue n the desgn of a transmsson prcng polcy but they must be consdered when the economcally adapted network s determned. The mpact of economes of scale n the determnaton of the economcally adapted network can be tested through the same example of secton 3.5 but consderng a non lnear nvestment curve. The optmal transmsson capacty s determned by solvng numercally the equatons 3-34 and 3-35, consderng the followng non lnear nvestment curve, nstead a lnear one: I ( F) / km 2, F = ( /km)

72 Appendx C Smulatons on the IEEE 24-Bus Network 63 By dong ths the optmal transmsson capacty s 700 MW, nstead of 607 MW for the lnear nvestment model. Fgure 3-20 shows the transmsson congeston (TCC) and nvestment cost curves shown n Fgure 3-10 ncludng the mpact of the economes of scale on the transmsson capacty. Naturally economes of scale mean a hgher optmal transmsson capacty due to the lower cost of addng one addtonal MW of capacty n comparson to the lnear modellng around the optmal value. As a consequence of ths result, n the real network of ths example the SRMC surplus (or TCC) covers only 48% of the nvestment cost (compared to 100% for the lnear modellng). Transmsson Congeston and Investment Costs Cost (Mllon ) Optmal Capacty 607 MW (lnear) TCC Lnear Inv. Non lnear Optmal Capacty 700 MW (non lnear) Transmsson Capacty (MW) Fgure 3-20 Impact of the transmsson nvestment modellng on the optmal transmsson capacty Therefore economes of scale n transmsson nvestments must be taken nto account when the economcally adapted network s determned by the regulator n order to recognse a relevant characterstc of the transmsson busness. So some revenue reconclaton method must be defned to complete the prcng process n real networks.

73 Appendx C Smulatons on the IEEE 24-Bus Network Securty of servce requrements Transmsson networks provde securty of servce to the electrcty consumers connected to them. The nterconnecton of power plants va the transmsson network reduces the mpact of any plant unexpected unavalablty over consumers. Therefore a consumer connected to the transmsson network have a better level of securty of servce compared to a consumer that s suppled by a generator not connected to the network (Espnosa, G., 1995). For nstance, Fgure 3-21 shows the stuaton of a consumer suppled ndependently to a generator wth a typcal fgure of 85% avalablty and ts comparson wth the stuaton of the same consumer when t s connected to the transmsson network wth a typcal 99.99% avalablty. GENERATOR TRANSMISSION NETWORK 85% avalablty 99.99% avalablty CONSUMER CONSUMER Fgure 3-21 Securty of servce provded by the network Furthermore, the outage cost s hgher for consumers than for generators and then a forced outage on the transmsson network affects them more deeply. On the other sde, an outage on a transmsson lne would cost a generator only the lost of revenues due to the energy not sold n the energy market (energy market prce less producton cost of the plant per MWh not sold). However, at the demand sde usually electrcty s a product that affects the whole producton for ndustral consumers, the whole busness for commercal consumers and the whole lfe for resdental consumers. So the outage cost or cost of the energy not suppled can be very hgh for consumers. Table 3-10 presents the mpact of dfferent values of the outage cost for consumers on the optmal transmsson capacty of the network of Fgure 3-22, for the same example of secton

74 Appendx C Smulatons on the IEEE 24-Bus Network , assumng that demand D k s suppled n a radal way from node j and G k represents a vrtual generator wth a margnal cost equal to the outage cost. Table 3-10 Impact of the outage cost on the network capacty Outage Optmal Suppled Unavalable Cost Capacty Demand Perod Avalablty /MWh MW % Hours % % % % % % % % % % % Whle hgher s the outage cost, hgher s the suppled demand D k (quotent between the optmal capacty F and the maxmum demand D k : 800 MW) and the avalablty at node k n the yearly perod. In summary, consumers must afford all transmsson overnvestments derved from securty of servce requrements, for nstance the use of the N-1 crtera. Nj Nk Vrtual gj gk generator Gj ----> f ----> <---- Gk dj -F < f < F dk Fgure 3-22 Securty of servce n a radal network.

75

76 CHAPTER 4 Transmsson rghts, SRMC surplus and nvestments Summary Ths chapter descrbes the use of SRMC surplus for transmsson prcng purposes and ts practcal applcaton as transmsson rghts. Transmsson rghts experences n the US are dscussed and the applcaton of frm access rghts n Englang and Wales s commented n detal. Transmsson prcng based on SRMC s tested on a three bus network and on the IEEE 24 bus Relablty Test System. Tests of the method probed that nodal SRMC cannot address a rght allocaton of revenues to recover nvestments n meshed networks. 4.1 Man concepts In recent years some of the world s deregulated electrcty ndustres have exhbted a preference for the use of market mechansms to prce the use of transmsson systems. These mechansms typcally rely on transmsson rghts whereby holders of rghts are guaranteed access to the transmsson network, to nject or wthdraw electrcty at dfferent locatons n the grd. Transmsson rghts schemes are currently n use n the PJM and Calforna markets n the US complementng the energy markets. A smlar scheme based on frm access rghts (FAR) has been proposed as part of the New Electrcty Tradng Arrangements (NETA) n England and Wales. Transmsson rghts are a market-based way to dscover the value of the transmsson network. They were developed as a market mechansm to nsure ther owners aganst the delvery rsk as a result of locaton-specfc energy contracts (Bushnell, J., 1999). The dea s to create a knd of property rght that, for a prce, can provde the equvalent of

77 Appendx C Smulatons on the IEEE 24-Bus Network 68 guaranteed access to the energy market at a locaton, regardless of transmsson capacty constrants. The frst scheme to deal wth transmsson access rghts n the short term was proposed by Hogan (Hogan, W., 1992) under the name of Transmsson Congeston Contracts (TCC) or Contract Network Rghts, wth the man purpose of protectng users of the grd from volatlty n spot prces caused by transmsson congeston. TCC permt ther owners to be pad the prce dfference between two nodes or nject power nto and wthdraw power out of the system. However, TCC are manly desgned as short term tools that hedge users of the grd aganst the nodal prcng mpact n a compettve bass and not as a mechansm to permt the full recovery and allocaton of the nvestment costs of the grd. Another market scheme s known as property rghts. Ths scheme developed by Chao and Peck (Chao, H.P. and Peck, S., 1996) was desgned to deal wth externaltes problem caused by loop flows n the grd. It allocates the transmsson rghts followng specfc tradng rules that recognse the mpact of externaltes. Transmsson rghts have been developed under a physcal or a fnancal form. A physcal rght of one MW enttles ts owner to nject one MW of power at a certan node and wthdraw one MW at other node. A physcal rght has been pared as rght-of way on the network for the power belongng to a gven generator. A one MW fnancal rght enttles ts owner to receve the dfference of prces between two nodes. A fnancal rght has been assmlated to an opton contract that guarantees ts owner the rght to sell power at the spot prce at a certan locaton n the network, regardless of where the power s njected nto the network. Transmsson rghts have also been broadly studed n relaton to the ncentves of partcpants n the energy market to exercse market power (Bushnell, J., 1999). Joskow and Trole (Joskow, P. and Trole J., 1998) have also analysed both fnancal and physcal rghts and ther nteracton wth market power.

78 Appendx C Smulatons on the IEEE 24-Bus Network Applcatons of transmsson rghts n the US In 1998 a Fxed Transmsson Rghts (FTR) scheme was ntroduced by the ndependent system operator (ISO) for the Pennsylvana, New Jersey and Maryland Interconnecton (PJM) to provde a hedgng mechansm aganst the fnancal mpact of the energy market s Locatonal Margnal Prces (LMP). In PJM, FTR are obtaned by one of the followng ways: As a network servce, based on annual peak load or desgnated from resources to loads. As a frm pont-to-pont servce from source to snk. By blateral tradng of exstng FTR n secondary markets. By FTR aucton of addtonal transmsson capacty n a centralsed market. In PJM, FTR are a fnancal (not physcal) enttlement, ndependent of the energy delvery and whose value (postve or negatve) s determned by hourly LMP and the drecton and magntude of congested flows. FTR monthly auctons provde a way for allocatng the remanng FTR capablty on the PJM transmsson system at the tme of closng the aucton quotng perod. They allow market partcpants to bd for or sell exstng FTR. The wnnng bds are determned by solvng a DC load flow model that maxmses the bd-based value of the FTR market, after checkng the smultaneous feasblty wth pror commtted FTR. Ths last process must ensure that there are enough revenues for transmsson congeston charges to satsfy all FTR oblgatons for the aucton. In 1999 the Calforna Independent System Operator (ISO) establshed a mechansm of Frm Transmsson Rghts (FTR) n a smlar way to PJM. However, the Calfornan energy market s based on a blateral model and the ISO operates three markets close to real tme: Ancllary Servces, Congeston Management and Energy Imbalance. The Congeston Management Market s used to adjust the schedules, allocatng transmsson capacty to those who value t most. The transmsson system has been dvded n four zones based on areas where congeston s nfrequent and they are prced on an average

79 Appendx C Smulatons on the IEEE 24-Bus Network 70 cost bass. Thus, zonal locatonal prces are calculated nstead of LMP for the whole grd as n PJM. Congeston management and prcng can be nter-zonal, meanng congeston between zones or ntra-zonal, meanng congeston wthn a zone. An FTR holder has both physcal schedulng and fnancal rghts n the ISO s day-ahead market but only fnancal rghts n the hour-ahead market. The revenues collected from auctons are credted to the enttes payng for the fxed costs of the transmsson system and therefore are netted off the access charges payable to the transmsson owners (TO). The new ISO of the New York Power Pool ntroduced n 1999 a scheme of Transmsson Congeston Contracts (TCC) smlar to FTR n PJM. A scheme of Frm Transmsson Rghts (FTR) was also ntroduced by the New England ISO n year Transmsson rghts and NETA In England and Wales, a new transmsson access and prcng regme based on Frm Access Rghts (FAR) has been recently proposed by the Offce of Gas and Electrcty Markets (Ofgem) as part of ts New Electrcty Tradng Arrangements (NETA). Accordng to Ofgem (Ofgem, 1998) the current tradng arrangements provde no shortterm locatonal prce sgnals to ether generators or demand. Among the objectves of NETA related to transmsson reforms s the applcaton of market-based rather than centrally admnstered mechansms to be used by NGC as system operator (SO) to accomplsh ts actvtes, allowng partcpants to express ther preferences. Fgure 4-1 shows a basc scheme of the proposed operaton of the energy and access markets. Both markets wll meet at the balancng mechansm, where energy unbalances of partcpants wll be solved by the SO, takng nto account ther FAR to nject nto or wthdraw energy from the network.

80 Appendx C Smulatons on the IEEE 24-Bus Network 71 Fgure 4-1 Energy and access markets under NETA Accordng to the NETA proposals, FAR wll be allocated to partcpants and subsequently traded n such a way that the market determnes the value of transmsson access and use. Some characterstcs of the proposed FAR scheme are: If the SO wshes to reduce a partcpant s access rght allocaton, t has to compensate t. Congeston management acheved by the SO buyng back exstng FAR. Defntons between the use of entry/ext rghts to a zone or transfer rghts between zones. Use t or lose t provsons to prevent holders not utlsng ther FAR. Over-run charges to dscourage partcpants from generatng or demandng n excess of ther FAR. Intal allocaton of FAR by auctons. Use of a top-down approach to determne the volume of FAR to be sold by the SO at the ntal allocaton. Combnaton of long-term and short-term FAR to be sold. 4.4 Frm Access Rghts ssues Followng the basc prncples of an effcent prcng scheme, revewed n secton 2.1, the man ssues concernng the use of FAR for transmsson prcng are:

81 Appendx C Smulatons on the IEEE 24-Bus Network Short term ssues n FAR Open access and economc effcency FAR scheme should provde a mechansm to trade energy freely n the network takng nto account the mpact of transmsson constrants. An ssue of concern s the proposed dscovery of the market value of transmsson access and use by the allocaton to and tradng of FAR by partcpants. In a blateral energy market, the short run value that partcpants are wllng to pay for transmsson can be explaned usng a perfect market representaton. In such a market, the value of the transmsson servce (ncludng costs of congeston and losses) s found as LMP dfferentals between nodes. In the proposed blateral market for England and Wales, partcpants n FAR auctons wll have to value the access to the network n coordnaton wth ther blateral energy contracts and self-despatch. SO should not allow a blateral transacton n the energy market or an unbalanced transacton wthout the correspondng FAR. Thus, economc effcency n the energy market wll be crtcally dependent on the level of competton acheved n the transmsson access market. Any gamng or exercsng market power could affect the economc effcency n the energy market. So, partcpants who own FAR should have the rght to nject nto (entry) or wthdraw power from (ext) the network n perfect tune wth ther balanced portfolo of generaton and demand. Also, partcpants who do not buy FAR are expected to pay over-run charges. In that sense, FAR would be a partcular type of physcal rghts. Volume of rghts to be sold Ths s a very crtcal ssue drectly related to the value of the rghts n the market. For example, the lower the amount of rghts offered by the SO (mplyng lower capacty declared n the access market), the hgher the value of FAR. The close relatonshp between the access and energy markets wll affect the wllngness to pay for FAR by users and ths fact could motvate the SO to rase the value of FAR. SO and TO actvtes performed by the same company, not an ndependent SO lke n the US, could potentally generate perverse ncentves and the exercse of market power by the SO to

82 Appendx C Smulatons on the IEEE 24-Bus Network 73 beneft ts TO sde. Instructng the SO to sell the maxmum level of capacty avalable (top-down approach) seems adequate to mtgate ts market power. Fgure 4-2 shows a 3-node lossless network used to llustrate the relatonshp between the value and the amount of rghts n a perfect market. Economc despatch, flows and nodal prces to supply a 690 MW demand were determned usng the model developed n secton Prces (P) at nodes are also shown n /MWh. If, for example, the thermal capacty of each crcut of lne L12 s 300 MW, we can examne the mpact of a SO reducng the capacty of path L12 from 300 MW (.e. sellng a lower amount of FAR). P1= 10 N2 95 MWh <---- G2A 395 MWh N1 P2= 35 G1 ----> > D > 0 MWh <---- G2B > > P3= MWh N3 D2 450 MWh D3 <---- G3 150 MWh 200 MWh Plant bds and max/mn gen. Lne reactance and capacty /MWh Gmn Gmax X (o/1) MW G L G2A L G2B L G L Fgure 4-2 Network to calculate the value of rghts Fgure 4-3 shows the market value of FAR measured as the dfference of prces between nodes 2 and 1, as a functon of the avalable capacty of path L12. The prce of rghts s zero for capactes over 287 MW and extremely hgh for capactes less than 103 MW. For capactes lower than 103 MW there s not enough generaton at node 2 and the

83 Appendx C Smulatons on the IEEE 24-Bus Network 74 prce at ths node should be equal to the customers cost of energy not suppled. So, the optmum socal welfare level could be lost under monopolstc gamng. So the queston s - what s the volume of rghts the SO should offer to prmary auctons? Nevertheless, who wll perform an ex-post analyss of thousands of transactons n the access and energy markets to check that all the capacty was really avalable? Fgure 4-3 Value and amount of rghts Market power One form of market power exercsed by the SO was descrbed above. Generators and supplers also could exercse market power n partcular operatonal condtons of the system. They could create constrants that do not exst by buyng and holdng FAR. In the presence of congeston n a partcular path, generators on the mportng sde ncrease ther market power, whle supplers on the exportng sde could also do the same. The SO could also be affected by market power exercsed by FAR holders when buyng back rghts to reduce the amount of FAR to manage congeston. The mpact of market power could be reduced by more partcpants offerng ther FAR for sale and provdng transparent nformaton n the market to facltate decsons. It could be done defnng zones wth enough partcpants.

84 Appendx C Smulatons on the IEEE 24-Bus Network 75 Congeston management The SO s expected to solve congeston by buyng back FAR n the access market. Under ths arrangement, the SO wll have to pay the market value of congeston to the partcpants who want to modfy ther energy schedulng. Nevertheless, those partcpants wll have to rearrange ther energy balance, for example by despatchng more expensve generaton at the mportng sde. However, the amount of congested paths could be a very dffcult ssue to manage, especally n large transmsson systems. Then, a nodal approach to manage congeston and defnng FAR from entry nodes to ext nodes could be a very dffcult task under a blateral market structure. A zonal approach must be preferred n that case, allowng an easer defnton of FAR between a few zones than many nodes. An adequate zonal defnton also should mtgate the mpact of market power of partcpants, allowng more of them offerng ther FAR for sale. Inter-zonal congeston would be solved usng transfer FAR between zones and the balancng mechansm n real tme, f the congeston could not be releved completely. However, ntra-zonal congeston cannot be solved n the same way. It s ntended to be resolved n the balancng mechansm, as shown n Fgure 4-1. Determnaton of zones and ther dynamcs Defnton of zones for the frst tme s a complex task. Only the SO has the experence and nformaton about more frequently congested paths. Nevertheless, the new tradng arrangements could change the pattern of generaton and demand, producng congeston at dfferent paths or modfyng the ntensty and frequency of congeston at typcally congested paths. Therefore, a frst defnton of zones should be based on the actual use of the network by exstng generators and demand but allowng a future redefnton of zones accordng to the real market operaton. The settng rule should consder the frequency and economc mpact of congeston at every path, to dentfy geographcally close sets of nodes whch could be consdered as one node, lnked to other sets of nodes by the frequently congested paths or nterfaces.

85 Appendx C Smulatons on the IEEE 24-Bus Network 76 Based on operatonal experence, the SO should modfy the extenson of the zones, transferrng nodes from one zone to another or creatng a new zone, for example f an ntra-zonal congeston becomes frequent and economcally relevant. Usng the same network of Fgure 4-2 but ncreasng capacty of generator 3 to 400 MW, Fgure 4-4 shows alternatve defntons of the nterface between Zones 1 and 2 dependng on the bddng strategy of generator 3. P1= 10 N2 30 MWh < ZONE 1 ZONE 2 > <---- G2A 330 MWh N1 P2= 35 G1 ----> > Generator G3 s bddng at 20 /MWh CONS 0 ---> 0 MWh <---- G2B > > P3= 20 CONS D1 90 MWh N3 D2 450 MWh D3 <---- G3 150 MWh 330 MWh P1= 10 N2 150 MWh < ZONE 1 ZONE 2 > <---- G2A 470 MWh N1 P2= 50 G1 ----> > Generator G3 s bddng at 60 /MWh CONS 0 ---> 20 MWh <---- G2B > > P3= 60 D1 CONS 90 MWh N3 D2 450 MWh D3 <---- G3 150 MWh 50 MWh Fgure 4-4 Determnaton of zones and bddng

86 Appendx C Smulatons on the IEEE 24-Bus Network Long term ssues n FAR Revenue recovery In the absence of market power, FAR would normally recover only part of the total costs of the transmsson network. World experence n the practcal use of LMP n transmsson shows that they only allow a recovery between 10% and 20% of the network total costs (Rudnck, H., et al 1999). It s expected to be lower n a strong transmsson system such as that of NGC. It means that some knd of revenue reconclaton wll be needed to recover NGC s total transmsson costs. So, the queston arses regardng the way the 80% to 90% of the total costs of transmsson wll be recovered, consderng that today those costs are allocated usng locatonal charges. Another ssue of concern relates to the way to recover the transmsson costs nsde a zone, where FAR are not on sale. Cost or value based prcng Prcng of transmsson servces can be based on ether cost of the servce or value of the servce concept (Mutale, J., 2000). Whle value based prcng s approprate for compettve markets lke electrcty generaton or supply, t s not obvous f such schemes are also approprate for monopoly functons such as transmsson. As ndcated earler, value based prcng would need some knd of revenue reconclaton mechansm or addtonal charge to cover the total costs of the transmsson system. In that sense, dscoverng the market value of transmsson by allocatng FAR does not seem an adequate mechansm for addressng short and long run ssues n a consstent manner. A scheme based on the cost of the servce concept seems more approprate to prce transmsson n the long term. Ablty to sgnal locaton of new generaton and demand In a stable market, long-term sgnals for allocaton of new generaton and demand are the response to short run costs of constrants. It means that electrcty wll get a

87 Appendx C Smulatons on the IEEE 24-Bus Network 78 locatonal value. In that sense, FAR s an effcent scheme to sgnal the entrance of new partcpants. Ablty to sgnal nvestments n transmsson Long-term sgnals for nvestments are the result of short run costs of constrants. Then, partcpants could determne the value that addtonal capacty have for them. However, uncertantes regardng revenue recovery, long perods of constructon and free rdng atttude of some partcpants should mpede the development of transmsson capacty on a market bass. Moreover, as t s demonstrated n secton 4.5, tests of an SRMC-based transmsson prcng scheme probed that nvestments are not matched wth SRMC revenues comng up from the energy market on a lne per lne bass. Therefore, short run sgnals am on the rght drecton but they are dstorted by the complextes of meshed networks. 4.5 Transmsson prcng based on SRMC Followng the analyss of the man concepts regardng the applcaton of SRMC for transmsson prcng n a two nodes network, performed n Chapter 3, n ths secton SRMC prcng wll be explored n bgger networks Tests on a 3-bus network The frst tests were performed on a 3-bus meshed network wth 3 demand perods, that contans the man dffcultes usually found on bgger networks Formulaton of the problem The 3-bus network optmal despatch and nvestment soluton was formulated as a lnear optmsaton problem and solved usng the routne Solver provded as one of the tools

88 Appendx C Smulatons on the IEEE 24-Bus Network 79 by MS Excel. The network equatons are formulated through the use of generalsed generaton dstrbuton factors, also known as GGDF (Ng, W., 1981). The problem was formulated on a long term bass n order to study the relatonshp between the SRMC transmsson revenue and transmsson nvestments. The formulaton of the long term optmsaton programme follows: NP NG nhp max p max Mnmse : OIC( g, F ) = c g a l F (4-1) G mn j j l p= 1 j= 1 j j p max g G 1 j NG; 1 p NP (4-2) j j NBR = 1 NG j= 1 g p j = d p 1 p NP (4-3) 0 (4-4) max F F f F (4-5) max p max f p = NBUS k= 1 GGDF k g p k (4-6) The symbols used n the above equatons are defned below: NP - Number of demand perods NG - Number of generators NBR - Number of branches (lnes) NBUS - Number of buses (nodes) nh p - Duraton of demand perod p d p c j p g j p g k mn G j max G j a l max F - Nodal demand for perod p - producton cost of generator j - despatch of generator j durng demand perod p - despatch of generators connected at node k, durng demand perod p - mnmum despatch of generator j - maxmum despatch of generator j - annutsed nvestment factor of lne ( /MW-km-year) - length of lne (km) - transmsson capacty of lne (MW)

89 Appendx C Smulatons on the IEEE 24-Bus Network 80 f p GGDF k - power flow by lne durng perod p - generalsed generaton dstrbuton factor for lne and node k The soluton of the optmsaton problem (4-1) calculates the generaton despatch g p j n each demand perod, power flows per lne n each demand perod f p and the optmal crcut capactes F max for every lne. Calculaton of nodal LRMC (λ kp, at node k durng demand perod p) s performed usng the general formulaton presented n Appendx A, wth a specfc equaton when GGDF are consdered. Analogously nodal SRMC are calculated n the same way when the problem s solved consderng a fxed transmsson capacty (short term formulaton). NBR p p p λ = λ GGDF τ (4-7) k = 1 k In equaton (4-7), λ p are the Lagrange multplers assocated to the demand constrant p equaton (4-3), also called system margnal costs, on every demand perod p and τ are the Lagrange multplers assocated to the transmsson constrants represented by equaton (4-5) Results The followng example presents a comparson of transmsson prcng based on SRMC and LRMC. The example has been developed on the three bus power system shown n Fgure 4-5 and three demand perods are consdered. The man data of the system are as follows: Generators capactes: G1= 400 MW, G2A= 210 MW, G2B= 60 MW, G3= 100 MW Producton costs: G1=10 /MWh, G2A=22 /MWh, G2B=40 /MWh, G3=15 /MWh Demand perods: 100%, 75% and 50% of peak demand wth a duraton of 720 hours, 2800 hours and 5240 hours respectvely Lne reactance and length: 0.2 p.u. and 300 km respectvely, every lne Transmsson annuty nvestment factor: 53 /MW-km-year

90 Appendx C Smulatons on the IEEE 24-Bus Network 81 P1= 18.5 N2 112 MW /MWh <---- G2A 400 MW N1 P2= 22.0 G1 ----> > /MWh 0 ---> 0 MW <---- G2B > > P3= 15.0 CONS D1 /MWh 100 MW N3 D2 400 MW D3 <---- G3 100 MW 88 MW Fgure 4-5: Three bus power system Economcally adapted network (EAN) The EAN s determned va the mnmsaton of the total annual operaton and transmsson annutsed nvestment costs. Optmal transmsson capactes per lne are determned as a result of the mnmsaton process. In the example the optmal transmsson capactes per lne that result are: L12= 208 MW, L23= 92 MW and L13= 117 MW. Those values of transmsson capactes permt a perfect trade off between operaton and nvestment costs from a system pont of vew. Total operaton cost s 34,627 Thousand and the optmal transmsson annutsed nvestment cost s 6,625 Thousand. Transmsson prcng based on LRMC Long-Run Margnal Costs (LRMC) are derved from the optmal soluton of the long term operaton plus nvestment problem. Thereby the soluton s the same already found to determne the EAN. The LRMC correspond to the margnal ncrease of the total operaton and nvestment costs when an addtonal unt of demand s requred. In the same way, nodal LRMC can be determned and they refer specfcally to the ncrease n the total long-term cost when an unt of demand s requred at a partcular node of the system. The values of the LRMC are shown n Table 4-1.

91 Appendx C Smulatons on the IEEE 24-Bus Network 82 Table 4-1: LRMC ( /MWh) Node 1 Node 2 Node 3 Perod Perod Perod The despatch by generators, power flows by lne and LRMC at every node of the system are deployed n Appendx B, for everyone of the perods. A remarkable characterstc of the applcaton of nodal LRMC as a transmsson prcng method s the fact that the nodal LRMC surplus s equal to the total transmsson nvestment cost. Table 4-2 presents the LRMC transactons n the system. Table 4-3 presents the transmsson nvestment costs. Table 4-2: LRMC Transactons (Thousand ) Generaton Perod 1 Perod 2 Perod 3 Total G G2A G2B G Total Generaton Demand Perod 1 Perod 2 Perod 3 Total D D D Total Demand Total Gen. Demand Transmsson Perod 1 Perod 2 Perod 3 Total L L L Total Transmsson

92 Appendx C Smulatons on the IEEE 24-Bus Network 83 Table 4-3: Transmsson Investment Costs (Thousand ) Optmal Transmsson Investment Annuty of Capacty Length Cost Investment Lne MW km /MW-km Th. L L L Total Transmsson Investments 6625 In spte of the equalty between the total LRMC surplus and the total transmsson nvestments, on a lne per lne bass ths stuaton cannot be obtaned. Comparng fgures of Table 4-2 and Table 4-3, although total fgures ft perfectly, LRMC surplus and nvestments per lne are qute dfferent. A smple example can be performed (for nstance openng one of the lnes of the network) to demonstrate that n a radal network ths lne per lne equalty s obtaned. Ths example for a radal network s presented n Appendx B. Therefore the problem relates to meshed networks. The reason to explan why the equalty on a lne per lne bass cannot be acheved s smple: LRMC depend on flows per lne that must follow the Krchhoff Voltage Law (KVL) on parallel paths, on the other sde, transmsson nvestments per lne do not have any relatonshp wth KVL. In other words, power flows and LRMC (or SRMC) revenue follow KVL but transmsson nvestments do not. Ths s a very mportant concluson regardng the applcaton of short-run transmsson prcng methods lke transmsson rghts, especally when they are consdered as physcal rghts and t s expected that n the long-run physcal rghts sgnal and drve nvestments n the transmsson network. KVL s a unque characterstc of electrcal networks and then the access prcng appled to electrcty networks must dffer from other smlar ndustres lke gas transportaton, where there s no such physcal restrcton. In concluson, f a method of physcal rghts lke FAR s put n place then

93 Appendx C Smulatons on the IEEE 24-Bus Network 84 some cross subsdes wll appear as a consequence of parallel paths on real networks (meshed) because a long-term prce dfference between two nodes not necessarly mean that every parallel lne needs a renforcement n ts transmsson capacty. Transmsson prcng based on SRMC Short-Run Margnal Costs (SRMC) are derved from the optmal soluton of the short term operaton problem, so only operatonal costs are consdered because transmsson capacty s fxed. The SRMC correspond to the margnal ncrease of the total operaton costs when an addtonal unt of demand s requred. In the same way, nodal SRMC can be determned and they refer specfcally to the ncrease n the total short-term cost when an unt of demand s requred at a partcular node of the system. Calculaton of SRMC s performed wth the same equatons ndcated for LRMC but the man dfference s on determnaton of Lagrange multplers, whch are dfferent for the longterm and short-term problem. The values of SRMC are shown n Table 4-4. Table 4-4: SRMC ( /MWh) Node 1 Node 2 Node 3 Perod Perod Perod It can be noted that SRMC fgures are qute smlar to LRMC, nevertheless they dffer on perod 2 because n the short term transmsson capacty s fxed and then an addtonal MWh requred at a node must be suppled only wth local generaton f a transmsson constrant s bndng. In the long term, an addtonal MWh demanded at a node can be suppled wth an optmal mx of local generaton and far generaton transported from neghbourng nodes va optmal adjustments on transmsson capacty of the correspondng lnks. Ths stuaton can be observed at nodes 1 and 2 n perod 2. Table 4-5 presents the SRMC transactons n the system. It can be observed that the SRMC revenue has no relatonshp wth the value of the transmsson nvestment costs presented n Table 4-3.

94 Appendx C Smulatons on the IEEE 24-Bus Network 85 Table 4-5: SRMC Transactons (Thousand ) Generaton Perod 1 Perod 2 Perod 3 Total G G2A G2B G Total Generaton Demand Perod 1 Perod 2 Perod 3 Total D D D Total Demand Total Gen. Demand Transmsson Perod 1 Perod 2 Perod 3 Total L L L Total Transmsson In concluson, the SRMC surplus cannot be consdered an effectve transmsson prcng method because t has no relaton wth transmsson nvestments. Therefore t s better to consder the SRMC surplus as a sub-product of the energy market whch can be used to create a set of fnancal rghts for hedgng purposes, for nstance Tests on the IEEE 24-bus network Formulaton of the problem The tests were carred out on the modfed IEEE 24-bus Relablty Test System (IEEE RTS, 1979) whose topology, basc parameters and data are gven n Appendx C. The 24-bus network optmal despatch and nvestment soluton was found usng a computatonal programme developed on a prevous research at UMIST (Neld, S.,

95 Appendx C Smulatons on the IEEE 24-Bus Network ). The programme s wrtten n C language and the problem s formulated as a lnear optmsaton usng CPLEX. The formulaton of the long term optmsaton programme follows: NP NG nhp max p max Mnmse : OIC( g, F ) = c g a l F (4-8) G mn j j l p= 1 j= 1 j j p max g G 1 j NG; 1 p NP (4-9) j j NBR = 1 NG j= 1 g p j = d p 1 p NP (4-10) 0 (4-11) max F The symbols used n the above equatons are the same already defned n secton Transmsson constrants generated as part of the optmsaton process are added as addtonal rows to the lnear programmng problem. These constrants are defned usng the Securty Constraned Optmal Power Flow (SCOPF) method, and they are wrtten as follows: F max f p0 NBUS k=1 h S k ( g g ) F (4-12) p k p0 k max where S represents the system topology (ntact, f the network s operatng wthout outages, or contngency, f the network s operatng wth a securty crtera lke N-1, S wth one lne out of servce) and h k are elements of the senstvty matrx 0 0 T H ] = [ Yd ] [ A ] 1. 0 [ Ybus ] [ r The soluton of the optmsaton problem (4-8) calculates the generaton despatch g p j n each demand perod, power flows per lne n each demand perod f p and the optmal crcut capactes F max for every lne. Calculaton of nodal LRMC (λ kp, at node k durng demand perod p) s performed usng the general formulaton presented n Appendx A, wth an specfc equaton when the

96 Appendx C Smulatons on the IEEE 24-Bus Network 87 SCOPF method s consdered. A specal functon wrtten n C language was developed and added to the optmal transmsson capacty programme to perform the calculatons. Analogously nodal SRMC are calculated n the same way when the problem s solved consderng a fxed transmsson capacty (short term formulaton). NSYS s= S1 NBR p p S ps λ = λ h τ (4-13) k = 1 k In equaton (4-7), λ p are the Lagrange multplers assocated to the demand constrant ps equaton (4-10), also called system margnal costs, on every demand perod p and τ are the Lagrange multplers assocated to the transmsson constrants added to the optmsaton problem, and represented by equaton (4-12) Results Departng from the data of the IEEE 24-bus network gven n Appendx C, the economcally adapted network was determned and consstency of LRMC prcng versus transmsson nvestment was verfed. Fve demand perods were consdered n the analyss. Economcally adapted network (EAN) The EAN s determned va the mnmsaton of the total annual operaton and transmsson annutsed nvestment costs of the problem descrbed by equaton (4-8). Total operaton cost s 147,059 Thousand and the optmal transmsson annutsed nvestment cost s 10,279 Thousand. Fgure 4-6 shows the maxmum power flows by branch of the network for the EAN and the network not constraned n comparson to the capactes of the exstent network. It s remarkable the need for more capacty on branches 12 and 13 whose maxmum flows are 69% over the exstent capacty wthout transmsson constrants and only 24% over the exstent capacty for the EAN.

97 Appendx C Smulatons on the IEEE 24-Bus Network % MAXIMUM FLOW BY LINE 160% Not Constraned EAN 140% Max.Flow / Capacty (%) 120% 100% 80% 60% 40% 20% 0% Branch Fgure 4-6: Maxmum power flows of the EAN and the network not constraned compared to the exstent capacty Transmsson prcng based on LRMC Table 4-6 presents the SRMC transactons for the not constraned network and the EAN, whose total revenue s equal to the annutsed transmsson nvestment cost (10,279 Thousand ). Table 4-7 presents the nodal LRMC and the SMP on every demand perod. Fnally Table 4-8 deploys the LRMC revenues by branch compared to the ndvdual optmal nvestment cost. In spte of the perfect match between total LRMC revenue and the transmsson nvestment cost for the EAN, once more LRMC prcng fals on a branch by branch bass.

98 Appendx C Smulatons on the IEEE 24-Bus Network 89 Table 4-6: SRMC Transactons (Thousand ) SRMC Revenues (Costs) Not Constraned network Perod 1 Perod 2 Perod 3 Perod 4 Perod 5 Total Generaton 19,083 99,471 79,733 56,916 25, ,836 Demand -19,083-99,471-79,733-56,916-25, ,836 Transmsson Operatonal Cost -9,732-43,303-45,132-35,253-13, ,958 Out-of-Mert G.Cost IEEE 24-Bus EAN Perod 1 Perod 2 Perod 3 Perod 4 Perod 5 Total Generaton 20,228 99,444 68,581 56,915 21, ,883 Demand -21,201-99,757-74,576-57,094-24, ,162 Transmsson , ,820 10,279 Operatonal Cost -9,786-43,351-45,132-35,253-13, ,059 Out-of-Mert G.Cost Table 4-7: Nodal LRMC and SMP by perod ( /MWh) Node SMP

99 Appendx C Smulatons on the IEEE 24-Bus Network 90 Table 4-8: LRMC revenue and Transmsson nvestment cost (Thousand ) SRMC revenue (Thousand ) Investment cost Branch Total (Thous. ) %Inv % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % Total % From Table 4-8 t s nterestng to observe that only the nvestment cost of branch 11 s fully pad by the LRMC revenue. Ths stuaton occurs because branch 11 s the only one radal branch n the IEEE 24 bus network. All other branches nvestments are overpad or under-pad usng the LRMC revenue for the EAN. It can also be notced that branches 33 and 34 have a negatve LRMC revenue as a consequence of the Krchhoff Voltage Law (KVL) constrant n the crcut formed by nodes 17, 18, 21 and 22.

100 Appendx C Smulatons on the IEEE 24-Bus Network 91 An SRMC prcng analyss for dfferent transmsson capactes probes to be not relevant due to the lack of relaton between the SRMC surplus and transmsson nvestment. Thus the SRMC surplus wll be lower or upper the transmsson nvestment cost of the optmal network dependng on the over-capacty or under-capacty of the actual network respectvely. In concluson, the SRMC surplus cannot prce transmsson nvestments because they are not related and moreover the LRMC surplus fals to provde the rght revenues for the optmal network. LRMC revenues don t have a match wth transmsson nvestments on a lne per lne bass n meshed networks for the EAN. Then the LRMC method must be dscarded for meshed networks nevertheless t can be appled for radal networks. Therefore an SRMC based method lke frm access rghts (FAR) wll fal to meet the objectves pursued by NETA and wll not allow the recovery of the nvestment cost of the exstent transmsson network for ts owner.

101

102 CHAPTER 5 Use of the concept of economcally adapted network for transmsson prcng Summary Ths chapter descrbes the desgn and tests of a prcng method based on optmal crcut prces derved from the EAN. Begnnng from the conceptual desgn, the method s then tested on a three bus network and on the IEEE 24 bus Relablty Test System. Several case studes are developed on the IEEE 24 bus system to probe the robustness and potental of the method. 5.1 Man ssues Ths prcng strategy comes from the need to fnd an economcally equtable allocaton for optmal crcut nvestments of the economcally adapted network (EAN). In ths case, nodal transmsson prces can be derved from crcut prces dependng on a predefned splt of payments among generators and consumers. Ths method requres that the energy regulatory authorty supervse the determnaton of the EAN and the calculaton of nodal transmsson prces although the calculaton process can be performed by the transmsson companes or the system operator. One very mportant ssue s the rght determnaton of the optmal crcut capactes and therefore, the assocated nvestment costs. Determnaton of the EAN s not an straght forward task because of the amount of data and operatonal smulatons nvolved, ncludng assumptons about the future lke demand forecastng and ts nodal dstrbuton, and development of new generaton power plants and ther locaton. Demand growth rates n developed countres are usually very small (no more than 1%

103 Appendx C Smulatons on the IEEE 24-Bus Network 94 per year) and then transmsson networks are already developed and they do not requre the constructon of new lnes or substatons. In those cases the determnaton of an EAN on a yearly bass s qute acceptable. Nevertheless, demand growth rates n developng countres are hgh (5 to 8% per year) and then transmsson networks are stll under development. In those cases, the defnton of the EAN must nvolve a long term perod of 7 to 10 years and consder the economes of scale provded by transmsson assets over a long term perod. 5.2 Transmsson prcng based on an EAN The proposton conssts of the allocaton of the optmal crcut nvestment costs over users of the transmsson network on the bass of the ntact network flows on the EAN, followng the basc prncples developed by J. Mutale n a PhD research work at UMIST (Mutale, J., 2000) Allocaton of transmsson costs Gong back to the example presented n secton for the two bus network of Fgure 3-1, n Table 3-5 the optmal crcut prce for lne j-k s equal to 17.0 /MWh n perod [0, To], when the flows are bndng. Ths prce multpled by the optmal transmsson capacty (607 MW) and tme To (3,528 hr) s equal to the optmal nvestment cost of lne j-k (36,402 Thousand ). Allocaton of crcut prces only to perods when flows are bndng mean the applcaton of tme-of-use prcng. Allocaton of crcut prces to nodes mean the applcaton of locaton-specfc prcng. Therefore once crcut prces are allocated to nodes the resultant transmsson prces are both locaton and tme-of-use specfc. Allocaton of crcut prces to nodal prces and therefore among generaton and demand users can be performed by defnng a reference node and usng senstvty factors. By defnton an ncremental change at njecton at any node wll be compensated by an equal and opposte njecton at the reference node, hence the nodal prce at the reference

104 Appendx C Smulatons on the IEEE 24-Bus Network 95 node s zero. Fgure 5-1 shows the smplfed verson of the two bus network of Fgure 3-1 and the defnton of the senstvty factors of power flow f to the njecton at nodes j or k. For example f node k s chosen as the reference, then an ncremental change n njecton at node j wll result n an ncrease on power flow f. Therefore the senstvty factor of flow f to the njecton at node j s equal to 1 and the crcut prce of 17.0 /MWh s allocated to node j as a postve nodal transmsson prce. Thus generaton G j pays the nodal transmsson prce for every MWh njected to the network and demand D j s pad the same nodal prce for every MWh wthdrawn from the network. In ths case generaton G k and demand D k do not pay transmsson charges. node j f node k G j G k D j D k f Senstvty factor of flow f to the njecton G j at node j : > 0 f Senstvty factor of flow f to the njecton G k at node k : < 0 Fgure 5-1: Allocaton of crcut prces usng senstvty factors G j G k If node j s chosen as the reference, the senstvty of flow f to njecton at node k s negatve because an ncrease n njecton at node k wll result n a reducton n flow f. Therefore, the nodal transmsson prce at node k s negatve and equal to /MWh. However, usng senstvty factors the nodal transmsson prces and charges to users depend on the choce of the reference node but the total transmsson revenue s ndependent of the choce. Another way to allocate the crcut prces s to splt the transmsson nvestment costs among generaton and demand, for example 50%:50%. The nodal transmsson prces np j and np k at nodes j and k respectvely that result n a splt φ g over generators of the total transmsson revenue (TTR) of the EAN can be found by solvng equatons (5-1) and (5-2) below.

105 Appendx C Smulatons on the IEEE 24-Bus Network 96 np np j j To 0 To To g ( t) dt np g ( t) dt = φ TTR (5-1) j k 0 To k g d j ( t) dt npk d k ( t) dt = (1 φ g ) TTR (5-2) 0 0 Table 5-1 presents the nodal transmsson prces to use n perod [0, To] for two choces of the reference node and for a 50%:50% splt of the transmsson revenue among generaton and demand, usng data from Table 3-2. Table 5-1: Nodal transmsson prces for dfferent allocaton crtera Reference at node j Reference at node k Splt 50% generaton, 50% demand np j ( /MWh) np k ( /MWh) np j np k ( /MWh) In summary, the results are concdent wth the sgnals gven by nodal transmsson prces determned accordng to LRMC for the EAN n secton Only demand D k pays the transmsson cost f the reference node s set at node j or only generator G j pays f the reference node s set at node k. Fnally transmsson cost s shared by generator G j and demand D k when a 50%:50% splt s set. Knowng the actual contrbuton of generators and consumers on the transmsson nvestment cost of the EAN s a queston that could have been answered postvely f Krchhoff Voltage Law (KVL) does not exst. However KVL mpedes the rght allocaton of transmsson costs va LRMC and therefore a market allocaton among generaton and demand cannot be found n meshed networks. In consequence a far 50%:50% splt among generaton and demand wll be consdered n the method.

106 Appendx C Smulatons on the IEEE 24-Bus Network Formulaton of the method The optmal capacty F max of every branch of the transmsson network s determned as a result of the soluton of the long term optmsaton problem that mnmsed transmsson nvestment costs plus generaton operatonal costs of the power system. Therefore transmsson nvestment costs of each branch, ntact and contngent power flows and the optmal generaton despatch n each demand perod are known. The prcng concept of tme-of-use s consdered n the method va the defnton of a threshold factor β, to look for bndng condtons on each branch wth a flow f (t) n a demand perod t : If f ( t) β F max f ( t) = ' f ( t) = 0 ' f ( t); n all other cases (5-3) Whle closer to 100% s factor β the bndng flows are closer to the transmsson capacty F max. Fgure 5-2 shows a duraton curve of flow f(t) n a branch and threshold factor β. f(t) F max β F max 0 t -β F max -F max Fgure 5-2 Applcaton of a threshold factor β over f(t) The maxmum flows per branch of the ntact or any contngent network n each perod that are bndng accordng to condton (5-3) are stored n a matrx arrangement.

107 Appendx C Smulatons on the IEEE 24-Bus Network 98 A securty factor SF (t) for branch n perod t s defned n order to adequate crcut prces for valuaton wth the ntact flows of the EAN. max F SF ( t) = (for f ( t) 0) (5-4) f ( t) where: F max : optmal capacty of branch f (t) : ntact flow of branch n perod t Then the optmal crcut prces cp (t) for branch n perod t are defned as follows: cp ( t) = a l SF ( t) nh NBR = 1 where: a : annutsed nvestment factor of branch ( /MW-km-year) l : length of branch (km) SF (t) : securty factor for branch n perod t nh : sum of hours n the perods when flows are bndng for a branch (5-5) The defnton of locaton-specfc prces s determned va the calculaton of nodal transmsson prces np k (t) at node k n perod t, usng senstvty factors: k NBR I np ( t) = cp ( t) h (5-6) = 1 k where h k I are elements of the senstvty matrx H for the ntact network I: 0 0 T H ] = [ Yd ] [ A ] 1 (5-7) 0 [ Ybus ] [ r In the calculaton of the senstvty matrx of equaton (5-7) t s necessary to defne a reference node or slack bus. The prce at ths node s zero. Therefore a shft on nodal transmsson prces wll be determned n order to splt the transmsson costs among generaton and demand, as t was explaned n secton np sh k ( t) = np ( t) δ ( t) (5-8) k

108 Appendx C Smulatons on the IEEE 24-Bus Network 99 where: np sh k (t) : shfted nodal transmsson prces at node k n perod t np k (t) : nodal transmsson prces at node k n perod t δ(t) : shft n nodal transmsson prces n perod t to obtan a desred splt The calculaton of the shft depends on the defnton of a splt of payments among generaton and demand. The splt of payments to generators φ g (t) n perod t s calculated as follows: NBUS ( npk ( t) δ ( t)) gk ( t) k= 1 φ g( t) = NBUS (5-9) np ( t) ( g ( t) d ( t)) k= 1 k k where: g k (t) : generaton despatch at node k n perod t d k (t) : demand at node k n perod t NBUS : number of nodes k Denomnator of equaton (5-9) corresponds to the total transmsson revenue TTR(t) n perod t, calculated wth nodal transmsson prces: NBUS TTR( t) = np ( t) ( g ( t) d ( t)) (5-10) k= 1 k k k Solvng equatons (5-9) and (5-10), the shft n nodal transmsson prces n perod t s calculated as follows: NBUS npk ( t) gk ( t) φ g ( t) TTR( t) k= 1 δ ( t) = NBUS (5-11) g ( t) k= 1 k In the tests that follow a splt of payments among generators and demand of 50%:50% s consdered (φ g =50%).

109 Appendx C Smulatons on the IEEE 24-Bus Network Tests on a 3-bus network The formulaton of the problem to determne the EAN and the relevant data to develop the tests of the method were performed usng the three bus network model descrbed n secton The same example developed n secton s used to present the method. Consderng a threshold of 90% of the transmsson capacty as defnton of bndng flows, Table 6 contans the sequence to derve crcut prces and crcut revenue. Table 5-2: Bndng flows and crcut prce Optmal Capacty (MW) Power Flows per Lne (MW) Lne Capacty Lne Perod 1 Perod 2 Perod 3 L L L23 92 L L L Bndng Flows per Lne (MW) Bndng Hours per Perod (Hours) Lne Perod 1 Perod 2 Perod 3 Perod 1 Perod 2 Perod 3 Total L L L Nhours Crcut Prces ( /MWh) Crcut Revenue (Thousand ) Lne Perod 1 Perod 2 Perod 3 Lne Perod 1 Perod 2 Perod 3 Total L L L L L L Total In ths case crcut revenues have a perfect match wth transmsson nvestment on a lne per lne bass, shown n Table 4-3.

110 Appendx C Smulatons on the IEEE 24-Bus Network 101 Fnally Table 5-3 shows the nodal transmsson prces derved from the EAN and Table 5-4 presents nodal charges to serve as revenues to pay transmsson nvestment costs of the EAN. Ths method always makes a perfect match between transmsson nvestments and revenues on a lne per lne bass. Table 5-3: Nodal transmsson prces derved from the EAN G - D (MW) Power Flows = [ H ] x [ G-D ] (MW) Node Perod 1 Perod 2 Perod 3 Lne Perod 1 Perod 2 Perod L L L [ H ]T Matrx Nodal Prces [ H ]T x [ Cp ] Lne Node Node Perod 1 Perod 2 Perod Table 5-4: Nodal revenues to pay transmsson nvestment costs Nodal Revenue (Thousand ) Node Perod 1 Perod 2 Perod 3 Total Total Tests on the IEEE 24-bus network The formulaton of the problem to determne the EAN and the relevant data to develop the practcal tests of the method were performed usng the IEEE 24 bus network model descrbed n secton A test for fve demand perods and a threshold of 90% to defne bndng flows was developed n order to compare the results wth those presented for the SRMC method n secton The complete results are ncluded n Appendx

111 Appendx C Smulatons on the IEEE 24-Bus Network 102 C. Table 5-5 presents the valuaton of nodal transmsson charges on generaton and demand sdes showng the 50%:50% allocaton. Table 5-5: Generaton and demand payments Generaton Demand Node (Thousand ) (Thousand ) Total A senstvty analyss to probe the mpact of the threshold, to defne flows that are bndng, on tme-of use allocaton of transmsson revenues s presented n Table 5-6. Threshold values between 50% and 100% were chosen and the temporal dstrbuton of crcut revenues was determned. For a threshold of 100% (only flows equal to the optmal capacty are bndng) the temporal dstrbuton of the transmsson revenues s smlar to the dstrbuton of LRMC transmsson revenues for the EAN (taken from Table 4-8). On the other sde, for a threshold of 50% (flows over 50% of the optmal capacty are bndng) the temporal dstrbuton of the revenues s smlar to the tme dstrbuton per perod, ndcatng that for a threshold less than 50% transmsson prces lose the tme-of-use sgnal.

112 Appendx C Smulatons on the IEEE 24-Bus Network 103 Table 5-6: Dstrbuton of payments by perod dependng on threshold Demand Perod Total LRMC revenue (Th. ) (%) 9% 3% 58% 2% 27% 100% Duraton per perod (Hours) (%) 3% 20% 28% 30% 18% 100% Crcut revenue (Th. ) Dstrbuton (%) Threshold 100% 9% 5% 61% 5% 20% 100% 90% 7% 8% 63% 11% 11% 100% 80% 5% 22% 32% 34% 7% 100% 70% 6% 21% 31% 32% 10% 100% 50% 3% 20% 29% 30% 18% 100% Some specal case studes wll be presented n the next secton to probe the robustness of the method. 5.3 Case studes on the IEEE 24-bus network A seres of case studes on the IEEE 24-bus network were performed consderng ffty demand perods and a threshold of 90% to defne bndng flows. The studes show the robustness of the proposed method usng the C-programme subroutne workng over a large network, smlar to a real one. Topology and parameters of the IEEE 24-bus network, generaton and demand data consdered n the case studes are gven n Appendx C. The power system has 31 generators, 38 branches and a peak demand of 2,850 MW and an N-1 securty crtera s consdered n the studes Network cost recovery Transmsson revenue for the EAN covers exactly the nvestment cost of the network as t s demonstrated n Table 5-7. For over-nvested networks a recovery n excess of the EAN revenue s permtted because there are benefts derved of lower generaton

113 Appendx C Smulatons on the IEEE 24-Bus Network 104 operatonal costs. For example, for a 50% over-capacty n the network only a 1.5% addtonal revenue s obtaned. Table 5-7: Transmsson revenue for the EAN and an over-nvested network Transmsson Capacty / Total Operaton and Operaton Costs Transmsson Transmsson Transmsson revenue / Optmal Capacty Investment Cost Investment Cost Revenue Optmal Revenue (Thousand ) (Thousand ) (Thousand ) (Thousand ) 1 134, , , , , , , , , , , , , , , , Table 5-8 presents the allocaton of the EAN costs among generators and consumers. Table 5-8: Generaton and demand payments for a 50%:50% splt Generaton Demand Node (Thousand ) (Thousand ) Total

114 Appendx C Smulatons on the IEEE 24-Bus Network Robust and weak networks The same IEEE 24-bus network was slghtly modfed n order to create a robust or a weak network compared to the reference network and evaluate the transmsson prcng method on those cases. Lookng at the network topology (Fgure C-1 of Appendx C), a robust transmsson network can be obtaned by movng generators G3, G4, G5 and G6 from nodes 1 and 2 to node 21. In that way the demand area formed by nodes 1 and 2 wll requre transmsson capacty renforcements and the generaton area formed by nodes 18, 21 and 22 wll also requre renforcements to evacuate the addtonal generaton njecton. In the same way, a weak transmsson network can be obtaned by movng generators G24, G25, G26 and G27 from node 22 to node 6 (2 unts) and node 4 (2 unts). In that way the generaton area formed by nodes 18, 21 and 22 wll not requre a bgger capacty and the demand area formed by nodes 2, 4 and 6 wll be suppled by the addtonal generaton njecton, reducng transmsson demands. Table 5-9 presents the mpact of the movements to obtan a robust or weak network on transmsson nvestment costs and revenues of the correspondng EAN. As a result of the changes the robust network has an nvestment cost 15.2% hgher than the reference network and the weak network has an nvestment cost 22.9% lower than the reference network. For each case transmsson revenues are equal to nvestment costs of the correspondng EAN. Table 5-9: Transmsson revenue for the reference, robust and weak networks Transmsson Total Operaton and Operaton Costs Transmsson Transmsson Transmsson nvestment / Network Investment Cost Investment Cost Revenue Reference nvestment (Thousand ) (Thousand ) (Thousand ) (Thousand ) Reference 134, , , , Robust 136, , , , Weak 132, , , ,

115 Appendx C Smulatons on the IEEE 24-Bus Network 106 Fgure 5-3 shows the optmal transmsson capacty by branch for the robust and weak networks compared to the capacty of the reference network and Table 5-10 presents the allocaton of transmsson costs of the EAN among generators and consumers Optmal Network Capactes Robust Reference Weak 500 Capacty (MW) Branch Fgure 5-3 Optmal transmsson capacty for a robust and weak network Table 5-10: Generaton and demand payments for a 50%:50% splt Robust network: Generaton Demand Node (Thousand ) (Thousand ) Total Weak network: Generaton Demand Node (Thousand ) (Thousand ) Total

116 Appendx C Smulatons on the IEEE 24-Bus Network Impact of securty n network desgn A study was performed to analyse the mpact of securty of servce n the nvestment cost of the EAN. The reference case study of secton was developed to determne network optmal capacty wth N-1 crtera. The next step was the development of a case study wthout consderng N-1 securty crtera, so capactes for pure transportaton arse from ths study. It s mportant to warn that all lnes except lne 11 were consdered n the analyss. Lne 11 cannot be ncluded because of ts radal characterstc. Fgure 5-4 shows the over-capacty produced by the applcaton of N-1 securty crtera n network desgn. Investment cost of the network wthout N-1 crtera s equal to 6.1 mllon and ths cost can be assocated to pure transportaton. Investment cost usng the N-1 crtera s equal to 10.3 mllon, therefore 4.2 mllon are the over-nvestment cost assocated to system securty, equvalent to 41% of the total nvestment cost. The evaluaton of the over-capacty nvestment cost s presented n Table Impact of Securty Crtera on Network Capacty N - 1 N 400 Optmal Capacty (MW) Branch Fgure 5-4 Optmal transmsson capacty wth and wthout N-1 crtera

117 Appendx C Smulatons on the IEEE 24-Bus Network 108 Transportaton costs can be allocated 50%:50% among generaton and demand but securty costs must be allocated 100% to demand because consumers have a hgher wllngness to pay for securty due to the deeper mpact of outages, as t was dscussed n secton Table 5-11 presents the resultng 30%:70% splt among generaton and demand respectvely and Table 5-12 shows the correspondng payments. Table 5-11: Investment costs and allocaton among generators and consumers Allocaton of transmsson nvestment costs Thousand Investment for electrcty transportaton Allocaton: 50% generaton % demand Investment for securty of servce Total allocaton Allocaton: 100% demand Generaton % Demand % Total Table 5-12: Generaton and demand payments for a 30%:70% splt Generaton Demand Node (Thousand ) (Thousand ) Total

118 Appendx C Smulatons on the IEEE 24-Bus Network 109 Generaton and demand charges for use of the network can be transformed nto nodal transmsson prces by dvdng the correspondng charges by the electrcty njecton or wthdraw at the respectve node, taken from the optmal despatch and demand for the EAN. The comparson of nodal transmsson prces for a 50%:50% splt generatondemand (based on payments shown n Table 5-8) and 30%:70% splt generaton-demand (based on payments shown n Table 5-12) s gven n Table Table 5-13: Comparson of nodal transmsson prces for 50/50% and 30/70% splt 50% generaton- 50% demand splt 30% generaton- 70% demand splt Generaton Demand Generaton Demand Node ( /MWh) ( /MWh) Node ( /MWh) ( /MWh) Total Total In both cases the total transmsson prce (generaton mnus demand) on an annual base s equal to only 0.66 /MWh. Ths value s less than 5% of the energy market cost n the IEEE 24-bus system, gven n Table 4-7 for fve demand perods. It s mportant to ndcate that a postve prce for generaton means a cost and analogously a negatve prce for demand also means a cost, because demand s evaluated wth a negatve sgn.

119 Appendx C Smulatons on the IEEE 24-Bus Network 110 Fgures 5-5 and 5-6 present graphcally the nodal transmsson prces Transmsson Charges ( /MWh) 0.50 /MWh Node Generaton Demand Fgure 5-5 Nodal transmsson prces for a 50/50% splt generaton/demand 1.00 Transmsson Charges ( /MWh) 0.50 /MWh Node Generaton Demand Fgure 5-6 Nodal transmsson prces for a 30/70% splt generaton/demand In summary, the method has probed to be an effcent transmsson access prcng methodology that allow the recovery of transmsson nvestment costs. For that reason, transmsson prcng based on the concept of economcally adapted network (EAN) s recommended. Prces derved from the EAN have the advantage to be tuned wth the

120 Appendx C Smulatons on the IEEE 24-Bus Network 111 maxmum revenue allowed to the owner of transmsson assets and facltate the optmal allocaton of transmsson costs among users of the network. 5.4 Implementaton on a real system: England & Wales and Chle cases Both cases were poneers n electrcty deregulaton n the 80 s and both are currently frontng a severe revew of the transmsson prcng scheme n use. England and Wales scheme s evolvng from a transmsson prcng scheme wthout locatonal sgnals n the short term to a market-based scheme of access rghts where an addtonal charge wll be requred to complete the revenue of the TO (NGC). Chle s evolvng from a scheme wth powerful short term sgnals based on nodal prcng but unable to fnd a rule to allocate the costs of the network among the users (today only focus on generaton companes) Implementaton n England and Wales The proposed method could be used as part of the NETA proposals to determne the TNUoS charges as an opton to the ICRP method. The proposed method has the vrtue to consder the spatal and temporal allocaton of costs n perfect tune wth margnal cost prncples and therefore sendng rght sgnals to the users regardng the costs they mpose on the network n the long term Implementaton n Chle The proposed method could be used by the Chlean Natonal Energy Commsson to replace the current method of area of nfluence and pro-rata to calculate transmsson tolls. Moreover t means the defnton of a unfed transmsson toll (replacng the current basc and addtonal tolls) that wll be allocated over generators and consumers. On that way, regulated nodal prces calculated as the transference prce among generaton companes and dstrbuton companes to supply consumers wth a demand

121 Appendx C Smulatons on the IEEE 24-Bus Network 112 below 2 MW should be complemented wth a transmsson charge to recover the payments from consumers to the owners of the transmsson assets.

122 CHAPTER 6 Concluson Summary Ths chapter presents the man conclusons, achevements and contrbutons of ths research and recommends topcs for future research. 6.1 Man conclusons Theoretcal and practcal experences n transmsson prcng arse to the concluson that there s no a global optmal method for prcng the use of the electrcty transmsson network. SRMC are well known optmal prces n the energy market nevertheless they are not able to remunerate the nvestment costs of the transmsson network. Therefore a second best soluton must be defned. Transmsson prcng based on the concept of economcally adapted network (EAN) has probed to be an effcent transmsson access prcng method that allow the recovery of transmsson nvestment costs. Prces derved from the EAN have the advantage to be tuned wth the maxmum revenue allowed to the owner of transmsson assets and also those prces facltate the optmal allocaton of transmsson costs among users of the network. In the process of allocaton of transmsson costs t s mportant to recognse that both generaton and demand users affect the dmensonng of the transmsson network and therefore they must pay for the use of the exstent assets. In meshed networks where t s not possble to dentfy transmsson facltes fully used by generators or consumers, the allocaton of transmsson costs must be shared n equal parts by both partes. Determnaton of allocaton accordng to the behavour of the energy market nodal

123 Appendx C Smulatons on the IEEE 24-Bus Network 114 prces s not a straghtforward process n meshed networks due to the complex nteractons derved from the physcal constrant assocated to the Krchhoff Voltage Law (KVL). Therefore, the allocaton of transmsson costs assocated to transportaton of electrcty can be shared 50%:50% among generators and consumers. Addtonal nvestments derved of the applcaton of securty of servce crtera (.e. N-1 ) must be allocated to consumers only. Securty of servce s requred mostly by demand sde due to the hgher economc mpact of outages on consumers rather than generators. Thus, consumers must pay most of transmsson over-capacty destned to mnmse the mpact of outages n the transmsson network. Transmsson prces to cover the nvestment costs of the exstent assets must be regulated because of the monopoly characterstc of the transmsson busness and payments must be compulsory to avod free rder atttudes from partcpants n the energy market. However the determnaton of the EAN s not an straght forward task because of the amount of data and operatonal smulatons nvolved, ncludng assumptons about the future lke demand forecastng and new generatng plants defnton and locaton. Small demand growth rates n developed countres mean transmsson networks already developed and there development of new lnes or substatons s not requred. In those cases the determnaton of an EAN on a yearly bass s qute acceptable. Nevertheless, hgh demand growth rates n developng countres mean transmsson networks under constant development. In those cases, the defnton of the EAN must nvolve a long term perod (.e. 10 years) and consderng economes of scale provded by transmsson assets over that perod. Transmsson prcng must be consstent wth prcng n the energy market. Usng system margnal prcng (SMP) plus congeston management technques to solve transmsson constrants or usng nodal short run margnal costs (SRMC) n the energy market (pool-based or blateral contracts) depend on the nterest of the regulatory authorty to let market partcpants to solve congeston by themselves (usng SRMC) or wth helpng of the system operator (usng SMP plus congeston management). Anyway SRMC surplus derved from the energy market transactons must be allocated to market

124 Appendx C Smulatons on the IEEE 24-Bus Network 115 partcpants rather than transmsson owners because of the perverse ncentve to cause congeston and ncrease the value of the SRMC surplus. Fnancal transmsson rghts (FTR) have probed to be an effcent market way to allocate the SRMC surplus among partcpants n the energy market. Prce dfferences between nodes as a result of SRMC applcaton are not the rght way to pay for the use of the transmsson network. SRMC do not have any relaton to transmsson nvestments and moreover, n real networks wth over-capacty the SRMC surplus can be very small or equal to zero. A better approach to defne transmsson prces s the use of long run margnal costs (LRMC) based on an economcally adapted network. Nevertheless n meshed networks LRMC revenues follow Krchhoff Voltage Law but transmsson nvestments do not. Then there s not a perfect match between LRMC revenues and nvestments for the optmal network on a lne per lne bass. The use of SRMC for transmsson prcng may result n under-capacty n the transmsson network, causng severe congeston and therefore breakng the energy market nto solated zones. The benefts of a robust grd go to every partcpant n the energy market, partcularly consumers, due to a more compettve scenaro and then less optons to exercse market power by any partcpant. In summary, a cost-based method lke nodal transmsson prces derved from the EAN s preferred to a value-based method for transmsson prcng. Development of the transmsson network can be performed on a market base only n partcular stuatons. Two types of transmsson expanson projects can be dentfed. One type of project refers to new transmsson facltes or upgradng on exstent facltes that affect a large amount of users and generally are lnked to global securty of servce. In that case t s very dffcult f not mpossble to get a negotated agreement wth users n tme to expand network capacty and regulatory support s necessary. Other type of project where there s only one or few users can be done based on market forces because ndvdual users are capable to measure the mpact of the project n the energy

125 Appendx C Smulatons on the IEEE 24-Bus Network 116 market. Regulatory partcpaton n nvestment decsons would not be necessary f market partcpants work n a co-operatve more than a compettve way. 6.2 Achevements and contrbutons of ths research A jont analyss of transmsson open access schemes and ts nteracton wth the energy market was presented, facltatng the selecton of an approprate method to prce the use of transmsson networks. A unfed methodology to analyse the energy and access market was developed n order to facltate the analyss of dfferent prcng strateges. Three dfferent models were developed (analytcal 2-bus, 3-bus network and IEEE 24- bus network) that provde a framework to evaluate dfferent prcng schemes and nvestment development n the energy and access markets. The lnk between short and long term ssues n electrcty transmsson was extensvely analysed, more specfcally n relaton to the allocaton of costs of the exstng network and development of nvestments to ncrease the capacty of the network. In that sense the foundatons of prcng and nvestment n electrcty transmsson have been revewed provdng a sold reference for future deregulaton processes. The man lmtatons of the short run margnal cost theory to prce the use of the transmsson network was dscovered. Partcularly n meshed transmsson networks SRMC revenues follow Krchhoff Voltage Law but nvestments do not. Therefore there s not a perfect match between SRMC (nor LRMC) revenues and nvestments for the optmal network on a lne per lne bass. The analyss were developed mplementng C-wrtten routnes to calculate SRMC and LRMC n a mult-node and mult-perod computer programme that determnes an economcally adapted network of a large power system. The routnes wll allow UMIST to have a powerful tool to evaluate the behavour of the energy market and determne locatonal margnal prces n the same way prces wll be dscovered n a compettve market.

126 Appendx C Smulatons on the IEEE 24-Bus Network 117 The recommended transmsson prcng method can be appled calculatng an economcally adapted network (EAN) or determnng a regulated prce control based on net replacement values (NRV) of the exstng assets n the network and then usng the allocaton technque to determne tme-of-use/locaton specfc nodal transmsson prces. 6.3 Recommendatons for future research From the regulatory pont of vew a defnton of the short run costs of not suppled energy could be an effcent way to send market sgnals regardng the value of securty for users. In that way a determnstc securty crtera lke N-1 would be replaced by a market based approach that justfes over-nvestments n transmsson capacty n order to provde the level of securty requred by consumers. Ths way could be the base to defne securty drven nvestments n transmsson. In developng countres the determnaton of the economcally adapted network requre the development of a new model that consders a long term perod (.e. 10 years), take nto account economes of scale n transmsson nvestments and uncertantes regardng the locaton of new generaton facltes and new demand. Partcularly dffcult s the determnaton of the EAN n power systems wth an mportant hydro-power generaton (.e. Brazl, Chle, Norway) due to the stochastc nature of the producton costs lnked to the optmal reservor management.

127

128 References Alaywan, Z. (1999) Facltatng the Congeston Management Market n Calforna, Calforna Independent System Operator, Calforna ISO web ste: com. Araneda, J., Mutale, J. and Strbac, G. (2000) Issues n Transmsson Rghts Schemes for Electrcty Transmsson Prcng, 35th Unverstes Power Engneerng Conference, UPEC 2000, Queen s Unversty of Belfast, September Bernsten, S. (1998) Competton, margnal cost tarffs and spot prcng n the Chlean electrc power sector, Energy Polcy, August Balek, J. (1998) Allocaton of Transmsson Supplementary Charge to Real and Reactve Loads, IEEE Transactons on Power Systems, Vol. 13, No. 3, August Bramer, B. and Bramer, S. (1997) C for Engneers, Arnold. Bushnell, J. and Stoft, S. (1997) Improvng Prvate Incentves for Electrc Grd Investment, Resource and Energy Economcs, March 1997, Vol. 19, Issue 1-2. Bushnell, J. (1999) Transmsson Rghts and Market Power, The Electrcty Journal, Vol. 12 No. 8. Cadwalader, M., Harvey, S., Hogan W. and Pope, S. (1999) Coordnatng Congeston Relef Across Multple Regons, October 1999, W. Hogan web ste: Cardell, J, Cullen Htt, C. and Hogan W. (1997) Market Power and Strategc Interacton n Electrcty Networks, Resource and Energy Economcs, March 1997, Vol. 19, Issue 1-2. Chao, H.P. and Peck, S. (1996) A Market Mechansm for Electrc Power Transmsson, Journal of Regulatory Economcs, Vol. 10 No. 1. Chao, H.P., Peck, S., Oren, S. And Wlson, R. (2000) Flow-Based Transmsson Rghts and Congeston Management, to appear n Electrcty Journal. Espnosa, G. (1995) Bases Conceptuales para la Asgnacón de Peajes en Transmsón (Document wrtten n Spansh. Ttle n Englsh: Conceptual Bass for the Allocaton of Transmsson Tolls ), TRANSELEC, August 1995.

129 Appendx C Smulatons on the IEEE 24-Bus Network 120 Green, R. et al. (1997) Specal Issue on Transmsson Prcng, Utltes Polcy, Vol. 6, Issue 3. Green, R. (1998) Electrcty Transmsson Prcng: How much does t cost to get t wrong?, Unversty of Calforna Energy Insttute (UCEI), PWP-058 paper, Aprl 1998, UCEI web ste. Green, R. (1998) England and Wales A Compettve Electrcty Market?, Unversty of Calforna Energy Insttute (UCEI), PWP-060 paper, September 1998, UCEI web ste. Grbk, P., Angelds, G. and Kovacs, R. (1999) Transmsson Access and Prcng wth Multple Separate Energy Forward Markets, IEEE Transactons on Power Systems, Vol. 14, No. 3, August Harvey, S. and Hogan, W. (2000) Nodal and Zonal Congeston Management and the Exercse of Market Power, January 2000, W. Hogan web ste: Henney, A. (1998) Contrasts n Restructurng Wholesale Electrc Markets: England/Wales, Calforna, and the PJM, The Electrcty Journal, Vol. 11 No 7. Hogan, W. (1992) Contract Networks for Electrc Power Transmsson, Journal of Regulatory Economcs, Vol. 4 No. 3. Hogan, W. (1998) Gettng the Prces Rght n PJM: What the Data Teaches Us, The Electrcty Journal, Vol. 11 No 7. Hogan, W. (1998) Transmsson Investment and Compettve Electrcty Markets, Harvard Unversty, Aprl 1998, W. Hogan web ste: harvard.edu/people/whogan. Hogan, W. (1999) Transmsson Congeston: The Nodal-Zonal Debate Revsted, Harvard Unversty, February 1999, W. Hogan web ste: harvard.edu/people/whogan. Hogan, W. (1999) Market-Based Transmsson Investments and Compettve Electrcty Markets, Harvard Unversty, August 1999, W. Hogan web ste: Hogan, W. (2000) Regonal Transmsson Organzatons: Desgnng market Insttutons for Electrc Network Systems, Harvard Unversty, January 2000, W. Hogan web ste: IEEE Relablty Test System, IEEE Transactons on Power Apparatus and Systems, Vol. PAS-98, No. 6, November/ December 1979.

130 Appendx C Smulatons on the IEEE 24-Bus Network 121 Joskow, P. and Trole, J. (1998) Transmsson Rghts and Market Power on Electrc Power Networks I: Fnancal Rghts, mmeo, MIT and IDEI. Joskow, P. and Trole, J. (1998) Transmsson Rghts and Market Power on Electrc Power Networks II: Physcal Rghts, mmeo, MIT and IDEI. Krschen, D., Allan, R.N. and Strbac, G. (1997) Contrbuton of Indvdual Generators to Loads and Flows, IEEE Transactons on Power Systems, Vol. 12, No. 1, February Mutale, J. (2000) Framework for Allocaton of Transmsson and Dstrbuton Network Costs, PhD Thess, Department of Electrcal Engneerng and Electroncs, UMIST. Mutale, J. and Strbac, G. (2000) Transmsson Network Renforcements Versus FACTS: An Economc Assessment, IEEE Transactons on Power Systems, Vol. 15, No. 3, August Nelson, J. R. (1967) Margnal Cost Prcng n Practce, Prentce-Hall. Neushloss, J. and Woolf, F. (1999) Revew of the England and Wales Tradng Arrangements: The Proposal to Cure the Ill by Euthanasa of the Pool, The Electrcty Journal, December 1999, Vol. 12, Issue 10. Ng, W. Y. (1981) Generalzed Generaton Dstrbuton Factors for Power System Securty Evaluatons, IEEE Transactons on Power Apparatus and Systems, Vol. PAS-100, No. 3, March Ncholson, W. (1998) Mcroeconomc Theory: Basc Prncples and Extensons, The Dryden Press. Neld, S. (2000) Optmal Transmsson System Investment Program V2.0 User Gude, MEng fnal work, Department of Electrcal Engneerng and Electroncs, UMIST. Offce of Gas and Electrcty Markets (1998) NGC System Operator Incentves, Transmsson Access and Losses under NETA, A Consultaton Document, December Offce of Gas and Electrcty Markets (2001) Transmsson Access and Losses under NETA, A Consultaton Document, May PJM Interconnecton (1998) FTR Aucton Tranng, PJM web ste: PJM Interconnecton (1999) FTR Aucton User s Gude, PJM web ste:

131 Appendx C Smulatons on the IEEE 24-Bus Network 122 PJM Interconnecton (1999) PJM Open Access Transmsson Tarff, PJM web ste: Rubo, F. J. and Perez-Arraga, I. (2000) Margnal Prcng of Transmsson Servces: A Comparatve Analyss of Network Cost Allocaton Methods, IEEE Transactons on Power Systems, Vol. 15, No. 1, February Rudnck, H., Soto, M. and Palma, R. (1999) Use of System Approaches for Transmsson Open Access Prcng, Internatonal Journal of Electrc Power and Energy Systems, Vol. 21, Issue 2, February Schweppe, F., Caramans, M., Tabors, R. and Bohn, R. (1988) Spot Prcng of Electrcty, Kluwer Academc Publshers. Sngh, H., Hao, S. and Papalexopoulos, A. (1998) Transmsson Congeston Management n Compettve Electrcty Markets, IEEE Transactons on Power Systems, Vol. 13, No. 2, May Stoft, S. (1999) Fnancal Transmsson Rghts Meet Cournot: How TCC Curb Market Power, The Energy Journal, January 1999, Vol. 20, Issue 1. Strbac, G., Krschen, D. and Ahmed, S. (1998) Allocatng Transmsson System Usage on the Bass of Traceable Contrbutons of Generators and Loads to Flows, IEEE Transactons on Power Systems, Vol. 13, No. 2, May Transpower New Zealand Lmted (2000) Submsson to The Mnsteral Inqury nto the Electrcty Industry, Vol. 1, 2 and 3, March Wu, F., Varaya, P., Spller, P. and Oren, S. (1996) Folk Theorems on Transmsson Access: Proofs and Counterexamples, Journal of Regulatory Economcs, Vol. 10, Issue 1.

132 Appendx A Nodal SRMC on a Transmsson Network A1. Objectve The objectve of ths appendx s the presentaton of two methods to calculate short run margnal costs (SRMC) by node on a transmsson network, consderng transmsson capacty and securty constrants. A2. Introducton In a compettve market lke electrcty, SRMC represent the optmal prce to nterchange energy among producers and users, mnmsng the system operatng costs and maxmsng socal welfare. In electrcty deregulated markets, based on blateral agreements (not pool based), SRMC are a very good reference to dscover the value of electrcty at dfferent locatons on the transmsson network. In absence of market power, prces must tend to SRMC at every locaton on the network. Therefore, t s worth for generaton and supply companes bddng n a new blateral energy market to dscover the locatonal value of electrcty and desgn commercal strateges to maxmse profts n that new envronment. In ths Appendx two methods to calculate SRMC are presented on a 3-node lossless network. A3. Nodal SRMC calculatons usng GGDF Fgure A-1 presents a 3-bus network wth three generators that supply nodal demand.

133 Appendx C Smulatons on the IEEE 24-Bus Network 124 g 1 f 12 g f 31 f 23 d 1 3 d 2 d 3 g 3 Fgure A-1: Three bus network Varable operaton costs of G 1, G 2 and G 3 are c 1, c 2 and c 3 respectvely. Power flows by the transmsson lnes L 1, L 2 and L 3 are f 12, f 23 and f 31 respectvely. Usng generalsed generaton dstrbuton factors or GGDF (Ng, W., 1981), the flows by the transmsson lnes can be wrtten as: f l = 3 = 1 a l g Where a l s the GGDF of the generator on the lne l. Values of GGDF are dependent of the network topology, reactance and demand dstrbuton. By mnmsng the total operaton costs of the system the calculaton of the system margnal cost (λ) s performed. The objectve functon s: Mn 3 = 1 c g

134 Appendx C Smulatons on the IEEE 24-Bus Network 125 subject to: g g - Gmn = 1,2,3 Gmax = 1,2,3 (generators) (generators) 3 = 1 3 = 1 a a l g l g Fmax l - Fmaxl l = 1,2,3 l = 1,2,3 (lnes) (lnes) d - 3 = 1 g = 0 (total demand, d = 3 = 1 d ) Then, Z = c g λ (d - g ) µ ( g G max ) τ l ( al g F maxl ) γ l ( al g ν ( g F max ) l G mn ) Z g = 0 = c λ µ ν ( τ γ ) a = 1,2,3 l l l And then, the system SRMC s: λ = c µ ν ( τ l γ l ) al Advancng nto the calculaton of SRMC by node, we must re-wrte the demand equaton consderng Krchhoff Current Law (KCL) at every one of the 3 nodes as follows: d 1 - g1 f12 f31 = 0 ( λ1 ) d d - g2 f12 f23 = 0 ( 2) 2 λ - g3 f31 f23 = 0 ( 3) 3 λ Then:

135 Appendx C Smulatons on the IEEE 24-Bus Network 126 ) ( ) ( ) ( ) max ( ) max ( ) mn ( ) max ( l f f g d f f g d f f g d F g a F g a G g G g g c Z l l l l l = λ λ λ γ τ ν µ ) ( ) ( ) ( ) ( g f g f g f g f g f g f a c g Z l l l = = λ λ λ λ γ τ ν µ = ) ( But l l l a c γ τ ν µ λ Then, ) ( ) ( ) 1 ( a a a a a a g Z = = λ λ λ λ ) ( ) 1 ( ) ( a a a a a a g Z = = λ λ λ λ ) 1 ( ) ( ) ( a a a a a a g Z = = λ λ λ λ They also can be wrtten as: ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( λ λ λ λ λ λ λ λ λ λ λ λ λ λ λ λ λ λ λ λ λ λ λ λ = = = a a a a a a a a a However, λ 2 - λ 1 represents the SRMC dfference between nodes 2 and 1 and t s equal to the sum of the Lagrange multplers for the transmsson constrants of Lne 1 or τ 1 - γ 1. Then, λ 2 - λ 1 = τ 1 - γ 1, λ 3 - λ 2 = τ 2 - γ 2 and λ 1 - λ 3 = τ 3 - γ 3.

136 Appendx C Smulatons on the IEEE 24-Bus Network 127 Thus, the system SRMC s related to the nodal SRMC by the followng equaton: λ = λ 3 l= 1 ( τ γ ) a l l l And the nodal SRMC at every node can be calculated as: λ = c µ ν = λ 3 l= 1 ( τ γ ) a l l l A4. Nodal SRMC calculatons based on a SCOPF Usng a securty constraned optmal power flow (SCOPF), nodal SRMC can also be calculated consderng the Lagrange multplers as an outcome of the optmsaton process. To ntroduce securty constraned calculatons we are gong to add a new varable renamng the lnes. Thus, as shown n Fgure A-2, f 1 = f 12 c1, f 2 = f 12 c2, f 3 = f 31 and f 4 = f 23. g 1 f 1 g 2 f f 3 f 4 d 1 3 d 2 d 3 g 3 Fgure A-2: Three bus network for SCOPF

137 Appendx C Smulatons on the IEEE 24-Bus Network 128 Mnmsng the total operaton costs of the system wthout consderng the transmsson capacty constrants, the problem can be wrtten as: Mn 3 = 1 c subject to: g g g Gmax = 1,2,3 - Gmn = 1,2,3 (generators) (generators) d - 3 = 1 g = 0 (total demand, d = 3 = 1 d ) As a result the power flows through the lnes can be calculated (F l 0 ), where the subscrpt l represents the lne number and the superscrpt 0 means belongng to the unconstraned system. Therefore, for the ntact system (I) the transmsson constrants can be wrtten usng the senstvty coeffcents h l, representng the ncremental change n power flow f l n lne l due to an ncremental change n generaton g at node. h l fl g T 0 0 and ther values are obtaned from [H] = [Yd ] [ A ] [ ] 1 0 [ Y ] = r bus Then, the constraned flows for the ntact system (I) can be wrtten as: F 0 l 3 = 1 h I l ( g g 0 ) F max l F 0 l 3 = 1 h ( g g I l 0 ) F max l l = 1,...4

138 Appendx C Smulatons on the IEEE 24-Bus Network 129 In the same way, for the contngent system (C) the transmsson constrants can be wrtten usng the senstvty coeffcents h l but they must be re-calculated accordng to the new network topology under the contngent condton, for example wth lne 1 out of servce. Then, the constraned flows for the contngent system (C) are: = 3 1 max ) ( l I C l I l F g g h F 1,...4 l max ) ( 3 1 = = l I C l I l F g g h F In ths case, the augmented Lagrangan can be wrtten as: = ) max ) ( ( ) max ) ( ( ) max ) ( ( ) max ) ( ( ) mn ( ) max ( ) ( C l C l 0 0 I l 0 0 I l l I l I C l l I l I C l l l I l l l I l F F g g h F F g g h F F g g h F F g g h G g G g g d g c Z γ τ γ τ ν µ λ And, = = C l C l C l I l I l I l h h u c g Z ) ( ) ( 0 γ τ γ τ ν λ Then, the system SRMC s: = C l C l C l I l I l I l h h u c ) ( ) ( γ τ γ τ ν λ And the SRMC at the node s: = = SYS S L l S l ) ( - S l S l h u c γ τ λ ν λ

139 Appendx C Smulatons on the IEEE 24-Bus Network 130 Then, nodal SRMC can be obtaned by calculatng SRMC for the unconstraned system (λ) and subtractng the sum of the Lagrange multplers assocated to transmsson constrants on every one of the system topologes evaluated n the SCOPF method.

140 Appendx B Smulatons on a 3-Bus Network B.1 Example of secton The three bus power system shown n Fgure 4-5 was analysed n the example ncluded n secton , presentng the applcaton of dfferent transmsson prcng methods. Partcularly the long term behavour of the system s shown n Fgure B-1. For each demand perod Fgure B-1 presents the optmal despatch of the generatng unts to supply the demand consderng transmsson constrants and the resultng power flows by lne. As a result LRMC at each node are dscovered. The man data of the system are as follows: Generators capactes: G1= 400 MW, G2A= 210 MW, G2B= 60 MW, G3= 100 MW Producton costs: G1= 10 /MWh, G2A= 22 /MWh, G2B= 40 /MWh, G3= 15 /MWh Three demand perods: 100%, 75% and 50% of peak demand wth duraton of 720 hours, 2800 hours and 5240 hours respectvely Lne reactances and length: 0.2 p.u. and 300 km respectvely, every lne Transmsson nvestment factor: 53 /MW-km-year Optmal transmsson capactes of the EAN are: L12= 208 MW, L23= 92 MW and L13= 117 MW.

141 Appendx C Smulatons on the IEEE 24-Bus Network 132 P1= 18.5 N2 112 MW /MWh <---- G2A 400 MW N1 P2= 22.0 G1 ----> > /MWh 0 ---> 0 MW <---- G2B > > P3= 15.0 CONS D1 /MWh 100 MW N3 D2 400 MW D3 <---- G3 100 MW 88 MW Perod 1 P1= 10.3 N2 0 MW /MWh <---- G2A 400 MW N1 P2= 17.0 G1 ----> > /MWh CONS 0 ---> 0 MW <---- G2B > > P3= 15.0 CONS D1 CONS /MWh 75 MW N3 D2 300 MW D3 <---- G3 75 MW 50 MW Perod 2 P1= 10.0 N2 0 MW /MWh <---- G2A 300 MW N1 P2= 10.0 G1 ----> > /MWh 0 ---> 0 MW <---- G2B > > P3= 10.0 D1 /MWh 50 MW N3 D2 200 MW D3 <---- G3 50 MW 0 MW Perod 3 Fgure B-1: Long term system behavour by perod (prces are LRMC) (note: cons ndcates an actve transmsson constrant)

142 Appendx C Smulatons on the IEEE 24-Bus Network 133 B.2 LRMC and SRMC prcng on a radal network The followng example presents the calculaton of the Economcally Adapted Network (EAN), LRMC and SRMC performed on the three bus radal system shown n Fgure B- 2, consderng three demand perods. Data are the same of the prevous example except producton costs (G1=12 /MWh, G2A= 17 /MWh, G2B= 40 /MWh, G3= 25 /MWh). P1= 22.4 N2 100 MW /MWh <---- G2A 400 MW N1 P2= 25.0 G1 ----> > /MWh CONS 0 ---> 0 MW <---- G2B 0 ---> 0 ---> P3= 25.0 D1 CONS /MWh 100 MW N3 D2 400 MW D3 <---- G3 100 MW 100 MW Fgure B-2: Three bus radal system The optmal transmsson capactes of the EAN that result are: L12= 300 MW and L23= 75 MW. Total operaton cost s 41,514 Thousand and the optmal transmsson annutsed nvestment cost s 5,962 Thousand. B2.1 LRMC Long-Run Margnal Costs (LRMC) are shown n Table B-1. Table B-1: LRMC ( /MWh) Node 1 Node 2 Node 3 Perod Perod Perod

143 Appendx C Smulatons on the IEEE 24-Bus Network 134 In Fgure B-3, despatch by generators, power flows by lne and LRMC at every node of the system are deployed, for everyone of the perods. As t was expected, the applcaton of LRMC for transmsson prcng means that nodal LRMC surplus s equal to the total transmsson nvestment cost. Table B-2 presents the LRMC transactons n the system. Table B-3 presents the transmsson nvestment costs. Table B-2: LRMC Transactons (Thousand ) Generaton Perod 1 Perod 2 Perod 3 Total G G2A G2B G Total Generaton Demand Perod 1 Perod 2 Perod 3 Total D D D Total Demand Total Gen. Demand Transmsson Perod 1 Perod 2 Perod 3 Total L L L Total Transmsson

144 Appendx C Smulatons on the IEEE 24-Bus Network 135 Table B-3: Transmsson Investment Costs (Thousand ) Optmal Transmsson Investment Annuty of Capacty Length Cost Investment Lne MW km /MW-km Th. L L L Total Transmsson Investments 5962 Moreover the equalty between the total LRMC surplus and the total transmsson nvestment cost, ndvdual lne nvestments are perfectly matched to ts correspondng LRMC surplus. Therefore the dscrepancy between LRMC surplus and transmsson nvestment cost on a lne per lne bass dsappears on radal networks. B2.2 Transmsson prcng based on SRMC Short-Run Margnal Costs (SRMC) are shown n Table B-4. Table B-4: SRMC ( /MWh) Node 1 Node 2 Node 3 Perod Perod Perod It can be noted that SRMC fgures are smlar to LRMC, nevertheless they dffer at node 1, perod 1, and node 3, perod 2, because n the short term transmsson capacty s fxed and then an addtonal MWh requred at a node must be suppled only wth local generaton f a transmsson constrant s bndng.

145 Appendx C Smulatons on the IEEE 24-Bus Network 136 Table B-5 presents the SRMC transactons n the system. It can be observed that the SRMC revenue has no relatonshp wth transmsson nvestment costs. Table B-5: SRMC Transactons (Thousand ) Generaton Perod 1 Perod 2 Perod 3 Total G G2A G2B G Total Generaton Demand Perod 1 Perod 2 Perod 3 Total D D D Total Demand Total Gen. Demand Transmsson Perod 1 Perod 2 Perod 3 Total L L L Total Transmsson Therefore, the SRMC surplus cannot be consdered for transmsson prcng and t s better to consder the SRMC surplus as a sub-product of the energy market whch can be used to create a set of fnancal rghts for hedgng purposes, for nstance. In Fgure B-4, despatch by generators, power flows by lne and SRMC at every node of the system are deployed, for everyone of the perods.

146 Appendx C Smulatons on the IEEE 24-Bus Network 137 B2.3 Senstvty Analyss B2.3.1 Optmal transmsson capactes A senstvty analyss on transmsson capactes was performed n order to demonstrate the robustness of the EAN calculatons. Transmsson capactes were slghtly ncreased and decreased wth respect to the optmal capacty fgures. The analyss probed that the calculated optmal transmsson capactes really acheved the mnmum cost soluton for the system. The results are shown n Table B-6. Table B-6: Operaton, Investment and Total Costs Senstvty Analyss (Thousand ) Transmsson Capacty L12 Optmal 300 MW 290 MW 310 MW 300 MW 300 MW L23 75 MW 75 MW 75 MW 70 MW 80 MW Costs: Operaton 41,514 41,748 41,374 41,626 41,514 Investment 5,962 5,803 6,121 5,883 6,042 Total Cost 47,476 47,551 47,495 47,509 47,557 B2.3.2 SRMC calculatons Another senstvty analyss was performed n order to demonstrate the robustness of the SRMC calculatons. The transmsson capacty of lne L12 was ncreased slghtly n 0.1 MW (to MW) n order to check ts mpact on nodal SRMC calculatons. Results are shown n Table B-7. Table B-7: SRMC ( /MWh) Node 1 Node 2 Node 3 Perod Perod Perod

147 Appendx C Smulatons on the IEEE 24-Bus Network 138 Table B-8 presents the SRMC transactons n the system. It can be noted that SRMC revenues are lower than fgures presented n Table B-5 due to the unconstraned stuaton at Lne L12 that occurs n Perod 1. In summary, SRMC calculatons are hghly volatle and therefore SRMC transmsson revenues are as low or hgh dependng on branches that are bndng at any partcular operatonal condton. Table B-8: SRMC Transactons (Thousand ) Generaton Perod 1 Perod 2 Perod 3 Total G G2A G2B G Total Generaton Demand Perod 1 Perod 2 Perod 3 Total D D D Total Demand Total Gen. Demand Transmsson Perod 1 Perod 2 Perod 3 Total L L L Total Transmsson In Fgure B-5, despatch by generators, power flows by lne and SRMC at every node of the system are deployed, for everyone of the perods.

148 Appendx C Smulatons on the IEEE 24-Bus Network 139 P1= 22.4 N2 100 MW /MWh <---- G2A 400 MW N1 P2= 25.0 G1 ----> > /MWh CONS 0 ---> 0 MW <---- G2B 0 ---> 0 ---> P3= 25.0 D1 CONS /MWh 100 MW N3 D2 400 MW D3 <---- G3 100 MW 100 MW Perod 1 P1= 12.0 N2 75 MW /MWh <---- G2A 375 MW N1 P2= 17.0 G1 ----> > /MWh CONS 0 ---> 0 MW <---- G2B 75 < > P3= 22.7 CONS D1 CONS /MWh 75 MW N3 D2 300 MW D3 <---- G3 75 MW 0 MW Perod 2 P1= 12.0 N2 0 MW /MWh <---- G2A 300 MW N1 P2= 12.0 G1 ----> > /MWh 0 ---> 0 MW <---- G2B 50 < > P3= 12.0 D1 CONS /MWh 50 MW N3 D2 200 MW D3 <---- G3 50 MW 0 MW Perod 3 Fgure B-3: Long term system behavour per perod (prces are LRMC) (note: cons ndcates an actve transmsson constrant)

149 Appendx C Smulatons on the IEEE 24-Bus Network 140 P1= 12.0 N2 100 MW /MWh <---- G2A 400 MW N1 P2= 25.0 G1 ----> > /MWh CONS 0 ---> 0 MW <---- G2B 0 ---> 0 ---> P3= 25.0 D1 CONS /MWh 100 MW N3 D2 400 MW D3 <---- G3 100 MW 100 MW Perod 1 P1= 12.0 N2 75 MW /MWh <---- G2A 375 MW N1 P2= 17.0 G1 ----> > /MWh CONS 0 ---> 0 MW <---- G2B 75 < > P3= 25.0 CONS D1 CONS /MWh 75 MW N3 D2 300 MW D3 <---- G3 75 MW 0 MW Perod 2 P1= 12.0 N2 0 MW /MWh <---- G2A 300 MW N1 P2= 12.0 G1 ----> > /MWh 0 ---> 0 MW <---- G2B 50 < > P3= 12.0 D1 CONS /MWh 50 MW N3 D2 200 MW D3 <---- G3 50 MW 0 MW Perod 3 Fgure B-4: Short term system behavour per perod (prces are SRMC) (note: cons ndcates an actve transmsson constrant)

150 Appendx C Smulatons on the IEEE 24-Bus Network 141 P1= 25.0 N2 100 MW /MWh <---- G2A 400 MW N1 P2= 25.0 G1 ----> > /MWh CONS 0 ---> 0 MW <---- G2B 0 ---> 0 ---> P3= 25.0 D1 /MWh 100 MW N3 D2 400 MW D3 <---- G3 100 MW 100 MW Perod 1 P1= 12.0 N2 75 MW /MWh <---- G2A 375 MW N1 P2= 17.0 G1 ----> > /MWh CONS 0 ---> 0 MW <---- G2B 75 < > P3= 25.0 CONS D1 /MWh 75 MW N3 D2 300 MW D3 <---- G3 75 MW 0 MW Perod 2 P1= 12.0 N2 0 MW /MWh <---- G2A 300 MW N1 P2= 12.0 G1 ----> > /MWh 0 ---> 0 MW <---- G2B 50 < > P3= 12.0 D1 /MWh 50 MW N3 D2 200 MW D3 <---- G3 50 MW 0 MW Perod 3 Fgure B-5: Short term system behavour per perod (prces are SRMC) Senstvty analyss: addng 0.1 MW more capacty at Lne L12 (note: cons ndcates an actve transmsson constrant)

151

152 Appendx C Smulatons on the IEEE 24-Bus Network C1. Descrpton of the IEEE 24-Bus Network G22 BUS 18 BUS 17 L28 L33 L34 G23 G24,25,26,27,28 BUS 21 BUS 22 L30 L29 BUS 23 L26 L35 L37 G21 L27 L36 L38 G29,30,31 BUS 19 BUS 16 BUS 20 L31 L32 L24 L kv L21 L22 L25 BUS 15 G15,16,17,18,19,20 BUS 14 L19 L18 L20 BUS 13 G12,13,14 BUS 24 BUS 11 BUS 12 VVV L7 L14 VVV VVV L16 L15 VVV VVV L17 BUS 3 L6 BUS 9 BUS 10 L kv L8 L12 L13 L2 BUS 4 L9 BUS 6 L3 L1 L4 BUS 5 L5 BUS 8 L11 BUS 1 BUS 2 BUS 7 G1,2,3,4 G5,6,7,8 G9,10,11 Fgure C-1 Topology of the IEEE 24-Bus Relablty Test System

153 Appendx C Smulatons on the IEEE 24-Bus Network 144 Table C-1 Transmsson network data for modfed IEEE 24-bus Relablty Test System Lne From To Reactance Capacty Length Incremental bus bus (p.u.) on nvestment cost 100 MVA base (MVA) (km) ( /MW-km-yr)

154 Appendx C Smulatons on the IEEE 24-Bus Network 145 Table C-2 Generaton data for modfed IEEE 24-bus Relablty Test System Generator Connecton Maxmum Mnmum Operatng bus output output cost (MW) (MW) ( /MWh)

155 Appendx C Smulatons on the IEEE 24-Bus Network 146 C2. Demand data for studes on the IEEE 24-Bus Network Table C-3 Demand peak for modfed IEEE 24-bus Relablty Test System Bus Peak demand (MW) Total 2850 Table C-4 Daly peak load n Percent of Weekly Peak Day Day Peak load number Monday 1 93 Tuesday Wednesday 3 98 Thursday 4 96 Frday 5 94 Saturday 6 77 Sunday 7 75

156 Appendx C Smulatons on the IEEE 24-Bus Network 147 Table C-5 Weekly peak load n Percent of Annual Peak Week Peak load Week Peak load Table C-6 Hourly peak load n Percent of Daly Peak Wnter weeks Summer weeks Sprng/Fall weeks Hour week 1-8 & week week 9-17 & Weekday Weekend Weekday Weekend Weekday Weekend

157 Appendx C Smulatons on the IEEE 24-Bus Network 148 Table C-7 Load duraton curve for modfed IEEE 24-bus Relablty Test System (samplng data for 50 demand perods) Demand Load Duraton n Demand Load Duraton n perod n MW Hours perod n MW Hours Table C-8 Load duraton curve for modfed IEEE 24-bus Relablty Test System (samplng data for 5 demand perods) Demand Load Duraton n perod n MW Hours

158 Appendx C Smulatons on the IEEE 24-Bus Network 149 C3. Results of smulatons on the IEEE 24-Bus Network (5 demand perods) Table C-9 Optmal capacty and power flows per perod Optmal Intact Flows per perod (MW) Contngency Flows per perod (MW) Capacty Branch (MW) Max Max

159 Appendx C Smulatons on the IEEE 24-Bus Network 150 Table C-10 Bndng flows and securty factor per perod Bndng Flows per perod (MW) Securty factor (o/1) Branch

160 Appendx C Smulatons on the IEEE 24-Bus Network 151 Table C-11 Crcut prces and crcut revenue Crcut Prces ( /MWh) Crcut Revenue (Thousand ) Investment perod perod Branch Total (Thous. ) Total

161 Appendx C Smulatons on the IEEE 24-Bus Network 152 Table C-12 Nodal transmsson prces Nodal Prces referred to node 1 ( /MWh) Shfted Nodal Prces ( /MWh) Node Shft

162 Appendx C Smulatons on the IEEE 24-Bus Network 153 Table C-13 Generaton and demand payments of transmsson charges Generaton payments (Thousand ) Demand payments (Thousand ) Node Total Total Total Total transmsson revenue

163 Appendx C Smulatons on the IEEE 24-Bus Network 154 C4. Results of case studes on the IEEE 24-Bus Network (50 demand perods) Table C-14 Optmal capacty of the Economcally Adapted Network Optmal Transmsson Capacty (MW) Impact on network Reference Robust Weak Robust/Ref. Weak/Ref. Branch Network Network Network Df. (%) Df. (%) % -36% % -32% % 3% % 1% % -50% % -25% % -21% % -32% % -24% % -59% % 9% % 0% % 0% % -24% % -18% % -26% % -24% % -17% % -27% % -22% % -13% % -14% % -20% % -24% % -21% % -24% % -26% % -12% % -53% % -73% % -22% % -22% % -21% % -21% % -30% % -30% % 31% % 31%

164 Appendx C Smulatons on the IEEE 24-Bus Network 155 Table C-15 Optmal capacty wth and wthout securty crtera Optmal Transmsson Capacty (MW) N N - 1 Overcapacty Branch Crtera Crtera (%) % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %

165 Appendx C Smulatons on the IEEE 24-Bus Network 156 Table C-16 Despatch and transmsson charges for the Reference Network Despatch and Demand by Node Nodal Transmsson Payments Nodal Transmsson Prces Generaton Demand Generaton Demand Generaton Demand Node (GWh) (GWh) Node (Thousand ) (Thousand ) Node ( /MWh) ( /MWh) Total Total Total Total