Interactive optimisation modelling using OPTIMISIR to support Melbourne water supply system operation

Transcription

1 19h Inernaional Congress on Modelling and Simulaion, Perh, Ausralia, December 2011 hp://mssanz.org.au/modsim2011 Ineracive opimisaion modelling using OPTIMISIR o suppor Melbourne waer supply sysem operaion M.D.U.P. Kularahna a, B. Baker a and S. Ennor b a Sraegic Planning, Melbourne Waer, Melbourne, Vicoria, Ausralia, b Operaions, Melbourne Waer, Melbourne, Vicoria, Ausralia. udaya.kularahna@melbournewaer.com.au Absrac: Melbourne Waer is a Vicorian governmen owned company ha provides waer supply and sewerage services o Melbourne s reail waer companies, and manages rivers and creeks, floodplains and he regional drainage sysem. The waer supply sysem managed by Melbourne Waer is a complex inerconneced sysem of 10 sorage reservoirs, over 40 service reservoirs, 160,000 hecares of cachmens and hundreds of kilomeres of unnels and pipelines. The complexiy of he sysem and he needs of sakeholders mean he way he sysem operaes has o be based on many consideraions. These consideraions include: he volumes o be sourced from various sources ha have differenial coss, he iming of volumes sourced, he disribuion of sorage wihin he waer supply sysem, minimising he risk of localised or sysem wide spills due o increased supply from various sources, minimising he risk of low sorage volumes due o inadequae inakes volumes, waer securiy, waer qualiy, scheduled ouages of sysem asses, operaing and capial coss, and environmenal facors such as healhy waer ways and lower greenhouse gas emissions. In planning for he sysem operaion, Melbourne Waer prepares an annual operaing plan (AOP) ha provides a forecas of anicipaed sysem operaion for he waer supply sysem. The AOP oulines he sysem operaions for he 12 monhs ahead, and idenifies for each monh he volumes of waer o be sourced, sored, moved from one locaion o anoher, or released o waerways. The preparaion of his forecas is a complex ask, as he decisions on sysem operaions need o be made under many consideraions as oulined earlier. In he pas, he preparaion of AOP was a manual process underaken by experienced operaors assised by decision suppor ools based around he calculaion of waer balance across he sysem. However, hese ools did no have he capabiliy o idenify opimal operaions. Insead, he ools were manually updaed by experienced operaors as documened in sysem operaion guidelines, in consulaion wih Melbourne s reail waer companies. The above approach was ime consuming, ofen requiring up o wo weeks o produce an AOP for one operaing scenario. In 2009, Melbourne Waer developed in-house an opimisaion modelling ool, OPTIMISIR, o assis in idenifying he opimal annual operaing plan. OPTIMISIR provides he flexibiliy for users o specify various sreamflow and demand scenarios, sysem sorage condiions, operaing requiremens and he aims of he opimisaion in he form of an objecive funcion. The choice wo opimisaion mehods; Linear Programming or Quadraic Programming is also available. The oupu of OPTIMISIR comprises he operaing decisions for each of he 12 monhs including: volumes o be aken from various waer sources, volumes o be released o waerways, volumes o be sored in each reservoir and volumes o be ransferred across he sysem. OPTIMISIR has wo modes of use; sandalone-use and he linked-use wih Melbourne Waer s REALM waer supply sysem simulaion model. This paper covers he sandalone-use mode. Wih OPTIMISIR, he opimisaion of 12-monh operaions can be compleed in seconds. This enables he operaors o generae a firs a greenfield soluion ha is no biased owards pas operaions and subsequenly assess he impacs of various operaing consideraions by generaing consrained soluions. The paper presens he OPTIMISIR modelling approach and an example applicaion. The paper paricularly highlighs he decision-suppor raher han decision-making naure of OPTIMISIR, in which he experience of sysem operaors and he power of an easy-o-use modelling ool are combined in idenifying a deailed operaing plan ha mees various operaing consideraions and he needs of sakeholders. The paper demonsraes he suiabiliy of OPTIMISIR, as par of a suie of opimisaion and simulaion modeling ools, for he operaion planning of Melbourne s waer supply sysem. Keywords: Waer supply sysem operaion, opimisaion, linear programming, OPTIMISIR, REALM 4057

2 Kularahna e al., Ineracive Opimisaion Modelling using OPTIMISIR o Suppor Melbourne Waer Supply Sysem Operaion 1. INTRODUCTION Operaion planning of a large waer supply sysem is a complex ask underaken based on many consideraions. These consideraions include: he volumes o be sourced from various sources ha have differenial coss, he iming of volumes sourced, he disribuion of sorage wihin he waer supply sysem, minimising he risk of localised or sysem wide spills due o increased supply from various sources, minimising he risk of low sorage volumes due o inadequae inakes volumes, waer securiy, waer qualiy, scheduled ouages of sysem asses, operaing and capial coss, operaional flexibiliy and seadiness, and environmenal facors such as healhy waer ways and lower greenhouse gas emissions. In planning for he sysem operaion, Melbourne Waer prepares an annual operaing plan (AOP) ha provides a forecas of anicipaed sysem operaion for he waer supply sysem. The AOP oulines he sysem operaions for he 12 monhs ahead, and idenifies for each monh he volumes of waer o be sourced, sored, moved from one locaion o anoher, or released o waerways. The preparaion of his forecas is a complex ask, as he decisions on sysem operaions need o be made based on he array of consideraions oulined earlier. In he pas, he preparaion of AOP was a manual process underaken by experienced operaors assised by decision suppor ools based around he calculaion of waer balance across he sysem. However, hese ools did no have he capabiliy o idenify opimal operaions. Insead, he ools were manually updaed by experienced operaors as documened in sysem operaion guidelines, in consulaion wih Melbourne s reail waer companies. The above approach was ime consuming, ofen requiring up o wo weeks o produce an AOP for one operaing scenario. Alhough many researchers have aemped o develop mehods o opimise he operaion planning problem oulined above, i sill remains largely a challenging research opic. This is parly due o he complexiy of he problem involving muliple objecives and sakeholders, nonlineariies, uncerainies, complexiy of waer supply sysems and he poenially long opimisaion ime horizon. Some of he research effors ha included he deailed opimisaion of shor-erm or real-ime operaions, in addiion o he long-erm-oriened operaing policy design, are as follows: Becker and Yeh (1974) developed a mehodology uilising dynamic programming (DP) for he selecion of an opimal reservoir sorage policy over a specified number of policy periods, and linear programming (LP) for period-by-period opimisaion. Gilber and Shane (1982) presened hree opimisaion mehods used for scheduling of a reservoir sysem ha posed a sochasic, nonlinear, mulireservoir problem. The mehods included linear programming for implemening prioriies, a nonlinear search mehod for minimising he cos funcion and a guide-developmen program based on sochasic dynamic programming. McLaughlin and Velasco (1990) described a real-ime opimal conrol approach for operaing wo hydropower reservoirs. The approach included solving a simplified sochasic problem formulaion of which he soluion is obained using a deerminisic approach. Soncini-Sessa e al. (1999) presened a decision suppor sysem designed o be used o generae waer reservoir managemen policies as well as o make daily operaion decisions using real-ime informaion. Casellei e al. (2008) reviewed, from a conrol heory perspecive, he advances in designing managemen policies for waer reservoir neworks and indicaed ha sochasic dynamic programming is he more naural algorihm for solving he problem of designing managemen policies for waer reservoir neworks. Simonović (2008) indicaes ha he mos widely used opimisaion mehodology for waer resource managemen has been LP. This is likely o be due is advanages including he wide availabiliy of sofware and he abiliy o handle relaively large problems, despie he difficulies in represening non-lineariies and uncerainies. Loucks e al. (2005) provides a horough inroducion o many aspecs and dimensions of waer resources managemen and presens pracical approaches, including sysems modeling and opimisaion. Despie he above effors, opimisaion ools are no widely available or used in waer resources managemen, paricularly for large-scale problems. OPTIMISIR is an easy-o-use opimisaion ool, developed in-house by Melbourne Waer, o assis wih he preparaion of AOP. The paper presens he OPTIMISIR modelling approach and an example applicaion. The opimisaion problem addressed by OPTIMISIR is one in which he operaing decisions are ineracively opimised over a period of one year, aking ino accoun many operaing consideraions in deail. OPTIMISIR enables operaions saff o invesigae many operaing sraegies in a shor period of ime. However i does no address he opimisaion of long-erm operaions or waer resources requiremens ha is addressed by Opimizer WSS (Kularahna e al., 2011) which can provide some key inpus o OPTIMISIR. The remainder of he paper is srucured as follows: Secion 2 oulines he Melbourne waer supply sysem. Secion 3 describes he model formulaion and modeling process. An example applicaion is presened in Secion 4, followed by summary and conclusions. 4058

3 Kularahna e al., Ineracive Opimisaion Modelling using OPTIMISIR o Suppor Melbourne Waer Supply Sysem Operaion 2. MELBOURNE WATER SUPPLY SYSTEM Melbourne Waer is a Vicorian governmen owned company ha provides wholesale waer supply and sewerage services o Melbourne s reail waer companies, and manages rivers and creeks, floodplains and he regional drainage sysem. In addiion, Melbourne Waer provides limied waer supplies o several regional waer auhoriies. The waer supply sysem managed by Melbourne Waer is a complex inerconneced sysem of 10 sorage reservoirs, over 40 service reservoirs, 160,000 hecares of cachmens and a ransfer sysem comprising hundreds of kilomeres of pipelines, unnels and aqueducs. The ransfer sysem links Melbourne's sorage reservoirs wih he areas serviced by he hree reail waer companies in Melbourne and regional waer auhoriies. Figure 1 shows he waer supply sysem managed by Melbourne Waer on behalf of he above companies and waer auhoriies who hold Melbourne s Bulk Waer Enilemens. Melbourne s en sorage reservoirs have a combined sorage capaciy of 1,810 GL. The average volume of waer supplied from he sysem for urban use over he las 5 years was abou 390 GL/year. The curren waer harvesing and ransfer sysem comprises graviy fed supplies from proeced cachmens as well as fully reaed and pumped supplies from unproeced cachmens. The exising and new supply sources ha involve waer reamen and pumping can be of higher cos. However, some of hese sources, such as he desalinaed supply can be largely unaffeced by climae. 3. OPTIMISIR MODELLING APPROACH 3.1. Overview Yan Yean Res Greenvale Res Ciy Wes Waer Yarra Valley Waer Souh Eas Waer Norh-Souh Pipeline (criical-need supply) Sugarloaf Res Armsrong Ck O Shannassy Res Maroondah Res Yarra River Coranderrk Ck Thomson Res Upper Yarra Res Thomson Releases McMahon Ck Sarvaion Ck Silvan Res Tarago Res Tarago Treamen Plan Sorage Reservoir Cardinia Res Waer Treamen Major Transfers Seawaer Desalinaion Seawaer Desalinaion (2012) Supply Area Figure 1. Melbourne waer supply sysem. OPTIMISIR enables modelling of he opimal operaion of Melbourne s waer supply headworks sysem comprising waer harvesing sies, sorage reservoirs and bulk ransfer pahs over a period of 12 consecuive monhs. OPTIMISIR provides he flexibiliy for users o specify various inpus including he objecive of he opimisaion, saring sysem sorage, sreamflow and demand scenario, asse capaciies, any operaing requiremens ha mus be me, environmenal flow requiremens and demand resricion rules. The oupu of OPTIMISIR comprises he opimal operaing decisions for each of he 12 monhs for all key sysem componens represened in he model, including: (1) volumes o be aken from various waer sources, (2) volumes o be released o waerways a various waer harvesing sies, (3) volumes o be sored in each reservoir, and (4) volumes o be ransferred across various ransfer pahs in he sysem. OPTIMISIR has wo modes of use: (1) as a sandalone opimisaion ool o assis preparing he AOP, and (2) as an add-on o he exising waer supply headworks sysem simulaion model, REALM, o improve is simulaion oucomes. The presen paper covers he sandalone mode. The second mode improves REALM simulaion oucomes by represening opimal annual operaion planning wihin a simulaion model. OPTIMISIR provides he choice of wo opimisaion mehods: Linear Programming (LP) or Quadraic Programming (QP), ogeher wih he flexibiliy for he user o specify he opimisaion problem hrough inpu daa. When using LP, he model developed for he Melbourne waer supply headworks sysem represening 10 sorage reservoirs and he bulk waer ransfer sysem runs in a couple of seconds in performing an opimisaion for 12 monhs on a sandard deskop compuer used a Melbourne Waer. The model archiecure of OPTIMISIR is shown in Figure 2 and comprises a Microsof Excel user inerface which communicaes wih a sandalone opimisaion module hrough inpu and oupu files. The user inerface provides a high level of flexibiliy o modify user inpu and oupu screens wihou requiring changes o he opimisaion module Model Formulaion An opimisaion model formulaion comprises hree key componens; (1) objecive funcion, OF, ha define he aim of opimisaion, (2) decisions ha can be varied o achieve he objecive, and (3) consrains ha 4059

4 Kularahna e al., Ineracive Opimisaion Modelling using OPTIMISIR o Suppor Melbourne Waer Supply Sysem Operaion indicae he requiremens ha mus be me or he limiaions imposed on he opimisaion. OPTIMISIR provides he flexibiliy o define all of hese componens hrough user inpus. OPTIMISIR performs a single objecive opimisaion, which means ha only one objecive funcion can be opimised a a ime. There are wo objecive funcions preconfigured wihin OPTIMISIR for opimisaion using LP. They are: (OF1) minimise he oal annual cos in operaing he sysem, and (OF2) minimise he oal ouflow from he sysem including localised spills from individual harvesing sies. In using OF1, OPTIMISIR provides he opion o accoun for he value of waer held in sorage, hrough an opional carryover waer value funcion (CWVF) (Kularahna, 2009) which can indicae how he value of waer changes wih he volume of waer in sorage. Such value funcions need o be idenified hrough economic assessmens or long-erm opimisaion approaches. CWVF is one way of providing a link beween he shorerm consideraions of OPTIMISIR and he longer-erm goals of sysem operaion. In using OPTIMISIR for annual operaion planning purposes, he above link may be provided hrough alernaive ways. These include he incorporaion of operaion arges or pre-defining of some decisions based on longer-erm opimisaion processes and/or pas experiences. The mahemaical formulaion of he model is included below wih reference o he example sysem configuraion shown in Figure 3. The objecive funcion OF1 has he form: Saring sorage volume Sreamflow Daa Demand Daa 12 4 Minimise ci, oi, CWVF = 13( s1, + s2, ) (1) = 1 i= 1 where c i, is he uni cos of waer aken from locaion i (i=1,..4) in monh, o i, is he volume aken from locaion i in monh, and CWVF =13 is he opional funcion ha indicaes he value of he oal volume of waer available in sorage for carryover o he 13 h monh. S i, (i=1,2) is he sorage volume in reservoir i a he beginning of monh. The second objecive funcion, OF2, has been consruced as he weighed sum of ouflows and spills, wih higher weighs for ouflows/spills from lower cos sources and vice versa. This provides OPTIMISIR wih an approximae represenaion of he coss of various sources, wihin an objecive funcion ha minimises he ouflows. The objecive funcion OF2 has he form: Inpus Objecives and Consrains Drough Response Acions Environmenal Releases Oher Releases Microsof Excel User Inerface Inpu files Problem Definiion and Inpu/Oupu Translaion Module Sandalone Opimisaion Module Opimisaion Rouines Oupus Oupu files AOP Graphs Resuls on Sysem Schemaic Figure 2. OPTIMISIR model archiecure Sorage Reservoir River Diversion weir Pipeline Supply Area Figure 3. Example sysem Minimise w i, ri, = 1 i= 1 where w i, is he weigh applied o he downsream release volume r i, of locaion i in monh. (2) The key decisions of he opimisaion problem are he oupus lised under Secion 3.1. The consrains of he opimisaion problem specify he waer balance a each modelled locaion, environmenal flow requiremens, waer supply asse capaciy limiaions such as reservoir sorage capaciies and pipeline capaciies, and, any operaing condiions ha mus be saisfied. The key consrains of he opimisaion problem are of he general form oulined below. s 1, 1= s1, + q1, + o3, o1, r1, e1, + = 1,..12 (3) s 2, 1= s2, + q2, + o4, o2, r2, e2, + = 1,..12 (4) 4060

5 Kularahna e al., Ineracive Opimisaion Modelling using OPTIMISIR o Suppor Melbourne Waer Supply Sysem Operaion q i, oi, + ri, = i = 3,4; = 1,..12 (5) d o1, + o2, = = 1,..12 (6) r r i = 1,..4; = 1,..12 (7) min i, i, s s s i = 1,2; = 1,..12 (8) min max i, i, i, o o o i = 1,..4; = 1,..12 (9) min max i, i, i, where q i, is he inflow in monh of he cachmen beween he locaion i and any upsream locaions modelled, e i, (i=1,2) is he volume los from locaion i in monh, d is he waer demand from he sysem in monh, r i, min is he minimum required flow downsream of locaion i in monh, s i, max and s i, min are he maximum and minimum allowable sorage volumes in reservoir i in monh, o i, max and o i, min are he maximum and minimum volumes ha can be ransferred ou of locaion i in monh. The opimisaion rouines used in OPTIMISIR were sourced from Visual Numerics (2006). The LP rouine is based on revised simplex mehod of which a descripion can be found in Mury (1983). The QP rouine is based on he implemenaion of Powel (1985) of he dual quadraic programming algorihm of Goldfarb and Idnani (1983) for convex QP problems subjec o general linear equaliy / inequaliy consrains. The LP rouine solves a problem wih a linear objecive funcion and consrains, whereas he QP rouine provides he flexibiliy of using a quadraic objecive funcion. In using he OF1, he opional and poenially non-linear CWVF was represened hrough a piecewise linearisaion approach Typical Modelling Process OPTIMISIR has no buil-in operaing rules and can idenify a firs a greenfield soluion which is no biased owards pas operaions. A greenfield soluion may show he bes way o opimise he sysem under ideal condiions. However, such greenfield soluions may no saisfy all operaing consideraions associaed wih a complex waer supply sysem. Hence, OPTIMISIR modeling process involves subsequen incremenal adjusmens o he greenfield model formulaion o idenify revised opimal soluions ( consrained soluions ) ha saisfy various operaing requiremens. The incremenal adjusmens enable users o idenify any sysem-wide implicaions of each specific adjusmen, prior o making furher adjusmens. 4. EXAMPLE APPLICATION The example applicaion is inended o illusrae: (1) he opimisaon capabiliies of OPTIMISIR, and (2) he modelling adjusmens indicaed in Secion 3.3 and does no indicae he poenial operaion of Melbourne waer supply sysem. Descripion of he assumpions used in he example including sreamflow scenarios has been inenionally omied from his paper o reflec he above inen. OPTIMISIR was run for hree separae sreamflow scenarios using OF2. The hree scenarios are named as drier, inermediae and weer scenarios for ease of reference, alhough he names do no imply he level of sreamflow in relaion o he whole range of possible sreamflow condiions. The consrains of he model formulaion specified environmenal flow requiremens and waer supply asse capaciy limiaions. Figure 4 70% presens he modelled oal sysem sorage volumes corresponding o he greenfield soluions obained using he hree scenarios. In Figure 4, he dashed line represens he operaing plan for he inermediae sreamflow scenario, prepared hrough he manual process referred o in Secion 1. Comparison of he oucomes of he manual process and OPTIMISIR indicae he closeness of he oucomes of he wo 50% 40% 30% Weer Scenario Inermediae Scenario Drier Scenario 20% approaches in erms of he oal sysem sorage volume. In erms of he esimaed operaing cos, he wo soluions were idenical. This resul highlighs he opimaliy achieved in he pas operaion planning process ha relied on operaing guidelines, pas experience and he experise of operaors. Sorage Level (% full) Inermediae Scenario (based on manual process) Jul Oc Jan Apr Jul Monh Figure 4. Modelled sorage volume. 4061

6 Kularahna e al., Ineracive Opimisaion Modelling using OPTIMISIR o Suppor Melbourne Waer Supply Sysem Operaion Figure 5 indicaes he normalised operaing cos and sysem ouflow associaed wih he hree sreamflow scenarios. In Figure 5, he opimal cos under he drier scenario is slighly lower han ha under he inermediae scenario. This is because he drier scenario represens limied availabiliy of relaively low-cos surface waer sources, and he maximisaion of higher cos sources. The weer scenario has he lowes cos due o he availabiliy of a large volume of low-cos surface waer and consequen reducion of inake from higher cos sources. The inermediae scenario has he highes cos as he available low-cos source volume is 100 Normalised Cos no large enough o cause a reducion in he inake of Normalised Ouflow 80 higher cos sources. 60 In erms of ouflow volumes, he drier scenario has he highes ouflow from he sysem, because of he larger 40 volume ha needs o be released from he sysem o 20 ensure environmenal flow requiremens are me, when he naural sreamflow downsream is relaively low. 0 Drier Inermediae Weer Conversely, he weer scenario has he lowes ouflow, Sreamflow Scenario no only because he sreamflow downsream is relaively high bu also because he opimal soluion avoided any Figure 5. Modelled cos and ouflow. poenial localised spills. Alhough OPTIMISIR is a single objecive opimisaion ool, i can also be used o: (1) check he opimaliy of a soluion in regards o anoher objecive, (2) generae alernaive soluions ha are opimal in erms of oher objecives, and (3) o idenify opimal soluions ha rade-off differen objecives o differen exens. Such alernaive soluions can be generaed by revising he opimisaion model formulaion ineracively. As an example, he soluion obained for he inermediae scenario using OF2 (which causes he end-sorage volume o be maximised) was esed by opimising he sysem under OF1, wih he end-sorage volume consrained o be no less han ha idenified using OF2. The wo soluions were idenical, indicaing ha for his paricular se of sar sorage, sreamflow scenario and consrains, he end-sorage volume has been maximised a he lowes cos. This is parly because he OF2 also has an approximae represenaion of coss, specified by he relaive weighs on ouflows/spills from differen sources. When using OF1, OPTIMISIR can be used o idenify opimal soluions ha rade-off differen objecives o differen exens by gradually varying he end-sorage consrain o generae soluions ha have differen end-sorage volumes and coss. A similar oucome can be obained using OF2 wih differen consrains on operaing coss. The soluions generaed using such an approach are equally good and opimal soluions, known as Pareo-Opimal soluions (or Pareo Fron), from which he selecion of a soluion involves a rade-off such as cos versus end-sorage volume. An alernaive mehod for generaing a Pareo Fron is o minimise he weighed sum of he differen objecives for various differen seings of he weighs. Alhough OPTIMISIR provides he flexibiliy o use he weighed sum approach, he single objecive funcion approach, wih consrains on end-sorage volume or cos, can represen he pracical consideraions in a more ransparen manner. This approach also enables focusing on he areas of he Pareo Fron ha are of pracical ineres and applicabiliy, wihou having o generae he enire Pareo Fron. Figure 6 presens he Pareo Fron generaed for he inermediae sreamflow scenario using OF2 and differen operaing-cos consrains. The soluions A and B shown in Figure 6 are he bes in erms of cos (B) 55% and end-sorage respecively. The soluions inbeween represen a rade-off cos and end-sorage o differen exens. The lower-cos end of Pareo Fron 50% indicaes he use of lower-cos sources. The highercos end indicaes ha furher relaxaion of operaing (A) 45% cos-consrain has no impac on end-sorage, due o limiaions in waer availabiliy and/or localised Normalised Cos capaciy consrains. The inermediae linear secion Figure 6. Pareo-Opimal soluions. indicaes he increasing use of a higher-cos source which was assumed o be of a consan uni cos. Figure 7 provides an example conaining greaer deails of a greenfield soluion and he subsequen consrained soluion, for wo reservoirs in he sysem. Figure 7(a) indicaes he oucome of adjusmens made o he greenfield formulaion o ensure ha he sorage volume of Maroondah reservoir is no drawn down excessively in anicipaion of higher sreamflows, and, a sufficien empy sorage space is mainained a he End Sorage Level (% full) Noramlised value 4062

7 Kularahna e al., Ineracive Opimisaion Modelling using OPTIMISIR o Suppor Melbourne Waer Supply Sysem Operaion 7(a) Maroondah Sorage 7(b) Yan Yean Sorage 100% 100% Sorage Level (% full) 80% 40% 20% Greenfield Consrained Sorage Level (% full) 90% 80% 70% Greenfield Consrained 0% Jul Oc Jan Apr Jul Monh end of he 12 monhs o maximise he poenial inake nex year. Figure 7(b) indicaes he oucome of adjusmens made o achieve sable operaion of Yan Yean reservoir. Jul Oc Jan Apr Jul Monh Figure 7. Deails of greenfield and consrained soluions. 5. SUMMARY AND CONCLUSIONS The paper described an ineracive waer supply sysem opimisaion ool, OPTIMISIR, developed o assis wih he preparaion of annual operaing plan for he Melbourne waer supply sysem. OPTIMISIR provides he flexibiliy for he user o generae an opimal operaing plan by ineracively defining he opimisaion problem hrough objecives, consrains and various inpu daa. OPTIMISIR modeling process involves firs idenifying an opimal greenfield soluion which is no biased owards pas operaions. Any specific operaing consideraions no represened in he greenfield soluion can subsequenly be incorporaed o generae an opimal consrained soluion ha bes saisfies he specific consideraions. The oupus of he ool provide valuable insighs ino he sysem-wide implicaions of various operaing consideraions. The abiliy o generae an opimal soluion wihin a couple of seconds is a key advanage of he ool. Recen use of OPTIMISIR has shown is poenial, as par of a suie of ools, for efficiency improvemens in annual operaing plan preparaion. The capabiliies of OPTIMISIR also highligh is poenial as a raining ool ha demonsraes opimal sysem operaions and he implicaions of various operaing consideraions. REFERENCES Becker, L., and W.W-G. Yeh (1974). Opimizaion of real ime operaion of a muliple-reservoir sysem. Waer Resources Research, 10(6), Casellei, A., F. Pianosi and R. Soncini-Sessa (2008). Waer reservoir conrol under economic, social and environmenal consrains. Auomaica, 44, Gilber, K.C., and R.M. Shane (1982). TVA Hydro Scheduling Model: Theoreical Aspecs. Journal of he Waer Resources Planning and Managemen Division, 108(1), Goldfarb, D., and A. Idnani (1983). A numerically sable dual mehod for solving sricly convex quadraic programs. Mahemaical Programming, 27, Kularahna, M.D.U.P. (2009). OPTIMISIR: An opimisaion ool o supplemen REALM models or perform sandalone opimisaion of waer resource sysems. 18h World IMACS / MODSIM Congress, Cairns, Ausralia, July. Kularahna, M.D.U.P., T.S.C. Rowan, H. Schulz-Byard, D.R. Broad, D. McIver, D. Flower, B. Baker, B. Rhodes and P.J. Smih (2011). Muli-Objecive opimisaion using Opimizer WSS o suppor operaion and planning decisions of Melbourne waer supply sysem. Submied o MODSIM 2011 Inernaional Congress on Modelling and Simulaion, Perh, Ausralia, December. Loucks, D.P., and E. van Beek (2005). Waer Resources Sysems Planning and Managemen: An Inroducion o Mehods, Models and Applicaions. Sudies and Repors in Hydrology, UNESCO, Paris. McLaughlin, D., and H.L. Velasco (1990). Real-ime conrol of a sysem of large hydropower reservoirs. Waer Resources Research, 26(4), Mury, K. G. (1983). Linear Programming. John Wiley and Sons, New York. Powell, M.J.D. (1985). On he quadraic programming algorihm of Goldfarb and Idnani. Mahemaical Programming Sudy, 25, Simonović, S.P. (2008). Managing waer resources: mehods and ools for a sysems approach. Sudies and Repors in Hydrology, UNESCO / Earhscan, London, Soncini-Sessa, R., A.E. Rizzoli, L. Villa, and E. Weber (1999). TwoLe: A sofware ool for planning and managemen of waer reservoir neworks. Hydrological Science Journal, 44(4), Visual Numerics (2006). IMSL Forran numerical library user s guide. 4063