SCENARIO SCOPING FOR DSR PRICE SIGNALS PROJECT
Contact details Name Email Telephone Mike Wilks Mike.wilks@poyry.com 01865 812251 Pöyry Management Consulting is Europe's leading management consultancy specialised in the energy sector, providing strategic, commercial, regulatory and policy advice to Europe's energy markets. The team of over 200 energy specialists, located across 14 European offices, offers unparalleled expertise in the rapidly changing energy sector. Pöyry is a global consulting and engineering firm. Our in-depth expertise extends to the fields of energy, industry, urban & mobility and water & environment, with over 7,000 staff operating from offices in 50 countries. Copyright 2011 Pöyry Management Consulting (UK) Ltd All rights reserved Unless prior written consent has been provided, this report is provided to the legal entity identified on the front cover for its internal use only. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of Pöyry Management Consulting (UK) Ltd. Should you wish to share this report for a particular project with an affiliate, shareholder or another party, prior written permission is required for which there may be an additional fee. Important This document contains confidential and commercially sensitive information. Should any requests for disclosure of information contained in this document be received (whether pursuant to; the Freedom of Information Act 2000, the Freedom of Information Act 2003 (Ireland), the Freedom of Information Act 2000 (Northern Ireland), or otherwise), we request that we be notified in writing of the details of such request and that we be consulted and our comments taken into account before any action is taken. Disclaimer While Pöyry Management Consulting (UK) Ltd ( Pöyry ) considers that the information and opinions given in this work are sound, all parties must rely upon their own skill and judgement when making use of it. Pöyry does not make any representation or warranty, expressed or implied, as to the accuracy or completeness of the information contained in this report and assumes no responsibility for the accuracy or completeness of such information. Pöyry will not assume any liability to anyone for any loss or damage arising out of the provision of this report.
TABLE OF CONTENTS EXECUTIVE SUMMARY 1 1. INTRODUCTION 6 1.1 Background 6 1.2 Scope of analysis and key assumptions 7 1.3 Structure of this report 7 2. CONTEXT OF THE ASSESSMENT 8 2.1 The GB electricity market in 2030 and beyond 8 2.2 Context of the study 10 3. BASIS OF OUR ASSESSMENT 14 3.1 Bounding the problem 14 3.2 Identifying value drivers of different stakeholders 15 3.3 Summary of the scenarios defined 18 4. RESULTS OF OUR ASSESSMENT 26 4.1 Summary of results 26 4.2 Shaving peak demand to avoid network investment 27 4.3 Boost peak demand to accommodate wind and optimise prices 37 4.4 Modify demand to accommodate low wind period 39 4.5 Modify demand to compensate for a generation trip 41 4.6 Modify demand to compensate for a network constraint 46 4.7 Modify demand to compensate for a distribution network fault 47 4.8 Modify demand to cope with volatile demand net wind profile 47 4.9 Conclusions 49 ANNEX A MODELLING DEMAND SIDE RESPONSE 53 ANNEX B DISTRIBUTION DEMAND PROFILES 57 ANNEX C PATHWAY ALPHA AND ASSOCIATED ASSUMPTIONS 65 C.1 Generation mix 65 C.2 Structure of demand 66 C.3 Network assumptions 72
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EXECUTIVE SUMMARY In 2010, Pöyry Management Consulting (UK) Ltd. (hereafter Pöyry ) conducted work for DECC to understand the potential role of demand side response ( DSR ) in helping deliver a secure and economic decarbonised energy sector by 2050. Pöyry has been jointly commissioned by Electricity North West Ltd ( ENW ) and National Grid to use this work to explore further the interactions of potential DSR use by ENW (as a DNO), National Grid (as TSO) and suppliers as different key end users and to examine relative strengths of DSR price signals that each might be able to provide to the market. Context The expectation is that a decarbonised generation sector will lead to the GB market containing large amounts of low marginal cost generation; much of this will be in the form of wind, which is also intermittent. Concurrent with the decarbonisation of electricity generation, further electrification of the heat and transport sectors is expected, particularly from the late 2020s onwards, in support of the 2050 emissions target. This will provide challenges for matching generation to the profile of demand; heat demand will be more variable than the existing electricity demand. The delivery of a low-carbon generation sector represents a departure from the status quo, which can be crudely characterised as a market dominated by load-following generation, a relatively predictable pattern of demand and limited opportunity for demandshifting. The implications of moving to a low-carbon energy system are that: the electricity generation sector could become more inflexible, which places a greater premium on having load that can follow generation; electricity demand could be more variable and more peaky, which increases the benefits of shifting load away from peak periods; and electricity demand may have much greater potential for flexibility through the storage associated with heat and transport. Therefore, there would be clear benefits from the implementation of demand-side flexibility i.e. demand side response (DSR) in helping to deliver a low-carbon, affordable and secure electricity supply. Defining the problem In the use of DSR, there are various stakeholders and different dimensions to its use. Furthermore depending on prevailing circumstances in the market and on the networks, stakeholder interest in DSR and their use of it may coincide or conflict. We were asked to: provide an understanding of when the key end users of DSR (TSO, DNO and supplier) will be in tandem or in conflict; and present an initial quantification of the value associated with various uses of DSR. Given the large number of specific situations and uses of DSR, we sought to capture these via an assessment of a number of specific, self-contained representative scenarios. The aim was to provide a representation of the full range of possible situations which might theoretically arise; the scenarios are summarised in Table 1. These were devised bearing in mind the need to quantify the relative value to each of the interested parties. 1
Table 1 Summary of the scenarios defined by Pöyry Scenario Shaving peak demand to avoid network investment Case A Case B Situation Demand is shifted at the national level and has an adverse affect on the local network Both Grid and DNO want to shift demand at the same peak Boost peak demand to accommodate wind and optimise prices Case C Case D Modify demand to accommodate low wind period Case E Modify demand to compensate for a generation trip Case F Case G Modify demand to compensate for a transmission constraint Case H Modify demand to compensate for a distribution network fault Case I Modify demand to cope with volatile demand net wind profile Case J Suppliers drive price optimisation through the use of DSR DSR is used to avoid wind curtailment DSR is used to avoid the costs associated with alternative solutions to the problem DSR is used instead of ancilliary services DSR is used to balance the system Use DSR to avoid bringing on another generator to meet demand Use DSR to avoid bringing on another generator to meet demand DSR is used to mitigate forecast error and wind volatility Implementing the method Using results from modelling work previous undertaken by Pöyry for DECC on their Pathway Scenario Alpha for 2030, and the potential role(s) of DSR, we analysed how often different situations, as represented by the scenarios above, could arise and the impact that DSR would have on them. This was done to quantify the value associated with the use of DSR to the three different key end users of DSR suppliers, transmission network owner/operators and distribution network owners. We used the scenarios to both analyse the price signals that will emerge in various situations and understand the potential implications for interactions in targeted use of DSR by the three key end users. Key assumptions In conducting this assessment we have made three key assumptions: DECC s Pathways Scenario Alpha is used for key market assumptions in 2030; DSR is provided on a voluntary basis driven by commercial signals from end users; and DSR providers act rationally in response to commercial signals i.e. they are reliable and take the highest price. 2
Furthermore we do not investigate the reduction of network asset ratings arising from flatter demand profiles and its impact on asset load cycles (and thus thermal stress). Assessing the results The relationship between different stakeholders and their use of DSR is complex for example, suppliers are interested in energy (MWh) DSR services; whereas DNOs and the TSO are interested in capacity (MW) DSR services. These differences present varied value propositions and natures of use to DSR providers. However, the pattern that emerged from the analysis is that the price signals given by the DNO will be far weaker than those given by other interested parties. The DNO probably won t be able to give the signals that it needs to attract DSR providers except in post-fault situations where spot value of DSR to the DNO would be very high. By contrast the TSO and suppliers should be able to give the desired price signals far more readily given the scale of potential benefits via, for example, asset investment avoidance and operational cost reductions. This ordering was also reflected in the benefits received by individual parties. Firstly, the supplier often has the most value as it gains on a frequent basis from wholesale price savings and from passing on the cost of incorporating wind generation onto its customers. The TSO follows as its investments are relatively large and infrequent; it is under certain operational obligations which drive sometimes high value for DSR. The DNO is lowest in the value chain, given the locality and lower scale typically of its requirements for DSR; and thus associated asset costs and operational savings. A summary of the findings can be found in Figure 2. 3
Figure 2 Scale of value of DSR to the users across the scenarios, thus reflecting the rate payable to provider (1 = highest value, 4 = lowest value) Scenario DNO TSO Supplier Shaving peak demand to avoid network investment Case A 4 - - Case B 3 1 2 Boost peak demand to accommodate wind and optimise prices Case C 3 2 1 Case D 3 2 2 Modify demand to accommodate low wind period Case E - 3 1 Modify demand to compensate for a generation trip Case F - 1 2 Case G - 1 2 Modify demand to compensate for a transmission constraint Case H - 1 - Modify demand to compensate for a distribution network fault Case I 1 - - Modify demand to cope with volatile demand net wind profile Case J - 1 2 In addition, a few other key observations can be drawn from this work and the previous work undertaken for DECC. There is clear potential for overspending. In the first case, this may happen when DSR is contracted by two or more different parties duplicating in part at least necessary payment for a DSR action. A different inefficiency can arise when the action to dispatch DSR minimises the costs for one stakeholder but results in additional costs for another. For example, when DSR is used to minimise demand at the national level it can cause demand on the distribution network to exceed the capacity limit and trigger need for local DSR previously not required. In many cases for both transmission and distribution networks deployment of DSR will defer but not avoid asset investment (given dramatically increasing underlying electricity demand under decarbonisation of energy as for example assumed in DECC s Pathway Scenario Alpha). Thus in many cases DSR can be used as an interim measure operationally to allow time for network investments to be made. However, it can only be sustained in situations where the scale of DSR required is reasonable in its impact on DSR providing consumers. DSR can only be relied on and sustained when the impact on consumer activities is both viable and acceptable to them. More generally, it is only reasonable to anticipate up to a certain scale of accessible DSR given the increasing impact on consumer activities and inconvenience. As they 4
face increased limitations/restrictions on their use of demand where higher levels and regularity of DSR delivery are sought, there will be competition for its use. For networks, there is most potential value for DSR to be used for events that are high in price but low in volume e.g. in post fault situations. However, such activities are either relatively rare (such as network outages) versus required availability commitment; or require prolonged use of DSR (such as generation trips). In these cases it is likely that while some role may be available for DSR over short timescales, dependent on commercial and physical commitments required. Given the overall value of competing services from suppliers in the longer term, DSR providers may prefer other service options. Furthermore, these operational situations require complete reliability of DSR provision when called upon to ensure suitable security of supply and to enable the TSO and DNO to realise the benefits of avoided asset investments and/or service costs. Thus we make the following conclusions: 1. Some form of common platform and process should be put in place to enable effective coordination and efficient use of DSR by different key end users. This is necessary to ensure that there is minimal wastage and maximised cost effectiveness. 2. For DSR services of highest value to networks, the requirements for reliability and the consequences of failure to deliver are such that commercial signals may well need to be reinforced or augmented by mandatory/enforced approaches which ensure the full benefits of DSR can be realised without risk to security of supply. 3. Where there is insufficient cross-stakeholder coordination in place and the dispatch of DSR purely comes down to price signals, the DNO will suffer the most as: DNO price signals will be swamped by those from other stakeholders; at the same time, the responsive demand lies on the distribution network; and thus it is the DNO that will face network capacity related problems when DSR is used to meet the objectives of other stakeholders. 5
1. INTRODUCTION 1.1 Background We were asked to provide a report to Electricity North West Ltd ( ENW ) which gave: "an initial indication of whether the strength of price signal we might be able to provide to a market where the distribution network is at risk would be strong enough to over-ride signals from Grid and suppliers. Subsequently ENW approached National Grid to participate in a joint study looking at the uses and interaction of DSR in general. Together we defined the following project objective: based on high level analysis, (i) provide an understanding of when the key end users of DSR (TSO, DNO and supplier) will be in tandem or in conflict; and (ii) present an initial quantification of the value associated with various uses of DSR. We adopted a two phase approach (which we describe below) for project delivery and agreed to use the following principles: Use DECC Pathways Alpha 2030 as the scenario baseline; The analysis and conclusions would be relatively high level and focus on drawing out key messages; and We would build on previous work completed for DECC on the optimal use of DSR to evaluate investments in the distribution and transmission networks. 1.1.1 Phase 1 of assessment In Phase 1 we set out and explained the different drivers for the use of DSR and the needs for it from each of the different parties - DNO, Supplier and TSO. We then derived snapshot examples to illustrate a representative set of potential scenarios, highlighting the interaction of the potential use of DSR by the different parties. 1.1.2 Phase 2 of assessment Based on Phase 1; we assessed the benefits for each different application of DSR by each different party, based on high level assessment of avoided costs and/or revenue obtained for each of the scenarios defined in Phase 1. This enabled us to understand the magnitude of relevant price signals that would be sent by stakeholders to use DSR. 1.1.3 Project deliverable As indicated above, it was agreed with ENW/National Grid that the end deliverable for the work conducted under Phase 1 and 2 is this report, which provides a full discussion of the assessment and findings. Thus this report encompasses the following key components: Identify the different values that each stakeholder has for the use of DSR; Derive scenarios that show when actions by stakeholders to use DSR are in conflict or in concert (by producing a set of scenarios to assess); From the above two points assess the financial drivers of signals that each party might give to a specific regional market;summarise the overall relative strength of price signals from each generic party in each scenario and the combined effect; and Identify key headline messages from our assessment regarding the effective future deployment of DSR. 6
1.2 Scope of analysis and key assumptions To reinforce the agreed scope and high level nature of analysis, the following are key assumptions: We have used DECC s Pathways Scenario Alpha for underlying market assumptions for 2030 and 2050; DSR is provided on a voluntary basis driven by commercial signals from end users ie. there is no mandatory enforcement of DSR provision e.g. for security of supply reasons; DSR providers act rationally in response to commercial signals such that (i) if the price is right they will respond i.e. it is reliable; and (ii) where there is competing buyers they will provide DSR to the highest bidder. Consumers behave in a compliant manner to the needs of smart grids. Furthermore there are issues for assessments which we recognise as needing exploration but which lie outside the scope of this analysis; We do not investigate the reduction of network asset ratings arising from flatter demand profiles and its impact on asset load cycles (and thus thermal stress); No costs of implementation of smart energy are taken into account; No changes to regulation are assessed; and We do not assess detailed pricing methodologies for different stakeholders. 1.3 Structure of this report This report is broken down into three further Chapters. In Chapter 2 we provide the background to this study by presenting an overview of the market context for 2030 and 2050 as provided by DECC s Pathway Scenario Alpha. This scenario originally triggered the consideration by DECC of how DSR might help deliver UK decarbonisation objectives securely and economically for which Pöyry undertook analysis 1, and is used as the starting point for the assessment undertaken in this report. In Chapter 3 we present our approach to answering the project objective. Therefore we firstly bound the problem and explain its key dimensions. Once these have been established, we describe the value drivers for each of the stakeholders. Once we have these two sets of information we then introduce scenarios in which DSR will be used by one or more of the stakeholders and also define potential conflicts that may arise. Therefore much of Chapter 2 is reporting the results of Phase 1 of the assessment. In Chapter 4 we present the results of our analysis (i.e. Phase 2 of the assessment). Therefore take each of the Scenarios in Chapter 3 and quantify the impact that the different uses of DSR have from each stakeholder. By comparing the different benefits derived by each stakeholder we give insight into the price signal that could be associated with a particular action and the relative strength of the price signals. At the end of Chapter 4 we present the conclusions from our analysis. 1 Demand side response: Conflict between supply and network driven optimisation ; a Pöyry report for DECC, November 2010 7
2. CONTEXT OF THE ASSESSMENT The starting points for this analysis came from work previously undertaken by Pöyry for DECC. DECC has set out six low-carbon pathways to 2050 within its 2050 Pathways report published in July 2010. DECC s Scenario Alpha has been used as the baseline for this study. The GB electricity market is expected to see some fundamental changes in the forthcoming years to 2020 and in particular beyond into 2030 and 2050 timeframes. This change is being driven in large part by environmental objectives set at both an EU and national level. However both economic and technology drivers are reinforcing this and the consequence is that the nature of generation and demand could change in ways which will have a fundamental impact on the whole electricity sector in GB, particularly for the wholesale market and for network investment and operation. 2.1 The GB electricity market in 2030 and beyond The expectation is that a decarbonised generation sector will lead to the market containing large amounts of low marginal cost generation, much of it in the form of wind, which is also intermittent. Concurrent with the decarbonisation of electricity generation, there is expected to be significant electrification of the heat and transport sectors, particularly from the late 2020s onwards, in support of the 2050 emissions target 2. DECC s Pathway Alpha assumes that GB electricity demand more than doubles from its current levels by 2050 to around 730TWh. This could provide challenges for matching generation to the profile of demand, particularly because heat demand is more variable than the existing electricity demand. The delivery of a low-carbon generation sector will represent a significant departure from the status quo, which can be crudely characterised as being a market dominated by loadfollowing generation with a relatively predictable pattern of demand and limited opportunity for demand-shifting. In contrast, the implications of a move towards a low-carbon energy system are that: the electricity generation sector could become more inflexible, which places a greater premium on having load that can follow generation; electricity demand could be more variable and more peaky, which increases the benefits of shifting load away from peak periods; and electricity demand may have much greater potential for flexibility through the storage associated with heat and transport. Therefore, there would be clear benefits from the implementation of demand-side flexibility in helping to deliver a low-carbon, affordable and secure electricity supply. 2 2050 Pathways Analysis, Department of Energy and Climate Change, July 2010. 8
Figure 3 Comparison of demand patterns from load flattening and generation balancing (2030 with January 2000 weather, GW) 80 Demand (GW) 60 40 20 0 24-Jan Wind generation (GW) Demand (GW) 80 60 40 20 0 24-Jan 80 60 40 20 0 24-Jan 80 Inflexible Flexible heat Flexible appliances Flexible EV Demand (GW) 60 40 20 Balancing generation Flattening load 0 24-Jan 25-Jan 26-Jan 27-Jan 28-Jan 29-Jan 30-Jan However, demand-side flexibility is one instrument trying to meet two policy objectives tracking generation, especially wind, in order to take maximum benefit from zero fuel cost generation (and reduce other generation costs); and flattening load in order to minimise network investment. At times, these policy objectives could be complementary, for example when wind is low and demand is high. However, at other times, they could conflict such as when wind is high and demand is high. This tension is illustrated in Figure 3, based on analysis undertaken with our Zephyr model. The top chart shows how the load curve can be flattened through demand shifting, particularly with respect to heating. The second chart illustrates how the pattern of wind generation can vary during a single week. Based on an assumed installed wind capacity of 31GW, output rises from an extended period of being at virtually zero to reach a load factor of nearly 100% in the latter half of the week. The third chart shows how different types of demand can be shifted in order to balance the change in wind generation. The 9
final chart then compares the load flattening approach with the balancing approach. It shows that the two approaches produce similar results when the wind is low in the early part of the week. However, a 10GW difference in demand emerges between the two approaches in 29 January when wind output is at its peak. This analysis highlights the potential benefits of demand side response for the wholesale market. However, it also raises the issue that the use of demand side response in this way could lead to the need for networks to have a higher capacity than necessary with an associated implication for overall system costs. The capacity of GB distribution networks may no longer able to accommodate the substantial demand created by the need to charge electric vehicles (EVs) or meet the demands of heat pumps (HPs). 2.2 Context of the study As we discuss above the GB electricity market is expected to see some fundamental changes in the forthcoming years. The DECC pathways analysis (Scenario Alpha) estimates that electricity demand could double by 2050 as a result of the electrification of heat and transport, which is required to meet the decarbonisation targets. In Figure 4 and Figure 5 below we show the impact of changing demand in 2009, 2030 and 2050 3. Figure 4 Changes in peak demand 140 120 2050 Demand 2030 Demand 2009 Demand Demand (GW).. 100 80 60 40 Year Peak demand (GW) Total demand (TWh) 2050 137 730 2030 96 505 2009 58 314 20 0 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% The demand duration curves shown in Figure 4 highlight that in general peak demand could grow at a slightly faster percentage rate than annual energy (this is quantified in the table in Figure 4), which would lead to a number of potential problems for electricity markets, including how to compensate generators with low load factors. The electrification of heat and transport would lead to an increase in the variation between the peak and minimum daily demand (assuming no use of demand side response), 3 This does not include losses (assumed at 8%) and was also calculated before DSR is applied. 10
making it harder to plan and manage the electricity systems, as shown in Figure 5 (overleaf). Figure 5 Variation in daily demand 140 2050 Demand 2030 Demand 2009 Demand 120 100 Demand (GW). 80 60 40 20 0 21-Jan 22-Jan 23-Jan 24-Jan There are two drivers of this effect; the first is the overall increase in demand; and the second is the roll out of electric heating and electric vehicles. The former means that average demand increases while the latter drives the peak up as the profile of electric heating and charging for electric vehicles amplifies demand at peak (i.e. heating systems are turned on and electric cars plugged in to charge during current peak demand hours note that these are the effects before the application of DSR). In the context of GB targets for decarbonisation of the electricity sector this higher demand will need to be met by substantial new low carbon generation much of which is likely to be intermittent. Figure 6 presents the current capacity mix in 2009 with the capacity in 2030 and 2050 consistent with DECC pathway Scenario Alpha. Under Scenario Alpha it is assumed that total generation output in 2050 is 730TWh assumed to be broadly split equally between nuclear, CCS and renewables. This will require 250GW of generation capacity, 165GW (>65%) of which in 2050 will be intermittent generation (see Figure 6 overleaf). 11
Figure 6 Capacity mix over time GW 2009 2030 2050 Wind+marine 1.9 65.9 93.3 Solar 0.0 5.8 70.4 Other renewables 1.8 3.3 3.7 Nuclear 10.9 16.4 40.0 CCS coal 0.0 10.2 39.0 Gas 32.6 28.3 0.0 Coal 23.0 1.3 1.3 Oil 3.8 0.0 0.0 Hydro 1.5 1.1 1.1 Pumped storage 2.7 2.8 2.8 Total 78 135 252 This level of intermittent generation will lead to changes in the relationship between prices and demand. Figure 7 shows this effect for an example day, with peak demand separating from peak net demand and hence probably peak price. Figure 7 Decoupling of peak demand and net demand for an example day In this example the demand peak occurs at c.5pm but due to the profile and magnitude of wind generation on the day the pricing peak is likely to occur at c.10am when the highest volume of conventional generation which have higher marginal costs than intermittent generation is operating (also known as the demand net wind peak). 12
A further complication will be that not only will demand decouple from price, but the timing of peak demand net of intermittent generation will become less certain. Figure 8 below shows the points at which peak demand (net of intermittent generation) occurs during the year. The results show that whereas peak demand will be contained within a 3 hour range in the evening (usually between 5pm and 8pm) as it is now, the peak of demand net of intermittent generation will occur over an 11 hour range (between 10 am and 9pm). These results also indicate that peak demand net of intermittent generation will, on some occasions, occur in the early hours of the morning, highlighting the variability in wind generation. Figure 8 Timing of peak demand (net of intermittent generation) This report will drive deeper into some of the challenges and possibilities this raises for a distribution network operator, such as ENW, when we consider the uses of DSR. Of course, these are not done in isolation from the issues raised in the various scenarios for National Grid and Suppliers. The discussion and assessments made of the extent to which demand side response can help mitigate the issues raised above will consider the relationship between the various parties. Therefore the focus of the study is to identify scenarios where DSR would be deployed and to measure the overall value that different parties are able to place on its use and therefore the strength of the price signals they are able to send. 13
3. BASIS OF OUR ASSESSMENT In this Chapter we describe the scenarios that will be analysed to derive price signals regarding the use of demand side response. Understanding the drivers and the scenarios allows us to begin to see when uses of DSR by different stakeholders may be in conflict and when they may be aligned. This in turn allows us to investigate the value to different parties of DSR in particular circumstances and hence the way in which it may be used. This will ultimately feed into the analysis of commercial arrangements that need to be struck between parties. 3.1 Bounding the problem In this Section we set out the boundaries of the problem by identifying the key dimensions that define the use of DSR and then comment on issues that are deemed out of scope. This enables us to proceed to the Section where we assess the drivers of different stakeholder value. In Section 3.2.7 we present the scenario snapshots we will use to quantify the value associated with the use of DSR by different parties. There are five key dimensions of understanding the uses of DSR: Magnitude. How much DSR will be needed (in MW terms)? Duration: How long will the DSR need to be used for (e.g. minutes, hours)? Timing: When will DSR be dispatched (time of year, time of day) and what is the frequency associated with this (how often within season, within week)? Notice period: Over what period of time will DSR be utilised and how far in advance will this be known (minutes, hours, days)? Location: When will the use of DSR need to consider location i.e. where and at what level of the T&D networks will DSR be used? Figure 9 relates to the final point and shows the fundamental reason why uses of DSR may conflict (or be in harmony). The perception of value attached to DSR from each stakeholder will depend on where they sit (e.g. national v. localised). Figure 9 Position of different stakeholders involved with DSR? 14
This means that the different stakeholders will want to use DSR for different things and will have different perspectives on the best use of DSR. For example, DNO s will be interested in the impacts of DSR at the Local and Regional levels whereas the Supplier will be interested in the impacts of DSR at the national level. As a result, there will be interaction; including conflict and harmony, across the different levels of DSR. The dotted lines in the right hand side of the Figure 10 indicate the influence of a level other levels. Figure 10 The different geographical interest of stakeholders drives DSR use Now we have identified the general uses of DSR, we are in a position to determine the drivers of each stakeholder, which we do in the following Section. 3.2 Identifying value drivers of different stakeholders In this Section we present the drivers for each of the different types of stakeholder: TSOs, DNOs, Suppliers, Aggregators and the UK government. The drivers for each stakeholder are laid out under the relevant headings and are driven by each stakeholders own value objectives. This allows us to understand the areas where conflict between stakeholders could arise. 3.2.1 TSO The main areas a TSO will use DSR are for: Optimising network investment. Avoiding additional (unnecessary) investment in transmission networks; Energy balancing. Operation (within the balancing mechanism) to balance the wholesale market; System balancing (within half hour to real time). There are a range of ancillary service (balancing services that the TSO uses; reserve, frequency response etc.; and Managing network constraints pre and post fault (to maintain system balancing). 15
3.2.2 DNO DNOs will use DSR to: Avoid or defer network investment and avoid investment in redundancy networks; Manage / Minimise (unacceptable) customer outages and use DSR to optimise operational costs and capital costs, and as an alternative to procuring energy generation or other measures; and Managing network constraints in operational timescales. 3.2.3 Supplier/ retailer For the purposes of this study we define two different types of supplier: those operating as part of a vertically integrated entity and those operating independently. In some cases the values will be the same. In general suppliers will use DSR to manage their position, including: Energy balancing (MWh / settlement period). Reducing exposure to cash out prices by optimising their contracting position / physical position (1/2 hourly resolution); Capacity. Avoid building or running peaking generation; Manage CO2 emissions (avoid running fossil generation); and Provision of DSR services to networks and TSO (i.e. develop into an aggregator). One area where suppliers from vertically integrated entities may differ from non-integrated entities is to use DSR to optimise their exposure to the market, taking into account their generation portfolio. 3.2.4 Aggregator We define two characteristics of value for aggregators: Aggregators generate revenue by providing flexibility and collecting revenues associated with price arbitrage; and Provide ancillary services to the market. 3.2.5 Consumer Consumers encompass a wide range from large industrial users to domestic users; and as such drivers, level of interest and priorities in relation to DSR will vary. In general the primary motivation for DSR provision will be driven by three factors: Cost (the prime driver) essentially the ability to reduce electricity costs, albeit this can be viewed from perspective of service value i.e. value available to them from user(s) of DSR Convenience/commitment the ability to provide DSR with minimal if any impact on business operations, domestic lifestyle etc. as relevant Complexity i.e. lack of. The ability to easily engage in DSR if cost and convenience criteria are met this can often be the last barrier to DSR deployment. 16
3.2.6 UK Government The UK Government may be the most suitable entity to represent the views of the public in terms of desirable outcomes for smart grids from the perspective of UK plc. We define these views as the integration of variable low carbon generation minimising overall cost to the consumer. 3.2.7 Scenarios introduction Now that we have set out the boundaries of the problem and defined the drivers of each stakeholder, we are in a position to begin to map out the scenarios that will be investigated in the analysis. These scenarios have been defined with the value drivers in mind to derive situations to investigate the different uses of DSR by different parties and the trade-offs that occur when using DSR to control the load on the system. In order to assign value to different DSR services it is necessary to define the level of saving and the frequency over which these occur. In this spirit we use snapshots so that we can quantify the frequency of a situation (using our historical data) and the magnitude. This enables us to distinguish between the impact of a low frequency high value event and a high frequency event that has lower but still significant value. This process is central to determining the value that a user allocates to a particular service and hence the different price signals that will be sent by the respective stakeholder. Once the scenarios are agreed we will define the goal of each stakeholder in a particular situation and then compare the uses of DSR, evaluate the value associated with it which will depend on the following issues; Does the stakeholder take an active interest in this situation? If the stakeholder has active interest in the situation, how will it want to use DSR? Are the values of different stakeholders aligned or in conflict in this situation? Identifying potential synergies and conflict in the use of DSR The next step is to identify and characterise the broad use modes of DSR and identify types of behaviour that result in conflict, inefficiency and harmony. 1. Two or more stakeholders interests align (i.e. they want to use DSR in the same way) and hence the risk is that the provider is paid for the same service twice (or more). Whilst the specific consumer/provider may benefit, consumers as whole end up paying more than is necessary for delivered service (assuming point 2 below does not apply) 2. Value of DSR is split between too many stakeholders and therefore no-one responds to the price signal from any one party as their respective individual price/value signals are too weak. 3. When two or more stakeholders interests conflict and there is only enough capability to meet the needs of one; it then becomes a question of who is willing to pay the most money for the use of DSR 4. The use of DSR by one party leads to the need to use DSR by another party i.e. the use of DSR creates a need for DSR to be used when it would not otherwise be needed e.g. price vs. demand on a windy day. In the following section we present the snapshot scenarios that we used to underpin the analysis of the interaction of different stakeholders potential use of DSR. It should be 17
noted that when we state modify demand we mean reduction/increase or relocation of demand; in most scenarios this may mean an emphasis on reduction, in others relocation. 3.3 Summary of the scenarios defined The various needs for DSR and its uses are varied and their relationship is complex. Thus in orders to make the solution quantifiable, a set of scenarios were defined. The aim was to investigate the various possible situations were DSR would be used and to analyse the drivers, the interested parties and value associated. Figure 11 below provides a summary of the scenarios defined. Note that for each of the main cases identified, there are various subtleties that required additional subcases. Figure 11 Summary of the scenarios defined by Pöyry Scenario Situation Problem Shaving peak demand to avoid network investment Case A1 Case A2 Case B1 Case B2 Demand is shifted at the national level and has an adverse affect on the local network Both Grid and DNO want to shift demand at the same peak Shifting demand creates a new problem for the DNO because peak demands do not coincide Shifting demand exascerbates an existing problem for the DNO The same service is contracted twice The same service is contracted three times: by NG, DNO and in order to minimise prices Boost peak demand to accommodate wind and optimise prices Case C1 Case C2 Case C3 Case D Modify demand to accommodate low wind period Case E1 Case E2 Modify demand to compensate for a generation trip Case F Case G Modify demand to compensate for a transmission constraint Case H Modify demand to compensate for a distribution network fault Case I Modify demand to cope with volatile demand net wind profile Case J1 Case J2 Suppliers drive price optimisation through the use of DSR DSR is used to avoid wind curtailment DSR is used to avoid the costs associated with alternative solutions to the problem DSR is used instead of ancilliary services DSR is used to balance the system Use DSR to avoid bringing on another generator to meet demand Use DSR to avoid bringing on another generator to meet demand DSR is used to mitigate wind forecast error Investment must be sufficient to avoid capacity constraints A network constraint exacerbates the volume of DSR needed because NG are trying to reduce peak demand A network constraint that suppliers are aware of prevents full price optimisation The value associated with this action may vary with different network capacity constraints There is a prolonged low wind period The transmission network has only been built to accommodate demand net embedded generation The alternative solutions have different values associated with them Price signal conflict between a supplier being out of balance and using DSM to balance Size and frequency of network constraints Size and frequency of fault on the distribution network and the value to the DNO The error/volatility is managed for network reasons The error/volatility is managed for supplier reasons The following sub-sections define the content of the scenarios in more detail. 18
3.3.1 Shaving peak demand to avoid network investment In this scenario we investigated the situation where national peak demand and local peak demand do not coincide (Case A) and where national peak demand and local peak demand do coincide (Case B). In the first situation, shifting the national peak can create the following situations: Case A1. Shifting demand at the national level creates a new problem for the DNO. This would be because the DNO network peak does not coincide with the national demand peak and National Grid would like to reallocate demand in a way that for the part of this provided within a given DNO area increases the DNO peak demand above existing network capacity capability and hence the DNO would need to invest in network reinforcement. The key trade-off to evaluate here would be the value of avoiding network investment at the Grid level to the value of avoiding network investment at the DNO level. Case A2. A variant of the above this is where the DNO already faces an issue locally. Thus shifting demand at the national level creates a worse problem for the DNO as action at the national level pushes up the peak on the DNO network. Figure 12 Modify demand at national level to avoid maximum peak demand and create a problem at the DNO level In the second situation, shifting the national peak can create the following situations: Case B1. DSR providers are paid twice for the same service; this is when the DNO demand profile and National Grid demand profile match and thus the DNO sends price signals to reduce demand at the same time that National Grid does. Case B2. A sub-set of the above case; the issue is to consider whether suppliers would also want to send price signals at the same time, resulting in a triple contracting of the same service. 19
Figure 13 Demand at DNO level and national level matches We considered the need to review a situation where action by a DNO to reduce a Local network peak demand would potentially drive a materially higher national peak demand which triggers transmission level action. Our modelling suggests this is not a realistic situation which will arise. For this to occur a lot of DNOs would need to shift substantive demand away from local peaks at points on the network where the local demand peaks do not coincide with the timing of the national peak demand in a way that causes the relocated demand to arise at time of national peak demand. Furthermore the shift would need to be a meaningful time shift given DNO load profiles (i.e. shifting demand off a local peak of 4.30pm to a 5pm time slot is not going to help the DNO reduce their local peak demand it will simply move it by half an hour and not address the driver of avoiding DNO capacity investment. The detailed distribution network demand profile data required for this scenario (Covering Case A and Case B) was provided by the University of Bath ( Bath ) who have modelled demand profiles and also costs of local reinforcement assets for urban, suburban and rural networks at EHV, HV and LV levels. Pöyry utilised national data from the work carried out for DECC with an artificial network constraint inserted into the modelling. The analysis used this data to estimate the frequency of occurrence of the respective actions taking place. The benefit for networks is the avoided investment cost and it is this value that they could pass on to DSR providers. There is also a further UK plc. benefit as investment in generation capacity will be avoided. 3.3.2 Boost peak demand to accommodate wind and optimise prices In this scenario we investigated the value associated with (supplier driven) price optimisation i.e. wholesale market costs are minimised. This was quantified using two scenarios from existing work; one scenario where we assumed the baseline capacity in 2030 was 80GW (enough to meet fixed demand) and the other when network investment 20
was made to allow wind free rein (96GW in 2030). There are three cases for this scenario: Case C1; DSR is used to optimise wholesale market costs but as a consequence demand is moved in way which leads to a boost in peak demand seen on the networks at a transmission and/or distribution level and hence there is the need to invest in network capacity (noting that this effect may not be seen at all points on the network pyramid for given situations and will have varying impact as the DSR varies). What is the cost of investing in the additional network capacity? Case C2; as above but in a situation where the transmission network already faces a network capacity constraint (and/or is unable to invest away this capacity constraint) where they are already seeking to reduce peak demand and thus the price driven DSR exacerbates the volume of DSR the networks need deploy. Therefore, what is the cost of operational actions? Case C3; use DSR to optimise wholesale market costs under a network constraint this assumes suppliers are aware of network capacity constraints and optimise within that (e.g. 94GW or 80GW constraint that Pöyry used for DECC work).this explores the curtailed value suppliers can offer to DSR providers versus cost of avoided network investment. Figure 14 Modify demand to boost peak, and take advantage of high wind Peak demand net intermittent generation is the important thing here as it drives market prices higher demand periods may be cheaper than lower demand periods depending on how much of the demand can be met by zero/low cost generation. We used data from two scenarios we ran for DECC; the first is where DSR was allowed to minimise prices subject to a network cap. The second is where DSR was allowed to boost demand. 21
Case D; this is similar to the above but where DSR is used to avoid wind curtailment where otherwise wind output exceeds the level of wind output which the network can securely carry. The key relationship to define here will be the instantaneous penetration of wind that is allowed (in Ireland it has been deemed to be 75%). This will define the value associated with curtailing wind compared to moving demand around. The critical value will occur when incorporating wind requires either demand to be curtailed (at prices specified by National Grid and Ofgem) or shifted to incorporate wind. Figure 15 Modify demand as peak wind and peak demand do not coincide 3.3.3 Modify demand to accommodate low wind period This scenario investigated the price signals associated with a low wind period. There are two separate cases: Case E1: Prolonged period of low wind. In this case we will investigate the value associated with curtailing demand compared to carrying additional capacity to meet demand in these periods (this could be generation, storage or interconnection). Case E2: When the transmission network has been built to accommodate demand net embedded generation where that includes substantive wind generation; and there is a prolonged period of low wind. In this case we will evaluate the additional investment costs associated with transmission network investment and capital costs for peaking plant compared to alternative of operational costs in the form of demand curtailment costs. Therefore this scenario answers the question: what is the value associated with building the transmission network to meet demand the transmission network sees from the distribution networks (i.e. demand net embedded generation) instead of latent demand (i.e. demand plus embedded generation)? 22
Figure 16 Illustrative difference between demand net embedded generation and demand plus embedded generation 3.3.4 Modify demand to compensate for generation trip In this scenario we investigated the price signals associated with modifying demand to compensate for a generation trip. There are two cases under this scenario; network driven and generator/vip driven. Case F; generation trip. At transmission level the price signal means avoiding the need to contract other ancillary services (at the STOR price). At the distribution network level it avoids demand curtailment i.e. interruption incentives. We take the modelled (Zephyr) pattern of random outages with each counting as a trip and then multiply by the respective cost of interrupting supply for the distribution network and ancillary services for the transmission network. Case G; generator/vip out of balance. In this case we estimate the price signal associated with avoiding imbalance charges and distress prompt trading. 3.3.5 Modify demand to include a transmission network constraint This scenario (Case H) is much the same as Case F above except it introduces a locational system balancing dynamic as it investigates the impact of a network constraint. Assuming that a network constraint is known about, there are two options; to reschedule appropriate generation (up and down as appropriate either side of the network constraint) as traditionally done or to use a combination of reduced generation ( above the constraint) reduce demand ( below the constraint). The key issue here is to determine the average cost of constraining demand and more importantly, how often the constraint arises. 23