Distribution Network Planning A ‘Real Options’ approach to support decisions on reinforcement versus post-fault demand-side-response (DSR) Dr Rita Shaw – Tuesday 7 th June 2016 Edinburgh University - International Centre for the Mathematical Sciences (ICMS) Conference on ‘Energy Management: Flexibility, Risk and Optimisation’ 1
Agenda Introducing Reinforce, or capacity from Electricity North West customers? A real-options decision Summary and questions support tool 2
Introducing Electricity North West 4.9 million 2.4 million 25 terawatt hours 56 000 km of network £12.3 billion assets 19 grid supply points 66 bulk supply substations 363 primary substations 33 000 transformers 3
The GB electricity structure All participants regulated by Ofgem Generation Trading Transmission Distribution Retail Free Market Regulated Free Market 4
RIIO regulatory framework RIIO = Total to be spent on the £24.6 GB distribution network BILLION 2015 - 2023 Revenue = Incentives + Resulting annual Innovation + Outputs average savings £10 in consumer bills in RIIO-ED1 ED1 = Electricity Distribution 14 DNO areas The power distribution Eight years 8% part of a dual fuel bill Total to be spent on £1.8 ENWL Network reliability 30% BILLION network increase since 2002 2015 - 2023 5
The lights are on – what's the problem? The network operator ‘Trilemma’ Reliability Smart Solutions Sustainability Affordability Smart solutions are the key to unlocking this puzzle 6
Our smart grid development Leading work on developing smart solutions Deliver value from existing Customer choice assets Five flagship products (second tier/NIC) £42 million 7
Capacity to Customers Capacity to Customers unlocks latent capacity on the electricity network Capacity Technical New commercial to Customers innovation contracts Utilised Current capacity demand Latent capacity Combines proven technology Remote control equipment on Innovative demand side and new commercial contracts HV circuit and close the NOP response contracts Enhanced network management software Facilitates connection of new Allow us to control a demand and generation customer’s consumption on a without reinforcement circuit at the time of fault Effectively doubles the available capacity of the circuit 8
Trial complete ... now when do we use? When is C 2 C cost ... or when should effective ...? we reinforce? Answer depends on costs, Spend £ every year for capacity capacity and views of future from customer demand OR spend £££ now to build capacity in new asset? 9
Outlook for future demand Why could demand go up? Why could demand fall? 10
Long-term electricity demand scenarios Average cold spell peak demand 72 Set of peak 70 demand scenarios, 68 tailored to a 66 specific 64 substation GW 62 Historic 60 Gone Green 58 Slow Progression 56 No Progression Methodologies 54 for annual Consumer 52 Power update of 2005/06 2007/08 2009/10 2011/12 2013/14 2015/16 2017/18 2019/20 2021/22 2023/24 2025/26 2027/28 2029/30 2031/32 2033/34 2035/36 long-term DNO load scenarios e.g. National Grid Future Energy Scenarios – July 2015 11
The problem (C 2 C) DSR provides Uncertain scale So uncertain BUT capacity a new source of and timing of scale and timing delivered in capacity future load of capacity location-specific requirements lumps. Sometimes large reinforcements, sometimes marginal release Objective – cost-effectively provide just the capacity required 12
DSR then reinforce if required Demand level Higher demand scenario Capacity after reinf. Capacity with DSR Short-term peak scenario Lower demand Initial capacity scenario Years Now Have DSR or RReinforce? Could start reinforce investment Is this new strategy cost-effective, and risk-appropriate? 13
Network Innovation Allowance project Demand Scenarios with Electric Heat and Commercial Capacity Options Create improved demand forecasts and Due to complete by implement in a end of 2016 DNO-appropriate Real Options approach Reports will be at www.enwl.co.uk/thefuture 14
A real-options approach (1) • Traditional CBA / NPV approach assumes 1 view of future. ‘Real options’ works with the uncertainty. • RO values flexibility of decision-making under uncertainty – Branch of mathematical finance, relevant to engineering – Ofgem expressed an interest (initially in relation to GDNs) – Useful as traditional reinforcement is financially material and irreversible Flexibility in when and how we invest for network capacity • – eg traditional large reinforcements, or marginal capacity release by DSR or incremental reinforcements • Based on uncertainty in long-term peak demand scenarios – And sensitivity to volatilities in demand and in other inputs – Information is delayed 15
A real options approach (2) Worked with University of Manchester on initial development of methodology and tool (Dr John Moriarty and Dr Pierluigi Mancarella) ‘Real options’ are useful for investments when... Decision to Uncertainty Investment is Flexibility invest based is financially at least partly exists on uncertain material irreversible information Invest Abandon Defer Expand 16
A real-options approach (3) Phase 1 report December 2013 “Flexible investment strategies in distribution networks with DSR: Real Options modelling and tool architecture” Phase 1 report can be shared Key findings A DNO-suitable Can be based on Many options exist for approach can be annually-updated set of decision-metrics on cost implemented in Excel. probability-weighted and risk demand scenarios, plus demand volatility around those scenarios. 17
Moriarty report – stages in RO model C D A B E Identify Probability For each of The decisions (one potential significant future weights (or more these states, the that would be metric) decision points in generally, a information that taken by rational the investment probability would be management in A probability project measure) are available to each of these weighted constructed for management as states are average is taken the possible a basis for their identified, for over these states of the decision making example using a possible futures world at those is identified. binomial tree. to arrive at the times, reflecting present project how likely the value. respective states are. 18
A real-options approach (4) When would we use RO? Before committing to investment – Scoping stage – find useful DSR Justifying efficiency of load-related scale and maximum price before expenditure before commitment to approaching DSR customers DSR or reinforcement Applied to every project with DSR potential, or to derive policy –TBC Options models provide the cost and risk metrics to support decisions about efficient investment Should we do DSR, Large or small reinforce, or DSR then reinforcement? maybe reinforce? How much DSR? When? At what price? Like-for-like or oversized DSR while wait for asset replacement? demand increase? 19
‘Real options’ methodology Working with University of Manchester to develop cost and risk metrics in a decision-support tool - with business and regulatory perspectives Strategy A Total NPC Weighted over All Scenarios Total Net Present Cost (£k) 18 Number of occurrences 16 14 Strategy A - Boxplot of Excess Load per Year by quartile Scenario 1 £10,000 12 10 4.00 8 6 3.50 £8,000 Scenario 2 Excess load (MVA) 4 3.00 2 0 2.50 £6,000 Scenario 3 2.00 Cost (£) 1.50 £4,000 1.00 Scenario 4 Strategy B Total NPC Weighted over All Scenarios £2,000 0.50 70 Number of occurrences 0.00 60 Scenario 5 50 £0 40 Network Losses Total Network Losses Total 30 Weighted 20 Strategy B - Boxplot of Excess Load per Year by quartile 10 mean 4.00 0 Excess load (MVA) 3.00 Cost (£) Analysis of general 2.00 reinforcement paid for by DNO 1.00 and customer in general, not just 0.00 connections reinforcement. 20
Objectives of our work Simple to Develop an Develop an Utilise the Support C 2 C apply v options informed ‘options’ for Reflects assessment position with expertise at dissemination actual project method for Ofgem UoM to and BAU strategic validate transition planning With appropriate recognition by Regulation and Finance colleagues 21
Real options – where we are now Creating prototype model harder than we thought, but now in use 2 strategies, each with up to 3 interventions Up to 5 demand scenarios, each with 2 x 100 Monte Carlo variations Electricity North West developed UoM’s early prototype Currently structured into one 34Mb Excel model Derive policy? Streamline for BAU stage after prototype complete? 22
RO model structure Inputs Calculations Summary metrics Cost and risk Site demand forecasts Strategy A distributions Framework inputs Strategy B Least regret cost and Strategy A inputs (repeated structure) risk analysis Strategy B inputs Capacity output per macro-scenario Cash flow output per macro-scenario 23
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