Dynamic Investments in Flexibility Services for Electricity Distribution with Multi-Utility Synergies Dr. Jesus Nieto-Martin Professor Mark A. Savill Professor Derek W. Bunn 40th IAEE International Conference Singapore, 19th June 2017

Why do we need flexibility? • Previous analysis shows significantly more investment is needed in absence of flexibility • Flexibility can support a cheaper low- carbon generation mix to meet a given carbon reduction target Source: Strbac, Imperial College

Real Options Valuation for Pricing Distribution Flexibility Services Understanding the role of flexibility is very complex and associated with a number of uncertainties: Evolution of future energy system Projected cost and availability of different flexibility options • Despite uncertainties, key investment decisions need to be made in the short-term but will have a lasting impact due to long lead times • This creates the possibility for regret i.e. additional cost due to suboptimal myopic decisions • Flexibility can provide option value – postponing decisions on larger investments until there is better information, hence reducing the need to make potentially high regret decisions • A proposed approach is about quantifying the possible outcomes for a set of strategic choices, and then identifying choices of the outcome for decision makers

Real Options Valuation for Pricing Distribution Flexibility Services DSO DSO

Business Options for contracting Flexibility © Cranfield University 2017

Milton Keynes, trials city © Cranfield University 2017

Scenario Investment Model Smart Grid trialed techniques Dynamic Asset Ratings Automated Load Transfer Meshed Networks Battery Storage Distributed Generation Demand-Side Management http://www.westernpowerinnovation.co.uk/Falcon.aspx

Methodology: Bottom-up Meta-heuristics

Planning Flexibility Investments

SIM Interfaces and results © Cranfield University 2017

A valuable source of learning: When Do Issues Occur? © Cranfield University 2017

Data visualisation: SIM Expansion trees

MURRA: Combining ROV with SIM locational resolution © Cranfield University 2017

Demand deterministic models Demand Scenarios Fuel efficiency Low Carbon heat Wall insulation DECC 1 Medium High DECC 2 DECC 3 Low DECC 4 *DECC: Department of Energy & Climate Change became part of Department for Business, Energy & Industrial Strategy in July 2016 © Cranfield University 2017

Results – Short-term planning (2015-2023) On the left DECC2, on the right DECC 4 Most demanding scenario requires 17% more of TOTEX

Results – Long-term planning (2015-2047) On the left DECC2, on the right DECC 4 DECC2 scenario requires spending 14% more on TOTEX

Optimal Investment Strategy 2015-2023

Optimal Investment Strategy 2015-2047 Optimal Path All SIM All DSO All Outs All Agg All P2P 1 1.17 1.92 1.47 1.38 1.52

Myopic Investment Strategy 2015-2047 Sub-Optimal All SIM All DSO All Outs All Agg All P2P 1.19 1.33 1.81 1.39 1.36 1.41

Benefits of meshing do not correlate to load Some learnings so far… Voltage issues appear in 2015 by changing Electrical Vehicles and Heat Pumps clustering assumptions Discovery of overbuilt primary networks, better to sign locational flexibility contracts Benefits of meshing do not correlate to load Voltage issues appear only in DECC2 and DECC3 scenarios Smart intervention techniques make up a greater proportion of the number of interventions over longer timeframes Smart techniques do not create extra capacity in the system © Cranfield University 2017

That is an on-going work where GANESH optimiser is being tailored to operate in parallel for using it in an HPC environment. That will allow us to start learning earlier reducing simulation costs where in model that takes days/weeks (depend on the resolution) to converge it can be have a notorious impact.