1. 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

2. 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

3. 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

4. Real Options Valuation for Pricing Distribution Flexibility Services
DSO
DSO

5. Business Options for contracting Flexibility
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6. Milton Keynes, trials city
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7. Scenario Investment Model
Smart Grid trialed techniques
Dynamic Asset Ratings
Automated Load Transfer
Meshed Networks
Battery Storage
Distributed Generation
Demand-Side Management

8. Methodology: Bottom-up Meta-heuristics

9. Planning Flexibility Investments

10. SIM Interfaces and results
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11. A valuable source of learning: When Do Issues Occur?
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12. Data visualisation: SIM Expansion trees

13. MURRA: Combining ROV with SIM locational resolution
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14. 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
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15. Results – Short-term planning (2015-2023)
On the left DECC2, on the right DECC 4
Most demanding scenario requires 17% more of TOTEX

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

17. Optimal Investment Strategy 2015-2023

18. 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

19. 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

20. 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
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21. 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.