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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© Cranfield University 2016

Why do we need flexibility?

Source: Strbac, Imperial College

• 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


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



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

Inspector

20152010 2020 2025 2030 2035 2040 2045 2050

42

Affected assets

Select All Focus Inspect

Patch Status Asset

1 added 3-A

1 changed 3-B

1 changed 3-C

2 changed 3-D

3 deleted 3-E

3 deleted 3-F

Column 4

4-A

4-B

4-C

4-D

4-E

4-F

Actions

1 2

3

Current year: 2030

State Metrics Year% 2030$CML% 5234$

CI% 20$Losses% 300$

Avg.%Utilisation% 0.70$Avg.%Max%Utilisation% 0.75$

Load%Factor% 0.9$Cost% 123$

$



A valuable source of learning: When Do Issues Occur?When do issues occur?

Project FALCON Closedown Dissemination Event

Initially a wider spread of days when issues occur – Winter Peak and Winter Weekday are most likely time for issues, some summer peak and other weekdays. Could reduce the number of days modelled. Weekends



Data visualisation: SIM Expansion trees


MURRA: Combining ROV with SIM locational resolution



*DECC: Department of Energy & Climate Change became part of Department for Business, Energy & Industrial Strategy in July 2016

Demand deterministic models


Demand Scenarios Fuel efficiency Low Carbon heatWall

insulation

DECC 1 Medium High High

DECC 2 High Medium High

DECC 3 High High Low

DECC 4 Low Low Medium


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


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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