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
www.cranfield.ac.uk
Why do we need flexibility?
• Previous analysis shows significantly
more investment is needed in absence of
flexibility
• Flexibility can support a cheaper lowcarbon generation mix to meet a given
carbon reduction target
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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 shortterm 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
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Real Options Valuation for Pricing
Distribution Flexibility Services
DSO
DSO
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Business Options for contracting Flexibility
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Milton Keynes, trials city
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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
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Methodology: Bottom-up Meta-heuristics
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Planning Flexibility Investments
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SIM Interfaces and results
Inspector
2010
2015
2020
1
2025
2
2
2030
2035
3
2040
2045
2050
4
Affected assets
Select All
Patch
Focus
Inspect
Status
Asset
1
added
3-A
4-A
1
changed
3-B
Column 4
4-B
1
changed
3-C
4-C
2
changed
3-D
4-D
3
deleted
3-E
4-E
3
deleted
3-F
4-F
Actions
Current year: 2030
State Metrics
Year%
CML%
CI%
Losses%
Avg.%
Utilisation%
Avg.%
Max%
Utilisation%
Load%
Factor%
Cost%
$
2030$
5234$
20$
300$
0.70$
0.75$
0.9$
123$
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Project FALCON Closedown Dissemination
A valuable source of learning:
Whendo
Doissues
Issues occur?
Occur?
When
Initially a w
spread of d
when issues
occur – Win
Peak and W
Weekday a
most likely t
for issues, s
summer pe
and other
weekdays.
Could reduc
number of d
modelled.
Weekends
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Data visualisation: SIM Expansion trees
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MURRA:
Combining ROV with SIM locational resolution
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Demand deterministic models
Demand Scenarios
Fuel efficiency
Low Carbon heat
DECC 1
DECC 2
DECC 3
DECC 4
Medium
High
High
Low
High
Medium
High
Low
Wall
insulation
High
High
Low
Medium
*DECC: Department of Energy & Climate Change became part of Department for Business, Energy & Industrial Strategy in July 2016
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Results – Short-term planning (2015-2023)
On the left DECC2, on the right DECC 4
Most demanding scenario requires 17% more of TOTEX
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Results – Long-term planning (2015-2047)
On the left DECC2, on the right DECC 4
DECC2 scenario requires spending 14% more on TOTEX
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Optimal Investment Strategy 2015-2023
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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
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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
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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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