jean-michel glachant (and vincent rious) loyola de palacio chair & florence school of regulation
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Robert Schuman Centre for Advanced Studies. Florence School of Regulation & Loyola de Palacio Chair. Coordination of the location and time of investment of generation and transmission in a liberalised power system. Jean-Michel Glachant (and Vincent Rious) - PowerPoint PPT PresentationTRANSCRIPT
Coordination of the location and time of investment of generation and transmission in
a liberalised power system
Jean-Michel Glachant (and Vincent Rious)
Loyola de Palacio Chair & Florence School of Regulation
European University Institute in Florence
30th July 201011th ACCC Regulatory Conference:
Surfers Paradise
Robert Schuman Centre for Advanced Studies Florence School of Regulation & Loyola de Palacio
Chair
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OutlineNeed for coordination between G & T
• Tool #1 = locational signals
• Tool #2 = anticipation
Scope for maximisation of social welfare?
• Conclusions
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Coordinated gen° and trans° investments by an integrated utility
G_Inv._CostE<
Min: Gen Invst Cost + Fuel Cost + Trans Invst Cost
Integrated Utility
Easy to know the cheapest investment strategy
Location + time of investment
G_Inv._CostW
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Unbundling and the need for coordination
<
Min: Gen Investment Cost + Fuel Cost + Tariff
G_Inv._CostW + Trans cost >
Suboptimal location ! Locational signals
Liberalised power system
Inefficient coordination
G_Inv._CostW G_Inv._CostE
G_Inv._CostE
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Unbundling and the need for coordination
Liberalised power system
>> Prompts investors to choose generation technologies with short
construction lead time
Right of way of powerlines facing increasing oppositions ~ 7 years to build a powerline from study to construction itself
because of administrative agreement >> 5 years!
Generation technology
Time to build (year)
Notional size (MW)
CCGT 2 200-800
Wind onshore 2 25
offshore 2 100
Coal 4-5 200-1600
Nuclear 5-7 1600
Congestion while the network is not upgraded
5
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yTransmission planning and generation location in a
liberalised power systemA story of chicken and egg
• The TSOs need to know generation location to develop the network
• The generators may be constrained because of network congestion
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Outline
• Need for coordination between G and T
• Tool #1 = locational signals
• Tool #2 = anticipation
Scope for maximisation of social welfare?Model and results
• Conclusions
FS
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Coordination with short term locat. signals
GICW GICE<
Min Gen Investment Cost + Fuel Cost + Tariff
PNW PNE<
Network constraints
Nodal pricing
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GICW GICE<
Min Gen Investment Cost + Fuel Cost + Tariff
PNW PNE<
Pb: lumpy transmission investment
Nodal pricing
marginal signals not enough to achieve coordination
Coordination with short term locat. signals
Network constraints
Lumpiness
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Min Gen Investment Cost + Fuel Cost + Tariff
+ Tariff
Coordination with long term locat. signals
TariffW TariffE>
GICW GICE<
+ Tariff
= 0
Network tariffs
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Outline
• Need for coordination between G and T
• Tool #1 = locational signals
• Tool #2 = anticipation
Scope for maximisation of social welfare?Model and results
• Conclusions
FS
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Problem
• Coordination of location of generation investments with lumpy transmission investments
• Efficiency of network tariffs– ‘Average participation’ tariff = each Gen. pays for
her ‘traced’ share of used lines• Thought as a good tariff in economic literature
– Game theory (Shapley value: fair & symmetric)
• Not been evaluated yet in interaction with investments (Ignacio!)
– Jointly implemented with nodal prices
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Structure of the model
• One generator (!)– Competitive setting -;)Min Gen° investt cost
+ fuel cost + tariff
• One TSO (at least!)– Benevolent -;)Min Trans° investt cost
+ cong° cost
Influenced by market design– Nodal pricing or Redispatch– No tariff or ‘Average participation’ tariff
Determined by the generator’s choice
Network costs
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Characteristics of the model
• Good news for simplicity of the model -;) Optimistic frame -;)
– No uncertainty• TSO perfectly informed of generator’s decision set• Generator perfectly informed of TSO’s decision set
• Bad news for simplicity of the model -;( Difficulty to solve -;(
– Double optimization• No sequential optimization• The 2 functions optimized at the same time
– Non linear problem• Lumpiness of transmission investment• ‘Average participation’ tariff
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Optimization algorithm for a realistic model!
2%/y
200 MW200 MW
200 MW
200 MW200 MW
200 MW400 MW400 MW
• Problem hard to solve
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yOptimization algorithm
for a realistic model!
2%/y
200 MW200 MW
200 MW
200 MW200 MW
200 MW400 MW400 MW
• Problem hard to solve– Solved using an heuristic Genetic algorithm
• System investment strategies as ’’individuals’’ • With reproduction and random mutation process to
better off from one period to another
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Optimization algorithm
• Use of a Pareto frontier to find the investment strategies equilibria of our double optimization problem
Simulated population
Measure of the coordination reached by the points on the Pareto frontier evaluation in terms of social cost
Value of the investment strategy for the Generator
Val
ue o
f the
inve
stm
ent s
trat
egy
for
the
TS
O
Investment strategies generated “at random” by the genetic algorithm
Pareto Frontier
Investment strategies = set of generation +
transmission investments
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• Average participation tariff
Results
• Nodal pricing– Weak effect on coordination– Even in favorable cases:
• No economies of scale• Perfect knowledge of economic signals
• Interaction between– network investments lumpy– locational signals
Locational signals with ‘bounded’ efficiency
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• Nodal pricing– Weak effect on coordination– Even in advantagous cases
• No economies of scale• Perfect anticipation of economic signals
Results
• Average participation tariff–Coordination always improved –But suboptimal coordination
• Not enough locational incentives• Or too much in some cases !
• Interaction between– locational signals and– network investments lumpy
Locational signals with limited efficiency
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Results
Network tariff more important than nodal pricing
for efficient location of G investment
• Interaction between– locational signals and– network investments lumpy
Locational signals with limited efficiency
FS
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Outline
• Need for coordination between G and T
• Tool #1 = locational signals
• Tool #2 = anticipation
Scope for maximisation of social welfare?Model and results
Conclusion #1
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Coordination with locational signals?
• No optimal coordination with locational signals• Nodal pricing + average participation
tariff– Because of lumpiness of transmission investment– Even if improved coordination
(so locational signals are needed)– And ‘average participation’ tariff more efficient than
nodal pricing for efficient location of investment
FS
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Coordination with locational signals?
• These signals = transmit information But limited to current grid and its current use
• And (+) many other locational constraints existing for generators: access to water, to fuel, to land, to social acceptance (NIMBY)
lasting congestions need to develop the network
FS
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Outline
• Need for coordination between G and T
• Tool #1 = locational signals
• Tool #2 = anticipation
Scope for maximisation of social welfare?Model and results
• Conclusion
FS
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Two alternative behaviors for the TSO
• Reactive behavior– Waits for generation connection request to study
the need of transmission investments
• Proactive behavior– Anticipates generation connection request in areas
with exploitable energy sources• Gas• Wind
– Starts first-step investment process as to get administrative green lights already agreed when generators will request for connection
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Pros & cons of the two alternative behaviors
• Reactive behavior
* No trsm investment cost put at risk**BUT excessive congestion if CCGTs or wind
farms connect while network is still to be upgraded
• Proactive behavior
* No excessive congestion put at risk**BUT proactive behavior is itself costly because
(if generation finally doesn’t come) TSO did already:• the study to upgrade the network • And went trough all procedures to obtain the
administrative green lights to build the line
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FS
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Unbundling and the need for coordination
Liberalised power system
>> Prompts investors to choose generation technologies with short
construction lead time
Right of way of powerlines facing increasing oppositions ~ 7 years to build a powerline from study to construction itself
because of administrative agreement >> 5 years!
Generation technology
Time to build (year)
Notional size (MW)
CCGT 2 200-800
Wind onshore 2 25
offshore 2 100
Coal 4-5 200-1600
Nuclear 5-7 1600
Congestion while the network is not upgraded
27
Need for coordination between generation and transmission
investments
FS
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Loyola
de P
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Outline
• Need for coordination between G and T
• Tool #1 = locational signals
• Tool #2 = anticipation
Scope for maximisation of social welfare?Model and results
• Conclusion
FS
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Problem
• Efficiency of anticipating generation connection for the TSO in terms of minimization of her total network costs?– If no anticipation congestion for quite long
period while network must be upgraded
– If TSO anticipes the ‘’first-stage costs’’ costly if anticipated generators do not connect
One must arbitrate between these two costs• weighed by a probability for the connection of
generation (been evaluated by an expert panel « à dire d’expert »)
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Construction
Generation Investment
Year
CU(d)CW(d)
Transmission Investment
d
0
Study, admin. proced. construction
Investment sequencing with reactive TSO 30
Excessive congestion Optimal value for network capacity
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Construction
Year
Study, admin. proced. construction
Generation and Network Investments
CU(0)
0
Investment sequencing with proactive TSO31
FS
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y“Probability limit”
and condition for proactive TSO with known first-stage costs32
First-stage costs = 10% of transmission investment costs
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With first-stage costs = 50% of transmission investment costs
Still efficient for the TSO to be proactive when 40% probability for the connection of a new plant
“Probability limit” and condition for a proactive TSO
with unknown first-stage costs
FS
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Outline
• Need for coordination between G and T
• Tool #1 = locational signals
• Tool #2 = anticipation
Scope for maximisation of social welfare?Model and results
• Conclusions
FS
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Conclusion (1/4)
• A model to evaluate the efficiency of anticipating plants investment to minimize the total network costs
• Illustration on simple realistic examples (like CCGT or wind farms)– Efficient to anticipate the connection of power
plants for the TSO– Planning in advance network reinforcement– Reduce congestion costs
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Conclusion (2/4)
– Efficient for TSO to anticipate the connection of new generator
• All the more that (a/)– Proactive behavior favours dialog between
TSO and market participants about planning assumptions
• Facilitates coordination through sharing information
• Facilitates dialog and acceptability from local populations
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Conclusion (3/4)
• All the more that (b/)– Possibility to send potential locational signals
related to the anticipated network• Volume signals: new generation capacity that will
not constrain the grid• Tariff and price signals: potential levels of
locational network access fees and energy prices for different network and generation scenarios
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Conclusion (4/4)
• All the more that (c/)– Possible to incentivise TSOs to be proactive
• Make them bear a part of congestion costs• Example in France: the TSO compensates the
wind farms when they have a ‘curtailment obligation’ for more than 3 years
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Future research on network anticipation
• Feedback effects with locational signals
• Effects of (milestones payment) in connection tariffs to create increasing locational commitment from generator
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Conclusion of conclusions
• Tool 1- Coordination of generation and transmission investment with signals because of intrinsic lumpy network cost structure– information sharing with Generation – Necessary but not sufficient locational
signal when generation quicker to develop than the network
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Conclusion of conclusions
• Tool 2- Importance of information / consultation platform for network planning– Information sharing building of common
knowledge about possible future(s) more certainty for investors
– All the more needed to integrate new generation technologies new location, new network usage
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That’s it: Even in a Surfers (non)Paradise more coordination between Gen. and
Transmission is not necessarily a luxury…
Jean-Michel Glachant
Loyola de Palacio Chair & Florence School of Regulation
European University Institute in Florence
Robert Schuman Centre for Advanced Studies Florence School of Regulation & Loyola de Palacio
Chair
Thank you for your attentionThank you for your attention
Questions ? Comments ?Questions ? Comments ?
Coordination of the location and time of investment of generation and transmission in
a liberalised power system