electricity markets, dry winters, and risk
DESCRIPTION
Electricity markets, dry winters, and risk. Department of Engineering Science. Andy Philpott Electric Power Optimization Centre. http://www.epoc.org.nz. joint work with Ziming Guan. The University of Auckland. Electricity supply in New Zealand. Department of Engineering Science. - PowerPoint PPT PresentationTRANSCRIPT
Electricity markets, dry winters, and risk
http://www.epoc.org.nz
Andy PhilpottElectric Power Optimization Centre
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joint work withZiming Guan
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Electricity supply in New Zealand
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Electricity markets, dry winters, and risk
"Private market disciplines are important in competitive industries. And the energy market is becoming increasingly competitive. And the government, in our experience, is not an adaptable, risk-adjusted 100 per cent owner of assets in competitive markets.“ Bill English, Energy News, Nov. 9.
Q: How competitive is the market?
Q: How can you tell?
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It wasn’t competitive two years ago
(New Zealand Herald May 21, 2009, downloaded from site: http://www.nzherald.co.nz)
“There is something fundamentally wrong in the way in which we’re marketing electricity in New Zealand,” Mr Brownlee said.
Power generators overcharged customers $4.3 billion over six years by using market dominance, according to a Commerce Commission report.
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Electricity markets, dry winters, and risk
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Source: Commerce Commission Report, 2009, p 200
Market power rents add up to $4.3 B
Benchmark against counterfactual
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What is wrong with this?
wet
dryThe realistic benchmarkThe optimal generation plan burns thermal fuel in stage 1 in case there is a drought in winter. The competitive price is high (marginal thermal fuel cost) in the first stage, but zero in the second (if wet).
The hindsight benchmarkIn the year under investigation, suppose all generators optimistically predicted high winter inflows and used all their water in summer. They were right, and no thermal fuel was needed at all. Counterfactual prices are zero.
summer winter
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Research question
What does a perfectly competitive market look like when it is dominated by a possibly insecure supply of hydro electricity?
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A welfare result
Suppose that the state of the world in all future times is known, except for reservoir inflows that are known to follow a stochastic process that is common knowledge to all generators. Suppose that, given electricity prices, these generators maximize their individual expected profits as price takers.
There exists a stochastic process of market prices that gives a price-taking equilibrium. These prices result in generation that maximizes the total expected welfare of consumers and generators.
So the resulting actions by the generators maximizing profits with these prices is system optimal. It minimizes total expected generation cost just as if the plan had been constructed optimally by a central planner.
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The EPOC benchmark
– Solve a multistage stochastic linear program (MSLP) to compute a centrally-planned generation policy, and simulate this policy.
– We account for shortages using lost load penalties.– In our model, we re-solve the MSLP every 13 weeks
and simulate the policy between solves using a detailed model of the system. We call this central.• includes transmission system with constraints and losses• river chains are modeled in detail• historical station/line outages included in each week• unit commitment and reserve are not modeled
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EPOC simulates this detailed system
MAN
HAW
WKO
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Cost assumptions
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Experiment 1: savings in daily fuel costs
Fuel costs from using central day by day, matching historical hydro reservoir levels on each day, but optimizing over 48 periods rather than period by period as in the market.
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Long-term optimization model
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demand
demandWKO
HAW
MAN
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demand
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Historical vs centrally planned storage
2005 2006 2007 2008 2009
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Experiment 2: savings in annual fuel cost
Total fuel cost = (NZ)$400-$500 million per annum (est)
Total wholesale electricity sales = (NZ)$3 billion per annum (est)
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Benmore prices over 2005
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Benmore prices over 2008
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How to represent (value at) risk
VaR0.95 = 150
5%
cost
frequency
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Conditional value at risk (CVaR) for costs
CVaR0.95 = 162
frequency
cost
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Including a risk measure
The system in each stage minimizes its fuel cost in the current week plus a measure of the future risk.
For two stages (next week’s cost is Z) this measure is:
(1-l)E[Z] + l CVaR1-a(Z)
for some l between 0 and 1:
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Simulated national storage 2006
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Four conclusions
When agents are risk neutral, competitive markets correspond to a central plan.
When agents are risk averse, competitive markets do not correspond to a central plan.
Risk-neutral optimal central plan can give higher prices than those observed in a historical realization, but they are best on average.
A new benchmark is needed: risk averse competitive equilibrium with incomplete markets for risk.
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