Electricity markets, dry winters, and risk

1. 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

2. Electricity supply in New Zealand Department of Engineering Science The University of Auckland

3. 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. Department of Engineering Science Q: How competitive is the market? Q: How can you tell? The University of Auckland

4. It wasn’t competitive two years ago Department of Engineering Science “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. The University of Auckland (New Zealand Herald May 21, 2009, downloaded from site: http://www.nzherald.co.nz)

5. Electricity markets, dry winters, and risk Department of Engineering Science The University of Auckland

6. Benchmark against counterfactual Market power rents add up to $4.3 B Department of Engineering Science The University of Auckland Source: Commerce Commission Report, 2009, p 200

7. What is wrong with this? The hindsightbenchmark In 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. wet Department of Engineering Science The realistic benchmark The 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). dry summer winter The University of Auckland

8. Research question Department of Engineering Science What does a perfectly competitive market look like when it is dominated by a possibly insecure supply of hydro electricity? The University of Auckland

9. 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. Department of Engineering Science The University of Auckland

10. 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 The EPOC benchmark Department of Engineering Science The University of Auckland

11. HAW MAN WKO EPOC simulates this detailed system Department of Engineering Science The University of Auckland

12. Cost assumptions Department of Engineering Science The University of Auckland

13. Experiment 1: savings in daily fuel costs Department of Engineering Science 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. The University of Auckland

14. HAW MAN WKO Long-term optimization model demand demand N Department of Engineering Science H S The University of Auckland demand

15. Historical vs centrally planned storage Department of Engineering Science The University of Auckland 2005 2008 2006 2007 2009

16. 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) Department of Engineering Science The University of Auckland

17. Benmore prices over 2005 Department of Engineering Science The University of Auckland

18. Benmore prices over 2008 Department of Engineering Science The University of Auckland

19. How to represent (value at) risk frequency 5% Department of Engineering Science cost The University of Auckland VaR0.95 = 150

20. Conditional value at risk (CVaR) for costs frequency Department of Engineering Science cost The University of Auckland CVaR0.95 = 162

21. 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: Department of Engineering Science The University of Auckland

22. Simulated national storage 2006 Department of Engineering Science The University of Auckland

23. 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. Department of Engineering Science The University of Auckland

24. Department of Engineering Science THE END The University of Auckland