ons sddp workshop, august 17, 2011 slide 1 of 31 andy philpott epoc () joint work with ziming guan...
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ONS SDDP Workshop, August 17, 2011 Slide 1 of 31
Andy PhilpottEPOC
(www.epoc.org.nz)
joint work with
Ziming Guan (now at UBC/BC Hydro)
Electricity Market Benchmarking Exploring
Risk
ONS SDDP Workshop, August 17, 2011 Slide 2 of 31
•Before 1996, the New Zealand wholesale electricity system was operated as a state monopoly.
•Since October 1996 this has been run as an electricity pool market.
•Generation ownership last changed in 1999 when ECNZ was broken up.
•The system is dominated by generation from hydro-electric reservoirs.
•This leads to unique and interesting problems when trying to understand how pool markets
should operate.
Electricity Supply in New Zealand
ONS SDDP Workshop, August 17, 2011 Slide 3 of 31http://www.electricityinfo.co.nz/
New Zealand national reservoir storage
ONS SDDP Workshop, August 17, 2011 Slide 4 of 31
NZ wholesale electricity market • Generators specify supply
curves defining prices at which they will generate.
• Curves fixed for each half hour
• Linear programming model runs every five minutes to determine – electricity generated– electricity flows in network– spot price (shadow price) of
electricity at 244 out of 470 network nodes
Waikato River
Waitaki system
ONS SDDP Workshop, August 17, 2011 Slide 5 of 31SPXII, Halifax, August 20, 2010 Slide 5 of 50
ONS SDDP Workshop, August 17, 2011 Slide 6 of 316/42
The economic dispatch problem
New Zealand electricity market
ONS SDDP Workshop, August 17, 2011 Slide 7 of 31
New Zealand electricity marketLake storage (blue) and price (pink)
ONS SDDP Workshop, August 17, 2011 Slide 8 of 31
Questions
• How should this system be operated to provide security of supply at low cost? As a pool market or some alternative?
• Do generators manage hydro-reservoir storage to minimize overall national thermal fuel cost or are they behaving strategically? (as discussed in Bushnell, 2003).
• If market power gives higher prices, is this accompanied by a deadweight loss from inefficient dispatch?
• The NZ Electricity Commission maintains a Centralized Data Set that can be used to address some of these questions.
ONS SDDP Workshop, August 17, 2011 Slide 9 of 31
(New Zealand Herald May 21, 2009, downloaded from site: http://www.nzherald.co.nz)
New Zealand Commerce Commission on Market Power
“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.
This has already been done
ONS SDDP Workshop, August 17, 2011 Slide 10 of 31Source: CC Report, p 177
The view from economicsNew Zealand electricity market
ONS SDDP Workshop, August 17, 2011 Slide 11 of 31
Deadweight loss = empirical price of anarchy
Offered cost curve
True cost curve
New Zealand electricity market
ONS SDDP Workshop, August 17, 2011 Slide 12 of 31
Deadweight loss = empirical price of anarchyNew Zealand electricity market
ONS SDDP Workshop, August 17, 2011 Slide 13 of 31
Deadweight loss = empirical price of anarchyNew Zealand electricity market
ONS SDDP Workshop, August 17, 2011 Slide 14 of 31
Deadweight loss = empirical price of anarchyNew Zealand electricity market
ONS SDDP Workshop, August 17, 2011 Slide 15 of 31Source: CC Report, p 200
The view from economics againNew Zealand electricity market
ONS SDDP Workshop, August 17, 2011 Slide 16 of 31
What is counterfactual 1?
– Fix hydro generation (at historical dispatch level).– Simulate market operation over a year with thermal plant
offered at short-run marginal (fuel) cost.– “The Appendix of Borenstein, Bushnell, Wolak (2002)* rigorously
demonstrates that the simplifying assumption that hydro-electric suppliers do not re-allocate water will yield a higher system-load weighted average competitive price than would be the case if this benchmark price was computed from the solution to the optimal hydroelectric generation scheduling problem described above” [Commerce Commission Report, page 190].
(* Borenstein, Bushnell, Wolak, American Economic Review, 92, 2002)
New Zealand electricity market
ONS SDDP Workshop, August 17, 2011 Slide 17 of 31
Counterfactual 1
Now set y=y0 not equal to y* (“fix hydro generation”)
(x*,y*,*)
(x0,y0,0)
Linear programming interpretation
ONS SDDP Workshop, August 17, 2011 Slide 18 of 31
Counterfactual 1What about uncertain inflows?
wet
dryStochastic program counterfactualThe 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).
Counterfactual 1In the year under investigation, suppose all generators optimistically predicted high 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
ONS SDDP Workshop, August 17, 2011 Slide 19 of 31
What is a better counterfactual?
– Solve a multistage stochastic linear program (MSLP) to compute a centrally-planned generation policy, and simulate this policy.
– Previous work does this with a dynamic program for Nordpool (Kauppi & Liski, 2008).
– In our model, we re-solve the MSLP every 13 weeks and simulate the policy between solves using a detailed model of the system.
• 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
New Zealand electricity market
ONS SDDP Workshop, August 17, 2011 Slide 20 of 31
Yearly problem represented by this system
S
N
demand
demandWKO
HAW
MAN
H
demand
Stochastic Counterfactual
ONS SDDP Workshop, August 17, 2011 Slide 21 of 31
Rolling horizon counterfactual
– Set s=0– At t=s+1, solve a DOASA model to compute a
weekly centrally-planned generation policy for t=s+1,…,s+52.
– In the detailed 18-node transmission system and river-valley networks successively optimize weeks t=s+1,…,s+13, using cost-to-go functions from cuts at the end of each week t, and updating reservoir storage levels for each t.
– Set s=s+13.
Application to NZEM
ONS SDDP Workshop, August 17, 2011 Slide 22 of 31
We simulate an optimal policy in this detailed system
MAN
HAW
WKO
Application to NZEM
ONS SDDP Workshop, August 17, 2011 Slide 23 of 31
Thermal marginal costs Application to NZEM
Gas and diesel prices ex MED estimatesCoal priced at $4/GJ
ONS SDDP Workshop, August 17, 2011 Slide 24 of 31
Gas and diesel industrial price data ($/GJ, MED)Application to NZEM
ONS SDDP Workshop, August 17, 2011 Slide 25 of 31
Load curtailment costsApplication to NZEM
ONS SDDP Workshop, August 17, 2011 Slide 26 of 31
Market storage and centrally planned storage New Zealand electricity market
2005 2006 2007 2008 2009
ONS SDDP Workshop, August 17, 2011 Slide 27 of 31
Risk aversion and competitive equilibrium New Zealand electricity market
Is the central plan the competitive equilibrium? • yes, if all agents are risk neutral, and share the
same probability distribution as the central planner
• no, if agents are risk averse• so the behaviour we are seeing could be risk
aversion in a perfectly competitive market
ONS SDDP Workshop, August 17, 2011 Slide 28 of 31
New Zealand electricity marketEstimated daily savings from central plan
$481,000 extra is saved from anticipating inflows during this week
ONS SDDP Workshop, August 17, 2011 Slide 29 of 31
Savings in annual fuel costTotal fuel cost = (NZ)$400-$500 million per annum (est)
Total wholesale electricity sales = (NZ)$3 billion per annum (est)
New Zealand electricity market
ONS SDDP Workshop, August 17, 2011 Slide 30 of 31
Benmore half-hourly prices over 2008 New Zealand electricity market
ONS SDDP Workshop, August 17, 2011 Slide 31 of 31
FIM
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