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NERI Conference, November 22, 2007 Modelling incentives and regulation in wholesale electricity markets Andy Philpott Electric Power Optimization Centre The University of Auckland (www.esc.auckland.ac.nz/epoc) (with acknowlegements to Geoff Pritchard and Golbon Zakeri)

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NERI Conference, November 22, 2007 What is the purpose of this talk? New Zealand faces some huge technical challenges in energy supply and delivery. This needs lots of research and development into new technology which is where NERI is currently focused. But technology is not enough – we need to understand the economic institutions for implementing this technology. Our work at EPOC studies how these institutions (e.g. taxes, trading schemes, regulations etc.) work using models. These models try to help us design mechanisms that will induce optimal behaviour in the agents of wholesale electricity markets – i.e. we study incentives and how they work.

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NERI Conference, November 22, 2007 Summary What is the wholesale electricity market? Examples of incentive/regulation problems –Generator offering –Transmission planning –Wind power –Emissions trading Takeaway: new energy technology is necessary but not sufficient without understanding the market mechanisms under which we expect it to be adopted.

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NERI Conference, November 22, 2007 NZEM is a uniform price auction (e.g. single node) price quantity price quantity combined offer stack demand p price quantity T 1 (q) T 2 (q) p

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NERI Conference, November 22, 2007 Example Capacity 250 lossless Thermal A: 400 @ $45 Wind: 100 forecast, @ $0 Thermal B: 400 @ $50 Hydro: 200 @ $30, 200 @ $90 Load 500

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NERI Conference, November 22, 2007 Least-cost dispatch Capacity 250 lossless Thermal A: 400 @ $45Wind: 100 forecast, @ $0 Thermal B: 400 @ $50 Hydro: 200 @ $30, 200 @ $90 Load 500 100 200 250 50 150

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NERI Conference, November 22, 2007 Least-cost dispatch with nodal prices Capacity 250 lossless Thermal A: 400 @ $45Wind: 100 forecast, @ $0 Thermal B: 400 @ $50 Hydro: 200 @ $30, 200 @ $90 Load 500 100 200 250 50 150 $45 $50 (1)Load pays $25000 (=$50*500) (2)Hydro makes profit $4000 and Wind makes profit $4500 (3)System operator makes congestion rent of $1250 (4)The dispatch has total cost $15250

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NERI Conference, November 22, 2007 The actual NZEM Generators specify supply curves defining prices at which they will generate. Curves fixed for each (1/2) hour Linear programming model runs every five minutes to determine –who produces how much –electricity flows in grid –spot price of electricity at each grid exit point around the country (244 of these)

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NERI Conference, November 22, 2007

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Wholesale electricity prices Five Minute Wholesale Electricity Prices on 28/08/06 ($/MWh) Time of Day Otahuhu Benmore 6am-9am3am-6am Source: comitfree

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NERI Conference, November 22, 2007 Capacity 250 lossless Thermal A: 400 @ $45 Wind: 100 forecast, @ $0 Thermal B: 400 @ $50 Hydro: 200 @ $30, 200 @ $90 Load 500 100 200 250 50 150 $45 $50 $89 Example 1: Dispatch with strategic bidding (1)Load pays $19500 extra (=$39*500) (2)Hydro makes extra $7800 and Thermal B makes extra $1950 (3)System operator makes extra congestion rent of $9750 (4)The dispatch is exactly the same, with cost $15250

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NERI Conference, November 22, 2007 Capacity 250 lossless Thermal A: 400 @ $45 Wind: 100 forecast, @ $0 Thermal B: 400 @ $50 Hydro: 200 @ $30, 200 @ $90 Load 500 100 200 250 50 150 $45 $50 Thermal A: 400 @ $45 149 @ $45 149 249 51 $50 Total cost of dispatch is $15255 which is $5 more than original cost!! (1)Load pays no extra money (2)System operator congestion rent goes down by $1250 to $0 (3)Wind makes $500 more, Thermal A makes $745 more… Example 2: Dispatch with strategic withholding

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NERI Conference, November 22, 2007 Strategic behaviour by firms can result in higher prices and a wealth transfer between agents. Strategic behaviour by firms can result in dispatch inefficiency. Prices that do not truly represent the cost of shortage can lead to inefficiencies in the wider economy. Dispatch inefficiency is a deadweight loss ($5 in example) Q: How bad can it get? Q: How do we prevent it? What can we learn from this example?

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NERI Conference, November 22, 2007 J.F. Nash Jr., Equilibrium points in n-person games, Proc Nat. Acad. Sci. USA, 36 (1950) 48-49.

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NERI Conference, November 22, 2007 If generators offer at marginal cost Capacity 1000 lossless Thermal A: 500 @ $50 Thermal B: 500 @ $50 Load = 500 - p Expect the price to be $50 a=450 b=450 Line contains no flow. Thermals make no profit. Load has high welfare. b a

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NERI Conference, November 22, 2007 If generators withhold strategically Thermal A: 500 @ $50 Thermal B: 500 @ $50 Load = 500 - p Total load = 1000-2p p = 500-(a+b)/2 A solves: max (p-50)a B solves: max (p-50)b b a (500-(a+b)/2-50)a has maximum at a = 450-b/2 (500-(a+b)/2-50)b has maximum at b = 450-a/2 Capacity 1000 lossless

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NERI Conference, November 22, 2007 Example of Cournot-Nash equilibrium Thermal A: 500 @ $50 Thermal B: 500 @ $50 Load = 500 - p 300 $200 Total load = 1000-2p p = 500-(a+b)/2 A solves: max (p-50)a B solves: max (p-50)b (500-(a+b)/2-50)a has maximum at a = 450-b/2 (500-(a+b)/2-50)b has maximum at b = 450-a/2 (300,300) Capacity 1000 lossless

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NERI Conference, November 22, 2007 Price = $200 Example of Cournot-Nash equilibrium Thermal A: 500 @ $50 Thermal B: 500 @ $50 Load = 500 - p 300 $200 Thermals each make profit of $45000. Load decreases welfare by $56250. Deadweight loss is $11250 x 2 Capacity 1000 lossless No flow in the line

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NERI Conference, November 22, 2007 What if the line has zero capacity? Thermal A: 500 @ $50 Thermal B: 500 @ $50 Load = 500 - p Each load = 500-p p = 500-a A solves: max (p-50)a b a (500-a-50)a has maximum at a = 225 (500-b-50)b has maximum at b = 225 Capacity 0 lossless

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NERI Conference, November 22, 2007 What if the line has zero capacity? Thermal A: 500 @ $50 Thermal B: 500 @ $50 Load = 500 - p Each load = 500-p p = 500-a A solves: max (p-50)a 225 (500-a-50)a has maximum at a = 225 (500-b-50)b has maximum at b = 225 $275 Capacity 0 lossless

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NERI Conference, November 22, 2007 Price = $275 What if the line has zero capacity? Thermal A: 500 @ $50 Thermal B: 500 @ $50 Load = 500 - p 225 $275 Thermals each make profit of $50625. Deadweight loss is $25312.50 x 2 Capacity 0 lossless The transmission line has significant value in encouraging competition even though it might never transport any electricity.

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NERI Conference, November 22, 2007 Does this matter in practice? Clause 10 of the Grid Investment Test states: Competition Benefits may be included in the market benefits of a proposed investment or alternative project if the Board reasonably considers this appropriate, provided the competition benefits can be separately identified and calculated NZ Electricity Commission 2006, Grid Investment Test.

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NERI Conference, November 22, 2007 AKL CNI SI Northland/Auckland Demand 2010 – 2288 MW 2015 – 2631 MW 2020 – 2987 MW Strategic Generators Huntly + E3P (1413 MW) Otahuhu B (390 MW) Lower North Island and South Island Demand 2010 – 3211 MW 2015 – 3492 MW 2020 – 3721 MW Strategic Generators Taranaki CC (365 MW) Waitaki Hydro (2718 MW) Clutha Hydro (1000MW) Central North Island Demand 2010 – 1794 MW 2015 – 1954 MW 2020 – 2109 MW Strategic Generators Waikato Hydro (776 MW) New Zealand example (Downward 2007)

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NERI Conference, November 22, 2007 New Zealand example Source: Anthony Downward, EPOC

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NERI Conference, November 22, 2007 Incentives for wind generation Capacity 250 lossless Thermal A: 400 @ $45Wind: 100 forecast, @ $0 Thermal B: 400 @ $50 Hydro: 200 @ $30, 200 @ $90 Load 500 Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 Least-cost dispatch Capacity 250 lossless Thermal A: 400 @ $45Wind: 100 forecast, @ $0 Thermal B: 400 @ $50 Hydro: 200 @ $30, 200 @ $90 Load 500 The best solution, on the assumption that the wind forecast is accurate. 100 200 250 50 150 Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 Wind above forecast Capacity 250 lossless Thermal A: 400 @ $45Wind: 120 actual, @ $0 Thermal B: 400 @ $50 Hydro: 200 @ $30, 200 @ $90 Load 500 100 200 250 50 150 spill 20 Wind is spilled – cheap energy is lost. Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 Wind below forecast Capacity 250 lossless Thermal A: 400 @ $45Wind: 80 actual, @ $0 Thermal B: 400 @ $50 Hydro: 200 @ $30, 200 @ $90 Load 500 Wind shortfall is made up with expensive water. 80 220 230 50 150 Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 Are better forecasts needed? Electricity Commission WGIP report June 2007

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NERI Conference, November 22, 2007 A flexible dispatch Capacity 250 lossless Thermal A: 400 @ $45Wind: 100 forecast, @ $0 Thermal B: 400 @ $50 Hydro: 200 @ $30, 200 @ $90 Load 500 100 175 225 100 125 Spare capacity on transmission line. Spare capacity in cheap hydro offer. Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 Wind above forecast Capacity 250 lossless Thermal A: 400 @ $45Wind: 120 actual, @ $0 Thermal B: 400 @ $50 Hydro: 200 @ $30, 200 @ $90 Load 500 120 155 245 100 125 Surplus wind is matched to hydro decrease. Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 Wind below forecast Capacity 250 lossless Thermal A: 400 @ $45Wind: 80 actual, @ $0 Thermal B: 400 @ $50 Hydro: 200 @ $30, 200 @ $90 Load 500 80 195 205 100 125 Lack of wind is matched by hydro. Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 Optimizing dispatch as a stochastic LP Generators offer to sell quantities q i, ask prices p i,regulation margins r i We find dispatches x i and Z i to minimize (p i x i + Ep i r iZ ix ip i r iZ ix i ) (expected cost of power, at offered prices, including re-dispatch) so that –demand is met (at both 1 st and 2 nd stages) –transmission network is operated within capacity –(x i, Z i ) satisfy plant constraints Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 Example Hydro 2: 40 @ $40 (+/- $5) Wind: capacity 40, @ $0 scenarios 0, 10, 20, 30 probabilities 0.5, 0.2, 0.2, 0.1 Load 60 Ensemble forecast for wind. Most likely scenario is 0. Hydros compete on both energy and regulation. What to dispatch? Hydro 1: 40 @ $39 (+/- $2) Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 Optimal hedged dispatch (initial) Hydro 2: 40 @ $40 (+/- $5) Wind: capacity 40, @ $0 scenarios 0, 10, 20, 30 probabilities 0.5, 0.2, 0.2, 0.1 Load 60 Hydros dispatched out of order to keep regulation cost down. Hydro 1: 40 @ $39 (+/- $2) 10 30 20 Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 Optimal hedged re-dispatch Hydro 2: 40 @ $40 (+/- $5) Wind: capacity 40, @ $0 scenarios 0, 10, 20, 30 probabilities 0.5, 0.2, 0.2, 0.1 Load 60 Hydro 1 wins the regulation business. Hydro 1: 40 @ $39 (+/- $2) 0, 10, 20, 30 40, 30, 20, 10 20 Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 Initial dispatch prices – the marginal cost of an additional unit of load in the initial dispatch. This is an appropriate price at which to trade energy, where that energy was present in the initial dispatch. Applies to: –inflexible load and generation –some flexible and intermittent generation Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 Re-dispatch prices R – the marginal cost of an additional unit of load in a re-dispatch. This is an appropriate price at which to trade energy, where that energy was added in a re-dispatch. Applies to: –some flexible and intermittent generation (both hydro & wind) Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 Example: initial dispatch prices Hydro 2: 40 @ $40 (+/- $5) Wind: capacity 40, @ $0 scenarios 0, 10, 20, 30 probabilities 0.5, 0.2, 0.2, 0.1 Load 60 Marginal additional load would be met by Hydro 2. The quantities x i are sold @ $40; load pays $40. Hydro 1: 40 @ $39 (+/- $2) 10 30 20 $40 Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 Example: re-dispatch prices Hydro 2: 40 @ $40 (+/- $5) Wind: capacity 40, @ $0 scenarios 0, 10, 20, 30 probabilities 0.5, 0.2, 0.2, 0.1 Load 60 1 st scenario: Wind buys back 10 @ $41; Hydro 1 sells 10 @ $41 2 nd scenario: no re-dispatch 3 rd scenario: Wind sells 10 @ $37; Hydro 1 buys back 10 @ $37 4 th scenario: Wind sells 20 @ $37; Hydro 1 buys back 20 @ $37 Hydro 1: 40 @ $39 (+/- $2) 0, 10, 20, 30 40, 30, 20, 10 20 $41, $41, $37, $37 1030 Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 Average selling prices Hydro 2: 40 @ $40 (+/- $5) Wind: capacity 40, @ $0 scenarios 0, 10, 20, 30 probabilities 0.5, 0.2, 0.2, 0.1 Load 60 Hydro 1: 40 @ $39 (+/- $2) 0, 10, 20, 30 40, 30, 20, 10 20 $41, $41, $37, $37 Average selling price achieved = (expected revenue) / (expected generation) Wind: $38.11 Hydro 1: $40.55 Hydro 2: $40 Source: Geoff Pritchard, EPOC WW2007

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NERI Conference, November 22, 2007 A price for uncertainty Prices earned by less predictable wind generation are lower on average. Prices earned by flexible generation are higher on average. Prices paid by less predictable loads are higher on average. New wind generation that decreases variation will increases price for all. Revenue adequate dispatch model means that wind backup can be suitably rewarded.

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NERI Conference, November 22, 2007 Emissions trading NZ ETS is a cap-and-trade scheme. How can generators act strategically in this setting? Little work done here, but see e.g. Chen, Hobbs et al 2007. Example conjecture: withholding generation decreases emissions so that emission permits become cheaper, and so are acquired by competitive firms who will increase output in equilibrium. Alternative is a carbon tax. Example conjecture: A $20/MWh carbon tax on thermal plant just increases the consumers price by $20/MWh with windfall to hydro. Try this out with a very stylized example…

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NERI Conference, November 22, 2007 Example: Least-cost dispatch Capacity 1000 lossless Thermal A: 500 @ $50 Hydro B: 500 @ $50 Load = 500 - p Expect the price to be $50 a=450 b=450 Line contains no flow. Thermals make no profit. Load has high welfare. b a $50

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NERI Conference, November 22, 2007 Least-cost dispatch with CO 2 tax Hydro B: 500 @ $50 Load = 500 - p Thermal A: 500 @ $50 plus $20 CO 2 tax Capacity 1000 500 360 70 $70 Price increases by $20. The carbon tax has been transferred to consumers. Hydro B makes $10000 profit.

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NERI Conference, November 22, 2007 Cournot-Nash equilibrium Thermal A: 500 @ $50 Hydro B: 500 @ $50 Load = 500 - p 300 $200 Total load = 1000-2p p = 500-(a+b)/2 A solves: max (p-50)a B solves: max (p-50)b (500-(a+b)/2-50)a has maximum at a = 450-b/2 (500-(a+b)/2-50)b has maximum at b = 450-a/2 (300,300) Capacity 1000 lossless

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NERI Conference, November 22, 2007 Cournot-Nash equilibrium with CO 2 tax Thermal A: 500 @ $50 plus $20 CO 2 tax Hydro B: 500 @ $50 Load = 500 - p $206.66 Total load = 1000-2p p = 500-(a+b)/2 A solves: max (p-50+20)a B solves: max (p-50)b (500-(a+b)/2-70)a has maximum at a = 430-b/2 (500-(a+b)/2-50)b has maximum at b = 450-a/2 Capacity 1000 (273,313) 313 273 20 Price increases by only $6.66.

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NERI Conference, November 22, 2007 The takeaways Markets are intended to provide incentives for agents to make optimal decisions. Understanding these is essential to formulating energy policy. For a poor market design, strategic behaviour might make decisions inefficient. Regulation is intended to restore some efficiency. Nash equilibrium models are indispensible in understanding whether incentives and or regulation will deliver the desired outcomes.

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NERI Conference, November 22, 2007 The last word is incentives Robert Aumann Nobel Prize Lecture December 8, 2005

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NERI Conference, November 22, 2007 The End

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