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Modelling incentives and regulation in wholesale electricity markets

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). What is the purpose of this talk?.

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Modelling incentives and regulation in wholesale electricity markets

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  1. 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)

  2. 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.

  3. 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.

  4. price T1(q) quantity demand NZEM is a uniform price auction (e.g. single node) price T2(q) p quantity price combined offer stack p quantity

  5. Example Wind: 100 forecast, @ $0 Thermal A: 400 @ $45 Capacity 250 lossless Hydro: 200 @ $30, 200 @ $90 Thermal B: 400 @ $50 Load 500

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

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

  8. The actual NZEM • Generators specify supply curves defining prices at which they will generate. • Curves fixed for each (1/2) hour • Linear programming model runsevery 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)

  9. 6am-9am 3am-6am Wholesale electricity prices Five Minute Wholesale Electricity Prices on 28/08/06 ($/MWh) Source: comitfree Otahuhu Benmore Time of Day

  10. $89 $89 Example 1: Dispatch with strategic bidding $45 100 150 Wind: 100 forecast, @ $0 Thermal A: 400 @ $45 250 Capacity 250 lossless Hydro: 200 @ $30, 200 @ $90 200 50 Thermal B: 400 @ $50 Load 500 $50 • Load pays $19500 extra (=$39*500) • Hydro makes extra $7800 and Thermal B makes extra $1950 • System operator makes extra congestion rent of $9750 • The dispatch is exactly the same, with cost $15250

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

  12. 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?

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

  14. If generators offer at marginal cost 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. Thermal A: 500 @ $50 a Capacity 1000 lossless Thermal B: 500 @ $50 b Load = 500 - p

  15. If generators withhold strategically Load = 500 - p Total load = 1000-2p p = 500-(a+b)/2 A solves: max (p-50)a Bsolves: max (p-50)b Thermal A: 500 @ $50 a Capacity 1000 lossless (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 Thermal B: 500 @ $50 b Load = 500 - p

  16. (300,300) Example of Cournot-Nash equilibrium Load = 500 - p Total load = 1000-2p p = 500-(a+b)/2 A solves: max (p-50)a Bsolves: max (p-50)b $200 Thermal A: 500 @ $50 300 Capacity 1000 lossless (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 Thermal B: 500 @ $50 300 $200 Load = 500 - p

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

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

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

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

  21. 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” NZElectricity Commission 2006, Grid Investment Test.

  22. AKL CNI SI New Zealand example (Downward 2007) Northland/Auckland Demand 2010 – 2288 MW 2015 – 2631 MW 2020 – 2987 MW Strategic Generators Huntly + E3P (1413 MW) Otahuhu B (390 MW) Central North Island Demand 2010 – 1794 MW 2015 – 1954 MW 2020 – 2109 MW Strategic Generators Waikato Hydro (776 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)

  23. New Zealand example Source: Anthony Downward, EPOC

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

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

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

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

  28. Are better forecasts needed? Electricity Commission WGIP report June 2007

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

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

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

  32. Optimizing dispatch as a stochastic LP Generators offer to sell quantities qi , ask prices pi ,regulation margins ri We find dispatches xi and Zi to minimize S (pi xi + E[ (pi +ri)(Zi - xi)+ - (pi -ri)(Zi - xi)- ] ) (expected cost of power, at offered prices, including re-dispatch) so that • demand is met (at both 1st and 2nd stages) • transmission network is operated within capacity • (xi , Zi ) satisfy plant constraints Source: Geoff Pritchard, EPOC WW2007

  33. Example Wind: capacity 40, @ $0 scenarios0, 10, 20, 30 probabilities0.5, 0.2, 0.2, 0.1 Hydro 1: 40 @ $39 (+/- $2) Hydro 2: 40 @ $40 (+/- $5) Load 60 • Ensemble forecast for wind. Most likely scenario is 0. • Hydros compete on both energy and regulation. • What to dispatch? Source: Geoff Pritchard, EPOC WW2007

  34. Optimal hedged dispatch (initial) Wind: capacity 40, @ $0 scenarios0, 10, 20, 30 probabilities0.5, 0.2, 0.2, 0.1 30 Hydro 1: 40 @ $39 (+/- $2) 10 20 Hydro 2: 40 @ $40 (+/- $5) Load 60 • Hydros dispatched “out of order” to keep regulation cost down. Source: Geoff Pritchard, EPOC WW2007

  35. Optimal hedged re-dispatch Wind: capacity 40, @ $0 scenarios0, 10, 20, 30 probabilities0.5, 0.2, 0.2, 0.1 Hydro 1: 40 @ $39 (+/- $2) 0, 10, 20, 30 40, 30, 20, 10 Hydro 2: 40 @ $40 (+/- $5) 20 Load 60 • Hydro 1 wins the regulation business. Source: Geoff Pritchard, EPOC WW2007

  36. Initial dispatch prices • p – 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

  37. Re-dispatch prices • pR– 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

  38. Example: initial dispatch prices Wind: capacity 40, @ $0 scenarios0, 10, 20, 30 probabilities0.5, 0.2, 0.2, 0.1 30 Hydro 1: 40 @ $39 (+/- $2) 10 20 Hydro 2: 40 @ $40 (+/- $5) $40 Load 60 • Marginal additional load would be met by Hydro 2. • The quantities xi are sold @ $40; load pays $40. Source: Geoff Pritchard, EPOC WW2007

  39. Example: re-dispatch prices Wind: capacity 40, @ $0 scenarios0, 10, 20, 30 probabilities0.5, 0.2, 0.2, 0.1 Hydro 1: 40 @ $39 (+/- $2) 0, 10, 20, 30 40, 30, 20, 10 Hydro 2: 40 @ $40 (+/- $5) 10 30 20 $41, $41, $37, $37 Load 60 • 1st scenario: Wind buys back 10 @ $41; Hydro 1 sells 10 @ $41 • 2nd scenario: no re-dispatch • 3rd scenario: Wind sells 10 @ $37; Hydro 1 buys back 10 @ $37 • 4th scenario: Wind sells 20 @ $37; Hydro 1 buys back 20 @ $37 Source: Geoff Pritchard, EPOC WW2007

  40. Average selling prices Wind: capacity 40, @ $0 scenarios0, 10, 20, 30 probabilities0.5, 0.2, 0.2, 0.1 Hydro 1: 40 @ $39 (+/- $2) 0, 10, 20, 30 40, 30, 20, 10 Hydro 2: 40 @ $40 (+/- $5) 20 $41, $41, $37, $37 Load 60 • Average selling price achieved • = (expected revenue) / (expected generation) • Wind: $38.11 • Hydro 1: $40.55 • Hydro 2: $40 Source: Geoff Pritchard, EPOC WW2007

  41. 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.

  42. 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 consumer’s price by $20/MWh with windfall to hydro. • Try this out with a very stylized example…

  43. Example: Least-cost dispatch 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. $50 Thermal A: 500 @ $50 a Capacity 1000 lossless Hydro B: 500 @ $50 b $50 Load = 500 - p

  44. 360 70 500 Least-cost dispatch with CO2 tax Load = 500 - p $70 Thermal A: 500 @ $50 plus $20 CO2 tax Capacity 1000 Hydro B: 500 @ $50 $70 Load = 500 - p Price increases by $20. The carbon tax has been transferred to consumers. Hydro B makes $10000 profit.

  45. (300,300) Cournot-Nash equilibrium Load = 500 - p Total load = 1000-2p p = 500-(a+b)/2 A solves: max (p-50)a Bsolves: max (p-50)b $200 Thermal A: 500 @ $50 300 Capacity 1000 lossless (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 Hydro B: 500 @ $50 300 $200 Load = 500 - p

  46. 273 (273,313) 20 313 Cournot-Nash equilibrium with CO2 tax Load = 500 - p Total load = 1000-2p p = 500-(a+b)/2 A solves: max (p-50+20)a Bsolves: max (p-50)b $206.66 Thermal A: 500 @ $50 plus $20 CO2 tax Capacity 1000 (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 Hydro B: 500 @ $50 $206.66 Load = 500 - p Price increases by only $6.66.

  47. 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.

  48. The last word is incentives Robert Aumann Nobel Prize Lecture December 8, 2005

  49. The End

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