Risk Sharing Under Renewable Portfolio Standards. Becky A. Lafrancois PhD Candidate, Department of Economics Syracuse University June 23, 2009

Transcription

1 Risk Sharing Under Renewable Portfolio Standards PhD Candidate, Department of Economics Syracuse University June 23, 2009

2 Come to Rio in 2010!! 2

3 Behavior of a Regulated Utility Subject to an RPS PhD Candidate, Department of Economics Syracuse University June 23, 2009

4 Presentation Outline Build model of electric utility subject to an RPS Analyze behavior of utility under different penalty structures Apply results to Southern California Edison Policy recommendations 4

5 Electricity Market: Participants Baseload Plants Coal, nuclear Produce fixed amount of power, c Price takers; paid p for each kwh Load Following and Peaking Plants Natural gas Produce variable amount power, c Q g Price takers; paid p for each kwh g Q 5

6 Electricity Market: Participants Intermittent Renewable Plants Wind, solar Replace baseload power Produce variable amount of power, r Q Output assumed to be uniformly distributed over range a to b r Price taker; paid p for each kwh generated The price is just high enough to induce investment by a risk neutral investor Public Utilities Commission Sets the price of electricity to end users, d p 6

7 Policy: Renewable Portfolio Standard Established by legislature Subjects load serving entities (utilities) to renewables target, φ Imposes noncompliance penalty, F N 7

8 The RPS: Assumptions Two possible penalty structures: Case 1: fine f h per kwh of shortfall, Case 2: fixed fine, Regulator enforces policy No renewable credit trading No renewable credit banking Single period model f a r ( φ Q ) 8

9 Electric Utility The Electric Utility Satisfies the demand of its customers in real time Demand given by Q d ( p ) Chooses number of contracts with renewable supplier, N Contract represents construction of a turbine All turbines located on same site Renewable contracts backed by gas to cover any shortfalls Procures electricity from distinct suppliers Renewable Suppliers: Dispatchable Suppliers: Baseload Suppliers: d r Q g r Q = N( b q% ) c d Q = Q Nb r = Nq% 9

10 REGULATOR RENEWABLE PLANTS CONVENTIONAL PLANTS (dispatchable) r p p Q g Q g r c p Sets RPS & Penalty UTILITY COMPANY Q c F Q Sets d p d d p PUBLIC UTILITIES COMMISSION END USER CONVENTIONAL PLANTS (baseload) 10

11 A Closer Look at the Electric Utility Risk neutral; chooses N to maximize expected profit: where u d d c d max π = pq ( pq + Nk) F N k = ( p p ) b+ ( p p ) q First Order Condition: k = N g c r g r F N N 11

12 Optimal Behavior: Case 1 Penalty imposed on each kwh of shortfall: r F = f ( φ Nq ) N h Utility s expected penalty: φ Na φ Na FN = fh Nb Na 2 First Order Condition: φ Na φ+ Na k = fh Nb Na 2N 12

13 Optimal Behavior: Case 1, continued The utility s optimal choice of N: N = 2( b a) k + a f h φ How does N change when the fine changes? dn df h 1 N = > 0 2 fh a fh 2 + ( b a ) k 2 13

14 Application: Southern California Parameters Edison Average Daily Sales (2007): 242,000 MWh Average Daily 20% RPS Requirement: 48,000 MWh Intermittent Renewables used to satisfy RPS: 20,000 MWh SCE obtains about 60% of renewables from geothermal Assume each renewable contract is for a 1 MW plant b = 24 MWh a = 0.1b Renewables premium: k = $10 per MWh Noncompliance penalty: F = $50 per MWh Optimal N derived from model: N = 1,400 Southern California Renewables Capacity: 1,700 MW 14

15 Optimal Behavior: Case 2 Lump sum penalty imposed for noncompliance: F N = Utility s expected penalty: F N f a φ Na Nb Na = fa First Order Condition: φ k = f a N 2 ( b a) 15

16 Optimal Behavior: Lump Sum Penalty The utility s optimal choice of N: N = f a 1 φ ( b a) k 16

17 Variable versus Lump Sum Penalty Lump sum penalty inducing same number of contracts as variable penalty: f a = φ fh 2 a fh 2 + ( b a ) k Using the Southern California Edison Data: f a φ fh = $170M 2 17

18 Future Research Expand to multi site model More realistic Explore correlations in output between sites Opportunity to incorporate costs of NIMBY problem Incorporate Renewable Energy Credits Analyze utility s decision to trade or bank credits Relevant to ongoing debates for national RPS Use to compare with other policy tools (e.g. carbon tax) 18

19 Thank You Questions and comments are greatly appreciated. If you would like a copy of the paper, please contact me at balafran@syr.edu (The version on the web site is out of date) 19

20 Extra Slides 20

21 Renewable Portfolio Standards Sets a target level or percentage of retail electricity sales that must come from eligible renewable resources by a given date Key features of RPS: Mandate is placed on the intermediary, not the end user or producer The supply of renewables is stochastic NOT the Economically Efficient Policy Pigouvian tax on exhaustible resources used to generate electricity Most widely used renewable energy policy in the United States 21

22 Application: Southern California Parameters Edison Approximate Retail Sales (2007): 10,275 MW 20% RPS Requirement: 2,055 MW Intermittent Renewables used to satisfy RPS: 825 MW SCE obtains about 60% of renewables from geothermal Assume each renewable contract is for a 1 MW plant a = 0.1 MW b = 1 MW Renewables premium: k = $10 per MWh Noncompliance penalty: F = $50 per MWh Optimal N derived from model: N = 1,356 Southern California Renewables Capacity: 1,700 MW 22