ELECTRICITY MARKET IMPACTS OF VARIABLE RENEWABLE ENERGY AND CARBON EMISSION POLICIES

Transcription

1 EVOLVING ENERGY REALITIES: ADAPTING TO WHAT'S NEXT ELECTRICITY MARKET IMPACTS OF VARIABLE RENEWABLE ENERGY AND CARBON EMISSION POLICIES TODD LEVIN Principal Energy Systems Engineer Energy Systems Division Argonne National Laboratory 36 th USAEE/IAEE North American Conference Washington, DC September 25, 2018 JONGHWAN KWON Postdoctoral Appointee Energy Systems Division Argonne National Laboratory AUDUN BOTTERUD Principal Energy Systems Engineer Energy Systems Division Argonne National Laboratory Laboratory for Information and Decision Systems Massachusetts Institute of Technology

2 IMPACTS OF VARIABLE RENEWABLE ELECTRICITY ON ELECTRICITY PRICES The merit order effect reduces electricity prices Empirical literature indicates a larger effect in Europe than the U.S. Low natural gas price main reason for lower electricity prices in U.S. The occurrence of negative prices has also increased with higher VRE penetration levels in many locations Wiser et al., LBNL/ANL Report, Nov

3 PREDICTION OF FUTURE PRICE IMPACTS OF VARIABLE RENEWABLE ELECTRICITY Wiser et al., LBNL/ANL Report, Nov

4 VARIABLE RENEWABLE ELECTRICITY (VRE) AND CARBON POLICIES VRE Technology Specific Policies Investment support: Investment Tax Credits (ITC) Generation support: Production Tax Credit (PTC), Feed-In Tariff (FIT) VRE quantity targets: Renewable Portfolio Standard (RPS) Carbon Policies Carbon taxation (CTAX) Cap and trade systems In the U.S., technology specific policies more common (ITC, PTC, RPS) What are the impacts of these policies on electricity markets, VRE penetration levels, and carbon emissions? 4

5 RESEARCH MOTIVATION Previous literature has: Explored the effectiveness of VRE and carbon policies Examined market impacts of high VRE penetrations However, many studies: Do not consider how VRE and carbon policies affect electricity markets Explore fixed VRE penetration scenarios Introduce VRE capacity on top of an existing power system We seek to understand the electricity market impacts of these policies New capacity investments Electricity and reserves prices Unit revenues and profitability Electricity system cost Consumer payments Consider two related (often conflated) social objectives Increasing VRE penetration Reducing carbon emissions 5

6 METHODOLOGY

7 MODEL FORMULATION Least-cost optimization for a single future year Mixed Integer Linear Program Includes capital, operating, fuel, VRE incentives, and carbon costs Includes standard operating constraints Ramp rates, unit commitment, reserves Hourly non-spinning reserves, spinning reserves, frequency regulation Regulation requirement increases with VRE penetration Two-stage modeling framework: Hourly Demand and VRE data 52 Weeks 52 Weeks Scenario Reduction Algorithm Representative Weeks Stage 1 Least-Cost Generation Capacity Expansion Planning Model Weekly Unit Commitment and Dispatch Expansion Decision Stage 2 System Portfolio, Carbon Emissions, and Market Outcome Analysis 7

8 FORMULATION Objective Minimizing costs to save space policy impacts are implicit. For example MC = marginal cost of generation which includes the PTC or Carbon Tax. I = investment cost which includes ITC etc. min σ i I u i I i + F i P i + σ i I σ t T MC i g i,t + RC i rr i,t + SUC i y i,t + SDC i x i,t + NLC i z i,t P i + σ t T VOLL es t + SSC ss t + NSC ns t Generation and Reserves Output i I, t T t T Load and Reserves Balance g i,t Ramping z i,t O i g i,t + es t = D t g i,t + rs i,t + rr i,t z i,t O i Gen is more than PMIN rr i,t z i P i RP i Gen + Res is less than PMAX Each reserve product less rr i,t = RR t i I rs i,t z i P i SP i than max for that unit (RP = regulation percentage etc.) rr i,t + ss t = SR t rn i,t ሺu i z i ) P i NP i ሺu i P i UF i ) D P ሺ1 + PRM) i I rr i,t + ns t = NR t # of units * capacity * i I unforced% >= peak demand * 1+PRM i I, t T i I i I i I, t T Shadow Prices Hourly demand and reserves requirements Unit Commitment g i,t g i,t 1 + z i,t RU i g i,t g i,t 1 z i,t 1 RD i Fairly simple ramping constraints Integer variables for expansion and commitment Significant reduction in computation time (up to 5000x*) * B. Palmintier and M. Webster, IEEE PES GM, z i,t = z i,t 1 + y i,t x i,t z i,t u i Fairly simple commitment constraints. Z = committed units Y = turn on unit X = turn off unit U = total units

9 MODEL APPLICATION Roughly based on ERCOT system in Texas 89,000 GW peak demand (2030 projection) 6 generation technology types 15% planning reserve margin Consistent levels of reliability Enables focus on policy impacts Technology Existing Capacity (MW) Nuclear 5,000 Coal 14,500 NGCC 35,800 NGCT 19,900 Solar 2,900 Wind 28,700 New capacity, including VRE, optimized in response to policy signals Three Cases Four Policies Case Reference (Ref) High Natural Gas (HighNG) No Existing Generation (NoGen) Existing Capacity Natural Gas Price ($/MMbtu) Yes $4.62 Yes $7.10 No $4.62 Policy Carbon Tax (CTAX) Investment Tax Credit (ITC) Production Tax Credit (PTC) Renewable Portfolio Standard (RPS) Metric Scenario Range $/ton $30-$90 % of capital cost 20%-60% $/MWh $10-$30 % of generation 30%-50% 9

10 RESULTS

11 GENERATION EXPANSION Reference Case Solar is initially preferred over wind Hits saturation point High Natural Gas Case More VRE Some new gas, mostly for reserves No Existing Generation Case Less VRE in baseline Note different scale 11

12 GENERATION DISPATCH Reference Case 22% VRE baseline Coal to NG switching under CTAX High Natural Gas Case 39% VRE baseline Wind replaces NG for most scenarios No Existing Gen. Case 2% VRE baseline 97% NGCC baseline No coal or nuclear 12

13 ENERGY PRICES VS. VRE PENETRATION (REFERENCE CASE) CTAX: increase in energy prices Increases marginal cost of coal and gas generation PTC: largest reduction in energy prices VRE has negative marginal cost of generation ITC and RPS: smaller reduction in energy prices More zero-marginal cost generation Other two cases show similar trends 13

14 ANNUAL ENERGY AND RESERVE PROFIT BY GENERATION TECHNOLOGY (REF. CASE) All units show negative total profit Planning reserve margin keeps energy prices low Profits do not include capacity payments or other revenue sufficiency measures Can calculate one potential capacity payment from NGCT revenue requirement 14

15 SYSTEM COST AND CONSUMER PAYMENTS System Costs Fixed Costs (Investment + FOM) Generation Costs (Fuel + VOM) Reg. Reserve Cost (RU/RD) Op. Reserve Costs (SU/SD/NS) Scarcity Costs (Energy/Spin/Non) Does NOT include policy transfers Consumer Payments Generation (Gen * LMP) Regulation (Reg * MCP R ) Spinning (Spin * MCP S ) Non-spinning (NonSpin * MCP NS ) Capacity Pmt. (UCap * RR NGCT ) Inv. Credit (Cap * ITC) Prod. Credit (Gen * PTC) VRE Credit (Gen * REC) Carbon Tax Rebate (-Gen * EMSF * CTAX) Assumes consumers ultimately fund policy transfers System Operator is revenue neutral 15

16 VRE PENETRATION VS. SYSTEM COST (REFERENCE CASE) VRE specific policies More efficient at reaching high VRE levels Carbon Tax Less efficient at encouraging VRE 16

17 CO2 EMISSIONS VS. SYSTEM COST (REFERENCE CASE) VRE specific policies Less efficient at reducing emissions Carbon Tax More efficient at reducing emissions 17

18 VRE PENETRATION VS. CONSUMER PAYMENTS (REFERENCE CASE) VRE technology specific policies ITC and PTC increase VRE with small increase in consumer payments Lower energy prices Carbon Tax Results in higher consumer payments for similar VRE penetration levels Due to increased energy prices 18

19 CO2 EMISSIONS VS. CONSUMER PAYMENTS (REFERENCE CASE) VRE specific policies ITC and PTC reduce emissions with limited increase in consumer payments Lower energy prices Carbon Tax Gives larger reductions in CO2 emissions Results in higher consumer payments Due to increased energy prices 19

20 CONCLUSIONS

21 CONCLUSIONS Energy environmental policy and electricity market design are closely interrelated Different policies will have different electricity market impacts Technology specific policies reduce energy prices (PTC more than ITC/RPS) Carbon tax increases energy prices Policies impact unit profits, which impact other market design elements E.g. capacity payments for revenue sufficiency and resource adequacy Policy makers should reflect true objectives in implemented policies VRE specific incentives most cost effective in increasing VRE levels Carbon tax is most cost-effective means of reducing carbon emissions Promotes fuel switching from coal to natural gas Supports other zero-carbon generation (e.g. hydro and nuclear) Minimizing system costs does not necessarily minimize consumer payments Differences between policies much smaller without existing capacity 21

22 ACKNOWLEDGEMENTS Sponsor: U.S. DOE Wind Power Technologies Office The views expressed in the article are the authors own and do not necessarily represent the views of the U.S. Department of Energy or the United States Government. 22

23 EVOLVING ENERGY REALITIES: ADAPTING TO WHAT'S NEXT ELECTRICITY MARKET IMPACTS OF VARIABLE RENEWABLE ENERGY AND CARBON EMISSION POLICIES TODD LEVIN Principal Energy Systems Engineer Energy Systems Division Argonne National Laboratory 36 th USAEE/IAEE North American Conference Washington, DC September 25, 2018 JONGHWAN KWON Postdoctoral Appointee Energy Systems Division Argonne National Laboratory AUDUN BOTTERUD Principal Energy Systems Engineer Energy Systems Division Argonne National Laboratory Laboratory for Information and Decision Systems Massachusetts Institute of Technology