Impact of wind resources and environmental regulations to future generation portfolio and its capacity factor in ercot
Joo Hyun Jin, University of Texas at Austin, email@example.com
Ross Baldick, Professor, University of Texas at Austin, firstname.lastname@example.org
As more renewable resources are added into the grid and environmental regulations are imposed to reduce emission from generators, they will result in dramatic market changes. Assessing the impact of these changes is important for policy makers, market participants, and general public to understanding where we are heading for. This paper addresses this issue by analyzing how the ERCOT generation portfolio evolves with different levels of wind penetration.
In order to assess the future power system, the study model should represent the long term dynamics of various factors to find out how investment decision is made economically in a competitive market with given assumptions. Another important aspect should be considered is the short term dynamics from real operation of power system. It is determined by operational characteristics of different units such as heat rate, maximum and minimum capacity, minimum up-time or down-time etc. For this study, AURORAxmp, a commercially available market simulator, is utilized to capture both long term and short term dynamics. This study runs 5 different scenarios from 2013 to 2040: two base cases with and without CO2
price, 18%, 25%, and 30% wind penetration level. Modeling assumptions for fuel prices, CO2
prices, CAPEX, inflation rate are assumed to follow the base case scenario of EIA AEO 2012. For demand forecast, ERCOT long term demand forecast is used.
price has huge impacts on accelerating coal units’ retirement as what environmental regulation is devoted to. CO2
price implementation makes average marginal cost of coal units exceed that of gas units, leaving difficult challenges for their economical operation. Consequently, it reduces production and penetration level of coal units throughout most of study periods.
One interesting observation is captured for the first period of the study: coal penetration level increases for the first three years, before starts to reduce consistently by the end of the study. This is due to operational inflexibility of coal units and the model’s capability to capture short term dynamics of a market.
Higher wind penetration level also affects future generation portfolio in ERCOT. As wind penetration increases, coal units’ retirement increases as well, while both retirement and addition of gas units diminish. Increase of coal retirement let existing gas units have chances to stay in the market, reducing amount of gas retirement. However, as both wind capacity and wind production increase, there will be less economical chances for new gas units to be built, so new gas units are lowered.
Given most likely assumptions from ERCOT and EIA, this study assesses the impact of increasing wind penetration on future outlook of ERCOT market. It also studies the impact of environmental regulation as well by the assumption that CO2
price from EIA GHG15 case is representative for any possible environmental regulations in the future. The study has maintained a practical point of view for the purpose of providing realistic results for future capacity expansion of ERCOT.
The result of this study is mostly what can be conjectured easily, but thanks to the modeling sophistication, the paper can provide more detail answers and values regarding future ERCOT resource portfolios varying wind penetration levels: amount of retirement and addition, production, profits made by a unit or a fuel type in every year, and so on.
Further study should be done regarding how much emission can actually be saved by having different wind penetration and environmental regulations, tax incentives, etc. and how the corresponding results would be in power generation portfolio of ERCOT and elsewhere.
R.Baldick, Wind Energy and Energy Markets: A Case Study of Texas,” IEEE Systems Journal, March 2012
H.Chavez, R. Baldick, S.Sharma, Regulation Adequacy Analysis Under High Wind Penetration Scenarios in ERCOT Nodal. IEEE Transactions on Sustainable Energy, May 2012
Department of Energy (DOE). 20% of Energy by 2030: Increasing Wind Energy’s Contribution to U.S. Electricity Supply. Oak Ridge, TN, July 2008, URL http://www.nrel.gov/docs/fy08osti/41869.pdf
Energy Information Administration (EIA). Annual Energy Outlook, 2012. Washington, DC.
EPIS. AURORAxmp Help. Sandpoint, ID. URL http://www.epis.com
Electricity Reliability Council of Texas (ERCOT). Report on the Capacity, Demand, and Reserves in the ERCOT Region. Austin, TX, May 2012 a. URL
Electricity Reliability Council of Texas (ERCOT). Historical System Peak and Energy by Month. Taylor, TX, August 2012 b. URL http:// planning.ercot.com
Electricity Reliability Council of Texas (ERCOT). System Planning Monthly Status Report. Taylor, TX, from January to August 2012 c. URL http://planning.ercot.com
Electricity Reliability Council of Texas (ERCOT). Mothballed Units Status for Summer 2012. Taylor, TX, April 2012 d. URL
Electricity Reliability Council of Texas (ERCOT). 2012 Long-Term Hourly Peak Demand and Energy Forecast. Taylor, TX, January 2012 e. URL
Electricity Reliability Council of Texas (ERCOT). Generation by Fuel Type. Taylor, TX, August 2011. URL http:// planning.ercot.com
Electricity Reliability Council of Texas (ERCOT), Analysis of Transmission Alternatives for Competitive Renewable Energy Zones in Texas, Taylor, TX, December 2006
Electric Power Research Institute (EPRI), Assessment of Achievable Potential from Energy Efficiency and Demand Response Programs in the U.S. Technical Report. Palo Alto, CA. January 2009.
A.M.Foley, B.P. O Gallachoir, J.Hur, R.Baldick, E.J. McKeogh. A Strategic review of electricity systems models. ELSEVIER Energy, 35(2010) 4522-4530. doi:10.1016
Global View Data, 2012
Mills, R. Wiser. Changes in Economic Value of Variable Generation at High Penetration Levels: A Pilot Case Study of California. Technical Report, Ernest Orlando Lawrence Berkeley National Laboratory, Berekly, CA, June 2012. URL http://eetd.lbl.gov/EA/EMP
S. Newell, K. Spees, J. Pfeifenberger, R. Mudge, M.DeLucia, R. Carlton. ERCOT Investment Incentives and Resource Adequacy, The Brattle Group report for ERCOT, Cambridge, MA. June 2012. URL
Platts. MWDaily. 2012. URL http://www.platts.com
SNL data base, 2012. URL http://www.snl.com
W.Short, N.Blair, P.Sullivan, T.Mai. ReEDS Model Ducumentation: Base Case Data and Model Description. National Renewable Energy Laboratory (NREL), Golden, CO, Augest 2009
Texas State Data Center (TSDC). Population projection. 2008. URL
A.J.Wood, B.F.Wollenberg, Power Generation, Operation, and Control, 2nd Edition