IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v80y2015icp10-19.html
   My bibliography  Save this article

Estimating zonal electricity supply curves in transmission-constrained electricity markets

Author

Listed:
  • Sahraei-Ardakani, Mostafa
  • Blumsack, Seth
  • Kleit, Andrew

Abstract

Many important electricity policy initiatives would directly affect the operation of electric power networks. This paper develops a method for estimating short-run zonal supply curves in transmission-constrained electricity markets that can be implemented quickly by policy analysts with training in statistical methods and with publicly available data. Our model enables analysis of distributional impacts of policies affecting operation of electric power grid. The method uses fuel prices and zonal electric loads to determine piecewise supply curves, identifying zonal electricity price and marginal fuel. We illustrate our methodology by estimating zonal impacts of Pennsylvania's Act 129, an energy efficiency and conservation policy. For most utilities in Pennsylvania, Act 129 would reduce the influence of natural gas on electricity price formation and increase the influence of coal. The total resulted savings would be around 267 million dollars, 82 percent of which would be enjoyed by the customers in Pennsylvania. We also analyze the impacts of imposing a $35/ton tax on carbon dioxide emissions. Our results show that the policy would increase the average prices in PJM by 47–89 percent under different fuel price scenarios in the short run, and would lead to short-run interfuel substitution between natural gas and coal.

Suggested Citation

  • Sahraei-Ardakani, Mostafa & Blumsack, Seth & Kleit, Andrew, 2015. "Estimating zonal electricity supply curves in transmission-constrained electricity markets," Energy, Elsevier, vol. 80(C), pages 10-19.
  • Handle: RePEc:eee:energy:v:80:y:2015:i:c:p:10-19
    DOI: 10.1016/j.energy.2014.11.030
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544214012857
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2014.11.030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Amarawickrama, Himanshu A. & Hunt, Lester C., 2008. "Electricity demand for Sri Lanka: A time series analysis," Energy, Elsevier, vol. 33(5), pages 724-739.
    2. Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-36, September.
    3. Dowds, Jonathan & Hines, Paul D.H. & Blumsack, Seth, 2013. "Estimating the impact of fuel-switching between liquid fuels and electricity under electricity-sector carbon-pricing schemes," Socio-Economic Planning Sciences, Elsevier, vol. 47(2), pages 76-88.
    4. Sahraei-Ardakani, Mostafa & Blumsack, Seth & Kleit, Andrew, 2012. "Distributional impacts of state-level energy efficiency policies in regional electricity markets," Energy Policy, Elsevier, vol. 49(C), pages 365-372.
    5. Stephen P. Holland & Erin T. Mansur, 2006. "The Short-Run Effects of Time-Varying Prices in Competitive Electricity Markets," The Energy Journal, , vol. 27(4), pages 127-156, October.
    6. Jonathan M. Fisk, 2013. "The Right to Know? State Politics of Fracking Disclosure," Review of Policy Research, Policy Studies Organization, vol. 30(4), pages 345-365, July.
    7. Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
    8. F J Nogales & A J Conejo, 2006. "Electricity price forecasting through transfer function models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(4), pages 350-356, April.
    9. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2013. "Evaluation of time series techniques to characterise domestic electricity demand," Energy, Elsevier, vol. 50(C), pages 120-130.
    10. Spees, Kathleen & Lave, Lester B., 2007. "Demand Response and Electricity Market Efficiency," The Electricity Journal, Elsevier, vol. 20(3), pages 69-85, April.
    11. Severin Borenstein & James B. Bushnell & Frank A. Wolak, 2002. "Measuring Market Inefficiencies in California's Restructured Wholesale Electricity Market," American Economic Review, American Economic Association, vol. 92(5), pages 1376-1405, December.
    12. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Ling & Zhou, Peng & Newton, Sidney & Fang, Jian-xin & Zhou, De-qun & Zhang, Lu-ping, 2015. "Evaluating clean energy alternatives for Jiangsu, China: An improved multi-criteria decision making method," Energy, Elsevier, vol. 90(P1), pages 953-964.
    2. Saeed Khojaste Effatpanah & Mohammad Hossein Ahmadi & Pasura Aungkulanon & Akbar Maleki & Milad Sadeghzadeh & Mohsen Sharifpur & Lingen Chen, 2022. "Comparative Analysis of Five Widely-Used Multi-Criteria Decision-Making Methods to Evaluate Clean Energy Technologies: A Case Study," Sustainability, MDPI, vol. 14(3), pages 1-33, January.
    3. Mestre, Guillermo & Sánchez-Úbeda, Eugenio F. & Muñoz San Roque, Antonio & Alonso, Estrella, 2022. "The arithmetic of stepwise offer curves," Energy, Elsevier, vol. 239(PE).
    4. Frew, Bethany A. & Becker, Sarah & Dvorak, Michael J. & Andresen, Gorm B. & Jacobson, Mark Z., 2016. "Flexibility mechanisms and pathways to a highly renewable US electricity future," Energy, Elsevier, vol. 101(C), pages 65-78.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sahraei-Ardakani, Mostafa & Blumsack, Seth & Kleit, Andrew, 2012. "Distributional impacts of state-level energy efficiency policies in regional electricity markets," Energy Policy, Elsevier, vol. 49(C), pages 365-372.
    2. Karakatsani Nektaria V & Bunn Derek W., 2010. "Fundamental and Behavioural Drivers of Electricity Price Volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-42, September.
    3. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    4. Clastres, Cédric & Khalfallah, Haikel, 2021. "Dynamic pricing efficiency with strategic retailers and consumers: An analytical analysis of short-term market interactions," Energy Economics, Elsevier, vol. 98(C).
    5. Jakub Nowotarski & Rafał Weron, 2015. "Computing electricity spot price prediction intervals using quantile regression and forecast averaging," Computational Statistics, Springer, vol. 30(3), pages 791-803, September.
    6. Carlo Fezzi & Derek Bunn, 2010. "Structural Analysis of Electricity Demand and Supply Interactions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(6), pages 827-856, December.
    7. Serinaldi, Francesco, 2011. "Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape," Energy Economics, Elsevier, vol. 33(6), pages 1216-1226.
    8. Assareh, E. & Behrang, M.A. & Assari, M.R. & Ghanbarzadeh, A., 2010. "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran," Energy, Elsevier, vol. 35(12), pages 5223-5229.
    9. Zhang, Wenbin & Tian, Lixin & Wang, Minggang & Zhen, Zaili & Fang, Guochang, 2016. "The evolution model of electricity market on the stable development in China and its dynamic analysis," Energy, Elsevier, vol. 114(C), pages 344-359.
    10. Bergaentzlé, Claire & Clastres, Cédric & Khalfallah, Haikel, 2014. "Demand-side management and European environmental and energy goals: An optimal complementary approach," Energy Policy, Elsevier, vol. 67(C), pages 858-869.
    11. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    12. Cédric Clastres & Haikel Khalfallah, 2021. "Dynamic pricing efficiency with strategic retailers and consumers: An analytical analysis of short-term market interactions," Post-Print hal-03193212, HAL.
    13. Shira Horowitz and Lester Lave, 2014. "Equity in Residential Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    14. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    15. Pao, Hsiao-Tien, 2009. "Forecast of electricity consumption and economic growth in Taiwan by state space modeling," Energy, Elsevier, vol. 34(11), pages 1779-1791.
    16. Usman Zafar & Neil Kellard & Dmitri Vinogradov, 2022. "Multistage optimization filter for trend‐based short‐term forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 345-360, March.
    17. C. Woo & J. Zarnikau & E. Kollman, 2012. "Exact welfare measurement for double-log demand with partial adjustment," Empirical Economics, Springer, vol. 42(1), pages 171-180, February.
    18. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    19. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2013. "Evaluation of time series techniques to characterise domestic electricity demand," Energy, Elsevier, vol. 50(C), pages 120-130.
    20. Jun Dong & Dongran Liu & Xihao Dou & Bo Li & Shiyao Lv & Yuzheng Jiang & Tongtao Ma, 2021. "Key Issues and Technical Applications in the Study of Power Markets as the System Adapts to the New Power System in China," Sustainability, MDPI, vol. 13(23), pages 1-29, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:80:y:2015:i:c:p:10-19. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.