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Spatially Explicit Prediction of Wholesale Electricity Prices

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  • James Wesley Burnett
  • Xueting Zhao

Abstract

Transmission constraints often limit the flow of electricity in a regional transmission network leading to strong interaction effects across different geographically distributed points within the system. In modern wholesale electricity markets, these transmission constraints lead to spatial patterns within the nodal electricity spot prices. This study exploits these spatial patterns to better predict spot prices within a wholesale electricity market. More specifically, we use the latest spatial panel data econometric models to compare within-sample and out-of-sample forecasts against nonspatial panel data models. The spatial panel data approach is explained by demonstrating a simple network optimization model. We find that a dynamic, spatial panel data model provides the best predictions within a forecasting error context. Our results may suggest that the spatial autocorrelation between node prices extends beyond the current market-defined zonal boundaries, which calls into question whether the zonal boundaries accurately reflect the congestion boundaries within the system.

Suggested Citation

  • James Wesley Burnett & Xueting Zhao, 2017. "Spatially Explicit Prediction of Wholesale Electricity Prices," International Regional Science Review, , vol. 40(2), pages 99-140, March.
    • Handle: RePEc:sae:inrsre:v:40:y:2017:i:2:p:99-140
    DOI: 10.1177/0160017615607055
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    References listed on IDEAS

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    1. Severin Borenstein & James Bushnell & Christopher R. Knittel & Catherine Wolfram, 2008. "Inefficiencies And Market Power In Financial Arbitrage: A Study Of California'S Electricity Markets," Journal of Industrial Economics, Wiley Blackwell, vol. 56(2), pages 347-378, June.
    2. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2008. "Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large," Journal of Econometrics, Elsevier, vol. 146(1), pages 118-134, September.
    3. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2012. "Estimation for spatial dynamic panel data with fixed effects: The case of spatial cointegration," Journal of Econometrics, Elsevier, vol. 167(1), pages 16-37.
    4. Noel D. Uri, 1976. "A Spatial Equilibrium Analysis of Electrical Energy Pricing and Allocation," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 58(4_Part_1), pages 653-662.
    5. Haldrup, Niels & Nielsen, Morten Orregaard, 2006. "A regime switching long memory model for electricity prices," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 349-376.
    6. Severin Borenstein & James Bushnell, 1999. "An Empirical Analysis of the Potential for Market Power in California’s Electricity Industry," Journal of Industrial Economics, Wiley Blackwell, vol. 47(3), pages 285-323, September.
    7. Eric Guerci & Alessandro Sapio, 2012. "High wind penetration in an agent-based model of the electricity market," Post-Print halshs-00871036, HAL.
    8. Hobbs, Benjamin F. & Kelly, Kevin A., 1992. "Using game theory to analyze electric transmission pricing policies in the United States," European Journal of Operational Research, Elsevier, vol. 56(2), pages 154-171, January.
    9. Haldrup Niels & Nielsen Morten Ø., 2006. "Directional Congestion and Regime Switching in a Long Memory Model for Electricity Prices," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-24, September.
    10. Weron, Rafal & Misiorek, Adam, 2007. "Heavy tails and electricity prices: Do time series models with non-Gaussian noise forecast better than their Gaussian counterparts?," MPRA Paper 2292, University Library of Munich, Germany, revised Oct 2007.
    11. Walton, Steven & Tabors, Richard D., 1996. "Zonal transmission pricing: methodology and preliminary results from the WSCC," The Electricity Journal, Elsevier, vol. 9(9), pages 34-41, November.
    12. Parent, Olivier & LeSage, James P., 2012. "Spatial dynamic panel data models with random effects," Regional Science and Urban Economics, Elsevier, vol. 42(4), pages 727-738.
    13. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    14. Juan M. Morales & Antonio J. Conejo & Henrik Madsen & Pierre Pinson & Marco Zugno, 2014. "Integrating Renewables in Electricity Markets," International Series in Operations Research and Management Science, Springer, edition 127, number 978-1-4614-9411-9, December.
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    Cited by:

    1. Eric Bowen & Donald J. Lacombe, 2017. "Spatial Dependence in State Renewable Policy: Effects of Renewable Portfolio Standards on Renewable Generation within NERC Regions," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    2. De Siano, Rita & Sapio, Alessandro, 2022. "Spatial merit order effects of renewables in the Italian power exchange," Energy Economics, Elsevier, vol. 108(C).
    3. Adam E. Clements & A. Stan Hurn & Zili Li, 2017. "The Effect of Transmission Constraints on Electricity Prices," The Energy Journal, , vol. 38(4), pages 145-163, July.
    4. John J. García & Jesús López-Rodríguez & Jhonny Moncada-Mesa, 2017. "Spatial effects in the bid price setting strategies of the wholesale electricity markets: The case of Colombia," Documentos de Trabajo de Valor Público 15660, Universidad EAFIT.
    5. Eric Bowen & Donald J. Lacombe, 2015. "Spatial interaction of Renewable Portfolio Standards and their effect on renewable generation within NERC regions," Working Papers 15-03, Department of Economics, West Virginia University.

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