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Spatially Explicit Prediction of Wholesale Electricity Prices


  • James Wesley Burnett
  • Xueting Zhao


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.

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  • 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

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    1. repec:cai:poeine:pope_804_0635 is not listed on IDEAS
    2. Richard Cebula, 2002. "Net interstate population growth rates and the Tiebout-Tullock hypothesis: New empirical evidence, 1990–2000," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 30(4), pages 414-421, December.
    3. Daniel P. McMillen & Christian L. Redfearn, 2010. "Estimation And Hypothesis Testing For Nonparametric Hedonic House Price Functions," Journal of Regional Science, Wiley Blackwell, vol. 50(3), pages 712-733.
    4. Richard Shearmur & Philippe Apparicio & Pauline Lizion & Mario Polèse, 2007. "Space, Time, and Local Employment Growth: An Application of Spatial Regression Analysis," Growth and Change, Wiley Blackwell, vol. 38(4), pages 696-722.
    5. Jan Eeckhout, 2004. "Gibrat's Law for (All) Cities," American Economic Review, American Economic Association, vol. 94(5), pages 1429-1451, December.
    6. Julie Gallo & Coro Chasco, 2008. "Spatial analysis of urban growth in Spain, 1900–2001," Empirical Economics, Springer, vol. 34(1), pages 59-80, February.
    7. Zhang, Kevin Honglin & Song, Shunfeng, 2003. "Rural-urban migration and urbanization in China: Evidence from time-series and cross-section analyses," China Economic Review, Elsevier, vol. 14(4), pages 386-400.
    8. Hernán D. Rozenfeld & Diego Rybski & Xavier Gabaix & Hernán A. Makse, 2011. "The Area and Population of Cities: New Insights from a Different Perspective on Cities," American Economic Review, American Economic Association, vol. 101(5), pages 2205-2225, August.
    9. Partridge, Mark D. & Rickman, Dan S., 2003. "The waxing and waning of regional economies: the chicken-egg question of jobs versus people," Journal of Urban Economics, Elsevier, vol. 53(1), pages 76-97, January.
    10. Edward L. Glaeser & Giacomo A. M. Ponzetto & Kristina Tobio, 2014. "Cities, Skills and Regional Change," Regional Studies, Taylor & Francis Journals, vol. 48(1), pages 7-43, January.
    11. Mark D. Partridge & Dan S. Rickman & Kamar Ali & M. Rose Olfert, 2008. "The Geographic Diversity of U.S. Nonmetropolitan Growth Dynamics: A Geographically Weighted Regression Approach," Land Economics, University of Wisconsin Press, vol. 84(2), pages 241-266.
    12. McMillen, Daniel P., 1996. "One Hundred Fifty Years of Land Values in Chicago: A Nonparametric Approach," Journal of Urban Economics, Elsevier, vol. 40(1), pages 100-124, July.
    13. Xavier Gabaix, 1999. "Zipf's Law for Cities: An Explanation," The Quarterly Journal of Economics, Oxford University Press, vol. 114(3), pages 739-767.
    14. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    15. Goerlich, Francisco José & Mas, Matilde, 2009. "Drivers of Agglomeration: geography VS. History," MPRA Paper 15802, University Library of Munich, Germany, revised 2009.
    16. Luis Lanaspa & Fernando Pueyo & Fernando Sanz, 2003. "The Evolution of Spanish Urban Structure during the Twentieth Century," Urban Studies, Urban Studies Journal Limited, vol. 40(3), pages 567-580, March.
    17. Gabaix, Xavier & Ioannides, Yannis M., 2004. "The evolution of city size distributions," Handbook of Regional and Urban Economics,in: J. V. Henderson & J. F. Thisse (ed.), Handbook of Regional and Urban Economics, edition 1, volume 4, chapter 53, pages 2341-2378 Elsevier.
    18. Goerlich, Francisco José & Mas, Matilde, 2008. "Empirical Evidence of Population Concentration in Spain, 1900-2001," MPRA Paper 15801, University Library of Munich, Germany, revised 2008.
    19. Mario Polèse & Richard Shearmur, 2004. "Is Distance Really Dead? Comparing Industrial Location Patterns over Time in Canada," International Regional Science Review, , vol. 27(4), pages 431-457, October.
    20. Edward L Glaeser & Jesse M Shapiro, 2003. "Urban Growth in the 1990s: Is City Living Back?," Journal of Regional Science, Wiley Blackwell, vol. 43(1), pages 139-165.
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    Cited by:

    1. 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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