Hybrid Bottom-Up/Top-Down Modeling of Prices in Deregulated Wholesale Power Markets

作者: James Tipping , E. Grant Read

DOI: 10.1007/978-3-642-12686-4_8

关键词:

摘要: “Top-down” models, based on observation of market price patterns, may be used to forecast prices in competitive electricity markets, once a reasonable track record is available and provided the structure stable. But many studies relate potential changes structure, while hydro-dominated markets are driven by inflow fluctuations reservoir management strategies, operating over such long timescale that an adequate not for decades, which time system itself will very different. “Bottom-up” analysis can readily model structural change hydro variation, but must make assumptions about fundamental data, commercial drivers, rational optimizing behavior leave significant unexplained volatility. Here we describe technique fitting hybrid model, “top-down” approach estimate parameters simplified “bottom-up” participant behavior, from along with stochastic process describing residual This fitted then simulate as vary. We briefly survey actual applications other differing characteristics, mainly illustrate application this New Zealand Electricity Market, where largely explained “marginal water value curves.” A second Australian National also provided.

参考文章(30)
Frédéric Ghersi, Mark Jaccard, Chris Bataille, Jean Charles Hourcade, Hybrid Modeling: New Answers to Old Challenges The Energy Journal. ,vol. 2, pp. 1- 12 ,(2006)
James M. Griffin, Long-run production modeling with pseudo data: electric power generation The Bell Journal of Economics. ,vol. 8, pp. 112- 127 ,(1977) , 10.2307/3003489
Les Clewlow, Chris Strickland, Energy Derivatives: Pricing and Risk Management ,(2000)
Iivo Vehviläinen, Tuomas Pyykkönen, Stochastic factor model for electricity spot price - the case of the Nordic market Energy Economics. ,vol. 27, pp. 351- 367 ,(2005) , 10.1016/J.ENECO.2005.01.002
D. Chattopadhyay, Multicommodity spatial Cournot model for generator bidding analysis IEEE Transactions on Power Systems. ,vol. 19, pp. 267- 275 ,(2004) , 10.1109/TPWRS.2003.821436
M PEREIRA, Optimal stochastic operations scheduling of large hydroelectric systems International Journal of Electrical Power & Energy Systems. ,vol. 11, pp. 161- 169 ,(1989) , 10.1016/0142-0615(89)90025-2
Pavlos S. Georgilakis, ARTIFICIAL INTELLIGENCE SOLUTION TO ELECTRICITY PRICE FORECASTING PROBLEM Applied Artificial Intelligence. ,vol. 21, pp. 707- 727 ,(2007) , 10.1080/08839510701526533
M. V. F. Pereira, L. M. V. G. Pinto, Multi-stage stochastic optimization applied to energy planning Mathematical Programming. ,vol. 52, pp. 359- 375 ,(1991) , 10.1007/BF01582895