Electricity Price Prediction Model Based on Simultaneous Perturbation Stochastic Approximation

Ko, Hee-Sang;Lee, Kwang-Y.;Kim, Ho-Chan

• Published : 2008.03.01
• 42 7

Abstract

The paper presents an intelligent time series model to predict uncertain electricity market price in the deregulated industry environment. Since the price of electricity in a deregulated market is very volatile, it is difficult to estimate an accurate market price using historically observed data. The parameter of an intelligent time series model is obtained based on the simultaneous perturbation stochastic approximation (SPSA). The SPSA is flexible to use in high dimensional systems. Since prediction models have their modeling error, an error compensator is developed as compensation. The SPSA based intelligent model is applied to predict the electricity market price in the Pennsylvania-New Jersey-Maryland (PJM) electricity market.

Keywords

electricity market;intelligent time series;price prediction;simultaneous perturbation stochastic approximation

References

1. M. Ilic, F. Galiana, and L. Fink (ed.), Power Systems Restructuring, Kluwer, Boston, 1994
2. F. C. Schweppe, M. C. Caramanis, R. D. Tabors, and R. E. Bohn, Spot Pricing of Electricity, Kluwer Academic Publishers, 1998
3. E. H. Allen and M. D. Ilic, Price-Based Commitment Decisions in the Electricity Market, London: Springer-Verlag, 1999
4. S. M. Ryan, and M. Mazumdar, 'Chronological influence on the variance of electric power production costs,' Operations Research, vol. 40, pp. 284-292, 1992 https://doi.org/10.1287/opre.40.3.S284
5. PJM Interconnection, LLC, http:// www.pjm.com, 2000
6. R. Green and D. M. Newbery, 'Competition in the British electricity spot market,' J. Political Econ., vol. 100, no. 5, pp. 929-953, 1992 https://doi.org/10.1086/261846
7. M. Michalik, E. Khan, and W. Mielczarski, 'Statistical analysis of electricity demand and spot prices in competitive markets,' in Proceedings of the $4^th$ International Conference on Advances in Power System Control, Operation and Management, Hong Kong, pp. 457-462, 1997
8. J. C. Spall, Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, Wiely, 2003
9. H. S. Ko, K. Y. Lee, and H.-C. Kim, 'An Intelligent based LQR controller design to power system stabilization,' Electrical Power System Research, vol. 71, no. 1, pp. 1-9, 2004 https://doi.org/10.1016/j.epsr.2003.12.015
10. K. Y., Lee and H. S. Ko, 'Power system stabilization using free-model based inverse dynamic neuro controller,' in Proceeding of Int. Joint Conf. Neural Network, Honolulu, USA, no. 3, pp. 2132-2137, 2002