Short-term nodal congestion price forecasting in a large-scale power market using ANN with genetic optimization training

In a daily power market, price and load forecasting are the most important signals for the market participants. In this paper, an accurate feed-forward neural network model with a genetic optimization Levenberg-Marquardt back propagation training algorithm is employed for short-term nodal congestion price forecasting in different zones of a large-scale power market. The use of genetic algorithms for neural network training optimization has a remarkable effect on the accuracy of price forecasting in a large-scale power market. The necessary data for neural network training are obtained by solving optimal power flow equations that take into account all effective constraints at any hour of the day in a single month. The structure of the neural network has 2 input signals of active and reactive powers for every load busbar in every hour of the programming model. These 2 signals are always available. In this study, an IEEE 118-bus power system is used to test the proposed method authenticity. This system is divided into 3 zones, and a neural network with genetic algorithm training optimization is employed for every zone. Performance of the proposed method is compared with ARIMA and GARCH time series for the same data. The simulation results show that the proposed algorithm is robust, efficient, and accurate. Therefore, the algorithm produces better results than the ARIMA and GARCH time series for short-term nodal congestion price forecasting.

Anahtar Kelimeler:

Price forecasting, nodal congestion price, artificial neural network, genetic algorithm

Short-term nodal congestion price forecasting in a large-scale power market using ANN with genetic optimization training

Keywords:

Price forecasting, nodal congestion price, artificial neural network, genetic algorithm,

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N. Amjady, “Day-ahead price forecasting of electricity market by a new fuzzy neural network”, IEEE Transactions on Power Systems, Vol. 21, pp. 888-896, 2006.
S.K. Aggarwal, L.M. Saini, A. Kumar, “Electricity price forecasting in deregulated markets: a review and evalua- tion”, Electrical Power and Energy Systems, Vol. 31, pp. 13-22, 2009.
J. Contreras, R. Espinola, F.J. Nogales, A.J. Conejo, “ARIMA models to predict next-day electricity prices”, IEEE Transactions on Power Systems, Vol. 18, pp. 1014-1020, 2003.
A.J. Conejo, M.A. Plazas, R. Espinola, A.B. Molina, “Day-ahead electricity price forecasting using the wavelet transform and ARIMA models”, IEEE Transactions on Power Systems, Vol. 20, pp. 1035-1042, 2005.
M. Olsson, L. S¨oder, “Modeling real-time balancing power market prices using combined SARIMA and Markov processes”, IEEE Transactions on Power Systems, Vol. 23, pp. 443-450, 2008.
R.C. Garcia, J. Contreras, M. van Akkeren, J.B.C. Garcia, “A GARCH forecasting model to predict day-ahead electricity prices”, IEEE Transactions on Power Systems, Vol. 20, pp. 867-874, 2005.
H. Mori, A. Awata, “Normalized RBFN with hierarchical deterministic annealing clustering for electricity price forecasting”, IEEE Power Engineering Society General Meeting, pp. 1-7, 2007.
P. Areekul, T. Senjyu, H. Toyama A. Yona, “A hybrid ARIMA and neural network model for short-term price forecasting in deregulated market”, IEEE Transactions on Power Systems, Vol. 25, pp. 524-530, 2010.
A.T. Lora, J.R. Santos, J.C.R. Santos, A.G. Exp´osito, J.L.M. Ramos, “A comparison of two techniques for next-day electricity price forecasting”, Proceedings of IDEAL, pp. 384-390, 2002.
V. Nanduri, T.K. Das, “A reinforcement learning model to assess market power under auction-based energy pricing”, IEEE Transactions on Power Systems, Vol. 22, pp 85-95, 2007.
J.J. Guo, P.B. Luh, “Improving market clearing price prediction by using a committee machine of neural networks”, IEEE Transactions on Power Systems, Vol. 19, pp. 1867-1876, 2004.
P. Mandal, A.K. Srivastava, J.W. Park, “An eﬀort to optimize similar days parameters for ANN-based electricity price forecasting”, IEEE Transactions on Power Systems, Vol. 45, pp. 1888-1896, 2009.
S.N. Pandey, S. Tapaswi, L. Srivastava, “Nodal congestion price estimation in spot power market using artiŞcial neural network”, IET Generation, Transmission & Distribution, Vol. 2, pp. 280-290, 2008.
A. Wang, B. Ramsay, “Prediction of system marginal price in the UK power pool using neural network”, Interna- tional Conference on Neural Networks, Vol. 4, pp. 2116-2120, 1997.
L. Zhang, P.B. Luh, “Neural network-based market clearing price prediction and conŞdence interval estimation with an improved extended Kalman Şlter method”, IEEE Transactions on Power Systems, Vol. 20, pp. 59-66, 2005.
B. Zhang, C. Zeng, S. Wang, “Forecasting market-clearing price in day-ahead market using SOM-ANN”, Proceedings of 39th International Universities Power Engineering Conference, Vol. 1, pp. 390-393, 2004.
G. Li, C.C. Liu, C. Mattson, J. Lawarr´ee, “Day-ahead electricity price forecasting in a grid environment”, IEEE Transactions on Power Systems, Vol. 22, pp 266-274, 2007.
N. Bigdeli, K. Afshar, N. Amjady, “Market data analysis and short-term price forecasting in the Iran electricity market with pay-as-bid payment mechanism”, Electric Power Systems Research, Vol. 79, pp. 888-898, 2009.
C.P. Rodriguez, G.J. Anders, “Energy price forecasting in the Ontario competitive power system market”, IEEE Transactions on Power Systems, Vol. 19, pp. 366-374, 2004.
B.J. Chen, M.W. Chang, C.J. Lin, “Load forecasting using support vector machines: a study on EUNITE compe- tition 2001”, IEEE Transactions on Power Systems, Vol. 19, pp. 1821-1830, 2004.
J.K. Lee, J.B. Park, J.R. Shin, “A system marginal price forecasting based on an artiŞcial neural network adapted with rough set theory”, IEEE Power Engineering Society General Meeting, Vol. 1, pp. 528-533, 2005.
A.J. Conejo, J. Contreras, R. Espinola, M.A. Plazas, ”Forecasting electricity prices for a day-ahead pool-based electric energy market”, International Journal of Forecasting, Vol. 21, pp. 435-462, 2005.
M. Shahidehpour, H. Yamin, Z. Li, Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management, New York, John Wiley & Sons, 2002.
M.T. Hagan, M.B. Menhaj, “Training feed forward networks with the Marquardt algorithm”, IEEE Transactions on Neural Networks, Vol. 5, pp. 989-993, 1994. http://motor.ece.iit.edu/data/118bus abreu.xls.
J. Wang, M. Shahidehpour, Z. Li, “Security-constrained unit commitment with volatile wind power generation”, IEEE Transactions on Power Systems, Vol. 23, pp. 1319-1327, 2008.