Abstract
In this chapter, a multi-layer perceptron neural network has been developed for prediction of nodal prices at various buses of power system under restructured environment. Levenberg-Marquardt algorithm has been applied to speed up the training of the multi-layer feed-forward neural network. To select the effective inputs for the Levenberg-Marquardt algorithm based artificial neural network (LMANN), an unsupervised vector quantization based clustering technique has been applied. Effectiveness of the proposed LMANN based approach for nodal price prediction has been demonstrated on benchmark 6-bus system and RTS 24-bus system. Since the training of artificial neural network is extremely fast and test results are accurate, they can be directly floated to OASIS (open access same time information system) web site. The Market Participants willing to make transactions can access this information instantly.
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Yuan-Kang Wu, Comparison of Pricing Schemes of Several Deregulated Electricity Markets in The World, IEEE/PES, Transmission and Distribution Conference and Exhibition: Asia and Pacific (2005) 1-6.
M. Pandit, L. Srivastava and J. Sharma, Voltage Security Assessment Employing Coherency Based Feature Selection for Neural Network, 14 (1) (2006) 25-37.
K.T. Chaturvedi, L. Srivastava and M. Pandit, Levenberg Merquardt Algorithm Based Economic Load Dispatch, Presented in 2006 IEEE Power India Conference, held at New Delhi (2006).
S. N. Pandey, S. Tapaswi, and L. Srivastava, Nodal congestion price estimation in spot power market using artificial neural network, IET Gener. Transm. and distrib., 1 (2008) 280-290.
Y. Y. Hong, C. Y. Hsiao, C.Y., Locational Marginal Price Forecasting in Deregulated Electricity Markets Using Artificial Intelligence, IEE Proc.-Generation, Transmission and Distribution, 149 (5) (2002) 621-626.
S. Aggarwal, L. Saini, Ashwani Kumar, Electricity Price Forecasting in Ontario Electricity Market Using Wavelet Transform in Artificial Neural Network Based Model, International Journal of Control, Automation, and Systems, 6 (5) (2008) 639-650.
J. P. S. Catalao, S. J. P. S. Mariano, V. M. F. Mendesb, and L. A. F. M. Ferreira, Short-Term Electricity Prices Forecasting in a Competitive Market: A Neural Network Approach, Electric Power Systems Research, 77 (2007) 1297–1304.
S. Fan, S., C. Mao, C. and L. Chen, Next-Day Electricity Price Forecasting Using a Hybrid Network, IET Generation, Transmission and Distribution, 1 (1) (2007) 176-182.
G. Li, C. C. Liu, C. Mattson, and J. Lawarrée, Day- Ahead Electricity Price Forecasting in a Grid Environment, IEEE Transactions on Power Systems, 22 (1) (2007) 266–274.
P. S. Georgilakis, Artificial Intelligence Solution to Electricity Price Forecasting Problem, Journal of Applied Artificial Intelligence,21 (8) (2007) 707-727.
C. P. Rodriguez, and G. J. Anders, Energy Price Forecasting in The Ontario Competitive Power System Market, IEEE Transactions on Power Systems,19 (1) (2004) 366–374.
L. Zhang, P. B. Luh and K. Kasiviswanathan, Energy Clearing Price Prediction and Confidence Interval Estimation With Cascaded Neural Networks, IEEE Power Engineering Review, 22 (12) (2002) 1-60.
L. Zhang, and P. B. Luh, Neural Network-Based Market Clearing Price Prediction and Confidence Interval Estimation With an Improved Extended Kalman Filter Method, IEEE Transactions on Power Systems, 20 (1) (2005) 59–66.
P. Mandal, A. K. Srivastava, and J. W. Park, An Effort to Optimize Similar Days Parameters for ANN-Based Electricity Price Forecasting, IEEE Transactions on Industry Applications, 45 (5) (2009) 1888–1896.
Y.Y. Hong, C. F. Lee, A Neuro Fuzzy Price Forecasting Approach in Deregulated Electricity Markets, Electrical Power and Energy System, 73 (2) (2005) 151-157.
M.T. Hagan and M.H. Mehnaj, Training Feed Forward Neural Networks with Marquardt Algorithm, IEEE Transaction on Neural Network, 5 (6) (1994) 989-993.
L.M. Saini and M.K. Soni, Artificial Neural Network Based Peak Load Forecasting using Levenberg Marquardt and Quasi- Newton Method, IET Proceeding Generation, Transmission and Distribution (2002) 578-584.
F. Milano, An Open Source Power System Analysis Toolbox, IEEE Transactions on Power Systems, 20 (3) (2005) 1199-1206.
F. Milano, C. A. Canizares, and A. J. Conejo, Sensitivity Based Security Constrained OPF Market Clearing Model, IEEE Transactions on Power Systems, 20 (4) (2005) 2051–2060.
S. Rajasekaran and G.A.V. Pai, Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis and Applications, Prentice-Hall Press, New Delhi, India, 2006.
S.N. Pandey, S. Tapaswi and L. Srivastava, Growing RBFNN Based Soft Computing Approach for Congestion Management, Neural Computing and Applications, 18 (8) (2009) 945-955.
H.A. Gil, F.D. Galiana, and E.L.D. Silva, Nodal Price Control: A Mechanism for Transmission Network Cost Allocation, IEEE Transactions on Power Systems, 21 (1) (2006) 3-10.
L. Chen et al., Mean Field Theory For Optimal Power Flow, IEEE Transactions on Power Systems, 12 (4) (1997) 1481–1486.
L. Chen et al., Surrogate Constraint Method for Optimal Power Flow, IEEE Transactions on Power Systems, 13 (3) (1998) 1084–1089.
L. Chen, Y. Tada, H. Okamoto, R. Tanabe, and A. Ono, Optimal Operation Solutions of Power Systems With transient Stability Constraints, IEEE Transactions On Circuits Syst. I, 48 (3) (2001) 327–339.
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Pal, K., Srivastava, L., Pandit, M. (2016). Levenberg-Marquardt Algorithm Based ANN for Nodal Price Prediction in Restructured Power System. In: Karampelas, P., Ekonomou, L. (eds) Electricity Distribution. Energy Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49434-9_13
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