EPSO-based Gaussian Process for electricity price forecasting

In this paper, a new method is proposed for Locational Marginal Pricing (LMP) forecasting in Smart Grid. The marginal cost is required to supply electric power to incremental loads in case where a certain node increases power demands in a balanced power system. LMP plays an important role to maintain economic efficiency in electric power markets in a way that electricity flows from a low-cost area to high-cost ones and the transmission network congestion is alleviated. The power market players are interested in maximizing the profits and minimizing the risks through selling and buying electricity. As a result, it is of importance to obtain accurate information on electricity pricing forecasting in advance so that their aim is achieved. This paper presents the Gaussian Process (GP) technique that comes from the extension of Support Vector Machine (SVM) in which hierarchical Bayesian estimation is introduced to express the model parameters as the probabilistic variables. The advantage is that the model accuracy of GP is better than others. GP is integrated with k-means of clustering to improve the performance of GP. Also, this paper makes use of the Mahalanobis kernel in GP rather than the Gaussian one so that GP is generalized to approximate nonlinear systems. EPSO of evolutionary computation is applied to GP to determine the parameters of the kernel function. The effectiveness of the proposed method is demonstrated for real data of ISO New England in USA.

[1]  Tongxin Zheng,et al.  Marginal loss modeling in LMP calculation , 2004, IEEE Transactions on Power Systems.

[2]  Marco van Akkeren,et al.  A GARCH forecasting model to predict day-ahead electricity prices , 2005, IEEE Transactions on Power Systems.

[3]  Hiroyuki Mori,et al.  Data mining for short-term load forecasting , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[4]  Parviz Rastgoufard,et al.  Forecasting Locational Marginal Pricing in deregulated power markets , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[5]  Tom Gedeon,et al.  A fuzzy-neural approach to electricity load and spot-price forecasting in a deregulated electricity market , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

[6]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[7]  Kristian Kersting,et al.  Stacked Gaussian Process Learning , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[8]  Vladimiro Miranda,et al.  EPSO - best-of-two-worlds meta-heuristic applied to power system problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[9]  B. Ramsay,et al.  A neural network based estimator for electricity spot-pricing with particular reference to weekend and public holidays , 1998, Neurocomputing.

[10]  Zuyi Li,et al.  Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets , 2004 .

[11]  Vladimiro Miranda,et al.  EPSO-evolutionary particle swarm optimization, a new algorithm with applications in power systems , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[12]  H. Daneshi,et al.  Some observations on market clearing price and locational marginal price , 2005, IEEE Power Engineering Society General Meeting, 2005.

[13]  Ying-Yi Hong,et al.  A neuro-fuzzy price forecasting approach in deregulated electricity markets , 2005 .

[14]  Shigeo Abe.  Training of Support Vector Machines with Mahalanobis Kernels , 2005, ICANN.

[15]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[16]  H. Mori,et al.  Probabilistic short-term load forecasting with Gaussian processes , 2005, Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems.

[17]  T. M. Usha,et al.  Knowledging on Tamil Nadu electricity board (TNEB) and electricity load demand forecasting by Gaussian processes using real time data , 2013, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[18]  C. Harris.  Electricity Markets: Pricing, Structures and Economics , 2006 .

[19]  H. Mori,et al.  Normalized RBFN with Hierarchical Deterministic Annealing Clustering for Electricity Price Forecasting , 2007, 2007 IEEE Power Engineering Society General Meeting.

[20]  Yaming Ma,et al.  A neural network-based method for forecasting zonal locational marginal prices , 2004, IEEE Power Engineering Society General Meeting, 2004..

[21]  H. Mori,et al.  A Hybrid Method of Clipping and Artificial Neural Network for Electricity Price Zone Forecasting , 2006, 2006 International Conference on Probabilistic Methods Applied to Power Systems.