Application of Gaussian Process to Locational Marginal Pricing Forecasting

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In this paper, a new method is proposed for Locational Marginal Pricing (LMP) forecasting in Smart Grid. The marginal cost is required to supply electricity 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 power markets in a way that electricity flows from a low-cost area to high-cost one 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 desire is reflected. This paper presents the Gaussian Process (GP) technique that comes from the extension of Support Vector Machine (SVM) in a way that hierarchical Bayesian estimation is introduced to express the model parameter as the stochastic variables. The advantage is that the model accuracy of GP is better than others. The proposed method is successfully applied to real data of ISONE(Independent System Operator New England) in USA.