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.