Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model

作     者：Gil-Gonzalez, Walter Danilo Montoya, Oscar Holguin, Erika Garces, Alejandro Fernando Grisales-Norena, Luis 

作者机构：Univ Tecnol Pereira AA 97 Pereira 660003 Colombia Univ Tecnol Bolivar Programa Ingn Elect & Ingn Elect Km 1 Via Turbaco Cartagena Colombia Inst Tecnol Metropolitano Dept Mecatron & Electromecan Medellin Colombia 

出 版 物：《JOURNAL OF ENERGY STORAGE》 (J. Energy Storage)

年 卷 期：2019年第21卷

页      面：1-8页

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：National Scholarship Program Doctorates of the Administrative Department of Science  Technology and Innovation of Colombia (COLCIENCIAS) [727-2015] 

主　　题：Economic dispatch Dc microgrids Energy storage systems Semidefinite programming Convex reformulation 

摘      要：A mathematical optimization approach for the optimal operation focused on the economic dispatch for dc microgrid with high penetration of distributed generators and energy storage systems (ESS) via semidefinite programming (SDP) is proposed in this paper. The SDP allows transforming the nonlinear and non-convex characteristics of the economic dispatch problem into a convex approximation which is easy for implementation in specialized software, i.e., CVX. The proposed mathematical approach contemplates the efficient operation of a dc microgrid over a period of time with variable energy purchase prices, which makes it a practical methodology to apply in real-time operating conditions. A nonlinear autoregressive exogenous (NARX) model is employed for training an artificial neural network (ANN) for forecasting solar radiation and wind speed for renewable generation integration and dispatch considering periods of prediction of 0.5 h. Four scenarios are proposed to analyze the inclusion of ESS in a dc microgrid for economic dispatch studies. Additionally, the results are compared with GAMS commercial optimization package, which allows validating the accuracy and quality of the proposed optimizing methodology.