Abstract
In recent years, a large number of wind power has been applied in the micro-grid (MG). Influenced by randomness characteristics of wind speed, the uncertainty in the power output of wind turbines imposes some safety and stability problems on the optimal energy management in MG. To address this problem, an expert energy management system (EEMS) considering wind power probability is developed in this study for optimal dispatching of a typical grid-connected MG. The EEMS composes of wind power probabilistic forecasting module, multi-objective optimization module and energy storage system (ESS) module. In the wind power forecasting module, wind power probabilistic forecasting based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Gaussian process regression (GPR) is proposed in this study. To improve the forecasting results, CEEMDAN, an effective signal processing method, is employed to decompose the wind power data, then, the decomposed subseries are utilized as the inputs of GPR for probabilistic forecasting. A two-step solution methodology combining an efficient and effective improved multi-objective bat algorithm (IMOBA) with fuzzy set theory (FST) is put forward to solve the optimal dispatching problems. In the first step, IMOBA is developed to optimize the energy dispatching of EEMS by minimizing both economic cost and pollutant emissions simultaneously, and obtain a well-distributed set of Pareto optimal front (POF), then, FST is employed to identify the best compromise solutions from POF. Six operational scenarios of a typical grid-connected MG based one-POF-one-day and one-POF-one-hour dispatching schemes are constructed to investigate the effectiveness of the proposed strategy and provide more flexibility for decision makers. The results illustrate that EEMS can effectively schedule power generation and energy storage by considering economic cost and pollutant emission objectives simultaneously.
Highlights
Environmental pollution and greenhouse effect caused by consumption of fossil fuel resources, diminution of conventional energy resources, and security caused by long distance electric power transmission, and bulk electric power transformation promote the wide development and application of renewable-energy-based micro-grid (MG) in the vicinity of
Prior to applying the proposed approach to solve the multi-objective optimization, some input information should be determined in advance, which are presented as follows
5: Apply fuzzy set theory (FST) approach to determine the compromise solutions O∗according to Eqs. (32) and (33). 6: while t < itermax do 7: for i = 1 : n do
Summary
Environmental pollution and greenhouse effect caused by consumption of fossil fuel resources, diminution of conventional energy resources, and security caused by long distance electric power transmission, and bulk electric power transformation promote the wide development and application of renewable-energy-based micro-grid (MG) in the vicinity of. In recent years, wind power probabilistic forecasting models have been developed to provide uncertainty information for studying the optimal energy management [12], [13]. Stage 1: Utilize CEEMDAN approach to break the original wind power empirical data into a few IMFs and one Res with different frequency and relatively stable features In this stage, the training sample data is added adaptively weighted noise signal, apply EMD to decompose the noise-assisted data, in the end, eliminate the noise signal by summing after the maximum iteration is reached. Ek,max , Ek,min and E(0) are set as 150kW, -150kW and 5kW, respectively
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