Energy Coordinative Optimization of Wind-Storage-Load Microgrids Based on Short-Term Prediction

Changbin Hu, Shanna Luo, Zhengxi Li, Xin Wang and Li Sun
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Changbin Hu: College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
Shanna Luo: College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
Zhengxi Li: College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
Xin Wang: College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
Li Sun: College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China

Energies, 2015, vol. 8, issue 2, 1-24

Abstract: According to the topological structure of wind-storage-load complementation microgrids, this paper proposes a method for energy coordinative optimization which focuses on improvement of the economic benefits of microgrids in the prediction framework. First of all, the external characteristic mathematical model of distributed generation (DG) units including wind turbines and storage batteries are established according to the requirements of the actual constraints. Meanwhile, using the minimum consumption costs from the external grid as the objective function, a grey prediction model with residual modification is introduced to output the predictive wind turbine power and load at specific periods. Second, based on the basic framework of receding horizon optimization, an intelligent genetic algorithm (GA) is applied to figure out the optimum solution in the predictive horizon for the complex non-linear coordination control model of microgrids. The optimum results of the GA are compared with the receding solution of mixed integer linear programming (MILP). The obtained results show that the method is a viable approach for energy coordinative optimization of microgrid systems for energy flow and reasonable schedule. The effectiveness and feasibility of the proposed method is verified by examples.

Keywords: microgrid; coordinative optimization of energy; predictive control; genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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