Energy Coordinative Optimization of Wind-Storage-Load Microgrids Based on Short-Term PredictionReport as inadecuate




Energy Coordinative Optimization of Wind-Storage-Load Microgrids Based on Short-Term Prediction - Download this document for free, or read online. Document in PDF available to download.

1

College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China

2

Inverter Technologies Engineering Research Center of Beijing, Beijing 100144, China

3

Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100144, China





*

Author to whom correspondence should be addressed.



Academic Editor: Josep M. Guerrero

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. View Full-Text

Keywords: microgrid; coordinative optimization of energy; predictive control; genetic algorithm microgrid; coordinative optimization of energy; predictive control; genetic algorithm





Author: Changbin Hu 1,2,* , Shanna Luo 1,2, Zhengxi Li 1,2, Xin Wang 1,2 and Li Sun 1,3

Source: http://mdpi.com/



DOWNLOAD PDF




Related documents