Coordinative Optimization Control of Microgrid Based on Model Predictive Control

Changbin Hu, Lisong Bi, ZhengGuo Piao, ChunXue Wen and Lijun Hou
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Changbin Hu: College of Electrical and Control Engineering, North China University of Technology, Beijing, China
Lisong Bi: College of Electrical and Control Engineering, North China University of Technology, Beijing, China
ZhengGuo Piao: College of Electrical and Control Engineering, North China University of Technology, Beijing, China
ChunXue Wen: College of Electrical and Control Engineering, North China University of Technology, Beijing, China
Lijun Hou: Resource Electric Tianjin Ltd, Tianjin, China

International Journal of Ambient Computing and Intelligence (IJACI), 2018, vol. 9, issue 3, 57-75

Abstract: This article describes how basing on the future behavior of microgrid system, forecasting renewable energy power generation, load and real-time electricity price, a model predictive control (MPC) strategy is proposed in this article to optimize microgrid operations, while meeting the time-varying requirements and operation constraints. Considering the problems of unit commitment, energy storage, economic dispatching, sale-purchase of electricity and load reduction schedule, the authors first model a microgrid system with a large number of constraints and variables to model the power generation technology and physical characteristics. Meanwhile the authors use a mixed logic dynamical framework to guarantee a reasonable behavior for grid interaction and storage and consider the influences of battery life and recession. Then for forecasting uncertainties in the microgrid, a feedback mechanism is introduced in MPC to solve the problem by using a receding horizon control. The objective of minimizing the operation costs is achieved by an MPC strategy for scheduling the behaviors of components in the microgrid. Finally, a comparative analysis has been carried out between the MPC and some traditional control methods. The MPC leads to a significant improvement in operating costs and on the computational burden. The economy and efficiency of the MPC are shown by the simulations.

Date: 2018
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