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Coordinative Optimization Control of Microgrid Based on Model Predictive Control

Coordinative Optimization Control of Microgrid Based on Model Predictive Control

Changbin Hu, Lisong Bi, ZhengGuo Piao, ChunXue Wen, Lijun Hou
Copyright: © 2018 |Volume: 9 |Issue: 3 |Pages: 19
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781522543541|DOI: 10.4018/IJACI.2018070105
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MLA

Hu, Changbin, et al. "Coordinative Optimization Control of Microgrid Based on Model Predictive Control." IJACI vol.9, no.3 2018: pp.57-75. http://doi.org/10.4018/IJACI.2018070105

APA

Hu, C., Bi, L., Piao, Z., Wen, C., & Hou, L. (2018). Coordinative Optimization Control of Microgrid Based on Model Predictive Control. International Journal of Ambient Computing and Intelligence (IJACI), 9(3), 57-75. http://doi.org/10.4018/IJACI.2018070105

Chicago

Hu, Changbin, et al. "Coordinative Optimization Control of Microgrid Based on Model Predictive Control," International Journal of Ambient Computing and Intelligence (IJACI) 9, no.3: 57-75. http://doi.org/10.4018/IJACI.2018070105

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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.

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