{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T14:59:07Z","timestamp":1781103547743,"version":"3.54.1"},"reference-count":20,"publisher":"IGI Global Scientific Publishing","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.4018\/ijaci.2018070105","type":"journal-article","created":{"date-parts":[[2018,4,12]],"date-time":"2018-04-12T10:16:37Z","timestamp":1523528197000},"page":"57-75","source":"Crossref","is-referenced-by-count":9,"title":["Coordinative Optimization Control of Microgrid Based on Model Predictive Control"],"prefix":"10.4018","volume":"9","author":[{"given":"Changbin","family":"Hu","sequence":"first","affiliation":[{"name":"College of Electrical and Control Engineering, North China University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisong","family":"Bi","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, North China University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ZhengGuo","family":"Piao","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, North China University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ChunXue","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, North China University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Hou","sequence":"additional","affiliation":[{"name":"Resource Electric Tianjin Ltd, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJACI.2018070105-0","doi-asserted-by":"crossref","unstructured":"Arora, P., White, R. 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