Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids

Sungmin Lim, Jaekyu Lee and Sangyub Lee ()
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Sungmin Lim: Energy IT Convergence Research Center, Korea Electronics Technology Institute, 25 Saenari-ro, Bundang-gu, Seongnam-si 13509, Gyeonggi-do, Republic of Korea
Jaekyu Lee: Energy IT Convergence Research Center, Korea Electronics Technology Institute, 25 Saenari-ro, Bundang-gu, Seongnam-si 13509, Gyeonggi-do, Republic of Korea
Sangyub Lee: Energy IT Convergence Research Center, Korea Electronics Technology Institute, 25 Saenari-ro, Bundang-gu, Seongnam-si 13509, Gyeonggi-do, Republic of Korea

Energies, 2025, vol. 18, issue 7, 1-29

Abstract: This paper presents a model predictive control (MPC)-based energy management system (EMS) for optimizing cooperative operation of networked microgrids (MGs). While the isolated operation of individual MGs limits system-wide optimization, the proposed approach enhances both stability and efficiency through integrated control. The system employs mixed-integer quadratic constrained programming (MIQCP) to model complex operational characteristics of MGs, facilitating the optimization of interactions among distributed energy resources (DERs) and power exchange within the MG network. The effectiveness of the proposed method was validated through a series of case studies. First, the performance of the algorithm was evaluated under various weather conditions. Second, its robustness against prediction errors was tested by comparing scenarios with and without disturbance prediction. Finally, the cooperative operation of MGs was compared with the independent operation of a single MG to analyze the impact of the cooperative approach on performance improvement. Quantitatively, integrating predictions reduced operating costs by 19.23% compared to the case without predictions, while increasing costs by approximately 3.7% compared to perfect predictions. Additionally, cooperative MG operation resulted in an average 46.18% reduction in external resource usage compared to independent operation. These results were verified through simulations conducted on a modified version of the IEEE 33-bus test feeder.

Keywords: model predictive control; energy management system; renewable energy; distributed energy resources; mixed-integer quadratically constrained programming (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: 2025
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