Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids
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 t...
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MDPI AG
2025-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/7/1696 |
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| author | Sungmin Lim Jaekyu Lee Sangyub Lee |
| author_facet | Sungmin Lim Jaekyu Lee Sangyub Lee |
| author_sort | Sungmin Lim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0a499784002d49cfbe077a52cdeff065 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-0a499784002d49cfbe077a52cdeff0652025-08-20T03:06:31ZengMDPI AGEnergies1996-10732025-03-01187169610.3390/en18071696Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected MicrogridsSungmin Lim0Jaekyu Lee1Sangyub Lee2Energy IT Convergence Research Center, Korea Electronics Technology Institute, 25 Saenari-ro, Bundang-gu, Seongnam-si 13509, Gyeonggi-do, Republic of KoreaEnergy IT Convergence Research Center, Korea Electronics Technology Institute, 25 Saenari-ro, Bundang-gu, Seongnam-si 13509, Gyeonggi-do, Republic of KoreaEnergy IT Convergence Research Center, Korea Electronics Technology Institute, 25 Saenari-ro, Bundang-gu, Seongnam-si 13509, Gyeonggi-do, Republic of KoreaThis 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.https://www.mdpi.com/1996-1073/18/7/1696model predictive controlenergy management systemrenewable energydistributed energy resourcesmixed-integer quadratically constrained programming |
| spellingShingle | Sungmin Lim Jaekyu Lee Sangyub Lee Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids Energies model predictive control energy management system renewable energy distributed energy resources mixed-integer quadratically constrained programming |
| title | Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids |
| title_full | Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids |
| title_fullStr | Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids |
| title_full_unstemmed | Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids |
| title_short | Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids |
| title_sort | model predictive control based energy management system for cooperative optimization of grid connected microgrids |
| topic | model predictive control energy management system renewable energy distributed energy resources mixed-integer quadratically constrained programming |
| url | https://www.mdpi.com/1996-1073/18/7/1696 |
| work_keys_str_mv | AT sungminlim modelpredictivecontrolbasedenergymanagementsystemforcooperativeoptimizationofgridconnectedmicrogrids AT jaekyulee modelpredictivecontrolbasedenergymanagementsystemforcooperativeoptimizationofgridconnectedmicrogrids AT sangyublee modelpredictivecontrolbasedenergymanagementsystemforcooperativeoptimizationofgridconnectedmicrogrids |