MPC based frequency control of an autonomous microgrid integrated with electric vehicles
Although electric vehicles (EVs) offer bidirectional charging and can serve as mobile energy storage units, integrating them into microgrids and enabling them to participate in load frequency control (LFC) has proven to be a significant challenge. One of the crucial issues is the unpredictability of...
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Elsevier
2025-06-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025008047 |
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| author | Abdullahi Bala Kunya Muhamad M. Mundu Aminu Babangida Péter Tamás Szemes |
| author_facet | Abdullahi Bala Kunya Muhamad M. Mundu Aminu Babangida Péter Tamás Szemes |
| author_sort | Abdullahi Bala Kunya |
| collection | DOAJ |
| description | Although electric vehicles (EVs) offer bidirectional charging and can serve as mobile energy storage units, integrating them into microgrids and enabling them to participate in load frequency control (LFC) has proven to be a significant challenge. One of the crucial issues is the unpredictability of EV usage patterns, which can lead to sudden fluctuations in generation-demand balance. This unpredictability makes it difficult to coordinate EV charging and discharging with the grid's frequency control needs. Classical control techniques lack the capability to handle the EV's stochastic behavior. To address this, the study proposes the integration of a battery energy storage system to maintain continuous generation – demand balance. Then, a finite horizon model predictive control (MPC) is applied for the LFC of a two-area islanded MG integrated with the EV charging station. The MPC generates the optimal signals for the optimal adjustment of the power generation of the dispatchable sources as well as the EV aggregators. To evaluate system stability, the local input-to-state stability (ISS) criterion is employed. Simulation results demonstrate that the proposed MPC-based LFC outperforms conventional methods in terms of frequency nadir, settling time, and rate of change of frequency (RoCoF). Under a 2 % step load perturbation in one of the control areas (CAs), the proposed MPC reduces frequency nadir to −0.0306 Hz (a 12.9 % improvement over the best alternative), limits RoCoF to −0.0524 Hz/s (a 17.4 % enhancement), and achieves a minimum steady-state frequency deviation of 4.14 × 10⁻³ Hz (a 1.9 % reduction). Furthermore, in response to random load fluctuations and renewable energy source (RES) variability, the proposed controller ensures that frequency deviations remain within ±0.02 Hz, while RoCoF is constrained within ±0.02 Hz/s, demonstrating superior robustness, constraint handling and faster convergence. |
| format | Article |
| id | doaj-art-9e8eec07e2b74d9093fd7383f7c0c258 |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-9e8eec07e2b74d9093fd7383f7c0c2582025-08-20T02:50:22ZengElsevierResults in Engineering2590-12302025-06-012610472710.1016/j.rineng.2025.104727MPC based frequency control of an autonomous microgrid integrated with electric vehiclesAbdullahi Bala Kunya0Muhamad M. Mundu1Aminu Babangida2Péter Tamás Szemes3Department of Electrical, Telecommunications and Computer Engineering, Kampala International University, Western Campus, Ishaka, Uganda; Department of Electrical Engineering, Ahmadu Bello University, Zaria, Nigeria; Corresponding author.Department of Electrical, Telecommunications and Computer Engineering, Kampala International University, Western Campus, Ishaka, UgandaDepartment of Vehicles Engineering, Vehicles and Mechatronics Institute, Faculty of Engineering, University of Debrecen, Ótemető u. 2-4, Debrecen 4028, HungaryDepartment of Vehicles Engineering, Vehicles and Mechatronics Institute, Faculty of Engineering, University of Debrecen, Ótemető u. 2-4, Debrecen 4028, HungaryAlthough electric vehicles (EVs) offer bidirectional charging and can serve as mobile energy storage units, integrating them into microgrids and enabling them to participate in load frequency control (LFC) has proven to be a significant challenge. One of the crucial issues is the unpredictability of EV usage patterns, which can lead to sudden fluctuations in generation-demand balance. This unpredictability makes it difficult to coordinate EV charging and discharging with the grid's frequency control needs. Classical control techniques lack the capability to handle the EV's stochastic behavior. To address this, the study proposes the integration of a battery energy storage system to maintain continuous generation – demand balance. Then, a finite horizon model predictive control (MPC) is applied for the LFC of a two-area islanded MG integrated with the EV charging station. The MPC generates the optimal signals for the optimal adjustment of the power generation of the dispatchable sources as well as the EV aggregators. To evaluate system stability, the local input-to-state stability (ISS) criterion is employed. Simulation results demonstrate that the proposed MPC-based LFC outperforms conventional methods in terms of frequency nadir, settling time, and rate of change of frequency (RoCoF). Under a 2 % step load perturbation in one of the control areas (CAs), the proposed MPC reduces frequency nadir to −0.0306 Hz (a 12.9 % improvement over the best alternative), limits RoCoF to −0.0524 Hz/s (a 17.4 % enhancement), and achieves a minimum steady-state frequency deviation of 4.14 × 10⁻³ Hz (a 1.9 % reduction). Furthermore, in response to random load fluctuations and renewable energy source (RES) variability, the proposed controller ensures that frequency deviations remain within ±0.02 Hz, while RoCoF is constrained within ±0.02 Hz/s, demonstrating superior robustness, constraint handling and faster convergence.http://www.sciencedirect.com/science/article/pii/S2590123025008047Autonomous microgrid;Electric vehicle aggregator;Energy storage system;Fuel cellLoad frequency controlModel predictive control |
| spellingShingle | Abdullahi Bala Kunya Muhamad M. Mundu Aminu Babangida Péter Tamás Szemes MPC based frequency control of an autonomous microgrid integrated with electric vehicles Results in Engineering Autonomous microgrid;Electric vehicle aggregator;Energy storage system;Fuel cell Load frequency control Model predictive control |
| title | MPC based frequency control of an autonomous microgrid integrated with electric vehicles |
| title_full | MPC based frequency control of an autonomous microgrid integrated with electric vehicles |
| title_fullStr | MPC based frequency control of an autonomous microgrid integrated with electric vehicles |
| title_full_unstemmed | MPC based frequency control of an autonomous microgrid integrated with electric vehicles |
| title_short | MPC based frequency control of an autonomous microgrid integrated with electric vehicles |
| title_sort | mpc based frequency control of an autonomous microgrid integrated with electric vehicles |
| topic | Autonomous microgrid;Electric vehicle aggregator;Energy storage system;Fuel cell Load frequency control Model predictive control |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025008047 |
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