Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval Prediction
With the integration of Renewable Energy Resources (RERs), the Day-Ahead (DA) scheduling for the optimal operation of the integrated Isolated Microgrids (IMGs) may not be economically optimal in real time due to the prediction errors of multiple uncertainty sources. To compensate for prediction erro...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/2198846 |
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| _version_ | 1832561390024392704 |
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| author | Huihui He Shengjun Huang Yajie Liu Tao Zhang |
| author_facet | Huihui He Shengjun Huang Yajie Liu Tao Zhang |
| author_sort | Huihui He |
| collection | DOAJ |
| description | With the integration of Renewable Energy Resources (RERs), the Day-Ahead (DA) scheduling for the optimal operation of the integrated Isolated Microgrids (IMGs) may not be economically optimal in real time due to the prediction errors of multiple uncertainty sources. To compensate for prediction error, this paper proposes a Robust Model Predictive Control (RMPC) based on an interval prediction approach to optimize the real-time operation of the IMGs, which diminishes the influence from prediction error. The rolling optimization model in RMPC is formulated into the robust model to schedule operation with the consideration of the price of robustness. In addition, an Online Learning (OL) method for interval prediction is utilized in RMPC to predict the future information of the uncertainties of RERs and load, thereby limiting the uncertainty. A case study demonstrates the effectiveness of the proposed with the better matching between demand and supply compared with the traditional Model Predictive Control (MPC) method and Hard Charging (HC) method. |
| format | Article |
| id | doaj-art-d9ed20949d4b459cbee54ccb42f9655d |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-d9ed20949d4b459cbee54ccb42f9655d2025-02-03T01:25:10ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/21988462198846Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval PredictionHuihui He0Shengjun Huang1Yajie Liu2Tao Zhang3College of Systems Engineering, National University of Defense Technology, Changsha 410073, Hunan, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, Hunan, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, Hunan, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, Hunan, ChinaWith the integration of Renewable Energy Resources (RERs), the Day-Ahead (DA) scheduling for the optimal operation of the integrated Isolated Microgrids (IMGs) may not be economically optimal in real time due to the prediction errors of multiple uncertainty sources. To compensate for prediction error, this paper proposes a Robust Model Predictive Control (RMPC) based on an interval prediction approach to optimize the real-time operation of the IMGs, which diminishes the influence from prediction error. The rolling optimization model in RMPC is formulated into the robust model to schedule operation with the consideration of the price of robustness. In addition, an Online Learning (OL) method for interval prediction is utilized in RMPC to predict the future information of the uncertainties of RERs and load, thereby limiting the uncertainty. A case study demonstrates the effectiveness of the proposed with the better matching between demand and supply compared with the traditional Model Predictive Control (MPC) method and Hard Charging (HC) method.http://dx.doi.org/10.1155/2021/2198846 |
| spellingShingle | Huihui He Shengjun Huang Yajie Liu Tao Zhang Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval Prediction Discrete Dynamics in Nature and Society |
| title | Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval Prediction |
| title_full | Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval Prediction |
| title_fullStr | Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval Prediction |
| title_full_unstemmed | Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval Prediction |
| title_short | Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval Prediction |
| title_sort | robust model predictive control for energy management of isolated microgrids based on interval prediction |
| url | http://dx.doi.org/10.1155/2021/2198846 |
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