Model Predictive Control Based Demand Response Scheme for Peak Demand Reduction in a Smart Campus Integrated Microgrid
This paper presents an effective solution to manage the power flows exchanges in a campus integrated microgrid for peak reduction/shaving purposes. The campus integrated microgrid is composed of photovoltaic parking shades, an energy storage system, electric vehicles and bikes, loads, an advanced me...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9638482/ |
| _version_ | 1819239099844788224 |
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| author | Yasmine Achour Ahmed Ouammi Driss Zejli |
| author_facet | Yasmine Achour Ahmed Ouammi Driss Zejli |
| author_sort | Yasmine Achour |
| collection | DOAJ |
| description | This paper presents an effective solution to manage the power flows exchanges in a campus integrated microgrid for peak reduction/shaving purposes. The campus integrated microgrid is composed of photovoltaic parking shades, an energy storage system, electric vehicles and bikes, loads, an advanced metering infrastructure, and a smart control unit. The latter is based on Model Predictive Control (MPC) whose objective is to reduce/shave the peak load of the campus while satisfying the Energy Storage System ESS, electrical Vehicles (EVs) and Electrical Bikes (EBs) state of charge. The proposed strategy aims to take the advantage of combining storage and photovoltaic (PV) systems to Vehicle to Campus (V2C) and Bike to Campus (B2C) concepts to support the microgrid to pay the minimum billing power while ensuring a good service quality to the EVs and EBs users. For that, the integration of the renewable energy sources and the different storage systems into the microgrid is modeled, and the MPC-based optimization framework is formulated. Besides, the results related to the application of the MPC to real case studies are presented, integrating the effects of static and dynamic weighting factors on the microgrid operation. |
| first_indexed | 2024-12-23T13:46:44Z |
| format | Article |
| id | doaj.art-71a84511923243d38da2d5b3c0268775 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T13:46:44Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-71a84511923243d38da2d5b3c02687752022-12-21T17:44:42ZengIEEEIEEE Access2169-35362021-01-01916276516277810.1109/ACCESS.2021.31328959638482Model Predictive Control Based Demand Response Scheme for Peak Demand Reduction in a Smart Campus Integrated MicrogridYasmine Achour0Ahmed Ouammi1https://orcid.org/0000-0002-1796-5496Driss Zejli2https://orcid.org/0000-0002-9207-3682Advanced Systems Engineering Laboratory, Ibn Tofail University, Kenitra, MoroccoCentre for Sustainable Development, College of Arts and Sciences, Qatar University, Doha, QatarAdvanced Systems Engineering Laboratory, Ibn Tofail University, Kenitra, MoroccoThis paper presents an effective solution to manage the power flows exchanges in a campus integrated microgrid for peak reduction/shaving purposes. The campus integrated microgrid is composed of photovoltaic parking shades, an energy storage system, electric vehicles and bikes, loads, an advanced metering infrastructure, and a smart control unit. The latter is based on Model Predictive Control (MPC) whose objective is to reduce/shave the peak load of the campus while satisfying the Energy Storage System ESS, electrical Vehicles (EVs) and Electrical Bikes (EBs) state of charge. The proposed strategy aims to take the advantage of combining storage and photovoltaic (PV) systems to Vehicle to Campus (V2C) and Bike to Campus (B2C) concepts to support the microgrid to pay the minimum billing power while ensuring a good service quality to the EVs and EBs users. For that, the integration of the renewable energy sources and the different storage systems into the microgrid is modeled, and the MPC-based optimization framework is formulated. Besides, the results related to the application of the MPC to real case studies are presented, integrating the effects of static and dynamic weighting factors on the microgrid operation.https://ieeexplore.ieee.org/document/9638482/Campus integrated microgridmodel predictive controldemand responsepeak reductionelectric vehicleselectric bikes |
| spellingShingle | Yasmine Achour Ahmed Ouammi Driss Zejli Model Predictive Control Based Demand Response Scheme for Peak Demand Reduction in a Smart Campus Integrated Microgrid IEEE Access Campus integrated microgrid model predictive control demand response peak reduction electric vehicles electric bikes |
| title | Model Predictive Control Based Demand Response Scheme for Peak Demand Reduction in a Smart Campus Integrated Microgrid |
| title_full | Model Predictive Control Based Demand Response Scheme for Peak Demand Reduction in a Smart Campus Integrated Microgrid |
| title_fullStr | Model Predictive Control Based Demand Response Scheme for Peak Demand Reduction in a Smart Campus Integrated Microgrid |
| title_full_unstemmed | Model Predictive Control Based Demand Response Scheme for Peak Demand Reduction in a Smart Campus Integrated Microgrid |
| title_short | Model Predictive Control Based Demand Response Scheme for Peak Demand Reduction in a Smart Campus Integrated Microgrid |
| title_sort | model predictive control based demand response scheme for peak demand reduction in a smart campus integrated microgrid |
| topic | Campus integrated microgrid model predictive control demand response peak reduction electric vehicles electric bikes |
| url | https://ieeexplore.ieee.org/document/9638482/ |
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