Optimal Infrastructure Design and Power Management for a Photovoltaic and Battery Assisted Electric Vehicle Charging Station in Southern California
This paper presents a framework for the optimal design of a solar and battery assisted electric vehicle (EV) charging station in southern California, with a focus on maximizing long-term profits while addressing operational uncertainties. The problem is conceptualized as an iterative two-stage decis...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10495054/ |
| _version_ | 1797197359618195456 |
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| author | Yu Yang Hen-Geul Yeh Nguyen Cam Thuy Dam |
| author_facet | Yu Yang Hen-Geul Yeh Nguyen Cam Thuy Dam |
| author_sort | Yu Yang |
| collection | DOAJ |
| description | This paper presents a framework for the optimal design of a solar and battery assisted electric vehicle (EV) charging station in southern California, with a focus on maximizing long-term profits while addressing operational uncertainties. The problem is conceptualized as an iterative two-stage decision process. In Stage I, the sampled designs of station infrastructure, including the number of chargers, the size of photovoltaic (PV) array, and capacity of the battery energy storage system (BESS), are specified. In Stage II, the EV charging rule is designed and simulated based on the charging request and solar power datasets. A model predictive controller and an empirical rule-based approach with incoming car forecasts are developed and compared for the vehicle charging management. The simulated annual operational profit and infrastructure investment with the consideration of long-term battery degradation is synthesized to build response surface for better design exploration in Stage I. Our results show that the proposed rule-based approach is computationally more efficient and suitable to integrate with response surface methodology (RSM) for design optimization. In addition, RSM is compared with adaptive particle swarm optimization (PSO) with multiple trials to demonstrate its superiority in high-profit design. |
| first_indexed | 2024-04-24T06:42:43Z |
| format | Article |
| id | doaj.art-e3f4fcf36b1e488eae009823c9674c7e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T06:42:43Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e3f4fcf36b1e488eae009823c9674c7e2024-04-22T23:00:31ZengIEEEIEEE Access2169-35362024-01-0112541015411410.1109/ACCESS.2024.338665910495054Optimal Infrastructure Design and Power Management for a Photovoltaic and Battery Assisted Electric Vehicle Charging Station in Southern CaliforniaYu Yang0https://orcid.org/0000-0002-7282-1452Hen-Geul Yeh1https://orcid.org/0000-0001-9209-3109Nguyen Cam Thuy Dam2Department of Chemical Engineering, California State University Long Beach, Long Beach, CA, USADepartment of Electrical Engineering, California State University Long Beach, Long Beach, CA, USADepartment of Chemical Engineering, California State University Long Beach, Long Beach, CA, USAThis paper presents a framework for the optimal design of a solar and battery assisted electric vehicle (EV) charging station in southern California, with a focus on maximizing long-term profits while addressing operational uncertainties. The problem is conceptualized as an iterative two-stage decision process. In Stage I, the sampled designs of station infrastructure, including the number of chargers, the size of photovoltaic (PV) array, and capacity of the battery energy storage system (BESS), are specified. In Stage II, the EV charging rule is designed and simulated based on the charging request and solar power datasets. A model predictive controller and an empirical rule-based approach with incoming car forecasts are developed and compared for the vehicle charging management. The simulated annual operational profit and infrastructure investment with the consideration of long-term battery degradation is synthesized to build response surface for better design exploration in Stage I. Our results show that the proposed rule-based approach is computationally more efficient and suitable to integrate with response surface methodology (RSM) for design optimization. In addition, RSM is compared with adaptive particle swarm optimization (PSO) with multiple trials to demonstrate its superiority in high-profit design.https://ieeexplore.ieee.org/document/10495054/Battery energy storage systemmodel predictive controlphotovoltaic systemsresponse surface methodology |
| spellingShingle | Yu Yang Hen-Geul Yeh Nguyen Cam Thuy Dam Optimal Infrastructure Design and Power Management for a Photovoltaic and Battery Assisted Electric Vehicle Charging Station in Southern California IEEE Access Battery energy storage system model predictive control photovoltaic systems response surface methodology |
| title | Optimal Infrastructure Design and Power Management for a Photovoltaic and Battery Assisted Electric Vehicle Charging Station in Southern California |
| title_full | Optimal Infrastructure Design and Power Management for a Photovoltaic and Battery Assisted Electric Vehicle Charging Station in Southern California |
| title_fullStr | Optimal Infrastructure Design and Power Management for a Photovoltaic and Battery Assisted Electric Vehicle Charging Station in Southern California |
| title_full_unstemmed | Optimal Infrastructure Design and Power Management for a Photovoltaic and Battery Assisted Electric Vehicle Charging Station in Southern California |
| title_short | Optimal Infrastructure Design and Power Management for a Photovoltaic and Battery Assisted Electric Vehicle Charging Station in Southern California |
| title_sort | optimal infrastructure design and power management for a photovoltaic and battery assisted electric vehicle charging station in southern california |
| topic | Battery energy storage system model predictive control photovoltaic systems response surface methodology |
| url | https://ieeexplore.ieee.org/document/10495054/ |
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