Bibliographic Details
| Title: |
Bi-objective collaborative optimization of a photovoltaic-energy storage EV charging station with consideration of storage capacity impacts |
| Authors: |
Wei Guo, Shengbo Sun, Kai Nan, Peng Tao, Kaibin Wu, Zhiqiang Wang, Huimin Wang, Mengmeng Yue, Xinlei Bai, Jianyong Ding |
| Source: |
Frontiers in Energy Research, Vol 12 (2024) |
| Publisher Information: |
Frontiers Media S.A., 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:General Works |
| Subject Terms: |
green building energy system (GBES), bi-objective optimization, electric vehicle (EV), photovoltaic (PV), energy storage system (ESS), General Works |
| More Details: |
The rapid growth of renewable energy and electric vehicles (EVs) presents new development opportunities for power systems and energy storage devices. This paper presents a novel integrated Green Building Energy System (GBES) by integrating photovoltaic-energy storage electric vehicle charging station (PV-ES EVCS) and adjacent buildings into a unified system. In this system, the building load is treated as an uncontrollable load and primarily utilized to facilitate the consumption of surplus photovoltaic (PV) power generated by EVCS. First, a strategy for determining the maximum value of the energy storage system (ESS) capacity is presented. Subsequently, to coordinate the charging and discharging plans of ESS, and EVs, a bi-objective optimization model was established focusing on GBES power purchase costs and the load peak-valley difference. The proposed GBES efficiently utilizes the integrated energy system comprising charging stations and adjacent buildings, maximizing the use of photovoltaic energy and external power grids during low-cost periods. In experiments, we compare the proposed optimized charging strategy with the unordered charging case, the simulation results demonstrate that the proposed method for coordinating ESS and EVs charging can respectively reduce the cost of purchased power by 33.2% and the peak-to-valley difference in load by 47.6%. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2296-598X |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fenrg.2024.1517011/full; https://doaj.org/toc/2296-598X |
| DOI: |
10.3389/fenrg.2024.1517011 |
| Access URL: |
https://doaj.org/article/7c23f53c6e0046cc920077d4edeea52a |
| Accession Number: |
edsdoj.7c23f53c6e0046cc920077d4edeea52a |
| Database: |
Directory of Open Access Journals |
