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| DOI | 10.1016/j.egyr.2024.01.004 |
| Hybrid soft computing based optimization for low carbon energy management considering nonlinear battery recharging patterns of electric vehicles | |
| Khalid, Muhammad | |
| 发表日期 | 2024 |
| ISSN | 2352-4847 |
| 起始页码 | 11 |
| 卷号 | 11 |
| 英文摘要 | Smart energy hubs and climate change are transforming traditional energy systems, fossil fuel use, and greenhouse gas emissions. To address these challenges, alternative solutions have been implemented, focusing on smart grid management. This study presents a strategy for optimizing electric vehicle (EV) recharging at energy hubs, aiming to minimize emissions and energy costs. Addressing the complexities of EV charging, the study considers four dynamic recharging modes, such as stochastic, off-peak, peak, and electric power research institute each with distinct patterns. The strategy employs advanced hybrid soft computing algorithms, incorporating genetic, simulated annealing, and interior point programming algorithms. The suggested approach achieves optimal cost and demand for vehicle -to -grid capacity as compared to other advanced approaches. Furthermore, scheduling of recharging patterns is conducted to prevent power outages and supply- demand mismatches. Additionally, compared to other schemes in the literature, this work also accounts for operational limitations of energy hubs, such as the valve -point loading effect (VPLE), energy capacity constraints, prohibited operating zones, and ramp -rate limitations. Simulation results validate the viability of recharging and discharging EVs with transferable load integration in the design protocol. This ensures effective control over economic costs, environmental impacts, and grid reliability. In addition, the developed approach enables energy operators, policymakers, and utilities to handle all grid loads, thereby reducing financial and environmental metrics. This research lays the groundwork for sustainable EV integration, fostering a cleaner and greener energy landscape. |
| 英文关键词 | Energy control; Electric vehicles charging patterns; Optimization; Smart grids; Energy and environmental dispatch; Nonlinear constraint |
| 语种 | 英语 |
| WOS研究方向 | Energy & Fuels |
| WOS类目 | Energy & Fuels |
| WOS记录号 | WOS:001172811500001 |
| 来源期刊 | ENERGY REPORTS
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/298244 |
| 作者单位 | King Fahd University of Petroleum & Minerals; King Fahd University of Petroleum & Minerals |
| 推荐引用方式 GB/T 7714 | Khalid, Muhammad. Hybrid soft computing based optimization for low carbon energy management considering nonlinear battery recharging patterns of electric vehicles[J],2024,11. |
| APA | Khalid, Muhammad.(2024).Hybrid soft computing based optimization for low carbon energy management considering nonlinear battery recharging patterns of electric vehicles.ENERGY REPORTS,11. |
| MLA | Khalid, Muhammad."Hybrid soft computing based optimization for low carbon energy management considering nonlinear battery recharging patterns of electric vehicles".ENERGY REPORTS 11(2024). |
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