可再生能源总局和电动汽车充电站的随机碳意识规划,在智能城市网络中提供需求灵活性
Shifeng Wang1, Qingji Tan2, Qishan Jiang3
1School of New Energy and intelligent Networked Automobile, University of Sanya, Sanya, 572099, Hainan, China. 13674596388@163.com.
Scientific reports
|October 2, 2025
概括
本研究介绍了一个碳意识的规划框架,以在城市电网中最佳地放置可再生能源和电动汽车充电站. 这种新的方法显著减少了能源损失,并改善了电压配置,同时考虑了不确定性.
科学领域:
- 电气工程 电气工程
- 可持续能源系统 可持续能源系统
- 优化技术 优化技术
背景情况:
- 城市配电网络面临的挑战是整合可再生分布式发电机 (RDGs) 和电动汽车充电站 (EVCS).
- 管理可再生能源发电和电动汽车充电需求的不确定性对于电网稳定性和效率至关重要.
- 尽量减少实际功耗损失,电压偏差,投资成本和碳排放是智能电网规划的关键目标.
研究的目的:
- 提出一个新的随机碳意识的规划框架,以实现 RDGs 和 EVCS 的最佳位置和尺寸.
- 共同将碳排放成本和基于场景的不确定性纳入统一的优化模型.
- 提高城市配电网络的可靠性和可持续性.
主要方法:
- 一个使用蒙特卡洛模拟的随机规划框架,使用K-means集群来处理不确定性.
- 一种模糊的决策方法,使用基于分析层次过程 (AHP) 的权重来汇总多个优化目标.
- 解决混合离散和连续优化问题的雪算法 (SGA),与灰狼优化器 (GWO) 和粒子群优化 (PSO) 相比.
主要成果:
- 拟议的框架在IEEE 33 总线和IEEE 69 总线系统上实现了损失的显著降低 (高达 96%) 和电压配置的改善 (高达 97.3%).
- 在所有经过测试的场景中,投资成本略有降低 (约为0.4%至4%).
- 与GWO和PSO相比,SGA在融合速度和解决方案质量方面表现优越.
结论:
- 随机碳意识框架为城市能源系统提供了强大而有弹性的规划决策.
- RDG和EVCS的联合优化有效地平衡了经济,环境和技术目标.
- 经验证的结果支持开发可持续和脱碳的城市能源基础设施.
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