随机优化以尽量减少智能混合能源网络的运营成本,考虑电动汽车
Nouman Qamar1, Mohammed Alqahtani2, Muhammad Rehan3,4
1Electrical Engineering Department, University of Engineering and Technology, Punjab, Pakistan.
PloS one
|June 9, 2025
概括
本研究介绍了住宅能源中心的随机模型,优化可再生能源和插电式混合动力电动汽车的整合. 智能充电和需求响应计划显著降低成本,提高可靠性.
科学领域:
- 电气工程 电气工程
- 能源系统 能源系统
- 优化优化 优化优化
背景情况:
- 住宅能源中心 (REH) 面临着整合可再生能源 (RES) 和插电式混合动力电动汽车 (PHEV) 的挑战,因为它们的性质不可预测.
- 精确的REH建模和优化因RES的间歇性和PHEV的可变使用模式而复杂.
研究的目的:
- 为REH开发一个随机模型,以最佳地管理来自RES和PHEV的不确定性.
- 引入和评估PHEV智能充电机制,以提高经济运行和可靠性.
- 评估需求响应计划 (DRP) 对REH运营成本和可靠性的影响.
主要方法:
- 使用混合整数线性编程 (MILP) 开发了REH的随机模型.
- 该模型是使用GAMS软件解决的,其性能通过四个案例研究来验证.
- 智能充电机制用于PHEV和需求响应程序被整合到REH模型中.
主要成果:
- 拟议的随机模型有效地处理了RES间歇性和PHEV不确定性所带来的局限性.
- 智能充电机制将总运营成本降低2.59%,同时保持消费者的舒适性和提高系统可靠性.
- 实施需求响应计划进一步降低了额外的3.7%的运营成本.
结论:
- 随机模型提供了一种优越的方法来管理REH操作中的不确定性.
- 智能充电和DRP集成导致运营成本大幅降低,并提高智能REH的可靠性.
- 拟议的框架为优化现代住宅能源系统性能提供了一个强大的解决方案.
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