通过使用多代理深度强化学习来提高积极能源地区的能源自主性
Jernej Hribar1,2, Mihael Mohorčič3, Andrej Čampa3,4
1Jozef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia. jernej.hribar@ijs.si.
Scientific reports
|July 30, 2025
概括
本研究介绍了一种新的多代理深度强化学习解决方案,用于正能量区,优化共享能源存储和电动汽车充电. 这种方法增强了能源自主性,并降低了可持续社区的成本.
科学领域:
- 可持续的能源系统可持续的能源系统
- 智能电网是一个智能电网.
- 在能源管理中的人工智能.
背景情况:
- 积极能源区 (PEDs) 整合了共享的能源存储系统 (ESS) 和电动汽车 (EVs) 以实现可持续的城市生活.
- 由于动态的电动汽车可用性,可变的可再生能源发电和用户舒适度要求,在PED中有效的能源管理具有挑战性.
- 车辆到电网 (V2G) 技术和共享ESS为能源优化和市场参与提供了潜力,需要先进的控制策略.
研究的目的:
- 提出一个新的社区多代理深度强化学习车辆到电网 (CoMAD V2G) 解决方案.
- 加强社区产生的能源的利用,并增加社区在PEDs的自治权.
- 在社区环境中优化支持V2G的电动汽车的充电和放电周期.
主要方法:
- 基于多代理强化学习 (MARL) 的解决方案的开发,CoMAD V2G.
- 利用家庭消费,太阳能生产,电动汽车动态和电价的真实数据进行验证.
- 在现实的社区环境中进行评估,以对比传统和替代的基于学习的方法来评估绩效.
主要成果:
- 与其他方法相比,CoMAD V2G解决方案实现了与外部电网的年度能源交换的改进.
- 家庭电力成本大幅降低,高达25%.
- 证明了加强社区能源自主权和优化能源共享.
结论:
- 拟议的CoMAD V2G解决方案有效地解决了PED中的能源管理挑战.
- 基于MARL的策略对于释放V2G和共享ESS的全部潜力至关重要.
- 该解决方案提供了实质性的经济和可持续性好处,为更高效和自主可持续社区铺平了道路.
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