基于深度强化学习的多能流楼宇低碳调度方法OACSTPCD
A low-carbon scheduling method for multi-energy flow buildings based on deep rein-forcement learning
建筑减排已成为中国达到"双碳"目标的重要途径,智慧楼宇作为多能流网络耦合的综合能源主体,面临碳排放量较多、多能流网络耦合程度高、负荷用能行为动态特性明显等问题.针对这一问题,提出基于深度强化学习的多能流楼宇低碳调度方法.首先,根据智慧楼宇的实际碳排放量,建立了一种奖惩阶梯型碳排放权交易机制.其次,面向碳市场和多能流耦合网络,以最小化运行成本为目标函数,建立多能流低碳楼宇调度模型,并将该调度问题转换为马尔可夫决策过程.然后,利用Rainbow算法进…查看全部>>
Building emissions reduction has become a crucial pathway for China to achieve its'dual-carbon'goals.As an integrated energy entity coupled with multi-energy flow networks,smart buildings face challenges such as high carbon emissions,a high degree of coupling in multi-energy flow networks,and distinct dynamic characteris-tics in load energy consumption behavior.In response to these challenges,a low-carbon scheduling method for multi-energy flow buildings bas…查看全部>>
胥栋;李逸超;李赟;徐刚;杜佳玮
国网上海市电力公司浦东供电公司,上海 200122国网上海市电力公司浦东供电公司,上海 200122国网上海市电力公司浦东供电公司,上海 200122国网上海市电力公司浦东供电公司,上海 200122国网上海市电力公司浦东供电公司,上海 200122
"双碳"目标多能流低碳调度深度强化学习
'dual-carbon'goalsmulti-energy flowlow-carbon schedulingdeep RL
《浙江电力》 2024 (2)
126-136,11
国网上海市电力公司浦东供电公司营销项目(640921220001)
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