With the rapid economic growth and improved living standards,electricity has become an indispensable energy source in our lives.Therefore,the stability of the grid power supply and the conservation of electricity is c...With the rapid economic growth and improved living standards,electricity has become an indispensable energy source in our lives.Therefore,the stability of the grid power supply and the conservation of electricity is critical.The following are some of the problems facing now:1)During the peak power consumption period,it will pose a threat to the power grid.Enhancing and improving the power distribution infrastructure requires high maintenance costs.2)The user’s electricity schedule is unreasonable due to personal behavior,which will cause a waste of electricity.Controlling load as a vital part of incentive demand response(DR)can achieve rapid response and improve demand-side resilience.Maintaining load by manually formulating rules,some devices are selective to be adjusted during peak power consumption.However,it is challenging to optimize methods based on manual rules.This paper uses SoftActor-Critic(SAC)as a control algorithm to optimize the control strategy.The results show that through the coordination of the SAC to control load in CityLearn,realizes the goal of reducing both the peak load demand and the operation costs on the premise of regulating voltage to the safe limit.展开更多
社区能量管理在整合可再生能源和实现“双碳”目标方面发挥着重要作用。在社区众多可调负荷中,电动汽车集群作为重要的需求响应资源受到了广泛关注。然而,电动汽车响应能力受用户主观意愿影响难以准确量化,直接影响了社区能量管理的有...社区能量管理在整合可再生能源和实现“双碳”目标方面发挥着重要作用。在社区众多可调负荷中,电动汽车集群作为重要的需求响应资源受到了广泛关注。然而,电动汽车响应能力受用户主观意愿影响难以准确量化,直接影响了社区能量管理的有效性。首先,构建了基于TSK模糊数学的响应意愿评估模型,将电价、电池荷电状态和温度作为关键影响因素,建立了27条模糊规则,实现对用户响应意愿的精确量化。其次,传统社区能量管理方法对复杂环境适应能力不足,社区能量管理系统很少充分考虑电动汽车的参与,针对此问题,在社区能量管理系统中加入了电动汽车集群,设计了基于软演员-评论家(soft Actor-Critic,SAC)算法的社区能量管理策略,通过Actor-Critic架构优化储能与电动汽车的协同调度。最后,在City Learn虚拟社区中的实证分析表明,相比基于规则控制(rule based control,RBC)策略,该方法使耗电量、电费支出和碳排放均降低了73%左右,平均每日峰值降低50%,为构建智能化社区能量管理系统提供了新的技术方案。展开更多
基金This research is supported by Jeollannam-do(2022 R&D supporting program operated by Jeonnam Technopark)and financially supported by the Ministry of Trade,Industry and Energy(MOTIE)and Korea Institute for Advancement of Technology(KIAT)under the research project:“National Innovation Cluster R&D program”(Grant Number:1415175592&P0016223).
文摘With the rapid economic growth and improved living standards,electricity has become an indispensable energy source in our lives.Therefore,the stability of the grid power supply and the conservation of electricity is critical.The following are some of the problems facing now:1)During the peak power consumption period,it will pose a threat to the power grid.Enhancing and improving the power distribution infrastructure requires high maintenance costs.2)The user’s electricity schedule is unreasonable due to personal behavior,which will cause a waste of electricity.Controlling load as a vital part of incentive demand response(DR)can achieve rapid response and improve demand-side resilience.Maintaining load by manually formulating rules,some devices are selective to be adjusted during peak power consumption.However,it is challenging to optimize methods based on manual rules.This paper uses SoftActor-Critic(SAC)as a control algorithm to optimize the control strategy.The results show that through the coordination of the SAC to control load in CityLearn,realizes the goal of reducing both the peak load demand and the operation costs on the premise of regulating voltage to the safe limit.
文摘社区能量管理在整合可再生能源和实现“双碳”目标方面发挥着重要作用。在社区众多可调负荷中,电动汽车集群作为重要的需求响应资源受到了广泛关注。然而,电动汽车响应能力受用户主观意愿影响难以准确量化,直接影响了社区能量管理的有效性。首先,构建了基于TSK模糊数学的响应意愿评估模型,将电价、电池荷电状态和温度作为关键影响因素,建立了27条模糊规则,实现对用户响应意愿的精确量化。其次,传统社区能量管理方法对复杂环境适应能力不足,社区能量管理系统很少充分考虑电动汽车的参与,针对此问题,在社区能量管理系统中加入了电动汽车集群,设计了基于软演员-评论家(soft Actor-Critic,SAC)算法的社区能量管理策略,通过Actor-Critic架构优化储能与电动汽车的协同调度。最后,在City Learn虚拟社区中的实证分析表明,相比基于规则控制(rule based control,RBC)策略,该方法使耗电量、电费支出和碳排放均降低了73%左右,平均每日峰值降低50%,为构建智能化社区能量管理系统提供了新的技术方案。
