As new power systems and dual carbon policies develop,virtual power plant cluster(VPPC)provides another reliable way to promote the efficient utilization of energy and solve environmental pollution problems.To solve t...As new power systems and dual carbon policies develop,virtual power plant cluster(VPPC)provides another reliable way to promote the efficient utilization of energy and solve environmental pollution problems.To solve the coordinated optimal operation and low-carbon economic operation problem in multi-virtual power plant,a multi-virtual power plant(VPP)electricity-carbon interaction optimal scheduling model considering integrated demand response(IDR)is proposed.Firstly,a multi-VPP electricity-carbon interaction framework is established.The interaction of electric energy and carbon quotas can realize energy complementarity,reduce energy waste and promote low-carbon operation.Secondly,in order to coordinate the multiple types of energy and load in VPPC to further achieve low-carbon operation,the IDR mechanism based on the user comprehensive satisfaction(UCS)of electricity,heat as well as hydrogen is designed,which can effectively maintain the UCS in the cluster within a relatively high range.Finally,the unit output scheme is formulated to minimize the total cost of VPPC and the model is solved using theCPLEX solver.The simulation results showthat the proposed method effectively promotes the coordinated operation among multi-VPP,increases the consumption rate of renewable energy sources and the economics of VPPC and reduces carbon emissions.展开更多
综合需求响应(integrated demand response,IDR)作为区域综合能源系统(regional integrated energy system,RIES)维持供需平衡、实现分布式能源就地消纳的重要途径,已成为当下的研究热点。但现有IDR研究存在3个方面不足:综合能源服务商(...综合需求响应(integrated demand response,IDR)作为区域综合能源系统(regional integrated energy system,RIES)维持供需平衡、实现分布式能源就地消纳的重要途径,已成为当下的研究热点。但现有IDR研究存在3个方面不足:综合能源服务商(integrated energy service provider,IESP)制定激励策略时,未考虑区域内用户的响应疲劳特性;在实施IDR的复杂场景下,未考虑区域IESP间的响应责任交易;忽略响应责任与碳排放责任的耦合关系。为此,首先分析用户响应意愿随响应次数的演化特性,通过引入响应疲劳函数实现对用户响应疲劳特性的有效建模;在此基础上,根据IESP的响应任务类型,设置多个IESP参与IDR的复杂场景。进一步,为实现各IESP参与IDR的经济性与低碳性,在考虑复杂场景下IESP间响应责任交易时的能源转移与碳排放责任转移基础上,建立响应责任-碳排放责任耦合交易机制。最终,采用基于自适应迭代步长的议价方法以确定最优交易价格。通过仿真算例验证所提模型的有效性:考虑用户响应疲劳特性的改进模型使用户的总收益提升27%;所提交易机制不仅使各场景下IESP的总成本分别降低15.8%、9.8%、94.1%,还使典型场景下IESP的碳排放量降低17.8%,提高IESP参与IDR的经济性与低碳性,实现IESP与用户的共赢。展开更多
为挖掘需求侧资源响应潜力,文中提出一种计及多重需求响应的综合能源系统(integrated energy system,IES)多时间尺度低碳调度策略。首先,考虑到需求侧资源在不同时间尺度下的响应差异性,建立计及价格型和激励型的多重综合需求响应(integ...为挖掘需求侧资源响应潜力,文中提出一种计及多重需求响应的综合能源系统(integrated energy system,IES)多时间尺度低碳调度策略。首先,考虑到需求侧资源在不同时间尺度下的响应差异性,建立计及价格型和激励型的多重综合需求响应(integrated demand response,IDR)模型。然后,为减少源、荷预测误差对IES运行的影响,分别构建日前低碳经济调度模型和日内双时间尺度滚动优化平抑模型。最后,算例仿真设置不同场景进行对比分析。结果表明,相比传统IDR,多重IDR能有效挖掘用户响应潜力,提升系统经济性。此外,计及多重IDR的多时间尺度调度策略能有效缓解源、荷误差带来的功率波动并降低系统碳排放量,实现IES低碳、经济和稳定运行。展开更多
为了准确描述负荷聚合商在与上级主体和同级主体进行能源交易过程中的各主体利益交互,以及用户侧储能昂贵且难以实施的问题。建立了综合能源运营商与负荷聚合商联盟之间的主从博弈模型,并特别考虑了拥有大量光伏用户的负荷聚合商之间的...为了准确描述负荷聚合商在与上级主体和同级主体进行能源交易过程中的各主体利益交互,以及用户侧储能昂贵且难以实施的问题。建立了综合能源运营商与负荷聚合商联盟之间的主从博弈模型,并特别考虑了拥有大量光伏用户的负荷聚合商之间的讨价还价博弈,以综合处理各主体之间的竞争与合作关系。为了实现负荷聚合商的低储高放策略,引入了云储能租赁的概念。采用二分法结合(alternating direction method of multipliers,ADMM)求解所构建的模型,以在各方持续互动过程中实现最大化的效益。研究结果表明,所建立的涵盖云储能租赁的混合博弈理论模型在保证各主体利益的同时能够制定合理的定价策略。展开更多
With the liberalization of the retail market,customers can sell their demand response(DR)resources to the distribution company(Disco)through the DR aggregator(DRA).In this paper,an intelligent DR resource trading fram...With the liberalization of the retail market,customers can sell their demand response(DR)resources to the distribution company(Disco)through the DR aggregator(DRA).In this paper,an intelligent DR resource trading framework between Disco and DRA is proposed by exploiting the benefits of deep reinforcement learning(DRL).The hierarchical decision process of the two players is modeled as a Stackelberg game.In the game,Disco is the leader who determines the retail price while DRA is the follower who responds to it.To protect their privacy,a dueling deep Q-network(dueling DQN)is then constructed to model the bi-level Stackelberg game,such that the lower-level problem doesn’t need to reveal its detailed model to the upperlevel.In the learning process,the uncertainties from the DRA’s baseline load and wind power are considered.In order to boost the robustness against the estimation error,the baseline load is discretized into symbols before being used as the input states of the dueling DQN.And to mitigate the uncertainty of wind power,the scenario-based method is introduced when designing the reward.We demonstrate that the proposed dueling DQNbased method has good performance and is more robust against uncertainties.展开更多
基金supported by the National Natural Science Foundation of China (NSFC) (Grant No.52107107).
文摘As new power systems and dual carbon policies develop,virtual power plant cluster(VPPC)provides another reliable way to promote the efficient utilization of energy and solve environmental pollution problems.To solve the coordinated optimal operation and low-carbon economic operation problem in multi-virtual power plant,a multi-virtual power plant(VPP)electricity-carbon interaction optimal scheduling model considering integrated demand response(IDR)is proposed.Firstly,a multi-VPP electricity-carbon interaction framework is established.The interaction of electric energy and carbon quotas can realize energy complementarity,reduce energy waste and promote low-carbon operation.Secondly,in order to coordinate the multiple types of energy and load in VPPC to further achieve low-carbon operation,the IDR mechanism based on the user comprehensive satisfaction(UCS)of electricity,heat as well as hydrogen is designed,which can effectively maintain the UCS in the cluster within a relatively high range.Finally,the unit output scheme is formulated to minimize the total cost of VPPC and the model is solved using theCPLEX solver.The simulation results showthat the proposed method effectively promotes the coordinated operation among multi-VPP,increases the consumption rate of renewable energy sources and the economics of VPPC and reduces carbon emissions.
文摘综合需求响应(integrated demand response,IDR)作为区域综合能源系统(regional integrated energy system,RIES)维持供需平衡、实现分布式能源就地消纳的重要途径,已成为当下的研究热点。但现有IDR研究存在3个方面不足:综合能源服务商(integrated energy service provider,IESP)制定激励策略时,未考虑区域内用户的响应疲劳特性;在实施IDR的复杂场景下,未考虑区域IESP间的响应责任交易;忽略响应责任与碳排放责任的耦合关系。为此,首先分析用户响应意愿随响应次数的演化特性,通过引入响应疲劳函数实现对用户响应疲劳特性的有效建模;在此基础上,根据IESP的响应任务类型,设置多个IESP参与IDR的复杂场景。进一步,为实现各IESP参与IDR的经济性与低碳性,在考虑复杂场景下IESP间响应责任交易时的能源转移与碳排放责任转移基础上,建立响应责任-碳排放责任耦合交易机制。最终,采用基于自适应迭代步长的议价方法以确定最优交易价格。通过仿真算例验证所提模型的有效性:考虑用户响应疲劳特性的改进模型使用户的总收益提升27%;所提交易机制不仅使各场景下IESP的总成本分别降低15.8%、9.8%、94.1%,还使典型场景下IESP的碳排放量降低17.8%,提高IESP参与IDR的经济性与低碳性,实现IESP与用户的共赢。
文摘为挖掘需求侧资源响应潜力,文中提出一种计及多重需求响应的综合能源系统(integrated energy system,IES)多时间尺度低碳调度策略。首先,考虑到需求侧资源在不同时间尺度下的响应差异性,建立计及价格型和激励型的多重综合需求响应(integrated demand response,IDR)模型。然后,为减少源、荷预测误差对IES运行的影响,分别构建日前低碳经济调度模型和日内双时间尺度滚动优化平抑模型。最后,算例仿真设置不同场景进行对比分析。结果表明,相比传统IDR,多重IDR能有效挖掘用户响应潜力,提升系统经济性。此外,计及多重IDR的多时间尺度调度策略能有效缓解源、荷误差带来的功率波动并降低系统碳排放量,实现IES低碳、经济和稳定运行。
文摘为了准确描述负荷聚合商在与上级主体和同级主体进行能源交易过程中的各主体利益交互,以及用户侧储能昂贵且难以实施的问题。建立了综合能源运营商与负荷聚合商联盟之间的主从博弈模型,并特别考虑了拥有大量光伏用户的负荷聚合商之间的讨价还价博弈,以综合处理各主体之间的竞争与合作关系。为了实现负荷聚合商的低储高放策略,引入了云储能租赁的概念。采用二分法结合(alternating direction method of multipliers,ADMM)求解所构建的模型,以在各方持续互动过程中实现最大化的效益。研究结果表明,所建立的涵盖云储能租赁的混合博弈理论模型在保证各主体利益的同时能够制定合理的定价策略。
基金supported by the National Key R&D Program of China(2021YFB2401203).
文摘With the liberalization of the retail market,customers can sell their demand response(DR)resources to the distribution company(Disco)through the DR aggregator(DRA).In this paper,an intelligent DR resource trading framework between Disco and DRA is proposed by exploiting the benefits of deep reinforcement learning(DRL).The hierarchical decision process of the two players is modeled as a Stackelberg game.In the game,Disco is the leader who determines the retail price while DRA is the follower who responds to it.To protect their privacy,a dueling deep Q-network(dueling DQN)is then constructed to model the bi-level Stackelberg game,such that the lower-level problem doesn’t need to reveal its detailed model to the upperlevel.In the learning process,the uncertainties from the DRA’s baseline load and wind power are considered.In order to boost the robustness against the estimation error,the baseline load is discretized into symbols before being used as the input states of the dueling DQN.And to mitigate the uncertainty of wind power,the scenario-based method is introduced when designing the reward.We demonstrate that the proposed dueling DQNbased method has good performance and is more robust against uncertainties.
