【作者】 黄俊杰；

【导师】 武新章；

【作者基本信息】 广西大学 ， 电气工程， 2025， 硕士

【摘要】 近年来,需求响应以其提高电力系统灵活性和消纳可再生能源的能力被广泛研究。其中,交易控制(Transactive control,TC)策略通过电价激励提升用户参与需求响应的积极性,能有效降低居民空调负荷占比,以保持电网供需平衡。然而,现有TC策略难以适应变频空调的设定温度调节尺度,且其报价机制对用户激励仍然较低;另外,相关研究并未考虑到TC策略下的用户行为。为了优化TC策略的控制性能并还原需求响应过程中用户的决策行为,具体做出如下工作:首先,针对现阶段居民空调负荷参与需求响应相关研究中对用户在单人博弈中的行为模型研究不足的问题,对用户可以直观接收的热舒适度和电价信息进行量化处理,并引入参数用户收益以总结描述决策信息。根据每一实时电价变化周期内用户收益最大化的原则提出并建立了用户决策模型。采用赌注模型类比并描述用户决策模型,并利用其中的跳变特性将用户决策模型离散化,使其从二维连续函数变为一维离散函数。实验结果表明,用户决策模型能使室内温度非舒适周期减少,用户收益提升,且能使决策误差率平均降低121.28%。其次,针对传统TC策略报价机制对用户激励不足以及无法实现设定温度静态控制的问题,本文在常规交易控制策略的基础上提出了改进交易控制(Modified transactive control,MTC)策略。在报价机制上,MTC引入用户意愿参数,并根据室内温度变量对热舒适度和电能消耗的耦合特性重新平衡了报价参数的权重,提出了全新的报价机制,且仿真验证了该报价机制的优越性。在调整部分,MTC提出静态设定温度控制方法,通过隐藏并记录变频空调的工作状态实现静态控制,提升用户收益。为实现用户决策模型代理用户决策的能力,本文提出基于用户决策模型的MTC策略算法流程。实验结果表明,与常规TC相比,MTC策略不仅能减少室内温度的非舒适周期,提升用户收益,还能减少24.22%的耗电量。另外,对比常规TC与MTC在不同意愿偏好的用户类型影响下的性能,结果表明MTC策略能为舒适偏好型用户提升1.8%的用户收益,为经济偏好型用户减少51.66%的电能消耗。更多还原

【Abstract】 In recent years,demand response has been extensively studied for its capability to enhance power system flexibility and accommodate renewable energy integration.Among various approaches,the transactive control(TC)strategy improves user participation enthusiasm in demand response through electricity price incentives,effectively reducing the proportion of residential air conditioning loads to maintain grid supply-demand balance.However,existing TC strategies exhibit limitations in adapting to the temperature adjustment range of inverter air-conditionings and insufficient user incentives in their bidding mechanisms.Furthermore,user behaviour under TC has not been considered in current research.To optimize TC strategy performance and reconstruct user decision-making processes during demand response,this study makes the following contributions:First,to reconstruct the user behavior under TC strategy,a user decision model is proposed by analyzing the single-player game in interaction between user and TC.The decision information is summarized by quantifing thermal comfort and real-time electricity price.The user profits are optimized within each real-time electricity pricing cycle.By analogizing with the gambler game and leveraging its jump characteristics,the user decision model is discretized,transforming it from a two-dimensional continuous function into a one-dimensional discrete function.The case studies indicated that the proposed model reduces indoor temperature discomfort periods by 8.7%,enhances user profits by 15.2%,and achieves an average 121.28%reduction in decision error rates compared to conventional TC.Second,a modified transactive control(MTC)strategy is proposed as an enhancement to conventional TC approaches.Regarding bidding mechanisms,A user willingness parameter is introduced in MTC and the weights of bidding parameters are rebalanced in bidding mechanism to optimize demand response participation incentives.Simulation results verifying the superiority of the MTC bidding mechanism.For adjustment part,MTC develops a static set-point temperature control method that enhances user profits through concealed monitoring and recording of inverter air-conditioning operational states.To enable user decision model-based automated decision-making,this study formulates the algorithmic workflow of the MTC strategy.The case studies indicated that compared to conventional TC,the MTC strategy reduces indoor temperature discomfort periods by 12.4%and improves user utility by 18.6%,achieves 24.22%energy consumption reduction.Additionally,comparative analyses of TC and MTC performance across different user preference types reveal that MTC enhances utility by 1.8%for comfort-oriented users and reduces energy consumption by 51.66%for economy-oriented users.更多还原