The main objective of electricity regulators when establishing electricity markets is to decrease the cost of electricity through competition. However, this scenario cannot be achieved without a full participation of ...The main objective of electricity regulators when establishing electricity markets is to decrease the cost of electricity through competition. However, this scenario cannot be achieved without a full participation of the electricity demand by reacting against electricity prices. The aim of this research is to develop tools for helping customers and aggregators to join price and demand response programs, while helping them to hedge against the risk of short-term price volatility. In this way, the capacity of and hybrid methodology (Self-Organizing Maps and Statistical Ward's Linkage) to classify high electricity market prices is analysed. Besides, with the help of Non-Parametric Estimation, some price-patterns were found in the abovementioned clusters. The contained knowledge within these patterns supplies customer market-based information on which to base its energy use decisions. The interest for this participation of customers in markets is growing in developed countries to obtain a higher elasticity in demand. Results show the capability of this approach to improve data management and select coherent policies to accomplish cleared demand offers amongst different price scenarios in a more flexible way.展开更多
Taking the agricultural companies listed in the A-share markets in Shanghai and Shenzhen as samples,the relationship between customer concentration and corporate social responsibility was studied. In addition,the regu...Taking the agricultural companies listed in the A-share markets in Shanghai and Shenzhen as samples,the relationship between customer concentration and corporate social responsibility was studied. In addition,the regulatory role of property rights and regional factors was examined. The results showed that the degree of customer concentration is negatively related to the degree of fulfillment of corporate social responsibility; for companies with different property rights and regions,there are significant differences in the influence of customer concentration on corporate social responsibility. In non-state-owned enterprises and mid-western areas,customer concentration has a greater impact on corporate social responsibility.展开更多
As a typical industrial Internet of things(IIOT)service,demand response(DR)is becoming a promising enabler for intelligent energy management in 6 G-enabled smart grid systems,to achieve quick response for supply-deman...As a typical industrial Internet of things(IIOT)service,demand response(DR)is becoming a promising enabler for intelligent energy management in 6 G-enabled smart grid systems,to achieve quick response for supply-demand mismatches.How-ever,existing literatures try to adjust customers’load profiles optimally,instead of electricity overhead,energy consumption patterns of residential appliances,customer satisfaction levels,and energy consumption habits.In this paper,a novel DR method is investigated by mixing the aforementioned factors,where the residential customer cluster is proposed to enhance the performance.Clustering approaches are leveraged to study the electricity consumption habits of various customers by extracting their features and characteristics from historical data.Based on the extracted information,the residential appliances can be scheduled effectively and flexibly.Moreover,we propose and study an efficient optimization framework to obtain the optimal scheduling solution by using clustering and deep learning methods.Extensive simulation experiments are conducted with real-world traces.Numerical results show that the proposed DR method and optimization framework outperform other baseline schemes in terms of the system overhead and peak-to-average ratio(PAR).The impact of various factors on the system utility is further analyzed,which provides useful insights on improving the efficiency of the DR strategy.With the achievement of efficient and intelligent energy management,the proposed method also promotes the realization of China’s carbon peaking and carbon neutrality goals.展开更多
为提高主动配电网(active distribution network,ADN)运行经济性和用户满意度,提出一种考虑需求响应和用户满意度的ADN优化调度方法。综合考虑ADN运行过程中的购电成本、发电成本、维护成本和需求响应成本,建立了以ADN总运行成本最小为...为提高主动配电网(active distribution network,ADN)运行经济性和用户满意度,提出一种考虑需求响应和用户满意度的ADN优化调度方法。综合考虑ADN运行过程中的购电成本、发电成本、维护成本和需求响应成本,建立了以ADN总运行成本最小为目标函数的优化调度模型。利用混沌映射、莱维飞行和收敛因子非线性变化等策略对斑点鬣狗优化算法(spotted hyena optimization,SHO)进行优化,以提高斑点鬣狗算法的优化性能。采用改进斑点鬣狗优化算法(ISHO)对ADN优化调度模型进行求解,算例分析结果表明,ISHO算法的优化效果优于其他算法,2种需求响应同时参与系统调度时的ADN总运行成本最小,经济性更好。展开更多
末端分散式资源的需求响应(demand response,DR)是支撑电力系统灵活调节的重要形式,但末端资源通常规模大且单体容量小,迫切需要虚拟电厂(virtual power plant,VPP)等市场主体进行聚合代理,从而间接参与电网公司的调节服务。聚焦于准线...末端分散式资源的需求响应(demand response,DR)是支撑电力系统灵活调节的重要形式,但末端资源通常规模大且单体容量小,迫切需要虚拟电厂(virtual power plant,VPP)等市场主体进行聚合代理,从而间接参与电网公司的调节服务。聚焦于准线型需求响应这一新兴的响应模式,重点研究VPP内部分散式资源的利益-风险互动协调策略,提出面向准线型DR的VPP两阶段优化及收益共享-风险共担决策方法,构建VPP与电网、内部用户互动协调新模式。首先,在准线型激励下VPP以整体收益最大化为目标,考虑柔性负荷响应积极度与新能源准确度因子,进行日前-日内两阶段DR优化;其次,在收益共享-风险共担决策方法下,VPP与柔性负荷共享准线型激励、与新能源共担不确定性风险,并以改进的Shapley法对内部柔性负荷进行利益分配。仿真结果表明,相较于传统DR,准线型DR给出全时段响应目标,更具有优越性;共享-共担决策吸引柔性负荷参与VPP调节,促使VPP与用户双赢。展开更多
在新型电力系统中,亟待深度挖掘需求侧资源以提升系统灵活性和新能源消纳能力。在“新基建”背景下,5G基站作为一种新型需求侧资源正迅速发展。研究如何在保证基站备用需求的前提下,由铁塔公司组建含大规模5G基站的虚拟电厂(virtual pow...在新型电力系统中,亟待深度挖掘需求侧资源以提升系统灵活性和新能源消纳能力。在“新基建”背景下,5G基站作为一种新型需求侧资源正迅速发展。研究如何在保证基站备用需求的前提下,由铁塔公司组建含大规模5G基站的虚拟电厂(virtual power plant,VPP)并常态化参与需求响应。首先,提出了考虑储能动态备用容量的5G基站运行可行域构建方法,建立了5G基站VPP的聚合模型。然后,建立了5G基站VPP响应负荷准线的日前优化模型,提出了适合对大规模5G基站进行协调控制的日内解聚合方法。最后,建立了含高比例新能源的区域电网仿真算例。仿真结果表明,聚合大规模基站参与准线型需求响应,可以显著降低5G基站的运行成本,同时提高电网的新能源消纳能力。展开更多
文摘The main objective of electricity regulators when establishing electricity markets is to decrease the cost of electricity through competition. However, this scenario cannot be achieved without a full participation of the electricity demand by reacting against electricity prices. The aim of this research is to develop tools for helping customers and aggregators to join price and demand response programs, while helping them to hedge against the risk of short-term price volatility. In this way, the capacity of and hybrid methodology (Self-Organizing Maps and Statistical Ward's Linkage) to classify high electricity market prices is analysed. Besides, with the help of Non-Parametric Estimation, some price-patterns were found in the abovementioned clusters. The contained knowledge within these patterns supplies customer market-based information on which to base its energy use decisions. The interest for this participation of customers in markets is growing in developed countries to obtain a higher elasticity in demand. Results show the capability of this approach to improve data management and select coherent policies to accomplish cleared demand offers amongst different price scenarios in a more flexible way.
文摘Taking the agricultural companies listed in the A-share markets in Shanghai and Shenzhen as samples,the relationship between customer concentration and corporate social responsibility was studied. In addition,the regulatory role of property rights and regional factors was examined. The results showed that the degree of customer concentration is negatively related to the degree of fulfillment of corporate social responsibility; for companies with different property rights and regions,there are significant differences in the influence of customer concentration on corporate social responsibility. In non-state-owned enterprises and mid-western areas,customer concentration has a greater impact on corporate social responsibility.
基金supported by the National Natural Science Foundation of China(62171218)。
文摘As a typical industrial Internet of things(IIOT)service,demand response(DR)is becoming a promising enabler for intelligent energy management in 6 G-enabled smart grid systems,to achieve quick response for supply-demand mismatches.How-ever,existing literatures try to adjust customers’load profiles optimally,instead of electricity overhead,energy consumption patterns of residential appliances,customer satisfaction levels,and energy consumption habits.In this paper,a novel DR method is investigated by mixing the aforementioned factors,where the residential customer cluster is proposed to enhance the performance.Clustering approaches are leveraged to study the electricity consumption habits of various customers by extracting their features and characteristics from historical data.Based on the extracted information,the residential appliances can be scheduled effectively and flexibly.Moreover,we propose and study an efficient optimization framework to obtain the optimal scheduling solution by using clustering and deep learning methods.Extensive simulation experiments are conducted with real-world traces.Numerical results show that the proposed DR method and optimization framework outperform other baseline schemes in terms of the system overhead and peak-to-average ratio(PAR).The impact of various factors on the system utility is further analyzed,which provides useful insights on improving the efficiency of the DR strategy.With the achievement of efficient and intelligent energy management,the proposed method also promotes the realization of China’s carbon peaking and carbon neutrality goals.
文摘为提高主动配电网(active distribution network,ADN)运行经济性和用户满意度,提出一种考虑需求响应和用户满意度的ADN优化调度方法。综合考虑ADN运行过程中的购电成本、发电成本、维护成本和需求响应成本,建立了以ADN总运行成本最小为目标函数的优化调度模型。利用混沌映射、莱维飞行和收敛因子非线性变化等策略对斑点鬣狗优化算法(spotted hyena optimization,SHO)进行优化,以提高斑点鬣狗算法的优化性能。采用改进斑点鬣狗优化算法(ISHO)对ADN优化调度模型进行求解,算例分析结果表明,ISHO算法的优化效果优于其他算法,2种需求响应同时参与系统调度时的ADN总运行成本最小,经济性更好。
文摘末端分散式资源的需求响应(demand response,DR)是支撑电力系统灵活调节的重要形式,但末端资源通常规模大且单体容量小,迫切需要虚拟电厂(virtual power plant,VPP)等市场主体进行聚合代理,从而间接参与电网公司的调节服务。聚焦于准线型需求响应这一新兴的响应模式,重点研究VPP内部分散式资源的利益-风险互动协调策略,提出面向准线型DR的VPP两阶段优化及收益共享-风险共担决策方法,构建VPP与电网、内部用户互动协调新模式。首先,在准线型激励下VPP以整体收益最大化为目标,考虑柔性负荷响应积极度与新能源准确度因子,进行日前-日内两阶段DR优化;其次,在收益共享-风险共担决策方法下,VPP与柔性负荷共享准线型激励、与新能源共担不确定性风险,并以改进的Shapley法对内部柔性负荷进行利益分配。仿真结果表明,相较于传统DR,准线型DR给出全时段响应目标,更具有优越性;共享-共担决策吸引柔性负荷参与VPP调节,促使VPP与用户双赢。
文摘在新型电力系统中,亟待深度挖掘需求侧资源以提升系统灵活性和新能源消纳能力。在“新基建”背景下,5G基站作为一种新型需求侧资源正迅速发展。研究如何在保证基站备用需求的前提下,由铁塔公司组建含大规模5G基站的虚拟电厂(virtual power plant,VPP)并常态化参与需求响应。首先,提出了考虑储能动态备用容量的5G基站运行可行域构建方法,建立了5G基站VPP的聚合模型。然后,建立了5G基站VPP响应负荷准线的日前优化模型,提出了适合对大规模5G基站进行协调控制的日内解聚合方法。最后,建立了含高比例新能源的区域电网仿真算例。仿真结果表明,聚合大规模基站参与准线型需求响应,可以显著降低5G基站的运行成本,同时提高电网的新能源消纳能力。