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
综合能源系统(integrated energy system,IES)以其高度灵活、环境友好的特点,在实现低碳经济和提高能源效率方面具有巨大潜力。然而,现有的IES建模方法难以挖掘氢能多设备的协同耦合性、且调度策略缺少市场机制的支持。因此,文中提出一...综合能源系统(integrated energy system,IES)以其高度灵活、环境友好的特点,在实现低碳经济和提高能源效率方面具有巨大潜力。然而,现有的IES建模方法难以挖掘氢能多设备的协同耦合性、且调度策略缺少市场机制的支持。因此,文中提出一种综合考虑绿色证书交易、阶梯型碳交易和需求响应的含氢IES优化调度策略。首先,建立基于电转气(power-to-gas,P2G)两阶段运行的氢能多元利用模型,推动新能源的使用;然后,建立绿证-碳联合交易机制,通过市场激励减少对化石燃料的依赖;最后,考虑综合需求响应优化用户侧用能行为,建立以经济运行成本最小为目标函数的IES优化调度模型,并通过CPLEX求解器进行求解。算例结果表明,文中所提模型能够实现IES多能耦合,提高新能源的消纳能力,减少碳排放量。展开更多
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.
文摘综合能源系统(integrated energy system,IES)以其高度灵活、环境友好的特点,在实现低碳经济和提高能源效率方面具有巨大潜力。然而,现有的IES建模方法难以挖掘氢能多设备的协同耦合性、且调度策略缺少市场机制的支持。因此,文中提出一种综合考虑绿色证书交易、阶梯型碳交易和需求响应的含氢IES优化调度策略。首先,建立基于电转气(power-to-gas,P2G)两阶段运行的氢能多元利用模型,推动新能源的使用;然后,建立绿证-碳联合交易机制,通过市场激励减少对化石燃料的依赖;最后,考虑综合需求响应优化用户侧用能行为,建立以经济运行成本最小为目标函数的IES优化调度模型,并通过CPLEX求解器进行求解。算例结果表明,文中所提模型能够实现IES多能耦合,提高新能源的消纳能力,减少碳排放量。
