Utilities around the world have been considering Demand Side Management (DSM) in their strategic planning. The costs of constructing and operating a new capacity generation unit are increasing everyday as well as Tran...Utilities around the world have been considering Demand Side Management (DSM) in their strategic planning. The costs of constructing and operating a new capacity generation unit are increasing everyday as well as Transmission and distribution and land issues for new generation plants, which force the utilities to search for another alternatives without any additional constraints on customers comfort level or quality of delivered product. De can be defined as the selection, planning, and implementation of measures intended to have an influence on the demand or customer-side of the electric meter, either caused directly or stimulated indirectly by the utility. DSM programs are peak clipping, Valley filling, Load shifting, Load building, energy conservation and flexible load shape. The main Target of this paper is to show the relation between DSM and Load Forecasting. Moreover, it highlights on the effect of applying DSM on Forecasted demands and how this affects the planning strategies for utility companies. This target will be clearly illustrated through applying the developed algorithm in this paper on an existing residential compound in Cairo-Egypt.展开更多
温控负荷(temperature control loads,TCLs)灵活性可行域的有效量化和聚合是其响应电价与调控信号的前提。由于TCLs能量在时间上耦合,且设备调节特性和物理参数各异,具有较高异质性,现有聚合方法面临计算复杂度高、适配性差等问题。该...温控负荷(temperature control loads,TCLs)灵活性可行域的有效量化和聚合是其响应电价与调控信号的前提。由于TCLs能量在时间上耦合,且设备调节特性和物理参数各异,具有较高异质性,现有聚合方法面临计算复杂度高、适配性差等问题。该文基于极端场景法建立了通用虚拟电池(virtual battery,VB)模型,即通过一套标准电池参数描述资源灵活性,其通用形式使得聚合可行域可以按一定规则线性求和获得。在此基础上,提出一种基于虚拟电池模型的外逼近闵可夫斯基聚合方法,并从数学上不失一般性地对不同异质性设备聚合的有效性进行了证明与推广。最后,提出了考虑系统灵活性供需平衡的负荷聚合商日前优化调度模型。算例结果表明,所提温控负荷可行域聚合方法在提高了外逼近精度的同时保证了较高的计算效率。相比传统模型,所提聚合模型有着更好的优化性能。展开更多
可再生能源和负荷的波动性、不确定性等给综合能源系统(integrated energy system,IES)的安全灵活运行带来了极大挑战。为提高IES灵活调节能力与可再生能源消纳水平,提出一种计及灵活性资源的IES源荷协调优化调度方法。针对系统内运行...可再生能源和负荷的波动性、不确定性等给综合能源系统(integrated energy system,IES)的安全灵活运行带来了极大挑战。为提高IES灵活调节能力与可再生能源消纳水平,提出一种计及灵活性资源的IES源荷协调优化调度方法。针对系统内运行灵活性需求,精细刻画各类资源灵活性能力,源侧根据电氢耦合单元运行特性构建热电联产机组(combined heating and power,CHP)和氢燃料电池(hydrogen fuel cell,HFC)联合运行模型,荷侧考虑综合需求响应的灵活性供给能力,建立系统综合灵活性供给模型。根据不同时刻运行灵活性不足问题分成2种调度模式,构建基于IES灵活性约束的优化调度模型,并进行仿真分析。仿真结果表明,所提出的优化调度方法能够有效提高IES灵活调节能力和可再生能源消纳水平。展开更多
需求侧管理可有效实现电力负荷的削峰填谷,提高电力系统的稳定性和运行效率。随着电力物联网的发展,不同用户在参与需求响应过程中的行为差异得以凸显,出于对用户隐私的保护,用户用电信息在采集后往往只能就地利用而不能进一步上传,给...需求侧管理可有效实现电力负荷的削峰填谷,提高电力系统的稳定性和运行效率。随着电力物联网的发展,不同用户在参与需求响应过程中的行为差异得以凸显,出于对用户隐私的保护,用户用电信息在采集后往往只能就地利用而不能进一步上传,给多元化负荷行为特征分析带来困难。提出了云边环境下基于A3C(asynchronous advantage actorcritic)强化学习算法和长期短期记忆(long short term memory,LSTM)网络的需求侧管理方法,通过强化学习解决需求侧管理决策中前瞻性不足的问题;通过基于LSTM网络的虚拟环境模拟多元用户行为特征,加速学习过程,降低算法实施成本。通过算例分析可知,所述决策方法在保证用户隐私的同时可有效加快学习进程,价格决策时可更准确地把握用户响应行为特征,从而保证决策的经济性。展开更多
This paper describes the significant cost saving opportunities for consumers in developing countries by the use of computational intelligence and demand-side-management techniques to mitigate the massive use of diesel...This paper describes the significant cost saving opportunities for consumers in developing countries by the use of computational intelligence and demand-side-management techniques to mitigate the massive use of diesel back-up during grid outages. Application of load scheduling optimization is investigated during scheduled power outages, for residential consumer in India. The specific load shifting approaches explored include a day ahead predicted load schedule which is generated by performing a DSM referring to the forecasted day ahead outage. Whereas in reality the predicted may not match the actual outage, thus in these cases a fuzzy logic rule base is referred on real time basis to take corrective action & reach the best optimal load schedule possible to attain the lowest cost. The load types modeled include passive loads and schedulable, i.e. typically heavy loads. It is found that this multi-level DSM schemes show excellent benefits to the consumer. The maximum diesel savings for the consumer due to load shifting can be approximately ranging from 45% to as high as 75% for a flat-tariff grid. The study also showed that the actual savings potential depends on the timing of power outage, duration and the specific load characteristics.展开更多
文摘Utilities around the world have been considering Demand Side Management (DSM) in their strategic planning. The costs of constructing and operating a new capacity generation unit are increasing everyday as well as Transmission and distribution and land issues for new generation plants, which force the utilities to search for another alternatives without any additional constraints on customers comfort level or quality of delivered product. De can be defined as the selection, planning, and implementation of measures intended to have an influence on the demand or customer-side of the electric meter, either caused directly or stimulated indirectly by the utility. DSM programs are peak clipping, Valley filling, Load shifting, Load building, energy conservation and flexible load shape. The main Target of this paper is to show the relation between DSM and Load Forecasting. Moreover, it highlights on the effect of applying DSM on Forecasted demands and how this affects the planning strategies for utility companies. This target will be clearly illustrated through applying the developed algorithm in this paper on an existing residential compound in Cairo-Egypt.
文摘温控负荷(temperature control loads,TCLs)灵活性可行域的有效量化和聚合是其响应电价与调控信号的前提。由于TCLs能量在时间上耦合,且设备调节特性和物理参数各异,具有较高异质性,现有聚合方法面临计算复杂度高、适配性差等问题。该文基于极端场景法建立了通用虚拟电池(virtual battery,VB)模型,即通过一套标准电池参数描述资源灵活性,其通用形式使得聚合可行域可以按一定规则线性求和获得。在此基础上,提出一种基于虚拟电池模型的外逼近闵可夫斯基聚合方法,并从数学上不失一般性地对不同异质性设备聚合的有效性进行了证明与推广。最后,提出了考虑系统灵活性供需平衡的负荷聚合商日前优化调度模型。算例结果表明,所提温控负荷可行域聚合方法在提高了外逼近精度的同时保证了较高的计算效率。相比传统模型,所提聚合模型有着更好的优化性能。
文摘可再生能源和负荷的波动性、不确定性等给综合能源系统(integrated energy system,IES)的安全灵活运行带来了极大挑战。为提高IES灵活调节能力与可再生能源消纳水平,提出一种计及灵活性资源的IES源荷协调优化调度方法。针对系统内运行灵活性需求,精细刻画各类资源灵活性能力,源侧根据电氢耦合单元运行特性构建热电联产机组(combined heating and power,CHP)和氢燃料电池(hydrogen fuel cell,HFC)联合运行模型,荷侧考虑综合需求响应的灵活性供给能力,建立系统综合灵活性供给模型。根据不同时刻运行灵活性不足问题分成2种调度模式,构建基于IES灵活性约束的优化调度模型,并进行仿真分析。仿真结果表明,所提出的优化调度方法能够有效提高IES灵活调节能力和可再生能源消纳水平。
文摘需求侧管理可有效实现电力负荷的削峰填谷,提高电力系统的稳定性和运行效率。随着电力物联网的发展,不同用户在参与需求响应过程中的行为差异得以凸显,出于对用户隐私的保护,用户用电信息在采集后往往只能就地利用而不能进一步上传,给多元化负荷行为特征分析带来困难。提出了云边环境下基于A3C(asynchronous advantage actorcritic)强化学习算法和长期短期记忆(long short term memory,LSTM)网络的需求侧管理方法,通过强化学习解决需求侧管理决策中前瞻性不足的问题;通过基于LSTM网络的虚拟环境模拟多元用户行为特征,加速学习过程,降低算法实施成本。通过算例分析可知,所述决策方法在保证用户隐私的同时可有效加快学习进程,价格决策时可更准确地把握用户响应行为特征,从而保证决策的经济性。
文摘This paper describes the significant cost saving opportunities for consumers in developing countries by the use of computational intelligence and demand-side-management techniques to mitigate the massive use of diesel back-up during grid outages. Application of load scheduling optimization is investigated during scheduled power outages, for residential consumer in India. The specific load shifting approaches explored include a day ahead predicted load schedule which is generated by performing a DSM referring to the forecasted day ahead outage. Whereas in reality the predicted may not match the actual outage, thus in these cases a fuzzy logic rule base is referred on real time basis to take corrective action & reach the best optimal load schedule possible to attain the lowest cost. The load types modeled include passive loads and schedulable, i.e. typically heavy loads. It is found that this multi-level DSM schemes show excellent benefits to the consumer. The maximum diesel savings for the consumer due to load shifting can be approximately ranging from 45% to as high as 75% for a flat-tariff grid. The study also showed that the actual savings potential depends on the timing of power outage, duration and the specific load characteristics.
