The increasing integration of intermittent renewable energy sources into distribution networks has exerted significant pressure on the frequency regulation of power systems.Meanwhile,integrating small-capacity battery...The increasing integration of intermittent renewable energy sources into distribution networks has exerted significant pressure on the frequency regulation of power systems.Meanwhile,integrating small-capacity battery energy storage systems into distribution network is a growing trend in the construction of virtual power plants(VPPs),which offer great potential advantages in improving the system frequency regulation capabilities.However,the process of power dispatch for VPPs may be hindered by imperfections in the communication network,which affects their frequency control performance.Simultaneously,the economic benefits associated with their frequency control services are often overlooked.As such,we propose a codesign method of power dispatch with dynamic power regulation and communication transmission optimization for frequency control in VPPs.First,a joint design scheme of power dispatch and routing optimization under cloud-edge collaborations is proposed.This scheme encompasses a power dispatch method considering the influences of communication network and a routing optimization policy based on graph convolutional neural networks,both of which are designed to ensure the accurate and real-time frequency control service.Further,we propose a dynamic power regulation strategy under edge-edge collaborations.Specifically,according to the established correction control objective,an adaptive distributed auction algorithm(ADAA)based dynamic power regulation control method is designed to determine the optimal regulation power of VPPs,thereby improving the economic benefits of frequency control service.Finally,the simulation results validate the feasibility and superiority of the proposed co-design method for frequency control.展开更多
Virtual Power Plants(VPPs)are integral to modern energy systems,providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data.Traditional anomaly detection methodolo...Virtual Power Plants(VPPs)are integral to modern energy systems,providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data.Traditional anomaly detection methodologies often need to adequately handle these fluctuations from solar radiation and ambient temperature variations.We introduce the Memory-Enhanced Autoencoder with Adversarial Training(MemAAE)model to overcome these limitations,designed explicitly for robust anomaly detection in VPP environments.The MemAAE model integrates three principal components:an LSTM-based autoencoder that effectively captures temporal dynamics to distinguish between normal and anomalous behaviors,an adversarial training module that enhances system resilience across diverse operational scenarios,and a prediction module that aids the autoencoder during the reconstruction process,thereby facilitating precise anomaly identification.Furthermore,MemAAE features a memory mechanism that stores critical pattern information,mitigating overfitting,alongside a dynamic threshold adjustment mechanism that adapts detection thresholds in response to evolving operational conditions.Our empirical evaluation of the MemAAE model using real-world solar power data shows that the model outperforms other comparative models on both datasets.On the Sopan-Finder dataset,MemAAE has an accuracy of 99.17%and an F1-score of 95.79%,while on the Sunalab Faro PV 2017 dataset,it has an accuracy of 97.67%and an F1-score of 93.27%.Significant performance advantages have been achieved on both datasets.These results show that MemAAE model is an effective method for real-time anomaly detection in virtual power plants(VPPs),which can enhance robustness and adaptability to inherent variables in solar power generation.展开更多
当前全国电力市场和碳交易市场正在同步改革,虚拟电厂(virtual power plant,VPP)内部聚合的各类分布式资源不断增多,VPP参与市场的角度出现转变。为研究VPP在电碳联合市场下兼顾经济性与低碳性的竞标策略,将VPP作为价格制定者,提出电碳...当前全国电力市场和碳交易市场正在同步改革,虚拟电厂(virtual power plant,VPP)内部聚合的各类分布式资源不断增多,VPP参与市场的角度出现转变。为研究VPP在电碳联合市场下兼顾经济性与低碳性的竞标策略,将VPP作为价格制定者,提出电碳联合市场下考虑风光不确定性的双层竞标模型。首先,对VPP的运营结构与VPP参与电碳联合市场的机制进行介绍与分析。其次,在此基础上构建上层以VPP参与电碳联合市场时自身收益最大为目标,下层以电碳联合市场的社会福利最大化为目标的双层竞标模型。然后,针对VPP内部资源中风光出力的不确定性,采用鲁棒优化理论进行处理,将VPP双层竞价模型转化为两阶段鲁棒优化模型。最后,通过应用列与约束生成算法、强对偶理论和Big-M法,将问题转化为混合整数线性规划进行求解。算例结果验证了竞标策略的可行性和有效性。展开更多
虚拟电厂(virtual power plant,VPP)通过先进的控制技术高效聚合容量小、数量多的分布式能源(distributed energy resource,DER)参与电力市场交易。随着DER数量的增加,其出力的波动性以及聚合后的收益问题需要解决。基于此,提出一种在...虚拟电厂(virtual power plant,VPP)通过先进的控制技术高效聚合容量小、数量多的分布式能源(distributed energy resource,DER)参与电力市场交易。随着DER数量的增加,其出力的波动性以及聚合后的收益问题需要解决。基于此,提出一种在日前电力市场下,多类型DER聚合于VPP的协同博弈调度模型。首先,提出多类型DER聚合于VPP的运营框架。其次,由于风光出力的不确定性严重影响系统的运行,建立基于变分模态分解(variational modal decomposition,VMD)和改进的双向多门控长短期记忆(bidirectional multi gated long short-term memory,Bi-MGLSTM)网络的组合预测模型。然后,同类型DER形成联盟,并以售电收益最大化为目标,构建VPP多联盟的合作博弈调度模型,为实现联盟及成员间收益分配的公平性,设计多因素改进shapley值法和基于奇偶循环核仁法的两阶段细化收益分配方案。最后,算例结果表明,所提方法能有效提高风光功率的预测精度,实现VPP内联盟间合作互补运行,保证了多个主体间收益分配的公平性与合理性。展开更多
随着大规模分散且多样化的分布式资源接入,虚拟电厂(virtual power plant,VPP)技术已成为有效管理和优化需求侧资源的重要工具。为使VPP更好地满足新型电力系统的发展需要,提出了一种考虑不确定性风险的VPP参与绿证-碳联合交易的优化调...随着大规模分散且多样化的分布式资源接入,虚拟电厂(virtual power plant,VPP)技术已成为有效管理和优化需求侧资源的重要工具。为使VPP更好地满足新型电力系统的发展需要,提出了一种考虑不确定性风险的VPP参与绿证-碳联合交易的优化调度模型。首先,构建了由风电机组、光伏机组、燃气轮机组、储能设备和用户侧柔性负荷组成的VPP优化运行模型,该模型以VPP运行成本最小为目标,并考虑了电市场、绿证-碳联合交易机制以及激励型需求响应。其次,综合考虑VPP中的源、荷以及需求响应等多重不确定性因素,运用条件风险价值(conditional value-at-risk ,CVaR)理论对不确定因素风险进行量化处理。最后,通过算例分析,验证了所提模型的经济性和环保性,所考虑的CVaR也为VPP利润与风险的平衡提供了有力的决策依据。展开更多
虚拟电厂(virtual power plant, VPP)作为一种新型的电力系统调度模式,通过聚合分布式能源资源,实现对新能源电力的高效利用。然而,传统以经济性为目标的调度策略已无法满足当前低碳发展的需求。为此,提出了一种兼顾经济性与碳排放的VP...虚拟电厂(virtual power plant, VPP)作为一种新型的电力系统调度模式,通过聚合分布式能源资源,实现对新能源电力的高效利用。然而,传统以经济性为目标的调度策略已无法满足当前低碳发展的需求。为此,提出了一种兼顾经济性与碳排放的VPP多目标优化调度策略。首先,在VPP系统中引入燃烧后碳捕集设备,并结合灵活的碳交易策略,构建考虑经济成本和碳排放量的多目标优化调度模型;其次,针对该模型采用增广ε-约束法求解Pareto解集,并运用熵权-优劣解距离(technique for order preference by similarity to ideal solution,TOPSIS)法对解集进行评价,以获得模型最优解;最后,围绕不同的碳捕集和碳交易策略开展多案例仿真实验,对比分析仅考虑经济性或低碳特性的单目标模型与兼顾经济性和碳排放的多目标模型在调度结果上的差异。实验结果表明:当采用阶梯型碳交易机制及相应的碳捕集运行方式时,VPP的碳排放量达到了最低水平;此外,与单目标模型相比,同时考虑经济性与碳排放的多目标优化策略能够有效降低碳排放并提升经济效益。展开更多
在“双碳”战略目标下,如何实现虚拟电厂(virtual power plant,VPP)之间的灵活交互并通过碳价作为激励促进VPP低碳运行,是一个值得研究的问题,为此,基于碳流理论研究VPP点对点(peer to peer,P2P)交易模型。首先,根据碳排放流理论分析碳...在“双碳”战略目标下,如何实现虚拟电厂(virtual power plant,VPP)之间的灵活交互并通过碳价作为激励促进VPP低碳运行,是一个值得研究的问题,为此,基于碳流理论研究VPP点对点(peer to peer,P2P)交易模型。首先,根据碳排放流理论分析碳流在网络中的分布特性,并引入天然气形成多能网络,建立低碳经济调度模型;其次,考虑各VPP参与交易的隐私问题,提出包含报量与报价交易信息的指标量化方法,建立基于综合优先权的P2P交易模型;同时,结合VPP在网络中承担的碳排放责任,在P2P交易机制中引入碳定价方法,建立基于碳税的“能源-碳”综合价格模型;最后,通过算例验证了所提方法不仅能降低VPP的运行成本,还能有效降低碳排放量。展开更多
区块链作为一种全新的去中心化基础构架和分布式计算范式,融合并创新了多种计算机技术。虚拟电厂(virtual power plant,VPP)是能源互联网中的一个重要分支,在聚合分布式发电资源和建立虚拟电力资源交易等方面发挥着重要的作用。该文针...区块链作为一种全新的去中心化基础构架和分布式计算范式,融合并创新了多种计算机技术。虚拟电厂(virtual power plant,VPP)是能源互联网中的一个重要分支,在聚合分布式发电资源和建立虚拟电力资源交易等方面发挥着重要的作用。该文针对现有的VPP模型中存在的问题,将区块链技术引入VPP,提出一种能源区块链网络模型,进而提出一种改进的VPP运行与调度模型。通过仿真实验,对比了该模型与原有的VPP运行模式在煤炭消耗、温室气体排放和经济成本等方面的数据。仿真结果表明,该模型可更好地反映需求侧实时信息,更有利于VPP进行环境友好、信息透明的稳定调度,也提高了系统的数据安全性和存储安全性。展开更多
基金supported in part by the Major Program of the National Natural Science Foundation of China(No.62293504)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX24_1212)。
文摘The increasing integration of intermittent renewable energy sources into distribution networks has exerted significant pressure on the frequency regulation of power systems.Meanwhile,integrating small-capacity battery energy storage systems into distribution network is a growing trend in the construction of virtual power plants(VPPs),which offer great potential advantages in improving the system frequency regulation capabilities.However,the process of power dispatch for VPPs may be hindered by imperfections in the communication network,which affects their frequency control performance.Simultaneously,the economic benefits associated with their frequency control services are often overlooked.As such,we propose a codesign method of power dispatch with dynamic power regulation and communication transmission optimization for frequency control in VPPs.First,a joint design scheme of power dispatch and routing optimization under cloud-edge collaborations is proposed.This scheme encompasses a power dispatch method considering the influences of communication network and a routing optimization policy based on graph convolutional neural networks,both of which are designed to ensure the accurate and real-time frequency control service.Further,we propose a dynamic power regulation strategy under edge-edge collaborations.Specifically,according to the established correction control objective,an adaptive distributed auction algorithm(ADAA)based dynamic power regulation control method is designed to determine the optimal regulation power of VPPs,thereby improving the economic benefits of frequency control service.Finally,the simulation results validate the feasibility and superiority of the proposed co-design method for frequency control.
基金supported by“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-002)the Technology Development Program(RS-2023-00266141)funded by the Ministry of SMEs and Startups(MSS,Republic of Korea).
文摘Virtual Power Plants(VPPs)are integral to modern energy systems,providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data.Traditional anomaly detection methodologies often need to adequately handle these fluctuations from solar radiation and ambient temperature variations.We introduce the Memory-Enhanced Autoencoder with Adversarial Training(MemAAE)model to overcome these limitations,designed explicitly for robust anomaly detection in VPP environments.The MemAAE model integrates three principal components:an LSTM-based autoencoder that effectively captures temporal dynamics to distinguish between normal and anomalous behaviors,an adversarial training module that enhances system resilience across diverse operational scenarios,and a prediction module that aids the autoencoder during the reconstruction process,thereby facilitating precise anomaly identification.Furthermore,MemAAE features a memory mechanism that stores critical pattern information,mitigating overfitting,alongside a dynamic threshold adjustment mechanism that adapts detection thresholds in response to evolving operational conditions.Our empirical evaluation of the MemAAE model using real-world solar power data shows that the model outperforms other comparative models on both datasets.On the Sopan-Finder dataset,MemAAE has an accuracy of 99.17%and an F1-score of 95.79%,while on the Sunalab Faro PV 2017 dataset,it has an accuracy of 97.67%and an F1-score of 93.27%.Significant performance advantages have been achieved on both datasets.These results show that MemAAE model is an effective method for real-time anomaly detection in virtual power plants(VPPs),which can enhance robustness and adaptability to inherent variables in solar power generation.
文摘当前全国电力市场和碳交易市场正在同步改革,虚拟电厂(virtual power plant,VPP)内部聚合的各类分布式资源不断增多,VPP参与市场的角度出现转变。为研究VPP在电碳联合市场下兼顾经济性与低碳性的竞标策略,将VPP作为价格制定者,提出电碳联合市场下考虑风光不确定性的双层竞标模型。首先,对VPP的运营结构与VPP参与电碳联合市场的机制进行介绍与分析。其次,在此基础上构建上层以VPP参与电碳联合市场时自身收益最大为目标,下层以电碳联合市场的社会福利最大化为目标的双层竞标模型。然后,针对VPP内部资源中风光出力的不确定性,采用鲁棒优化理论进行处理,将VPP双层竞价模型转化为两阶段鲁棒优化模型。最后,通过应用列与约束生成算法、强对偶理论和Big-M法,将问题转化为混合整数线性规划进行求解。算例结果验证了竞标策略的可行性和有效性。
文摘虚拟电厂(virtual power plant,VPP)通过先进的控制技术高效聚合容量小、数量多的分布式能源(distributed energy resource,DER)参与电力市场交易。随着DER数量的增加,其出力的波动性以及聚合后的收益问题需要解决。基于此,提出一种在日前电力市场下,多类型DER聚合于VPP的协同博弈调度模型。首先,提出多类型DER聚合于VPP的运营框架。其次,由于风光出力的不确定性严重影响系统的运行,建立基于变分模态分解(variational modal decomposition,VMD)和改进的双向多门控长短期记忆(bidirectional multi gated long short-term memory,Bi-MGLSTM)网络的组合预测模型。然后,同类型DER形成联盟,并以售电收益最大化为目标,构建VPP多联盟的合作博弈调度模型,为实现联盟及成员间收益分配的公平性,设计多因素改进shapley值法和基于奇偶循环核仁法的两阶段细化收益分配方案。最后,算例结果表明,所提方法能有效提高风光功率的预测精度,实现VPP内联盟间合作互补运行,保证了多个主体间收益分配的公平性与合理性。
文摘随着大规模分散且多样化的分布式资源接入,虚拟电厂(virtual power plant,VPP)技术已成为有效管理和优化需求侧资源的重要工具。为使VPP更好地满足新型电力系统的发展需要,提出了一种考虑不确定性风险的VPP参与绿证-碳联合交易的优化调度模型。首先,构建了由风电机组、光伏机组、燃气轮机组、储能设备和用户侧柔性负荷组成的VPP优化运行模型,该模型以VPP运行成本最小为目标,并考虑了电市场、绿证-碳联合交易机制以及激励型需求响应。其次,综合考虑VPP中的源、荷以及需求响应等多重不确定性因素,运用条件风险价值(conditional value-at-risk ,CVaR)理论对不确定因素风险进行量化处理。最后,通过算例分析,验证了所提模型的经济性和环保性,所考虑的CVaR也为VPP利润与风险的平衡提供了有力的决策依据。
文摘虚拟电厂(virtual power plant, VPP)作为一种新型的电力系统调度模式,通过聚合分布式能源资源,实现对新能源电力的高效利用。然而,传统以经济性为目标的调度策略已无法满足当前低碳发展的需求。为此,提出了一种兼顾经济性与碳排放的VPP多目标优化调度策略。首先,在VPP系统中引入燃烧后碳捕集设备,并结合灵活的碳交易策略,构建考虑经济成本和碳排放量的多目标优化调度模型;其次,针对该模型采用增广ε-约束法求解Pareto解集,并运用熵权-优劣解距离(technique for order preference by similarity to ideal solution,TOPSIS)法对解集进行评价,以获得模型最优解;最后,围绕不同的碳捕集和碳交易策略开展多案例仿真实验,对比分析仅考虑经济性或低碳特性的单目标模型与兼顾经济性和碳排放的多目标模型在调度结果上的差异。实验结果表明:当采用阶梯型碳交易机制及相应的碳捕集运行方式时,VPP的碳排放量达到了最低水平;此外,与单目标模型相比,同时考虑经济性与碳排放的多目标优化策略能够有效降低碳排放并提升经济效益。
文摘在“双碳”战略目标下,如何实现虚拟电厂(virtual power plant,VPP)之间的灵活交互并通过碳价作为激励促进VPP低碳运行,是一个值得研究的问题,为此,基于碳流理论研究VPP点对点(peer to peer,P2P)交易模型。首先,根据碳排放流理论分析碳流在网络中的分布特性,并引入天然气形成多能网络,建立低碳经济调度模型;其次,考虑各VPP参与交易的隐私问题,提出包含报量与报价交易信息的指标量化方法,建立基于综合优先权的P2P交易模型;同时,结合VPP在网络中承担的碳排放责任,在P2P交易机制中引入碳定价方法,建立基于碳税的“能源-碳”综合价格模型;最后,通过算例验证了所提方法不仅能降低VPP的运行成本,还能有效降低碳排放量。
文摘区块链作为一种全新的去中心化基础构架和分布式计算范式,融合并创新了多种计算机技术。虚拟电厂(virtual power plant,VPP)是能源互联网中的一个重要分支,在聚合分布式发电资源和建立虚拟电力资源交易等方面发挥着重要的作用。该文针对现有的VPP模型中存在的问题,将区块链技术引入VPP,提出一种能源区块链网络模型,进而提出一种改进的VPP运行与调度模型。通过仿真实验,对比了该模型与原有的VPP运行模式在煤炭消耗、温室气体排放和经济成本等方面的数据。仿真结果表明,该模型可更好地反映需求侧实时信息,更有利于VPP进行环境友好、信息透明的稳定调度,也提高了系统的数据安全性和存储安全性。
