In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes ...In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.展开更多
In vehicle Ad-hoc netwok （VANET）, traffic load is often unevenly distributed among access points （APs）. Such load imbalance hampers the network from fully utilizing the network capacity. To alleviate such imbalanc...In vehicle Ad-hoc netwok （VANET）, traffic load is often unevenly distributed among access points （APs）. Such load imbalance hampers the network from fully utilizing the network capacity. To alleviate such imbalance, the paper introduces a novel pricing game model. The research scene is at the intersection when the traffic light is green. As vehicles are highly mobile and the network typology changes dynamically, the paper divides the green light time into equal slots and calculates APs＇ prices with the presented pricing game in each time slot. The whole process is a repeated game model. The final equilibrium solution set is APs＇ pricing strategy, and the paper claim that this equilibrium solution set can affect vehicles＇ selection and ensure APs＇ load-balancing. Simulation results based on a realistic vehicular traffic model demonstrate the effectiveness of the game method.展开更多
With the large-scale connection of 5G base stations(BSs)to the distribution networks(DNs),5G BSs are utilized as flexible loads to participate in the peak load regulation,where the BSs can be divided into base station...With the large-scale connection of 5G base stations(BSs)to the distribution networks(DNs),5G BSs are utilized as flexible loads to participate in the peak load regulation,where the BSs can be divided into base station groups(BSGs)to realize inter-district energy transfer.A Stackelberg game-based optimization framework is proposed,where the distribution net-work operator(DNO)works as a leader with dynamic pricing for multi-BSGs;while BSGs serve as followers with the ability of demand response to adjust their charging and discharging strategies in temporal dimension and load migration strategy in spatial dimension.Subsequently,the presence and uniqueness of the Stackelberg equilibrium(SE)are provided.Moreover,differential evolution is adopted to reach the SE and the optimization problem in multi-BSGs is decomposed to solve the time-space coupling.Finally,through simulation of a practical system,the results show that the DNO operation profit is increased via cutting down the peak load and the operation costs for multi-BSGs are reduced,which reaches a winwin effect.展开更多
Composite load model of Western Electricity Coordinating Council(WECC)is a newly developed load model that has drawn great interest from the industry.To analyze its dynamic characteristics with both mathematical and e...Composite load model of Western Electricity Coordinating Council(WECC)is a newly developed load model that has drawn great interest from the industry.To analyze its dynamic characteristics with both mathematical and engineering rigors,a detailed mathematical model is needed.Although composite load model of WECC is available in commercial software as a module and its detailed block diagrams can be found in several public reports,there is no complete mathematical representation of the full model in literature.This paper addresses a challenging problem of deriving detailed mathematical representation of composite load model of WECC from its block diagrams.In particular,we have derived the mathematical representation of the new DERA model.The developed mathematical model is verified using both MATLAB and PSS/E to show its effectiveness in representing composite load model of WECC.The derived mathematical representation serves as an important foundation for parameter identification,order reduction and other dynamic analysis.展开更多
文章提出了一种自底向上的虚拟电厂(virtual power plant, VPP)自组织聚合运行调度方法,旨在通过分布式能源(distributed energy resources, DER)间的动态自组织聚合,降低调度过程中的调控量。首先,针对分布式能源的出力特性,以跟踪系...文章提出了一种自底向上的虚拟电厂(virtual power plant, VPP)自组织聚合运行调度方法,旨在通过分布式能源(distributed energy resources, DER)间的动态自组织聚合,降低调度过程中的调控量。首先,针对分布式能源的出力特性,以跟踪系数量化评估其与负荷的一致性水平,并给出相关优化调控模型,同时考虑DER具备的基础智能,作为自组织聚合的基础。其次,引入联盟博弈的框架,给出了聚合效用函数及其对应的利益分配机制,并以其作为依据确立了DER的自组织聚合条件。最后,基于Pareto规则提出"聚合-分裂"机制,促进虚拟电厂的有序进化,形成虚拟电厂自组织聚合运行策略。算例表明,所提方法可根据DER出力特性动态组合,其总体调控量与集中优化时相当,相比各DER独立优化则显著下降;其计算量和计算时间,与各DER独立优化时相当,较集中优化显著下降。展开更多
基金supported by the National Science Foundation(NSF)grant ECCF 1936494.
文摘In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.
基金supported by the Open Research Fund from the Key Laboratory for Computer Network and Information Integration (Southeast University, Ministry of Education, China)the Fundamental Research Funds for the Central Universities+4 种基金National Key Technology R&D Program (2011BAK02B02-01),National Key Technology R&D Program of China (2011BAK02B02)the Hi-Tech Research and Development Program of China (2012AA111902)State Key Development Program for Basic Research of China (2011CB302902)the National Natural Science Foundation of China (61073180)National Science and Technology Major Project (2010ZX03006-002-03)
文摘In vehicle Ad-hoc netwok （VANET）, traffic load is often unevenly distributed among access points （APs）. Such load imbalance hampers the network from fully utilizing the network capacity. To alleviate such imbalance, the paper introduces a novel pricing game model. The research scene is at the intersection when the traffic light is green. As vehicles are highly mobile and the network typology changes dynamically, the paper divides the green light time into equal slots and calculates APs＇ prices with the presented pricing game in each time slot. The whole process is a repeated game model. The final equilibrium solution set is APs＇ pricing strategy, and the paper claim that this equilibrium solution set can affect vehicles＇ selection and ensure APs＇ load-balancing. Simulation results based on a realistic vehicular traffic model demonstrate the effectiveness of the game method.
基金supported by the National Natural Science Foundation of China(No.51877076).
文摘With the large-scale connection of 5G base stations(BSs)to the distribution networks(DNs),5G BSs are utilized as flexible loads to participate in the peak load regulation,where the BSs can be divided into base station groups(BSGs)to realize inter-district energy transfer.A Stackelberg game-based optimization framework is proposed,where the distribution net-work operator(DNO)works as a leader with dynamic pricing for multi-BSGs;while BSGs serve as followers with the ability of demand response to adjust their charging and discharging strategies in temporal dimension and load migration strategy in spatial dimension.Subsequently,the presence and uniqueness of the Stackelberg equilibrium(SE)are provided.Moreover,differential evolution is adopted to reach the SE and the optimization problem in multi-BSGs is decomposed to solve the time-space coupling.Finally,through simulation of a practical system,the results show that the DNO operation profit is increased via cutting down the peak load and the operation costs for multi-BSGs are reduced,which reaches a winwin effect.
基金supported by the Power Systems Engineering Research Center(No.S-84G)
文摘Composite load model of Western Electricity Coordinating Council(WECC)is a newly developed load model that has drawn great interest from the industry.To analyze its dynamic characteristics with both mathematical and engineering rigors,a detailed mathematical model is needed.Although composite load model of WECC is available in commercial software as a module and its detailed block diagrams can be found in several public reports,there is no complete mathematical representation of the full model in literature.This paper addresses a challenging problem of deriving detailed mathematical representation of composite load model of WECC from its block diagrams.In particular,we have derived the mathematical representation of the new DERA model.The developed mathematical model is verified using both MATLAB and PSS/E to show its effectiveness in representing composite load model of WECC.The derived mathematical representation serves as an important foundation for parameter identification,order reduction and other dynamic analysis.
文摘文章提出了一种自底向上的虚拟电厂(virtual power plant, VPP)自组织聚合运行调度方法,旨在通过分布式能源(distributed energy resources, DER)间的动态自组织聚合,降低调度过程中的调控量。首先,针对分布式能源的出力特性,以跟踪系数量化评估其与负荷的一致性水平,并给出相关优化调控模型,同时考虑DER具备的基础智能,作为自组织聚合的基础。其次,引入联盟博弈的框架,给出了聚合效用函数及其对应的利益分配机制,并以其作为依据确立了DER的自组织聚合条件。最后,基于Pareto规则提出"聚合-分裂"机制,促进虚拟电厂的有序进化,形成虚拟电厂自组织聚合运行策略。算例表明,所提方法可根据DER出力特性动态组合,其总体调控量与集中优化时相当,相比各DER独立优化则显著下降;其计算量和计算时间,与各DER独立优化时相当,较集中优化显著下降。
