Vehicle electrification,an important method for reducing carbon emissions from road transport,has been promoted globally.In this study,we analyze how individuals adapt to this transition in transportation and its subs...Vehicle electrification,an important method for reducing carbon emissions from road transport,has been promoted globally.In this study,we analyze how individuals adapt to this transition in transportation and its subsequent impact on urban structure.Considering the varying travel costs associated with electric and fuel vehicles,we analyze the dynamic choices of households concerning house locations and vehicle types in a two-dimensional monocentric city.A spatial equilibrium is developed to model the interactions between urban density,vehicle age and vehicle type.An agent-based microeconomic residential choice model dynamically coupled with a house rent market is developed to analyze household choices of home locations and vehicle energy types,considering vehicle ages and competition for public charging piles.Key findings from our proposed models show that the proportion of electric vehicles(EVs)peaks at over 50%by the end of the first scrappage period,accompanied by more than a 40%increase in commuting distance and time compared to the scenario with only fuel vehicles.Simulation experiments on a theoretical grid indicate that heterogeneity-induced residential segregation can lead to urban sprawl and congestion.Furthermore,households with EVs tend to be located farther from the city center,and an increase in EV ownership contributes to urban expansion.Our study provides insights into how individuals adapt to EV transitions and the resulting impacts on home locations and land use changes.It offers a novel perspective on the dynamic interactions between EV adoption and urban development.展开更多
In the process of my country’s energy transition,the clean energy of hydropower,wind power and photovoltaic power generation has ushered in great development,but due to the randomness and volatility of its output,it ...In the process of my country’s energy transition,the clean energy of hydropower,wind power and photovoltaic power generation has ushered in great development,but due to the randomness and volatility of its output,it has caused a certain waste of clean energy power generation resources.Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption,this paper establishes a quantitative model of clean energy restricted electricity fromthe perspective of power system supply and demand balance.Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid-to long-term electricity market and the day-ahead market.Through the multi-scenario analysis method,the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene,and the typical scene is obtained by using the hierarchical clustering algorithm.This paper establishes a electricity retailer’s risk decisionmodel for purchasing and selling electricity in themid-and long-term market and reduce-abandonment market,and takes the maximum profit expectation of the electricity retailer frompurchasing and selling electricity as the objective function.At the same time,in themediumand longterm electricity market and the day-ahead market,the electricity retailer’s purchase cost,electricity sales income,deviation assessment cost and electricity purchase and sale risk are considered.The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies,on the premise of balancing profits and risks.展开更多
In the United States, emission regulations are enacted at a state level;individual states are allowed to define what methods they will use to mitigate their carbon emissions. The consequence of this is especially inte...In the United States, emission regulations are enacted at a state level;individual states are allowed to define what methods they will use to mitigate their carbon emissions. The consequence of this is especially interesting in the state of Texas where new legislation has created a “deregulated” electricity market in which end-users are capable of choosing their electricity provider and subsequently the type of electricity they wish to consume (generated by fossil fuels or renewable sources). In this paper we analyze the effects of carbon tax on the development of renewable generation capacity at the utility level while taking into account expected adoption of rooftop PV systems by individual consumers using agent based modeling techniques. Monte Carlo simulations show carbon abatement trends and proffer updated renewable portfolio standards at various levels of likelihood.展开更多
This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integrati...This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council(WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methods presented.展开更多
With the development of electricity market mechanism and advanced metering infrastructure(AMI),demand response has become an important alternative solution to improving power system reliability and effi-ciency. In thi...With the development of electricity market mechanism and advanced metering infrastructure(AMI),demand response has become an important alternative solution to improving power system reliability and effi-ciency. In this paper, the agent-based modelling and simulation method is applied to explore the impact of symmetric market mechanism and demand response on electricity market. The models of market participants are established according to their behaviors. Consumers’ response characteristics under time-of-use(TOU) mechanism are also taken into account. The level of clearing price and market power are analyzed and compared under symmetric and asymmetric market mechanisms. The results indicate that the symmetric mechanism could effectively lower market prices and avoid monopoly.Besides, TOU could apparently flatten the overall demand curve by enabling customers to adjust their load profiles,which also helps to reduce the price.展开更多
Currently,critical peak load caused by residential customers has attracted utility companies and policymakers to pay more attention to residential demand response(RDR)programs.In typical RDR programs,residential custo...Currently,critical peak load caused by residential customers has attracted utility companies and policymakers to pay more attention to residential demand response(RDR)programs.In typical RDR programs,residential customers react to the price or incentive-based signals,but the actions can fall behind flexible market situations.For those residential customers equipped with smart meters,they may contribute more DR loads if they can participate in DR events in a proactive way.In this paper,we propose a comprehensive market framework in which residential customers can provide proactive RDR actions in a day-ahead market(DAM).We model and evaluate the interactions between generation companies(GenCos),retailers,residential customers,and the independent system operator(ISO)via an agent-based modeling and simulation(ABMS)approach.The simulation framework contains two main procedures—the bottom-up modeling procedure and the reinforcement learning(RL)procedure.The bottom-up modeling procedure models the residential load profiles separately by household types to capture the RDR potential differences in advance so that residential customers may rationally provide automatic DR actions.Retailers and GenCos optimize their bidding strategies via the RL procedure.The modified optimization approach in this procedure can prevent the training results from falling into local optimum solutions.The ISO clears the DAM to maximize social welfare via Karush-Kuhn-Tucker(KKT)conditions.Based on realistic residential data in China,the proposed models and methods are verified and compared in a large multi-scenario test case with 30,000 residential households.Results show that proactive RDR programs and interactions between market entities may yield significant benefits for both the supply and demand sides.The models and methods in this paper may be used by utility companies,electricity retailers,market operators,and policy makers to evaluate the consequences of a proactive RDR and the interactions among multi-entities.展开更多
The electricity market is a complex system in which participants interact and compete with each other,which makes description of them with mathematical models difficult.To solve these difficulties,computer simulation ...The electricity market is a complex system in which participants interact and compete with each other,which makes description of them with mathematical models difficult.To solve these difficulties,computer simulation has become one of the main methods for studying electricity market problems.How to establish a reasonable electricity market has always been a major research issue in the electric power industry,for which a key point is the bidding mechanism.Agent-based modeling and a simulation(ABMS)method are used in this paper to study the imperfect competitive electricity market.An agent-based simulation method of multilateral bargaining game theory in the dynamics of the power bidding market is presented,and a multi-agent power market bidding dynamics simulation model based on game theory is established.The dynamic bidding game behavior among the government,power grid companies,power plant companies,and consumer parties is simulated in the market,and the simulation method is realized by Anylogic software.Finally,an agent-based four-party competitive dynamic game simulation in the electricity market is implemented,which provides a theoretical reference for further understanding resource optimization problems in the electricity market.展开更多
在电力市场由计划向市场转型过程中,工商业(industrial and commercial,I&C)用户逐步由目录电价转向市场化定价。为推动工商业用户全部进入市场,中国建立了代理购电机制,暂未直接参与市场购电的工商业用户由电网公司以代理方式购电...在电力市场由计划向市场转型过程中,工商业(industrial and commercial,I&C)用户逐步由目录电价转向市场化定价。为推动工商业用户全部进入市场,中国建立了代理购电机制,暂未直接参与市场购电的工商业用户由电网公司以代理方式购电,确保工商业市场化电价改革政策平稳实施。由此,首先分析现行电网企业代理购电价格形成机制,建立代理购电价格模型,针对由购电结构差异所产生的代理购电-市场化用户价差及代理购电用户价格季节波动大问题,提出基于计划-市场电量分配的代理购电价格优化方法;随后结合规划周期内计划-市场电量供需平衡关系,以工商业市场用户和代理购电用户价差最小为目标、代理购电价格波动幅值为约束条件,优化调整风光发电量进入计划和市场电量比例以及外购电月度分配比例,实现代理购电用户价格的合理动态调整;最后基于国内某省年度源-荷数据进行算例分析。分析结果表明:所提代理购电价格优化方法可以有效降低两类用户价差、代理购电价格波动,对电网代理购电机制的顺利运行和工商业用户有序进入市场起到积极的作用。展开更多
针对当前省内分时电价机制忽略省级以上电力市场交易成本影响且忽视代理购电商购电成本传导风险的问题,提出一种考虑多级市场代理购电成本传导风险的分时电价定价模型。首先,基于购电成本最小化目标,设计多级市场购电决策模型;然后,采...针对当前省内分时电价机制忽略省级以上电力市场交易成本影响且忽视代理购电商购电成本传导风险的问题,提出一种考虑多级市场代理购电成本传导风险的分时电价定价模型。首先,基于购电成本最小化目标,设计多级市场购电决策模型;然后,采用概率场景描述现货市场价格预测偏差和用户响应预测偏差,并以条件风险价值(conditional value at risk,CVaR)作为传导风险评估指标,建立最大化传导上级市场购电成本变动和最小化代理购电商传导风险为目标的分时电价模型;最后,采用k-means和粒子群算法进行求解。算例分析结果表明,所提出的分时电价模型能更准确传导多级市场的成本与风险,向用户释放多级电力市场的综合价格信号,并能够为代理购电商提供多级市场交易情境下的风险分析。展开更多
针对电网企业购售电交易策略需兼顾自身合理运营成本与用户电力保供稳价的核心问题,文章通过分析既有的关键业务流程和规则,说明了结合风险传导协同优化购电成本与购售电偏差费用的必要性。由此设置年度优化购电成本、月度优化成本与偏...针对电网企业购售电交易策略需兼顾自身合理运营成本与用户电力保供稳价的核心问题,文章通过分析既有的关键业务流程和规则,说明了结合风险传导协同优化购电成本与购售电偏差费用的必要性。由此设置年度优化购电成本、月度优化成本与偏差费用的不同目标,从全局与前瞻视角提出电网企业“年度逐月、月度分时”的两阶段一体化交易决策框架。进而综合期望和风险分析,构建了一种电网企业年度与月度两阶段交易决策模型。主要围绕供需双侧电量与市场价格的不确定性,梳理电网企业的年度、月度购电成本及月度购售电偏差费用等决策要素,并结合条件风险价值(conditional value at risk,CVaR)进行了风险测度。最后通过算例验证了所提出的两阶段交易决策模型的可行性和有效性,结果表明协同优化决策相对于成本或偏差的单一优化具有综合性优势。展开更多
A low utilization rate of public chargers and unmatched deployment of public charging sta-tions(CSs)are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation.This study...A low utilization rate of public chargers and unmatched deployment of public charging sta-tions(CSs)are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation.This study proposes an optimization methodology for public CS deployment,considering real charging behavior and interactions between battery elec-tric vehicle(BEV)users and CSs.Realistic charging choice behavior is modeled based on surveys,and a dynamic charging decision chain is simulated,allowing interactions between BEV users and CSs through an agent-based modeling(ABM)approach.The charging-related activities are triggered by state of charge(SOC)levels randomly generated from distributions derived from real BEV operating data,including the random SOC levels at the start of a trip,the SOC level that prompts the user to charge the BEV,and the SOC level at which the user stops charging the BEV.A bi-level programming model is proposed to optimize the deployment schemes for building new CSs considering the existing CSs,to determine the location and the capacity of new CSs.The objective is to minimize the total time cost per BEV user,including travel time,charging time and waiting time in the queue.An application is conducted,for the deployment of fast CSs in Washington State,USA.The results show that our method could provide effective guidance for allocating new CSs that are good supplements to the existing heavy-load CSs to share their charging load and relieve their serious queuing problems.The optimized deployment scheme can efficiently alleviate long waiting times at existing CSs,leading to a more balanced utilization among CSs.The proposed approach is expected to contribute to better planning and deployment of public CSs,satisfaction of the booming charging demand,and increased utilization of pub-lic CSs.展开更多
基金supported by National Natural Science Foundation of China(72288101,72361137002,and 72101018)the Dutch Research Council(NWO Grant 482.22.01).
文摘Vehicle electrification,an important method for reducing carbon emissions from road transport,has been promoted globally.In this study,we analyze how individuals adapt to this transition in transportation and its subsequent impact on urban structure.Considering the varying travel costs associated with electric and fuel vehicles,we analyze the dynamic choices of households concerning house locations and vehicle types in a two-dimensional monocentric city.A spatial equilibrium is developed to model the interactions between urban density,vehicle age and vehicle type.An agent-based microeconomic residential choice model dynamically coupled with a house rent market is developed to analyze household choices of home locations and vehicle energy types,considering vehicle ages and competition for public charging piles.Key findings from our proposed models show that the proportion of electric vehicles(EVs)peaks at over 50%by the end of the first scrappage period,accompanied by more than a 40%increase in commuting distance and time compared to the scenario with only fuel vehicles.Simulation experiments on a theoretical grid indicate that heterogeneity-induced residential segregation can lead to urban sprawl and congestion.Furthermore,households with EVs tend to be located farther from the city center,and an increase in EV ownership contributes to urban expansion.Our study provides insights into how individuals adapt to EV transitions and the resulting impacts on home locations and land use changes.It offers a novel perspective on the dynamic interactions between EV adoption and urban development.
文摘In the process of my country’s energy transition,the clean energy of hydropower,wind power and photovoltaic power generation has ushered in great development,but due to the randomness and volatility of its output,it has caused a certain waste of clean energy power generation resources.Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption,this paper establishes a quantitative model of clean energy restricted electricity fromthe perspective of power system supply and demand balance.Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid-to long-term electricity market and the day-ahead market.Through the multi-scenario analysis method,the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene,and the typical scene is obtained by using the hierarchical clustering algorithm.This paper establishes a electricity retailer’s risk decisionmodel for purchasing and selling electricity in themid-and long-term market and reduce-abandonment market,and takes the maximum profit expectation of the electricity retailer frompurchasing and selling electricity as the objective function.At the same time,in themediumand longterm electricity market and the day-ahead market,the electricity retailer’s purchase cost,electricity sales income,deviation assessment cost and electricity purchase and sale risk are considered.The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies,on the premise of balancing profits and risks.
文摘In the United States, emission regulations are enacted at a state level;individual states are allowed to define what methods they will use to mitigate their carbon emissions. The consequence of this is especially interesting in the state of Texas where new legislation has created a “deregulated” electricity market in which end-users are capable of choosing their electricity provider and subsequently the type of electricity they wish to consume (generated by fossil fuels or renewable sources). In this paper we analyze the effects of carbon tax on the development of renewable generation capacity at the utility level while taking into account expected adoption of rooftop PV systems by individual consumers using agent based modeling techniques. Monte Carlo simulations show carbon abatement trends and proffer updated renewable portfolio standards at various levels of likelihood.
文摘This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council(WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methods presented.
基金supported by National Natural Science Foundation of China(No.51577115)the Ministry of Industry and Information Technology of the People’s Republic of China(No.2016YFB0901302)
文摘With the development of electricity market mechanism and advanced metering infrastructure(AMI),demand response has become an important alternative solution to improving power system reliability and effi-ciency. In this paper, the agent-based modelling and simulation method is applied to explore the impact of symmetric market mechanism and demand response on electricity market. The models of market participants are established according to their behaviors. Consumers’ response characteristics under time-of-use(TOU) mechanism are also taken into account. The level of clearing price and market power are analyzed and compared under symmetric and asymmetric market mechanisms. The results indicate that the symmetric mechanism could effectively lower market prices and avoid monopoly.Besides, TOU could apparently flatten the overall demand curve by enabling customers to adjust their load profiles,which also helps to reduce the price.
基金supported in part by the National Key Research and Development Program of China(2016YFB0901100)the National Natural Science Foundation of China(U1766203)+1 种基金the Science and Technology Project of State Grid Corporation of China(Friendly interaction system of supply-demand between urban electric power customers and power grid)the China Scholarship Council(CSC).
文摘Currently,critical peak load caused by residential customers has attracted utility companies and policymakers to pay more attention to residential demand response(RDR)programs.In typical RDR programs,residential customers react to the price or incentive-based signals,but the actions can fall behind flexible market situations.For those residential customers equipped with smart meters,they may contribute more DR loads if they can participate in DR events in a proactive way.In this paper,we propose a comprehensive market framework in which residential customers can provide proactive RDR actions in a day-ahead market(DAM).We model and evaluate the interactions between generation companies(GenCos),retailers,residential customers,and the independent system operator(ISO)via an agent-based modeling and simulation(ABMS)approach.The simulation framework contains two main procedures—the bottom-up modeling procedure and the reinforcement learning(RL)procedure.The bottom-up modeling procedure models the residential load profiles separately by household types to capture the RDR potential differences in advance so that residential customers may rationally provide automatic DR actions.Retailers and GenCos optimize their bidding strategies via the RL procedure.The modified optimization approach in this procedure can prevent the training results from falling into local optimum solutions.The ISO clears the DAM to maximize social welfare via Karush-Kuhn-Tucker(KKT)conditions.Based on realistic residential data in China,the proposed models and methods are verified and compared in a large multi-scenario test case with 30,000 residential households.Results show that proactive RDR programs and interactions between market entities may yield significant benefits for both the supply and demand sides.The models and methods in this paper may be used by utility companies,electricity retailers,market operators,and policy makers to evaluate the consequences of a proactive RDR and the interactions among multi-entities.
基金This work was supported by the National Key R&D Projects of China(No.2017 YFB1400105).
文摘The electricity market is a complex system in which participants interact and compete with each other,which makes description of them with mathematical models difficult.To solve these difficulties,computer simulation has become one of the main methods for studying electricity market problems.How to establish a reasonable electricity market has always been a major research issue in the electric power industry,for which a key point is the bidding mechanism.Agent-based modeling and a simulation(ABMS)method are used in this paper to study the imperfect competitive electricity market.An agent-based simulation method of multilateral bargaining game theory in the dynamics of the power bidding market is presented,and a multi-agent power market bidding dynamics simulation model based on game theory is established.The dynamic bidding game behavior among the government,power grid companies,power plant companies,and consumer parties is simulated in the market,and the simulation method is realized by Anylogic software.Finally,an agent-based four-party competitive dynamic game simulation in the electricity market is implemented,which provides a theoretical reference for further understanding resource optimization problems in the electricity market.
文摘在电力市场由计划向市场转型过程中,工商业(industrial and commercial,I&C)用户逐步由目录电价转向市场化定价。为推动工商业用户全部进入市场,中国建立了代理购电机制,暂未直接参与市场购电的工商业用户由电网公司以代理方式购电,确保工商业市场化电价改革政策平稳实施。由此,首先分析现行电网企业代理购电价格形成机制,建立代理购电价格模型,针对由购电结构差异所产生的代理购电-市场化用户价差及代理购电用户价格季节波动大问题,提出基于计划-市场电量分配的代理购电价格优化方法;随后结合规划周期内计划-市场电量供需平衡关系,以工商业市场用户和代理购电用户价差最小为目标、代理购电价格波动幅值为约束条件,优化调整风光发电量进入计划和市场电量比例以及外购电月度分配比例,实现代理购电用户价格的合理动态调整;最后基于国内某省年度源-荷数据进行算例分析。分析结果表明:所提代理购电价格优化方法可以有效降低两类用户价差、代理购电价格波动,对电网代理购电机制的顺利运行和工商业用户有序进入市场起到积极的作用。
文摘针对当前省内分时电价机制忽略省级以上电力市场交易成本影响且忽视代理购电商购电成本传导风险的问题,提出一种考虑多级市场代理购电成本传导风险的分时电价定价模型。首先,基于购电成本最小化目标,设计多级市场购电决策模型;然后,采用概率场景描述现货市场价格预测偏差和用户响应预测偏差,并以条件风险价值(conditional value at risk,CVaR)作为传导风险评估指标,建立最大化传导上级市场购电成本变动和最小化代理购电商传导风险为目标的分时电价模型;最后,采用k-means和粒子群算法进行求解。算例分析结果表明,所提出的分时电价模型能更准确传导多级市场的成本与风险,向用户释放多级电力市场的综合价格信号,并能够为代理购电商提供多级市场交易情境下的风险分析。
文摘针对电网企业购售电交易策略需兼顾自身合理运营成本与用户电力保供稳价的核心问题,文章通过分析既有的关键业务流程和规则,说明了结合风险传导协同优化购电成本与购售电偏差费用的必要性。由此设置年度优化购电成本、月度优化成本与偏差费用的不同目标,从全局与前瞻视角提出电网企业“年度逐月、月度分时”的两阶段一体化交易决策框架。进而综合期望和风险分析,构建了一种电网企业年度与月度两阶段交易决策模型。主要围绕供需双侧电量与市场价格的不确定性,梳理电网企业的年度、月度购电成本及月度购售电偏差费用等决策要素,并结合条件风险价值(conditional value at risk,CVaR)进行了风险测度。最后通过算例验证了所提出的两阶段交易决策模型的可行性和有效性,结果表明协同优化决策相对于成本或偏差的单一优化具有综合性优势。
基金supported by the National Natural Science Foundation of China(No.71971162)Key Research Project from Shanxi Transportation Holdings Group(No.20-JKKJ-1).
文摘A low utilization rate of public chargers and unmatched deployment of public charging sta-tions(CSs)are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation.This study proposes an optimization methodology for public CS deployment,considering real charging behavior and interactions between battery elec-tric vehicle(BEV)users and CSs.Realistic charging choice behavior is modeled based on surveys,and a dynamic charging decision chain is simulated,allowing interactions between BEV users and CSs through an agent-based modeling(ABM)approach.The charging-related activities are triggered by state of charge(SOC)levels randomly generated from distributions derived from real BEV operating data,including the random SOC levels at the start of a trip,the SOC level that prompts the user to charge the BEV,and the SOC level at which the user stops charging the BEV.A bi-level programming model is proposed to optimize the deployment schemes for building new CSs considering the existing CSs,to determine the location and the capacity of new CSs.The objective is to minimize the total time cost per BEV user,including travel time,charging time and waiting time in the queue.An application is conducted,for the deployment of fast CSs in Washington State,USA.The results show that our method could provide effective guidance for allocating new CSs that are good supplements to the existing heavy-load CSs to share their charging load and relieve their serious queuing problems.The optimized deployment scheme can efficiently alleviate long waiting times at existing CSs,leading to a more balanced utilization among CSs.The proposed approach is expected to contribute to better planning and deployment of public CSs,satisfaction of the booming charging demand,and increased utilization of pub-lic CSs.
