考虑用户出行成本预算的电力-交通耦合网络充电站定价策略OA北大核心CSTPCD
Pricing Strategy of Charging Station in Power-Transportation Coupling Network Considering User Travel Cost Budget
随着电动汽车的规模化发展,研究如何有效考虑用户的出行行为机理并制定合理的充电站充电价格,对电力-交通网络的协同优化调度具有重大意义.针对此问题,提出了考虑用户出行成本预算的电力-交通耦合网络充电站定价策略.首先,建立考虑出行成本预算的交通用户均衡模型,将均衡状态通过变分不等式进行等效描述,从而对电动汽车出行需求和充电行为进行刻画.其次,构建考虑功率削减的配电网二阶锥优化模型,将充电站定价问题转化为含有变分不等式约束的优化问题,并根据问题设计交替迭代算法和外梯度算法进行求解.最后,通过算例对所提模型和方法的有效性进行验证,结果表明了考虑出行成本预算对耦合网络充电定价的必要性.
With the large-scale development of electric vehicles,it is of great significance to study how to effectively consider the traveling behavior mechanism of users and formulate rational charging prices for charging stations for the collaborative optimization and scheduling of power-transportation coupling networks.To solve this problem,this paper proposes a pricing strategy for charging stations in the power-transportation coupling network considering the user travel cost budget.Firstly,a transportation user equilibrium model considering the travel cost budget is established,and the equilibrium state is equivalently described through variational inequalities,so as to characterize the traveling demands and charging behaviors of electric vehicles.Secondly,a second-order cone optimization model for distribution networks considering power reduction is constructed.The charging station pricing problem has been transformed into an optimization problem with variational inequality constraints,and an alternating iteration algorithm combined with an extra-gradient algorithm is designed to solve the problem.Finally,the effectiveness of the proposed model and methods is verified through a case,and the results show the necessity of considering the travel cost budget for charging pricing in coupling networks.
谢龙韬;谢仕炜;陈铠悦;张亚超;陈之栋
福州大学电气工程与自动化学院,福建省福州市 350108
电动汽车电力-交通耦合网络变分不等式出行成本预算交通用户均衡充电站定价
electric vehicle(EV)power-transportation coupling networkvariational inequalitiestravel cost budgettransportation user equilibriumcharging station pricing
《电力系统自动化》 2024 (007)
201-209 / 9
国家自然科学基金青年基金资助项目(52307087). This work is supported by National Natural Science Foundation of China(No.52307087).
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