摘要
在“双碳”目标背景下,为构建更加公平、绿色低碳的新型电力市场,文中提出一种微网内部双重需求响应和微网间点对点(peer-to-peer, P2P)合作的协同调度策略。首先,构建电热气多能协同的微电网模型,考虑以电动汽车(electric vehicle, EV)为代表的柔性负荷。然后,提出多微网之间和微网内部的电能共享优化策略。在多微网层,以每个微网个体和整个微网集群参与P2P合作后获得最优利益为目标,建立基于纳什谈判理论的优化调度模型;在微网内部,以降低微网碳排放量、缓解电负荷峰谷差和减少EV充电成本为目标,建立基于动态电价和EV碳配额的双重需求响应优化调度模型。最后,仿真结果表明,所提优化调度策略可有效促进微网间能源共享和新能源消纳,进一步降低系统碳排放量。
In the context of the'dual-carbon'goal,a dual demand response within each microgrid and a cooperative peer-to-peer(P2P)scheduling strategy among microgrids are proposed to build a fairer,green,and low-carbon new electricity market.Firstly,a microgrid model incorporating integrated electricity,heat and gas is constructed,with explicit modeling of flexible loads such as electric vehicles(EVs).Then,the optimization strategies for electricity sharing among multiple microgrids and within microgrids are proposed.At the multi-microgrid level,an optimal scheduling model based on Nash bargaining theory is established,aiming to achieve the optimal benefits for both individual microgrids and the entire microgrid group after their participation in P2P collaboration.Within each microgrid,a dual demand response optimal scheduling model based on dynamic tariffs and EV carbon quota is established with the objective of reducing the microgrid carbon emissions,the peak-to-valley difference of the electric loads,and the charging cost of EVs.Finally,simulation results demonstrate that the proposed optimal scheduling strategy effectively enhances energy sharing and renewable energy consumption among microgrids,and further reduces the system carbon emissions.
作者
张嘉蕾
宣文举
杨栋
吕高
关燕鹏
ZHANG Jialei;XUAN Wenju;YANG Dong;LÜGao;GUAN Yanpeng(School of Electric Power,Civil Engingeering and Architecture,Shanxi University,Taiyuan 030006,China;State Grid Shanxi Electric Power Co.,Ltd.,Xinzhou Power Supply Company,Xinzhou 034000,China;School of Automation and Software Engineering,Shanxi University,Taiyuan 030006,China)
出处
《电力工程技术》
北大核心
2025年第6期103-113,共11页
Electric Power Engineering Technology
基金
国家自然科学基金资助项目(62473242)。
