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"双碳"目标下电热-电蓄热接入配电网拓扑多目标规划OA北大核心CSTPCD

Multiobjective Planning of Electric Heating-Electric Heat Storage Device Access to Distribution Network Topology under Dual Carbon Target

中文摘要英文摘要

在"双碳"背景下,电热供暖成为主流供暖方式.大量电热负荷接入会增大配电网运行压力,因此需要对配电网进行扩展规划.传统的规划方法难以解决含大量电热-电蓄热设备的灵活配电网规划问题.因此,提出-种考虑电热-电蓄热设备接入的配电网低碳规划方法.首先,根据采暖建筑最佳室温,综合考虑风速等多方面因素建立采暖建筑室内温度时变方程,根据方程求解获得规划场景下精确热负荷需求;其次,结合热负荷需求及各时段风、光电源出力特性,以碳排放量最低为目标优化蓄热电锅炉运行方式;最后,将负载率极差与方差加权和作为配电网均匀性指标,以综合成本和系统均匀性为目标对配电网进行多目标扩展规划.采用嵌套的混合粒子群优化算法进行求解,引入动态惯性权重增强粒子群算法寻优能力.以东北某地实际数据对电热-电蓄热接入场景下的配电网进行仿真,验证了所提方法的有效性.

ABSTRACT:In the context of"carbon emission peaks and carbon neutrality,"electric heating has become the mainstream heating method.Electric heating loads typically increase the operating pressure of the distribution network;therefore,it is necessary to expand the distribution network.Traditional planning methods have difficulty in solving the problem of flexible distribution network planning with a large number of electric heating-electric heat storage devices.Therefore,a low-carbon expansion planning method for a distribution network considering access to electric heating-electric heat storage devices is proposed.First,based on the optimal room temperature for heating buildings,wind speed and other factors were comprehensively considered to derive a temperature time-varying equation and solve it to obtain the heat load demand.Next,the output characteristics of the heat load demand,wind power,and photovoltaic power were used during the planning process to optimize the operation mode of the thermal storage electric boiler to achieve the lowest carbon emissions.Finally,the range and variance of the load rate was used as the uniformity index of the distribution network,and the multiobjective expansion plan for the distribution network was carried out to achieve comprehensive cost and line load rate uniformity.A case study was solved using a nested hybrid particle swarm optimization algorithm,which introduces a dynamic inertia weight to enhance the optimization ability of particle swarm optimization.Actual data from a specific area in Northeast China were used in the case studies to verify the feasibility of the proposed method.

宋卓然;李剑峰;范宇航;姜涛;刘宇;黄南天

国网辽宁省电力有限公司,沈阳市 110006东北电力大学电气工程学院,吉林省吉林市 132012

动力与电气工程

扩展规划电蓄热多目标规划系统均匀性混合粒子群优化

expansion planelectric heat storagemultiobjective planningsystem uniformityhybrid particle swarm optimization algorithm

《电力建设》 2024 (004)

57-65 / 9

This work is supported by National Key R8D Program of China(No.2022YFB2404002)and State Grid Liaoning Electric Power Co.,Ltd.Technology Projects(No.SGTYHT/21-JS-226).国家重点研发计划项目(2022YFB2404002);国网辽宁省电力有限公司科技项目(SGTYHT/21-JS-226)

10.12204/j.issn.1000-7229.2024.04.006

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