含柔性电热负荷响应阈值的源-荷-储协调优化模型  

Source-load-storage Cooperative Optimization Model with Flexible Electric and Thermal Load Response Threshold

作　　者：彭院院 周任军[1] 方绍凤 李斌[1] 许燕燕 石亮缘 PENG Yuanyuan;ZHOU Renjun;FANG Shaofeng;LI Bin;XU Yanyan;SHI Liangyuan(Hunan Province Collaborative Innovation Center of Clean Energy and Smart Grid(Changsha University of Science and Technology),Changsha 410004,China;Guangzhou Power Supply Bureau Co.,Ltd,Guangzhou 510620,China)

机构地区：[1]湖南省清洁能源与智能电网协同创新中心(长沙理工大学),长沙410004 [2]广州供电局有限公司,广州510620

出　　处：《电力系统及其自动化学报》2020年第5期30-37,共8页Proceedings of the CSU-EPSA

基　　金：国家自然科学基金资助项目(71331001,91746118);湖南省自然科学基金资助项目(2019JJ40302);湖南省战略性新兴科技攻关与重大科技成果转化项目(2018GK4002)。

摘　　要：大量热电联产机组在"以热定电"的传统方式下运行,机组受供热负荷的限制,导致系统调峰能力不足,进而造成大量弃风弃光。为了提高系统新能源消纳能力,在电源侧增设电锅炉、储能装置进行热电解耦,在负荷侧利用柔性电负荷的激励响应特性和柔性热负荷的供热舒适度模糊性来调整负荷变化,提出一种含柔性电热负荷响应阈值的源-荷-储协调优化模型。模型以新能源消纳量最大和系统运行成本最小为目标;定义柔性电热负荷响应阈值约束和用户响应满意度,利用用户响应满意度修正柔性电热负荷响应阈值约束,通过改进多目标粒子群算法对模型进行求解。算例仿真表明:相比于源、荷单方面优化调度,源-荷-储协调优化能够进一步提高新能源消纳能力,降低系统运行成本;决策者可选择合适的柔性电热负荷响应阈值来兼顾新能源消纳目标和用户满意度。When a large number of combined heat and power(CHP)units are operating in the traditional mode of"determining power by heat",the units are limited by thermal load,resulting in insufficient peak regulation capacity of the system and further causing a large amount of wind and photovoltaic powers to be abandoned. To improve the system’s renewable energy accommodation capacity,electric boilers and energy storage devices are added on the power supply side for thermoelectric decoupling,and the load response is adjusted on the load side by the excited response characteristics of flexible electric load and the heating comfortable ambiguity of flexible thermal load. A source-load-storage cooperative optimization model with flexible electric and thermal load response threshold is proposed,which aims at maximizing the accommodation of renewable energy and minimizing the system operating costs. In addition,flexible electric and thermal load response threshold constraint and the customer responsive satisfaction degree are defined,and the latter is used to modify the former. This model is solved by the improved multi-objective particle swarm optimization algorithm. The simulation result of an example shows that compared with the single scheduling of source or load,the sourceload-storage cooperative optimization can further improve the renewable energy accommodation capacity and reduce the system operating costs. Decision makers can choose a suitable threshold to balance the objective of renewable energy accommodation and the customer responsive satisfaction degree.

关 键 词：新能源消纳 源-荷-储协调优化 热电解耦 柔性电热负荷响应阈值 改进多目标粒子群算法 

分 类 号：TM74[电气工程—电力系统及自动化]