摘要
针对能耗高且增速快的建筑楼宇优化调度问题,构建了含光伏发电、冷热电联供系统、燃气锅炉和储能装置的建筑级综合能源系统。在对系统内各个能源设备进行建模分析的基础上,考虑需求响应补偿价,以建筑运行成本最低为目标函数,建立了基于需求响应的建筑级综合能源系统优化调度模型,采用基于云模型改进的粒子群算法对模型优化求解。引入算例进行仿真,对比是否参与需求响应的两种不同模式,以及云模型粒子群算法与基本粒子群算法的优化性能。结果表明,基于需求响应的云模型粒子群算法模式可有效节约建筑级综合能源系统运行成本,同时降低电网侧负荷峰谷差。
Aiming at the problem of optimal scheduling of buildings with high energy consumption and fast growth rate, a inte. grated energy system in buildings including photovoltaic power generation, cogeneration system, gas boiler and energy storage de. vice is constructed. Modeling and analyzing various energy devices in the system, considering the demand response compensation price,taking the lowest building operating cost as the objective function, optimization scheduling model of integrated energy sys. tem in buildings based on demand response is established. The par. ticle swarm optimization algorithm based on cloud model is used to solve the model optimization. A simulation is introduced to simu. late. The two different modes of participation in demand response, and optimization performance of cloud model particle swarm opti. mization algorithm and basic particle swarm optimization algorithm are compared. The results show that the cloud model particle swarm optimization algorithm model based on demand response can effectively save the operating cost of the integrated energy sys. tem in buildings, and reduce the peak.to.valley difference of grid side load.
作者
张雪纯
高广玲
张智晟
杨连河
ZHANG Xuechun;GAO Guangling;ZHANG Zhisheng;YANG Lianhe(Electrical Engineering College, Qingdao University, Qingdao 266071, China;State Grid Institute of Technology, Jinan 250000, China;State Grid Zhucheng Power Supply Company, Zhucheng 262200, China)
出处
《电力需求侧管理》
2019年第4期28-34,共7页
Power Demand Side Management
基金
国家自然科学基金项目(51477078)~~
关键词
建筑级综合能源系统
需求响应
优化调度
云模型粒子群算法
负荷峰谷差
building integrated energy system
demand response
optimized scheduling
cloud model particle swarm optimization
load peak.tovalley difference