Optimal scheduling of building integrated energy system based on demand response

张雪纯; 高广玲; 张智晟; 杨连河

doi:10.3969/j.issn.1009-1831.2019.04.008

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Optimal scheduling of building integrated energy system based on demand response

更新时间：2026-03-05

- Optimal scheduling of building integrated energy system based on demand response
- Vol. 21, Issue 4, (2019)
- 作者机构：
  
  1. 青岛大学电气工程学院,山东,青岛,266071
  2. 国网技术学院,济南,250000
  3. 国网诸城市供电公司,山东诸城,262200
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2019.04.008
  CLC：
- Published：2019
- 稿件说明：
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张雪纯, 高广玲, 张智晟, et al. Optimal scheduling of building integrated energy system based on demand response[J]. 2019, 21(4).
DOI：

张雪纯, 高广玲, 张智晟, et al. Optimal scheduling of building integrated energy system based on demand response[J]. 2019, 21(4). DOI： 10.3969/j.issn.1009-1831.2019.04.008.

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。

Abstract

Aiming at the problem of optimal scheduling of buildings with high energy consumption and fast growth rate

a integrated energy system in buildings including photovoltaic power generation

cogeneration system

gas boiler and energy storage device 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 system in buildings based on demand response is established. The particle swarm optimization algorithm based on cloud model is used to solve the model optimization. A simulation is introduced to simulate. The two different modes of participation in demand response

and optimization performance of cloud model particle swarm optimization 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 system in buildings

and reduce the peak-to-valley difference of grid side load.

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