Demand response cost assessment of HVAC based on model and data driven

赵本源; 赵建立; 张沛超; 姚 刚; 杜 江

doi:10.3969/j.issn.1009-1831.2024.02.005

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Demand response cost assessment of HVAC based on model and data driven

更新时间：2026-03-05

- Demand response cost assessment of HVAC based on model and data driven
- Vol. 26, Issue 2, (2024)
- 作者机构：
  
  1. 贵州电网有限责任公司电力调度控制中心,贵阳,550002
  2. 国网上海市电力公司,上海,200030
  3. 电力传输与功率变换控制教育部重点实验室（上海交通大学）,上海,200240
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2024.02.005
  CLC：
- Published：2024
- 稿件说明：
移动端阅览
赵本源, 赵建立, 张沛超, et al. Demand response cost assessment of HVAC based on model and data driven[J]. 2024, 26(2).
DOI：

赵本源, 赵建立, 张沛超, et al. Demand response cost assessment of HVAC based on model and data driven[J]. 2024, 26(2). DOI： 10.3969/j.issn.1009-1831.2024.02.005.

摘要

为实现楼宇暖通空调与电网友好互动

提出了模型与数据驱动相结合的需求响应成本分析。首先

利用人工神经网络的拟合能力

从物理模型中自动学习并构建动态模型。然后

考虑温度变化对楼宇人员工作效率的影响

建立暖通空调设定温度的优化模型并基于粒子群优化算法进行求解。最后

将用户因偏离最优设定温度而产生的额外成本定义为响应成本

据此提出暖通空调的需求响应成本分析方法。仿真结果表明

通过设定暖通空调的最优温度

可显著降低用户的总成本；响应成本曲线可反映用户的工作效率损失因素

从而帮助用户在需求响应中理性确定投标价格。

Abstract

To realize friendly interaction between building heating

ventilation and air conditioning（HVAC）and the power grid

a demand response cost assessment method combining model and data driven is proposed. Firstly

the fitting ability of artificial neural network（ANN） is used to automatically learn from the physical model and build a dynamic model of HVAC. Then

considering the impact of temperature changes on the personnel efficiency of building

an optimization model of the HVAC setting temperature is established and solved based on particle swarm optimization（PSO）algorithm. Finally

additional cost incurred by users due to deviation from the optimal set temperature is defined as response cost

and demand response cost assessment for HVAC is proposed accordingly. Simulation results show that by setting optimal temperature of HVAC

total cost of users can be significantly reduced. The response cost curve can reflect users’productivity loss factors

thereby helping users determine the bid price rationally in demand response.

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