考虑用户热适应性的含跨季储热综合能源系统两阶段容量配置

闫宇声; 高建伟; 吴浩宇; 孟琪琛; 王紫莹

doi:doi:10.19912/j.0254-0096.tynxb.2024-1408

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考虑用户热适应性的含跨季储热综合能源系统两阶段容量配置

更新时间：2026-01-08

- 考虑用户热适应性的含跨季储热综合能源系统两阶段容量配置
- Vol. 46, Issue 12, Pages: 448-459(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1408
  CLC： TM73
- Published Online：07 January 2026，
  
  Published：2025
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闫宇声, 高建伟, 吴浩宇, et al. 考虑用户热适应性的含跨季储热综合能源系统两阶段容量配置[J]. 2025, 46(12): 448-459.
DOI：

闫宇声, 高建伟, 吴浩宇, et al. 考虑用户热适应性的含跨季储热综合能源系统两阶段容量配置[J]. 2025, 46(12): 448-459. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1408.

摘要

针对月度发电量分布不均以及用户用能差异带来的季节性供需匹配问题

构建一种考虑用户热适应性的含跨季储热综合能源系统两阶段容量配置模型。首先

对不同供暖时期用户舒适度特征进行刻画

提出一种用户热经历影响下的供暖温度动态设置及用户舒适度差异化评估策略。其次

考虑经济与环境成本

构建含跨季储热综合能源系统的一阶段容量配置模型

在时间序列重构下采用多目标粒子群算法求解

并生成跨季储热全年参考容量状态序列。然后

建立第二阶段多时间尺度运行优化模型

研究不同配置条件下考虑长短时电热综合需求响应的系统运行情况

最终确定使得用户满意度最高的设备最优配置容量。通过算例仿真验证所提出模型及其求解方法的可行性。

Abstract

In response to the seasonal supply-demand mismatch caused by uneven distribution of monthly power generation and differences in user energy consumption

a two-stage capacity allocation model for an integrated energy system with seasonal thermal energy storage is constructed by considering the thermal adaptability of users. Firstly

the comfort characteristics of users in different heating periods are portrayed

and a strategy for dynamic setting of heating temperatures and differentiated assessment of user comfort is proposed

taking into account the influence of user thermal experience. Secondly

considering the economic and environmental costs

a one-stage capacity allocation model is constructed for an integrated energy system with seasonal thermal energy storage

which is solved by a multi-objective particle swarm algorithm under time series reconstruction and generates an annual reference capacity state sequence for seasonal thermal energy storage. Then

a two-stage multi-timescale operation optimization model is developed to study the system operation under different configuration conditions considering the integrated demand response of long and short-term electricity and heat

and ultimately determine the optimal configuration capacity of the system that makes the highest user satisfaction. The model is simulated with examples to verify the feasibility of the proposed model and its solution method.

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