Scholarlink

<p>Optimizing the scheduling of integrated electric-heat systems (IEHS) is complex due to fluctuating user-side loads and their associated uncertainties. To address this, this paper proposes an integrated demand response (DR) optimization strategy for IEHS that accounts for load uncertainty. First, a probabilistic model leveraging Copula functions was formulated to capture the temporal correlation of load uncertainties. A non-parametric Kernel Density Estimation method was then employed to fit the load distribution, and randomized load fluctuation data were generated using Monte Carlo sampling to simulate uncertainty. Second, a DR model that incorporates the characteristics of the electric-heat system is introduced. The electrical and heating load are coordinated through distinct energy storage devices. Finally, the effectiveness of the strategy is validated through the application of an improved column-and-constraint generation algorithm. Simulation outcomes indicate that the presented optimization approach substantially improves the operational flexibility and performance of IEHS.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An optimization method for integrated demand response strategies for electricity and heat considering the uncertainty of user-side loads

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月5日

查看全文
  • Jiaqi Li,
  • Delong Zhang,
  • Yuheng Wei,
  • Xuesong Zhou,
  • Xiangyu Kong,
  • Xianxu Huo,
  • Chao Pang

摘要

Optimizing the scheduling of integrated electric-heat systems (IEHS) is complex due to fluctuating user-side loads and their associated uncertainties. To address this, this paper proposes an integrated demand response (DR) optimization strategy for IEHS that accounts for load uncertainty. First, a probabilistic model leveraging Copula functions was formulated to capture the temporal correlation of load uncertainties. A non-parametric Kernel Density Estimation method was then employed to fit the load distribution, and randomized load fluctuation data were generated using Monte Carlo sampling to simulate uncertainty. Second, a DR model that incorporates the characteristics of the electric-heat system is introduced. The electrical and heating load are coordinated through distinct energy storage devices. Finally, the effectiveness of the strategy is validated through the application of an improved column-and-constraint generation algorithm. Simulation outcomes indicate that the presented optimization approach substantially improves the operational flexibility and performance of IEHS.