DRP-FLR：智能电网中用于灵活负荷调节的需求响应潜力的数据驱动评估

中文摘要 AI 辅助

人工智能工作负载和可再生能源资源的快速增长加剧了电力系统的供需不平衡，使得传统的负荷调节方式不足，需求响应（DR）机制变得必要。然而，现有面向DR的方法存在局限性。为此提出DRP-FLR，它通过将外部知识嵌入历史负荷表示来实现准确的短期负荷预测，通过聚类构建特定实体负荷模式配置文件并估计DR潜力，将灵活负荷调节制定为混合整数优化问题并求解。实验表明，DRP-FLR可降低调节偏差36.63%-91.87%，平均提高参与者收益44.66%。

英文摘要

The rapid growth of AI workloads and renewable energy resources exacerbates supply-demand imbalance in power systems, making traditional load regulation designed for efficient allocation inadequate and motivating demand response (DR) mechanisms to enable load controllability in smart grids. However, existing DR-oriented approaches either focus on optimizing electricity cost or occupant comfort with limited benefit to system-level balance. Others overlook the diverse and dynamic consumption patterns of heterogeneous energy entities, leading to significant over- or under-regulation. Therefore, we propose DRP-FLR. First, DRP-FLR achieves accurate short-term load forecasting by embedding exogenous knowledge (e.g., entity information, prediction time) into historical load representations. Next, it constructs entity-specific load-pattern profiles by clustering historical load curves, and estimates DR potential by matching forecasted loads with pattern profiles. Finally, DRP-FLR formulates flexible load regulation as a mixed-integer optimization problem and solves it with an MILP solver to jointly optimize DR utilization, participant economic benefit, and renewable accommodation, while enforcing supply-demand balance and economic feasibility. Experiments on a regional grid and a campus microgrid show that DRP-FLR reduces regulation deviation by 36.63%-91.87% and improves participant benefit by 44.66% on average.

发表机构

• University of Science and Technology of China (USTC)（中国科学技术大学）
• Anhui Zhongxin Jiyuan Information Technology Co., Ltd.（安徽中信济源信息技术有限公司）

机构由 AI 辅助整理，请以论文原文为准。

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10 pages, 6 pages