用深度强化学习调控居民用电，降低电网峰值负荷22.82%。
Capacity-constrained demand response in smart grids using deep reinforcement learning
- 基于实时电价和用电量，动态调整激励费率
- 使峰值负荷与平均负荷比下降22.82%
- 考虑用户偏好与不满成本，适合智能电网调度
本文提出一种面向住宅智能电网的容量约束型激励式需求响应方法，旨在通过经济激励促使终端用户减少或转移用电，以维持电网容量限制、防止拥堵。框架采用分层结构，服务提供商根据批发电价和聚合居民负荷，动态调整每小时激励费率，同时兼顾服务方与用户的经济利益。利用深度强化学习，在显式容量约束下学习最优实时激励策略。用户异质性通过家电级家庭能源管理系统与不满成本建模。基于三个家庭的真实用电与电价数据的仿真显示，该方法有效降低峰值负荷，平滑聚合负荷曲线，相比无需求响应情形，峰值-均值比降低约22.82%。
原文摘要 · Abstract (English)
This paper presents a capacity-constrained incentive-based demand response approach for residential smart grids. It aims to maintain electricity grid capacity limits and prevent congestion by financially incentivising end users to reduce or shift their energy consumption. The proposed framework adopts a hierarchical architecture in which a service provider adjusts hourly incentive rates based on wholesale electricity prices and aggregated residential load. The financial interests of both the service provider and end users are explicitly considered. A deep reinforcement learning approach is employed to learn optimal real-time incentive rates under explicit capacity constraints. Heterogeneous user preferences are modelled through appliance-level home energy management systems and dissatisfaction costs. Using real-world residential electricity consumption and price data from three households, simulation results show that the proposed approach effectively reduces peak demand and smooths the aggregated load profile. This leads to an approximately 22.82% reduction in the peak-to-average ratio compared to the no-demand-response case.
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