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.

翻译：本文提出了一种面向住宅智能电网的容量约束激励型需求响应方法。该方法旨在通过经济激励促使终端用户减少或转移其能源消耗，从而维持电网容量限制并防止拥塞。所提出的框架采用分层架构，其中服务提供商根据批发电价和聚合住宅负荷调整小时级激励费率。该框架明确考虑了服务提供商与终端用户双方的经济利益。研究采用深度强化学习方法，在显式容量约束下学习最优实时激励费率。通过设备级家庭能源管理系统与不满意成本对异质性用户偏好进行建模。利用来自三个家庭的实际住宅用电量与价格数据进行仿真，结果表明所提出的方法能有效降低峰值需求并平滑聚合负荷曲线。相较于无需求响应场景，该方法使峰均比降低了约22.82%。