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[2602.16525] Capacity-constrained demand response in smart grids using deep reinforcement learning
Summary
This paper explores a capacity-constrained demand response strategy for smart grids using deep reinforcement learning, aiming to optimize energy consumption and reduce peak demand.
Why It Matters
As energy consumption patterns evolve, managing grid capacity is crucial for sustainability. This research introduces a novel approach that leverages deep reinforcement learning to incentivize users to adjust their energy use, potentially leading to significant reductions in peak demand and improved grid stability.
Key Takeaways
- The proposed framework uses financial incentives to manage residential energy consumption.
- Deep reinforcement learning is employed to determine optimal incentive rates under capacity constraints.
- Simulation results indicate a 22.82% reduction in peak-to-average load ratios.
- The approach considers heterogeneous user preferences, enhancing its applicability.
- This research contributes to the development of smarter, more efficient energy management systems.
Computer Science > Machine Learning arXiv:2602.16525 (cs) [Submitted on 18 Feb 2026] Title:Capacity-constrained demand response in smart grids using deep reinforcement learning Authors:Shafagh Abband Pashaki, Sepehr Maleki, Amir Badiee View a PDF of the paper titled Capacity-constrained demand response in smart grids using deep reinforcement learning, by Shafagh Abband Pashaki and 1 other authors View PDF HTML (experimental) Abstract: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 2...
