Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
Green buildings (GBs) with renewable energy and building energy management systems (BEMS) enable efficient energy use and support sustainable development. Electric vehicles (EVs), as flexible storage resources, enhance system flexibility when integrated with stationary energy storage systems (ESS) for real-time scheduling. However, differing degradation and operational characteristics of ESS and EVs complicate scheduling strategies. This paper proposes a model-free deep reinforcement learning (DRL) method for joint real-time scheduling based on a combined battery system (CBS) integrating ESS and EVs. We develop accurate degradation models and cost estimates, prioritize EV travel demands, and enable collaborative ESS-EV operation under varying conditions. A prediction model optimizes energy interaction between CBS and BEMS. To address heterogeneous states, action coupling, and learning efficiency, the DRL algorithm incorporates double networks, a dueling mechanism, and prioritized experience replay. Experiments show a 37.94 percent to 40.01 percent reduction in operating costs compared to a mixed-integer linear programming (MILP) approach.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 55 references that are in the map. First posted on arXiv 7 Jun 2025.
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5 of 55 references.
References · 5
- 2021Random vector functional link neural network based ensemble deep learning for short-term load forecastingcited by 6
- 2021LoRa-RL: Deep Reinforcement Learning for Resource Management in Hybrid Energy LoRa Wireless Networkscited by 6
- 2017Optimal Battery Participation in Frequency Regulation Marketscited by 4
- 2017Factoring the Cycle Aging Cost of Batteries Participating in Electricity Marketscited by 3
- 2023Life cycle economic viability analysis of battery storage in electricity marketcited by 2
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- 2025Green Energy Management for Sustainable Data Centers Using Deep Reinforcement Learning
- 2026Reinforcement Learning-Based Energy Management for Industrial Park with Heterogeneous Batteries under Demand Response ↗
- 2024Advancements in Electric Vehicle Charging Optimization: A Survey of Reinforcement Learning Approaches
- 2024SOC-Boundary and Battery Aging Aware Hierarchical Coordination of Multiple EV Aggregates Among Multi-stakeholders with Multi-Agent Constrained Deep Reinforcement Learning ↗
- 2025Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards
- 2025Algorithmic Control Improves Residential Building Energy and EV Management when PV Capacity is High but Battery Capacity is Low