# Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices

arXiv: https://arxiv.org/abs/2506.06824 · HTML: https://mlsanity.com/paper/2506.06824/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 5 of 5)

- [Random vector functional link neural network based ensemble deep learning for short-term load forecasting](https://mlsanity.com/paper/2107.14385/) (2021)
- [LoRa-RL: Deep Reinforcement Learning for Resource Management in Hybrid Energy LoRa Wireless Networks](https://mlsanity.com/paper/2109.02392/) (2021)
- [Optimal Battery Participation in Frequency Regulation Markets](https://mlsanity.com/paper/1710.10514/) (2017)
- [Factoring the Cycle Aging Cost of Batteries Participating in Electricity Markets](https://mlsanity.com/paper/1707.04567/) (2017)
- [Life cycle economic viability analysis of battery storage in electricity market](https://mlsanity.com/paper/2305.15079/) (2023)

## 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.