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

2025-06-07 · Chi Liu, Zhezhuang Xu, Jiawei Zhou, Yazhou Yuan, Kai Ma, Meng Yuan

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.

📄 PDF Abstract BibTeX arXiv:2506.06824

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement Learningenergy managementScheduling

Methods 이 논문이 사용한 방법론

Travel 설명 없음

Electric Electric is an energy-based cloze model for representation learning over text. Like BERT, it is a conditional generative model of tokens given their contexts. However,…

Energy-aware Scheduling of Jobs in Heterogeneous Cluster Systems Using Deep Reinforcement Learning

2019-12-11 · Amirhossein Esmaili, Massoud Pedram

Energy consumption is one of the most critical concerns in designing computing devices, ranging from portable embedded systems to computer cluster systems. Furthermore, in the past decade, cluster systems have increasing…

Deep Reinforcement Learning Management Reinforcement Learning Reinforcement Learning (RL)+1

Safe Hierarchical Reinforcement Learning for CubeSat Task Scheduling Based on Energy Consumption

2023-09-21 · Mahya Ramezani, M. Amin Alandihallaj, Jose Luis Sanchez-Lopez, Andreas Hein

This paper presents a Hierarchical Reinforcement Learning methodology tailored for optimizing CubeSat task scheduling in Low Earth Orbits (LEO). Incorporating a high-level policy for global task distribution and a low-le…

Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning Scheduling

Forecast Aware Deep Reinforcement Learning for Efficient Electricity Load Scheduling in Dairy Farms

2026-01-12 · Nawazish Ali, Rachael Shaw, Karl Mason arxiv

Dairy farming is an energy intensive sector that relies heavily on grid electricity. With increasing renewable energy integration, sustainable energy management has become essential for reducing grid dependence and suppo…

Reinforcement Learning

Real-time scheduling of renewable power systems through planning-based reinforcement learning

2023-03-09 · Shaohuai Liu, Jinbo Liu, Weirui Ye, Nan Yang 외

The growing renewable energy sources have posed significant challenges to traditional power scheduling. It is difficult for operators to obtain accurate day-ahead forecasts of renewable generation, thereby requiring the …

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Scheduling

Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space

2020-11-19 · Jernej Hribar, Andrei Marinescu, Alessandro Chiumento, Luiz A. DaSilva

Millions of battery-powered sensors deployed for monitoring purposes in a multitude of scenarios, e.g., agriculture, smart cities, industry, etc., require energy-efficient solutions to prolong their lifetime. When these …

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)Scheduling