Application-oriented assessment of grid-connected PV-battery system with deep reinforcement learning in buildings considering electricity price dynamics
Authors:
- Qi ChenQCQi Chen
- Zhonghong KuangZKZhonghong Kuang
- Xiaohua LiuXLXiaohua Liu
- Tao ZhangTZTao Zhang
- 13 April 2024
- journal article
- research article
- Published by Elsevier in Applied Energy
- Vol. 364
Abstract
No abstract availableKeywords
Funding Information
- Renmin University of China
- National Natural Science Foundation of China (52394223, 72192801, 72103190, 52278114)
- National Key Research and Development Program of China
- Tsinghua University (2022YFC3802505, 2023YFC3807001)
This publication has 47 references indexed in Scilit:
- Optimal sizing and techno-economic analysis of the hybrid PV-battery-cooling storage system for commercial buildings in ChinaApplied Energy, 2023
- A data-driven DRL-based home energy management system optimization framework considering uncertain household parametersApplied Energy, 2023
- 50% reduction in energy consumption in an actual cold storage facility using a deep reinforcement learning-based control algorithmApplied Energy, 2023
- Comparison of reinforcement learning and model predictive control for building energy system optimizationApplied Thermal Engineering, 2023
- Operational optimization for off-grid renewable building energy system using deep reinforcement learningApplied Energy, 2022
- Site demonstration and performance evaluation of MPC for a large chiller plant with TES for renewable energy integration and grid decarbonizationApplied Energy, 2022
- Energy storage to solve the diurnal, weekly, and seasonal mismatch and achieve zero-carbon electricity consumption in buildingsApplied Energy, 2022
- Assessing the CO2-emission risk due to wind-energy uncertaintyApplied Energy, 2022
- Optimal and rule-based control strategies for energy flexibility in buildings with PVApplied Energy, 2016
- Optimal design and techno-economic analysis of a hybrid solar–wind power generation systemApplied Energy, 2009