An Online Reinforcement Learning-Based Energy Management Strategy for Microgrids With Centralized Control
Zhejiang Ocean University · Zhejiang University
Abstract
To address the issue of significant unpredictability and intermittent nature of renewable energy sources, particularly wind and solar power, this paper introduces a novel optimization model based on online reinforcement learning. Initially, an energy management optimization model is designed to achieve plan adherence and minimize energy storage (ES) operation costs, taking into account the inherent challenges of wind power-photovoltaic energy storage systems (WPESS). An online reinforcement learning framework is employed, which defines various state variables, action variables, and reward functions for the energy management optimization model. The state-action-reward-state-action (SARSA) algorithm is applied…
Citation impact
- FWCI
- 43.89
- Percentile
- 100%
- References
- 33
Authors
5Topics & keywords
- Reinforcement learning
- Energy management
- Computer science
- Control (management)
- Load management
- Energy (signal processing)
- Control engineering
- Engineering
- Affordable and clean energy