Abstract
With the growing integration of distributed energy resources (DERs), flexible loads, and other emerging technologies, there are increasing complexities and uncertainties for modern power and energy systems. This brings great challenges to the operation and control. Besides, with the deployment of advanced sensor and smart meters, a large number of data are generated, which brings opportunities for novel data-driven methods to deal with complicated operation and control issues. Among them, reinforcement learning (RL) is one of the most widely promoted methods for control and optimization problems. This paper provides a comprehensive literature review of RL in terms of basic ideas, various types of algorithms, and their applications in power and energy systems. The challenges and further works are also discussed.
Highlights
WITH the gradual depletion of fossil energy and in‐ creasing environmental pressure, a revolution in ener‐ gy sector is going on globally [1]
This paper summarizes the recent researches on reinforcement learning (RL) for the optimization and control of power and energy systems and discusses the potential research directions
1) Optimization of Distribution Network The voltage fluctuation and power quality issues caused by the increasing penetration of distributed energy resources (DERs) and electric vehicles (EVs) in distribution networks bring great challenges to the operation of the distribution network
Summary
WITH the gradual depletion of fossil energy and in‐ creasing environmental pressure, a revolution in ener‐ gy sector is going on globally [1]. With the increasing pene‐ tration of distributed energy resources (DERs) and flexible demands, both the generation and demand sides are facing growing uncertainties In this context, real-time control strat‐ egy based on the latest observation may achieve a better per‐ formance than the pre-determined ones. The learned strategy is scalable, it can be exploited in an on‐ line manner to inform decisions based on the latest informa‐ tion Owing to these advantages, RL has been widely ap‐ plied in industrial manufacturing [19], operation and schedul‐ ing [20], robotic control [21], etc. Sec‐ tion IV discusses the challenges and prospects of RL in pow‐ er systems and conclude this paper
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