基于强化学习的配网参数优化方法

A Parameter Optimization of Distribution System Method by Reinforcement Learning

随着我国科技、经济的快速发展,人们对电量的需求日益递增。当前,智能化体系不断完善,智能化的配网自动化系统将成为未来的主要研究方向。论文中主要解决自动配网系统中的配网参数设置问题。具体地,论文基于强化学习框架提出了一种配网参数优化的方法,该方法能够在尽可能少的人为干预下实现配网自动化,从而高效的解决传统配网问题。该智能体以最大化配网系统的供电质量为目标,利用配网系统正常供电的时长作为奖赏值,通过使用强化学习算法促使智能体不断学习,最终智能体能够为配网系统选择出一系列高质量的配网参数,从而实现高效、可靠的配网系统。

With the rapid development of technology and economy, people’s demand for electricity is increasing day by day. This paper mainly solves the problem of setting the distribution parameters in the automatic distribution system. Specifically, this paper proposes a distribution network parameter optimization method based on reinforcement learning framework, which can automate the distribution network with as little human intervention as possible, thereby efficiently solving the traditional distribution network problem. The agent aims to maximize the power supply quality of the distribution system, and uses the reinforcement learning algorithm to promote the agent to learn continuously, and finally the agent can select a series of high-quality distribution network parameters, thus achieving an efficient and reliable distribution network system.