Learning the optimal power flow:Environment design matters  

作　　者：Thomas Wolgast Astrid Nieße 

机构地区：[1]Carl von Ossietzky Universität Oldenburg,Department of Computing Science,Digitalized Energy Systems,Ammerländer Heerstraße 114-118,Oldenburg,26129,Germany

出　　处：《Energy and AI》2024年第3期432-443,共12页能源与人工智能（英文）

摘　　要：To solve the optimal power flow(OPF)problem,reinforcement learning(RL)emerges as a promising new approach.However,the RL-OPF literature is strongly divided regarding the exact formulation of the OPF problem as an RL environment.In this work,we collect and implement diverse environment design decisions from the literature regarding training data,observation space,episode definition,and reward function choice.In an experimental analysis,we show the significant impact of these environment design options on RL-OPF training performance.Further,we derive some first recommendations regarding the choice of these design decisions.The created environment framework is fully open-source and can serve as a benchmark for future research in the RL-OPF field.

关 键 词：Reinforcement learning Optimal power flow Environment design Economic Dispatch Voltage control Reactive power market 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]