Open AccessTwo‐level area‐load modelling for OPF of power system using reinforcement learning
Author(s) -
Jiang Changxu,
Li Zhigang,
Zheng J.H.,
Wu Q.H.,
Shang Xiaoya
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2019.0554
Subject(s) - reinforcement learning , weighting , computer science , electric power system , function (biology) , identification (biology) , mathematical optimization , power (physics) , control theory (sociology) , control (management) , artificial intelligence , mathematics , medicine , physics , botany , quantum mechanics , evolutionary biology , biology , radiology
Load modelling is essential to the planning and operation of a power system. This study proposes a two‐level hierarchical framework of real‐time area‐load modelling for optimal power flow (OPF). The upper‐level problem is a parameter identification for an area‐load model improved via using a weighting strategy decayed with time, whereas the lower‐level optimisation is a dynamic OPF considering N − 1 static security constraints. In the framework, the error of the lower‐level optimisation is added into the upper‐level model, which guides a search direction for the load modelling toward minimising the error between the online measurement and equivalent model output as much as possible. An improved method is proposed based on function optimisation by reinforcement learning to identify the parameters of the area‐load model online in real time. Simulation studies verify the effectiveness of the proposed framework, algorithm and improved strategies.