Two-level area-load modelling for OPF of power system using reinforcement learning

• Published in: IET generation, transmission & distribution transmission & distribution, 2019-09, Vol.13 (18), p.4141-4149
• Main Authors: Jiang, Changxu, Li, Zhigang, Zheng, J.H, Wu, Q.H, Shang, Xiaoya
• Format: Article
• Language: English
• Subjects: dynamic OPF; equivalent model output; error minimization; function optimisation; learning (artificial intelligence); load flow; lower‐level optimisation; N−1 static security constraints; online measurement; optimal power flow; optimisation; parameter identification; power system parameter estimation; power system planning; power system security; power system simulation; real‐time area‐load modelling; reinforcement learning; Research Article; two‐level area‐load modelling; two‐level hierarchical framework; upper‐level model; upper‐level problem; weighting strategy
• ISSN: 1751-8687; 1751-8695; 1751-8695
• DOI: 10.1049/iet-gtd.2019.0554

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.