Power System Dispatch Based on Improved Scenario Division with Physical and Data-Driven Features

Author:

Huang Wenqi1,Cao Shang1,Liang Lingyu1,Zhang Huanming1,Zhao Xiangyu1,Li Hanju1,Ren Jie2,Che Liang2

Affiliation:

1. Southern Power Grid Digital Grid Research Institute, Guangzhou 510000, China

2. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

Abstract

In power systems with high penetration of renewable energy, traditional physical model-based optimal dispatch methods suffer from modeling difficulties and poor adaptability, while data-driven dispatch methods, represented by reinforcement learning, have the advantage of fast decision making and reflecting long-term benefits. However, the performances of data-driven methods are much limited by the problem of distribution shift under insufficient power system scenario samples in the training. To address this issue, this paper proposes an improved scenario division method by integrating the power system’s key physical features and the data-driven variational autoencoder (VAE)-generated features. Next, based on the scenario division results, a multi-scenario data-driven dispatch model is established. The effectiveness of the proposed method is verified by a simulation conducted on a real power system model in a province of China.

Funder

Science and Technology Project of China Southern Power Grid Digital Grid Research Institute

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

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