Work

A Grid Partition Planning Method of AC–DC Interconnected Main Grid for Large‐Scale Renewable Energy Integration

Year: 2025

Type: article

Abstract: ABSTRACT Amid the goals of carbon peaking and carbon neutrality, the unpredictability and fluctuations associated with integrating large‐scale renewable energy pose substantial challenges to the new electric power system. This situation calls for robust grid partition planning methods to maintain the power grid's safety and stability. This paper addresses the problem of 500 kV main grid partition planning by proposing a partition method based on complex network theory and an improved multi‐objective cuckoo search optimisation (MOCSO) algorithm to achieve grid optimal partition and the generation of key channel set. First, a comprehensive reliability indicator system is constructed to comprehensively assess and optimise the reliability of AC–DC hybrid grid based on spatial and temporal distribution characteristics. Second, a grid partition planning method based on the information gap decision theory (IGDT) and MOCSO algorithm is proposed. It can quickly find the global or near‐optimal solution, adapt to the change of system demand and take into account various practical constraints. Finally, taking a 500‐kV grid in a province of China as an example, the effectiveness of the proposed method is verified through simulation analysis. Results show that this method significantly reduces short‐circuit current, enhances system stability and provides technical support and feasible partition scheme for scientific planning and safe operation of the grid. The proposed method has high practical value and promotion significance as it achieves an efficient balance between short‐circuit current control and system stability while taking into account both economy and reliability.

Source: IET Renewable Power Generation

DOI: 10.1049/rpg2.70023

Authors: Xun Lu, Xianfu Gong, Peng Wang

Institutions: Guangdong Power Grid Company (China), Jiangsu University

Language: English

Topic: Electric Power System Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,500