A Review of Optimization Scheduling for Active Distribution Networks with High-Penetration Distributed Generation Access

Kewei Wang, Yonghong Huang (), Yanbo Liu, Tao Huang and Shijia Zang
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Kewei Wang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Yonghong Huang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Yanbo Liu: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Tao Huang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Shijia Zang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

Energies, 2025, vol. 18, issue 15, 1-23

Abstract: The high-proportion integration of renewable energy sources, represented by wind power and photovoltaics, into active distribution networks (ADNs) can effectively alleviate the pressure associated with advancing China’s dual-carbon goals. However, the high uncertainty in renewable energy output leads to increased system voltage fluctuations and localized voltage violations, posing safety challenges. Consequently, research on optimal dispatch for ADNs with a high penetration of renewable energy has become a current focal point. This paper provides a comprehensive review of research in this domain over the past decade. Initially, it analyzes the voltage impact patterns and control principles in distribution networks under varying levels of renewable energy penetration. Subsequently, it introduces optimization dispatch models for ADNs that focus on three key objectives: safety, economy, and low carbon emissions. Furthermore, addressing the challenge of solving non-convex and nonlinear models, the paper highlights model reformulation strategies such as semidefinite relaxation, second-order cone relaxation, and convex inner approximation methods, along with summarizing relevant intelligent solution algorithms. Additionally, in response to the high uncertainty of renewable energy output, it reviews stochastic optimization dispatch strategies for ADNs, encompassing single-stage, two-stage, and multi-stage approaches. Meanwhile, given the promising prospects of large-scale deep reinforcement learning models in the power sector, their applications in ADN optimization dispatch are also reviewed. Finally, the paper outlines potential future research directions for ADN optimization dispatch.

Keywords: renewable energy; distribution network; optimal scheduling; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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