Open Access

REVIEW

A Review of Optimization and Solution Methods for New Power Systems with Uncertainty

Zemin Liang, Songyu Gao, Qi Yao^*

Energy and Electricity Research Center, Jinan University, Zhuhai, 519070, China

* Corresponding Author: Qi Yao. Email:

Energy Engineering 2026, 123(4), 2 https://doi.org/10.32604/ee.2025.072877

Received 05 September 2025; Accepted 25 November 2025; Issue published 27 March 2026

Abstract

For mixed-integer programming (MIP) problems in new power systems with uncertainties, existing studies tend to address uncertainty modeling or MIP solution methods in isolation. They overlook core bottlenecks arising from their coupling, such as variable dimension explosion, disrupted constraint separability, and conflicts in solution logic. To address this gap, this paper focuses on the coupling effects between the two and systematically conducts three aspects of work: first, the paper summarizes the uncertainty optimization methods suitable for addressing uncertainty-related issues in power systems, along with their respective advantages and disadvantages. It also clarifies the specific forms and operational mechanisms through which these uncertainty optimization methods are integrated into MIP models. Meanwhile, based on the application scenarios of new power systems, the paper delineates the applicable boundaries of different optimization methods; second, the paper organizes three categories of solution methods, which are exact solution methods, decomposition-based methods, and meta-heuristic algorithms. It focuses on analyzing the improvement paths of various solution methods for resolving coupling bottlenecks, as well as their applicability in different types of power system optimization problems; finally, providing a summary and presenting an outlook on future directions: artificial intelligence-enabled optimization, development of dedicated solvers for extreme scenarios, and dynamic modeling of multi-source uncertainties. This study aims to help researchers in the field of new power systems quickly grasp uncertainty optimization methods and core solution methods, bridge existing research gaps, and promote the development of this field.

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