In the field of renewable energy (RE) development, optimization models are critical for addressing decision-making challenges across the renewable energy development and utilization chain (REDUC). However, existing research has three key limitations: First, single-model approaches struggle to handle the complexity of RES (e.g., uncertainty of RE resources, multi-energy carrier interactions), leading to incomplete decision support. Second, there is a lack of systematic integration between different optimization models (e.g., programming, game theory) and solution methods (e.g., analytical algorithms, AI), resulting in inefficient problem-solving for complex scenarios. Third, micro-level applications (e.g., firm/equipment-level optimization) are insufficient due to data constraints, failing to provide targeted guidance for practical operations.

Therefore, a research team composed of scholars from the China University of Geosciences (Wuhan) has carried out a research entitled “A Review of Optimization Modeling and Solution Methods in Renewable Energy Systems”.

This study conducts a systematic review of 32,806 literature entries (1990–2023) to address the above gaps. First, it classifies RES into five subsystems (resource exploitation, production, transmission, consumption, storage) and clarifies key optimization scenarios (e.g., site selection, generation expansion, storage scheduling). Second, it summarizes four core optimization models: 1) Programming models (LP/MILP/DP/SP) dominate economic-technical optimization, with MILP widely used for infrastructure decisions (e.g., transmission line construction); 2) MADM models (AHP/TOPSIS) excel in RE resource assessment and policy evaluation; 3) Game models (cooperative/non-cooperative) capture multi-agent interest conflicts (e.g., power generation-transmission coordination); 4) Hybrid models (combining prediction/simulation/assessment) enhance applicability (e.g., coupling LEAP with HOMER for system simulation). Third, it identifies solution method trends: Conventional methods (analytical/decomposition) are used for model simplification, probabilistic methods (MCS) handle uncertainty, AI methods (GA/PSO/DRL) improve nonlinear problem-solving efficiency, and hybrid methods (e.g., GA+MC) balance accuracy and speed.

The paper “A review of optimization modeling and solution methods in renewable energy systems” is authored by Shiwei YU, Limin YOU, and Shuangshuang ZHOU. Full text of the open access paper: https://doi.org/10.1007/s42524-023-0271-3.