Abstract

Renewable energy systems are increasingly essential for sustainable development, yet their design and planning require robust optimization methods. This study is motivated by the need to understand how different optimization approaches and modelling tools support transparent and efficient energy strategies. The central aim is to evaluate whether specific optimization frameworks can be regarded as universally superior or whether their suitability depends on contextual factors. A systematic literature review was conducted by examining 54 peer-reviewed articles published between 2017 and 2025, covering linear programming, mixed-integer linear programming, metaheuristic algorithms, and models based on artificial intelligence, with a particular focus on open-source energy modelling frameworks. The results show that no single method consistently outperforms others, as effectiveness is strongly influenced by system complexity, data quality, and planning objectives. This leads to the conclusion that integrated deterministic and adaptive strategies are necessary for reproducible, transparent, and sustainable renewable energy modelling.