# Optimizing Sustainable Energy Transitions in Small Isolated Grids Using Multi-Criteria Approaches
## 1 Introduction
### Modeling and Optimization--Tools and Techniques  
Islands and remote regions rely heavily on fossil fuel-based electricity due to their isolation, which complicates achieving green and cost-economical energy solutions [1; 14]. Dependence on diesel generates environmental problems and raises fuel costs due to small transport quantities and a remote location [8; 26]. To address these challenges and reduce GHG emissions, many islands are aligning with the EU's goal of sustainable energy by 2050 [27]. Recent research has explored various renewable energy models to provide reliable and emission-free electricity, such as those of Lampedusa Island [28], Terecira Island [29] and the Galapagos Archipelago [30]. Different simulation tools can help in modeling these energy scenarios. For example, HOMER software was used for Grand Canary Island's 2040 decarbonization plan [14], EnergyPLAN for South Tyrol [31], and H2 RES for Cape Verde's S. Vicente Island [32]. Comprehensive reviews of these tools highlight their effectiveness at scales ranging from tiny islands and microgrids to expansive continental and macro-grid levels, demonstrating their adaptability in diverse energy system contexts [33; 34]. EnergyPLAN, developed by Aalborg University in 1999, is an appropriate instrument for analyzing integrated energy systems and was an early adopter of smart energy systems concepts [35]. Consequently, the current EnergyPLAN version 16.3 is the result of more than 25 years of development. Continuously updated, it connects demand sectors like transportation, industrial and buildings with different supply technologies, including electricity, gas, and district heating. It performs hourly simulations over a year, making it suitable for scenarios with high renewable energy shares, and allows comparisons with fossil fuel and nuclear-based systems. The software also calculates costs and integrates with platforms like Excel, MATLAB R2023a, and Python 3.11.13. The review work of [PERSON] et al. [36] highlighted EnergyPLAN's effectiveness in modeling sustainable energy solutions, its application in over 315 peer-reviewed studies, and its credibility when combined with other models. Most of the previous research documents reach the optimal system only through cost reduction. In contrast, EnergyPLAN is often used in conjunction with supplemental models and advanced optimization algorithms to enhance its effectiveness. For example, the EPLANopt model combines EnergyPLAN with a specialized optimization algorithm designed specifically for capacity expansion [31]. EPLANopt is a Python-based framework tool that facilitates defining optimization problems and constraints [37; 38]. The tool uses NSGA-II, one of the most used Multi-Objective Optimization with Evolutionary Algorithm (MOEA), as an optimization algorithm. The EPLANopt source code is freely offered in an open access repository [39]. [PERSON] et al. [40] reviewed various tools and identified EPLANopt as widely adopted among optimization algorithms, further demonstrating its value in smart energy systems research. Multiple algorithms are available for addressing multi-objective optimization problems, with Evolutionary Algorithms (EAs) and Particle Swarm Optimization (PSO) being two of the most commonly applied methods. EAs are inspired by the principles of natural selection and genetics, employing mechanisms such as crossover and mutation to evolve a population of candidate solutions. In contrast, PSO draws inspiration from the collective behavior of bird flocks or fish schools, where each solution (particle) navigates the search space by considering both its own experience and that of its neighbors. Among EAs, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is particularly prevalent in energy system planning optimization [37].