Multi-Criteria Optimization of a Standalone Photovoltaic System in Cyprus (Techno-Economic Analysis)

Athina Vogiatzoglou (), Konstantinos Alexakis and Dimitris Askounis
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Athina Vogiatzoglou: Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., 15773 Athens, Greece
Konstantinos Alexakis: Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., 15773 Athens, Greece
Dimitris Askounis: Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., 15773 Athens, Greece

Energies, 2025, vol. 18, issue 11, 1-28

Abstract: Photovoltaic systems are increasingly recognized as one of the most advanced, efficient, and rapidly developing methods of electricity generation, utilizing the limitless potential of solar radiation while offering environmentally sustainable solutions to contemporary energy challenges. However, despite their clear benefits, issues such as high initial investment costs and relatively low energy efficiency must be carefully addressed during the design phase. Key considerations include the quantity and type of panels, battery capacity and number, environmental conditions, site-specific factors, and the mathematical models and interconnection strategies of system components. This study proposes a two-stage optimization approach for standalone photovoltaic systems, employing three distinct optimization algorithms—NSGA-II, DEMO, and Particle Swarm Optimization—to minimize both the Loss of Load Probability (LLP) and the life cycle cost (LCC). In the second stage, optimal solutions from the Pareto front are evaluated using three multi-criteria decision-making techniques: the hybrid AHP-TOPSIS method, VIKOR, and PROMETHEE. The proposed framework is applied to systems with storage batteries designed for deployment in three Cypriot cities, aiming to meet energy demands of 10, 15, and 20 kWh. The findings reveal a strong correlation between economic and energy performance and the degree of load coverage, with the combination of the DEMO algorithm and the AHP-TOPSIS method emerging as the most effective solution.

Keywords: LLP; LCC; Npm; Cbat; non-dominated sorting genetic algorithm II (NSGA-II); differential evolution for multi-objective optimization (DEMO); particle swarm optimization (PSO) (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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