In a significant stride towards sustainable energy solutions, researchers have developed an intelligent decision support model that optimizes the development of photovoltaic (PV) solar power. Published in the journal “Energy Nexus” (formerly known as “Energy Reports”), this study, led by Ibrahim Alrashdi from Jouf University in Saudi Arabia, introduces a novel approach to navigating the complexities of solar energy deployment.
The model addresses a critical need in the energy sector: the transition from fossil fuels to cleaner, more sustainable sources. Fossil fuels have long been a double-edged sword, providing energy at the cost of environmental degradation and health risks. Solar energy, particularly PV, offers a promising alternative, but its widespread adoption requires strategic planning and decision-making.
Alrashdi and his team have tackled this challenge by proposing a four-stage decision support model. The model employs multi-criteria decision-making (MCDM) procedures to handle conflicting criteria, using two specific methods: Stepwise Weight Assessment Ratio Analysis (SWARA) to obtain factor weights and Evaluation Based on Distance from Average Solution (EDAS) to order alternatives. To manage vague information, the researchers integrated a spherical fuzzy set (SFS) framework with the MCDM methods.
The four stages of the model are applied to different aspects of solar PV development. The first stage focuses on site selection, using the SF-SWARA-EDAS procedure to identify the best location. Following this, the second stage involves selecting the best manufacturer. The third stage addresses technology selection, and the final stage is dedicated to strategy selection, aiming to overcome issues in solar PV development.
“The main results show that location 10, manufacturer 10, technology 1, and strategy 1 are the best alternatives,” Alrashdi explained. “The sensitivity analysis indicates that the ranking of alternatives in each stage is stable, and the comparative study demonstrates the effectiveness of the SF-SWARA-EDAS procedure compared to other MCDM approaches.”
This research has significant implications for the energy sector. By providing a robust framework for decision-making, it can aid governance in achieving sustainability and promoting renewable energy. The benefits are manifold: clean energy production, reduced carbon emissions, minimized dependence on fossil fuels, and cost savings.
As the world grapples with the urgent need to transition to sustainable energy sources, this decision support model offers a valuable tool for optimizing solar PV development. It not only addresses the immediate challenges but also paves the way for future advancements in the field. By integrating advanced decision-making techniques with a focus on sustainability, this research is poised to shape the future of renewable energy.