Analytical Hierarchal Process based Multi-Criteria Sustainability Assessment of Renewable Energy Systems

Climate change and other sustainability challenges indicate that there is a pressing need to shift away from fossil fuels to renewable energy systems (RES) but there are numerous obstacles facing the design of these systems, such as the complex trade-offs that must be made between energy production, land use, water availability, and economic viability. The fundamental objective of this study is to build a sustainability framework that optimizes the design of RES by combining economic, technological, and environmental dimensions. Employing the Analytic Hierarchy Process (AHP) approach, the study conducts a pairwise evaluation of several influencing elements, giving suitable weights to each factor under each dimension, and formulates a multi-criteria decision-making model that applies linear programming techniques for optimization using Python programming language and the Python Linear Programming (PULP) module. The optimal process aimed at finding the best set of decision variables that minimize the objective function which incorporated economic, technical, and environmental factors, weighted according to their AHP-derived priorities while satisfying all constraints. The results from the AHP revealed that the investment costs, resource availability, and Green-house gas emissions are the most important factors across economic, technical, and environmental respectively also a total sustainability score of 0.662, implies a moderately sustainable system derived from the test values. Sensitivity and scenario studies further highlighted the model's resilience, revealing modest sensitivity to changes in priority weights, which underlines the significance of a balanced approach in decision-making. Ultimately, this study provides vital insights into optimizing renewable energy systems, advocating for measures that enhance energy security, mitigate climate change, and promote sustainable development.