A global framework for maximizing sustainable development indexes in agri-photovoltaic-based renewable systems: Integrating DEMATEL, ANP, and MCDM methods

The high dependence of single and dual objective optimization algorithms and commercial energy optimization tools on economic criteria has a negative impact on the sub-goals of energy systems. This is due to the typical inverse relationship between economic affairs and other objectives, such as reducing emissions, minimizing energy waste, and enhancing energy security. Optimizing energy systems with high sustainability indexes becomes even more challenging, particularly for off-grid applications that cater to essential human needs like food and water. This need for a practical global decision-making framework based on objective/subjective prioritization has led to a knowledge gap in maximizing sustainability indexes through optimization methods. In this study, for the first time, the 169 targets of Sustainable Development Goals (SDG17) developed by the United Nations are applied to determine the influence of criteria in the relation map using the DEMATEL (Decision Making Trial and Evaluation Laboratory) method. The developed method is applicable to all countries; however, Iran was selected as a case study due to the significant challenge of providing a sustainable power supply for agricultural demands in this country. The resulting relation matrix is then imported into the Analytic Network Process (ANP) to enhance the reliability of criteria weighting. Finally, the weighted multi-criteria decision-making (MCDM) method is employed to address the real greenhouse demand, wherein potential power supply solutions are determined using the predictive dispatch strategy in the HOMER tool. The hybrid solutions include conventional PV, agri-photovoltaic (APV) units, wind turbines, a diesel generator, and a battery bank. The results of the SDG17-DEMATEL-ANP method indicate the prioritization of environmental, economic, technical, energy security, and social objectives, with total decision influence weights of 22.6%, 22.1%, 21.2%, 18.4%, and 15.7% respectively. While the commercial optimization tool shows a higher affordability of conventional PV compared to APV units, the proposed method demonstrates that implementing APV units, despite a slight increase in energy cost (reaching 0.137 $/kWh), can result in a higher sustainability index of about 75.5%, over 48% renewable fraction, <1% unmet load, and >7% improvement in CO2 emission reduction. These advantages are achieved while significantly enhancing the land usage index and without any negative effects on excess electricity levels, capacity factor, and total energy system life cycle emissions. Therefore, the developed framework can be utilized by investors and agricultural demand owners to select energy systems with higher sustainability indexes.