Soft-clustering for conflict management around the water-energy-carbon nexus and energy security

This work explores the implementation of an energy supply chain considering the analysis of photovoltaic, biogas, biomass and conventional grid energy systems to satisfy electricity demand minimizing costs, greenhouse gas emissions, water consumption and maximizing energy security. To address this problem, a multi-criteria decision-making environment was implemented that takes into consideration priorities over economic, environmental and energy security objectives obtaining a compromise solution that minimizes stakeholder dissatisfaction and conflicts and, in addition, possible alliances between stakeholders are analyzed using fuzzy C-means clustering. The compromise solution of multi-objective optimization shows the optimal configuration of the electric power and biomass distribution network, the sizing, behavior, installation and location of the technologies. A case study of the Patzcuaro lake region in Mexico is used to evaluate the performance of the proposed methodology. The results indicate that the implementation of this type of methodology leads to attractive solutions for stakeholders, who are the main influencers in the design and implementation of energy supply chains. The compromise solution found reduces emissions by 73.5% and water consumption by 71.8% compared to grid power and achieves economic returns in most locations of up to $517,000 per year. In addition, the compromise solution satisfies on average 70% of the stakeholders either individually or in alliance.