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This paper presents a two-stage stochastic mixed-integer nonlinear programming model, which is linearized to a mixed-integer linear programming (MILP) form, for optimizing the energy–water–carbon (EWC) nexus in remote AC/DC microgrids aimed at sustainable community development. The model optimizes the selection, location, and operation of diesel generators, voltage source converters , photovoltaic systems , wind turbines , and battery energy storage systems while managing CO2 emissions . It accurately models the AC/DC microgrid’s operation, determining optimal voltages, currents, and converter states (inverter or rectifier). Uncertainties in power demand, water consumption, solar irradiation , and wind speed are modeled using stochastic scenarios generated via k-means clustering. The MILP model is implemented in GAMS and solved with the commercial solver CPLEX, ensuring global optimization. Results show that integrating distributed energy resources , compared to a case without these elements, reduces CO2 emissions of the diesel generators and the operational costs of the EWC nexus, highlighting the proposed approach’s environmental and economic benefits. These findings underline the importance of incorporating distributed energy resources in the sustainable development of microgrids.
Valencia-Díaz et al. (Sat,) studied this question.