Multi-Objective and Multi-Variable Optimization Models of Hybrid Renewable Energy Solutions for Water–Energy Nexus

Abstract

A new methodology, called HY4RES models, includes hybrid energy solutions (HESs) based on the availability of renewable sources, for 24 h of water allocation, using WaterGEMS 10.0 and PVGIS 5.2 as auxiliary calculations. The optimization design was achieved using Solver, with GRG nonlinear/evolutionary programming, and Python, with the non-dominated sorting genetic algorithm (NSGA-II). The study involves the implementation of complex multi-objective and multi-variable algorithms with different renewable sources, such as PV solar energy, pumped hydropower storage (PHS) energy, wind energy, grid connection energy, or battery energy, and also sensitivity analyses and comparisons of optimization models. Higher water allocations relied heavily on grid energy, especially at night when solar power was unavailable. For a case study of irrigation water needs of 800 and 1000 m3/ha, the grid is not needed, but for 3000 and 6000 m3/ha, grid energy rises significantly, reaching 5 and 14 GWh annually, respectively. When wind energy is also integrated, at night, it allows for reducing grid energy use by 60% for 3000 m3/ha of water allocation, yielding a positive lifetime cashflow (EUR 284,781). If the grid is replaced by batteries, it results in a lack of a robust backup and struggles to meet high water and energy needs. Economically, PV + wind + PHS + grid energy is the most attractive solution, reducing the dependence on auxiliary sources and benefiting from sales to the grid.