Accounting for meteorological and load data uncertainty in the optimal design of off-grid hybrid renewable energy systems

Abstract

This study presents a methodological contribution to the optimal design of an off-grid hybrid renewable energy systems (HRES) producing both electricity and drinking water. Beyond simulating the operation of a system combining solar photovoltaic and wind generation with battery and hydrogen storage, the work focuses on a critical yet often overlooked issue: the uncertainty associated with meteorological and consumption input data. A multi-objective optimization model, implemented in Julia, is used to determine system configurations that minimize the cost of energy and water while maximizing the share of renewable energy. The analysis demonstrates that the selection of input data has a significant influence on system design results. A methodology is proposed to identify the most favorable and most unfavorable input datasets. A novel shortage indicator is introduced to quantify energy deficits during periods when renewable production is insufficient to meet demand. This indicator enables interpretation of the underlying causes of cost and sizing variations, by linking them to storage requirements. The methodology is applied to the island of Molène (France) using meteorological and consumption data from 2018 to 2023. The results highlight the strong sensitivity of system design to input variability, and provide a framework for robust analysis and planning under uncertainty.