Addressing extreme weather events for the renewable power-water-heating sectors in Neom, Saudi Arabia

Abstract

A renewable energy design optimization model is proposed to plan investments in power, water, and heat technologies. The intermittent nature of renewables requires that these models capture the variability and complementarity of resources at high spatial and temporal resolutions. However, most planning models use time-series reduction methods that, while capturing data variance, often smooth out extreme weather or demand patterns. To account for extreme patterns and design reliable energy systems, we propose a clustering-optimization framework that considers extreme weather days. This framework is applied to design an integrated multi-sector energy system for the Neom region in Saudi Arabia. Our results show that fully renewable systems designed without considering extreme days could not meet demands and instead required external power or water supplies during a post-optimization simulation. Once extreme days were considered in the optimization, system reliability increased at the expense of larger generation and storage capacity investments.