On the impact of load profile data on the optimization results of off-grid energy systems

Abstract

Access to electricity via large scale power grids is seen as one of the solutions for a fully renewable power system. However, it remains a huge technical, economical, and geopolitical challenge. In the meantime, millions of people across the world have none or limited access to electricity and quite often to rely on autonomous solutions such as diesel generators. With the decreasing cost of renewable energy generation technologies in recent years, one could observe a simultaneous increase in studies dedicated to optimal sizing of renewable off-grid systems. Many of these studies rely on the usage of typical daily load profiles to model the electricity demand, sometimes enhanced with seasonal or random components. Such approaches tend to overlook the existing potential case-specific correlation between availability of renewable energy and energy demand and in particular the natural variability of the load in terms of its extreme values or ramp rates. The objective of this study is to investigate the impact of different types of load input data (for instance real load, monthly adjusted typical load, and typical daily load) on the cost of energy provided by off-grid PV-battery systems supplying various loads with different reliability levels. For this purpose, we determine the optimal capacity of PV-battery systems based on commonly used energy management strategies and optimization algorithms. The analysis of the obtained results indicates that, on average, using daily load profiles tends to underestimate the cost by 1.2% points (pp) for a system with 100% reliability and by over 5 pp for a system characterized by 95% reliability. Using monthly adjusted typical daily load profiles leads to slight differences compared to the results obtained by using real load as input. Although the obtained average values indicate a tendency of underestimating the energy cost, some outliers have been also observed reaching values of up to 15% of overestimating the cost of energy.