Multi-criteria sizing of hybrid renewable energy systems taking into account the reliability factor

The use of hybrid renewable energy systems (HRES) based on diesel power plants with integration of renewable energy sources (RES) improves the efficiency of electricity supply to decentralized consumers in conditions of expensive fuel delivery. The study aims to develop methods for accounting for the reliability of backup diesel generator sets (DGS) during multi-criteria optimization of HRES equipment composition, since their failure during periods of low RES generation and discharged batteries becomes the primary cause of electricity undersupply. A two-level approach is used for multi-criteria optimization of HRES equipment composition. At the upper level, a set of Pareto-optimal HRES configurations is formed; at the lower level, hourly simulation of HRES operation is performed for a detailed assessment of each option according to the criteria of economic and environmental efficiency, and reliability of power supply. Two approaches to accounting for the reliability criterion are proposed: a simulation-dynamic approach, which models DGS failures as events in time using the Weibull distribution, with consequences of these failures assessed using short-term simulation and the Monte Carlo method, and a combinatorial-probabilistic approach based on the binomial distribution law for analyzing all possible static states of a group of DGS. The impact of the decision-maker's preference structure on the equipment composition is demonstrated through a case study of selecting an HRES for the village of Kovran in Kamchatka Krai. The simulation-dynamic approach provides a more accurate assessment of the risk of long power supply interruptions by accounting for the temporal correlation between DGS failures and restorations. The developed approaches make it possible to substantiate the appropriate composition and capacity of HRES equipment, including the number and unit capacity of DGS, taking into account the stochastic nature of electricity consumption and RES generation, and equipment failure.

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