Multiobjective Optimization of System Configuration and Component Capacity in an AC Minigrid Hybrid Power System

This article proposes a two-stage optimization algorithm to effectively determine the system configuration at one stage, as well as the capacity of components at the other stage in the middle of the former. This algorithm fits in best with the hybrid systems with more possible types of components. The studied system, in this article, includes diesel generators, wind turbines, photovoltaic arrays, and tidal generators as the power generation components, as well as battery banks and flywheels as the energy storage components. It also includes fuel cells and electrolyzers that either work as batteries or generate electricity in the presence of biomass. When the number of components (decision variables) increases, it becomes difficult to find an optimal solution by the conventional methods. Therefore, in this study, a two-stage multiobjective optimization algorithm is applied to design a cost effective and environmental friendly ac minigrid hybrid power system. In each iteration of the proposed algorithm, first, renewable energy sources and energy storage components are selected to form a hybrid power system along with the diesel generator. Then, the capacities of the components are optimized based on the two objective functions, including levelized cost of electricity and emissions. The optimization model uses real annual data in hourly time intervals for the load, solar insolation, ambient temperature, and wind speed. It is found that the proposed algorithm decreases the susceptibility of the solutions to multiple runs compared with the conventional algorithms and achieves a better optimized power system design.

Automated summary

This article proposes a two-stage optimization algorithm for designing cost effective and environmentally friendly AC minigrid hybrid power systems. The algorithm selects renewable energy sources and energy storage components to form a hybrid power system and optimizes the capacities of the components based on two objective functions.