Techno-economic optimization of standalone hybrid power systems in context of intelligent computational multi-objective algorithms

Abstract

The use of renewable energy (RE) for meeting some load power demand in the present global developmental dealings is realistically unavoidable. However, many challenges conspicuously stand the way of RE penetration into the global power sector. Some of the perceived problems require aggressive research attentions. The utilization of single RE energy structure for the supply of electricity in off-grid isolated communities is usually not a technically dependable system with to regards reliability, security and stability. The core challenge is usually connected to some spontaneous variable weather conditions. It is based on this perspective that the implementation of integrated hybrid RE becomes a promising solution for mitigation of RE intermittent behaviors. In this study, an autonomous hybrid energy system was examined based on simulations for optimal sizing configurations of solar photovoltaic (PV), wind turbine (WT), diesel generator (DG) and battery storage (BS) system. Modern intelligent optimization algorithms of Ant Colony Optimization (ACO), Flower Pollination Algorithm (FPA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were applied for providing solutions to the set of selected focal technoeconomic objectives in the framework of this study. Compare with others, FPA provided better results in terms of the net present cost (NPC), cost of energy (COE) and deficit power supply probability (DPSP). The proposed hybrid power systems are configured in four different scenarios: PV/BS, PV/DG/BS, PV/WT/BS and PV/WT/DG/BS. It was consequently established that the configuration of PV/DG/BS with NPC of $85112.08, COE of 0.145 $/kWh and zero DPSP gave the best overall technoeconomic results through the FPA optimization technique.