Abstract
An increase in the world’s population results in high energy demand, which is mostly fulfilled by consuming fossil fuels (FFs). By nature, FFs are scarce, depleted, and non-eco-friendly. Renewable energy sources (RESs) photovoltaics (PVs) and wind turbines (WTs) are emerging alternatives to the FFs. The integration of an energy storage system with these sources provides promising and economical results to satisfy the user’s load in a stand-alone environment. Due to the intermittent nature of RESs, their optimal sizing is a vital challenge when considering cost and reliability parameters. In this paper, three meta-heuristic algorithms: teaching-learning based optimization (TLBO), enhanced differential evolution (EDE), and the salp swarm algorithm (SSA), along with two hybrid schemes (TLBO + EDE and TLBO + SSA) called enhanced evolutionary sizing algorithms (EESAs) are proposed for solving the unit sizing problem of hybrid RESs in a stand-alone environment. The objective of this work is to minimize the user’s total annual cost (TAC). The reliability is considered via the maximum allowable loss of power supply probability ( L P S P m a x ) concept. The simulation results reveal that EESAs provide better results in terms of TAC minimization as compared to other algorithms at four L P S P m a x values of 0%, 0.5%, 1%, and 3%, respectively, for a PV-WT-battery hybrid system. Further, the PV-WT-battery hybrid system is found as the most economical scenario when it is compared to PV-battery and WT-battery systems.
Highlights
The growth in the world’s population results in high electricity demand
When the total energy produced by the PV and wind turbines (WTs) is greater than ξ ld, the battery bank is in the state of charge (SoC) at time slot t, which is obtained by Equation (6) [29]
The hourly WTs’ power produced along with the energy storage level achieved by enhanced evolutionary sizing algorithms (EESAs) during a year at four LPSPmax values is depicted in Figure 10 for the WT-battery system
Summary
The growth in the world’s population results in high electricity demand. Most of this demand is fulfilled by the consumption of fossil fuel (FF) resources. These resources are intermittent by nature due to their dependency on varying climate and weather conditions like solar irradiation, wind speed, temperature, and other factors [4] To overcome this issue, hybrid RESs (HRESs) in conjunction with energy storage systems (ESSs) are proposed to complement one another to some extent. Among the other available sources, PVs and WTs are considered as the most popular RESs due to the presence of solar irradiation and wind in almost every location of the world Due to their intermittent nature, ESSs in the form of batteries, fuel cells, flywheels, etc., are integrated into the system to make it more reliable during varying weather conditions [6]. This paper proposes meta-heuristic algorithms with their hybridization to achieve objectives, including good convergence speed, efficiency, and flexibility for the unit sizing problem.
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