Abstract
In this paper, the design of a hybrid renewable energy PV/wind/battery system is proposed for improving the load supply reliability over a study horizon considering the Net Present Cost (NPC) as the objective function to minimize. The NPC includes the costs related to the investment, replacement, operation, and maintenance of the hybrid system. The considered reliability index is the deficit power-hourly interruption probability of the load demand. The decision variables are the number of PV panels, wind turbines and batteries, capacity of transferred power by inverter, angle of PV panels, and wind tower height. To solve the optimization problem, a new algorithm named improved crow search algorithm (ICSA) is proposed. The design of the system is done for Zanjan city, Iran based on real data of solar radiation and wind speed of this area. The performance of the proposed ICSA is compared with crow search algorithm (CSA) and particle swarm optimization methods in different combinations of system. This comparison shows that the proposed ICSA algorithm has better performance than other methods.
Highlights
In recent years, the applications of hybrid renewable energy sources have grown rapidly [1,2,3]
The optimum capacity of the hybrid system component is determined with the aim of minimizing NPCS and improving the reliability index (DPHIP) using the new improved crow search algorithm (ICSA) meta-heuristic algorithm
The optimum capacity of the hybrid system components is presented in various combinations and in different amounts of deficit power-hourly interruption probability (DPHIP) constraint
Summary
The applications of hybrid renewable energy sources have grown rapidly [1,2,3]. The optimal design of a solar–wind–battery hybrid system has been presented in [13] to minimize the annual energy generation costs by taking into account the reliability constraints of the probability of load energy not supplied using the genetic algorithm (GA). In [16], the sizing of solar–wind hybrid systems by utilizing a battery bank with the objective of achieving the reliability constraint of desired load supply and the minimum annual system cost is presented using GA. In [17], a three-criterion optimization objective function with the minimization of total costs, environmental fuel emissions, and load power deficiency is proposed using Pareto-evolutionary algorithm to determine the optimal size of energy hybrid system. The main contributions of this work can be listed as follows: 1. The optimal design of a hybrid system for Zanjan city to minimize the NPCS considering the DPHIP hybrid constraint
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