Comparative study between classical methods and genetic algorithms for sizing remote PV systems

Abstract

Uncertain renewable energy supplies, load demands and the non-linear characteristics of some components of photovoltaic (PV) systems make the design problem not easy to solve by classical optimization methods, especially when relevant meteorological data are not available. To overcome this situation, modern methods based on artificial intelligence techniques have been developed for sizing PV systems. However, simple methods like worst month method are still largely used in sizing simple PV systems. In the present study, a method for sizing remote PV systems based on genetic algorithms has been compared with two classical methods, worst month method and loss of power supply probability (LPSP) method. The three methods have been applied to a PV lighting system with orientation due south and inclination angles between 0°and 90°in Adrar city (south Algeria). Because measured data for the chosen location were not available, a year of synthetic hourly meteorological data of this location, generated by PVSYST software, have been used in the simulation. Genetic algorithms and worst month methods give results close to each other between 0°and 60°but the system is largely oversized by the worst month method when the tilted angle is over 60°. The results obtained by LPSP method show that the system is very undersized. Hence, a proposition has been made to improve results obtained by this method.

Optimum Design of a Standalone Solar Photovoltaic System Based on Novel Integration of Iterative-PESA-II and AHP-VIKOR Methods

“…Meanwhile, other studies used heuristic techniques in order to find the optimal size of a SAPV system using techno-economic objective functions such as artificial bee colony (ABC) [41], genetic algorithm (GA) [42,43], generalized regression neural network (GRNN) [44], firefly (FL) [45], and particle swarm optimization (PSO) [46]. The main advantage of these searching algorithms is the ability to converge the optimal solution in a short time.…”

Gradyan Tabanlı Optimize Edici Algoritmasının Parametre Ayarlaması

“…Ancak en iyi sonucu elde etmek için uğraşırlar. Metasezgisel algoritmalar klasik yöntemlere göre daha iyi sonuç vermektedirler (Makhloufi, 2015). Bundan dolayı optimizasyon algoritmaları inşaat mühendisliği, makine mühendisliği gibi pek çok alanda kullanılmaktadır (Aala Kalananda & Komanapalli, 2021;Beşkirli & Dağ, 2020;Dhiman & Kaur, 2019;Houssein, Saad, Hashim, Shaban, & Hassaballah, 2020;Huerta et al, 2022;Kutlu Onay & Aydemı̇r, 2022;Salgotra, Singh, Singh, Mittal, & Gandomi, 2021;Shabani, Asgarian, Salido, & Asil Gharebaghi, 2020;Sulaiman, Mustaffa, Saari, & Daniyal, 2020;Umam, Mustafid, & Suryono, 2021).…”

Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia

“…This may be due to the improved accuracy and reduced execution time of AI methods. Furthermore, the ability of AI systems such as genetic algorithm (GA) [20], artificial bee colony (ABC) [21], generalized artificial neural network (GRNN) [22], fuzzy logic [23], firefly methods (FL) [24], and particle swarm optimization (PSO), to cope up with missing meteorological data, is a significant advantage. However, the complexity in designing of an SAPV system is the main drawback of AI methods.…”

“…However, the complexity in designing of an SAPV system is the main drawback of AI methods. The authors of [20] proposed GA to optimize the size of remote PV systems based on synthetic hourly meteorological data, in Adrar city in South Algeria. In [20], the capital cost of the system was employed as an objective function without considering the availability of the system.…”

“…The authors of [20] proposed GA to optimize the size of remote PV systems based on synthetic hourly meteorological data, in Adrar city in South Algeria. In [20], the capital cost of the system was employed as an objective function without considering the availability of the system. However, the proposed GA compared with LPSP method and worst month method based on cost criteria.…”