**1. Introduction**

Nowadays, solar photovoltaic energy is being utilised in electrical energy generation to meet the quick-growing consumption and the urgent need for power [1]. Grid-connected photovoltaic (PV) systems with a capacity of 3 kW PV modules could meet the electric demand of a 60–90 m<sup>2</sup> for residential building [2]. By contrast, large-scale PV power plants face some major challenges for the use of vast amounts of components in relation to the cost, reliability, and efficiency, requiring an optimal design of the PV power plant. Recently, the drop in PV module prices of up to 86% from 2010 to 2017 [3] resulted in a decrement in the levelised cost of energy (LCOE) of large-scale PV power plants reaching 0.03 (\$) [4].

TRNSYS software has been used to determine the optimum PV inverter sizing ratios [5]. The simulation has been carried out using three types of inverters with low, medium and high efficiency to determine the maximum total output of the PV system. Furthermore, the PV inverter sizing ratio of the grid-connected has been investigated for eight European locations. Mondol et al. suggested that the installation of a PV system with high-efficiency inverter in the sizing of PV and inverter is more flexible than that of a low-efficiency inverter. Artificial intelligence (AI) methods have also been used to optimise the grid-connected PV power plant, as presented in [6], whereas the PV plant global solution is solved through particle swarm optimisation technique (PSO) and compared with a genetic algorithm (GA), based on the total net economic benefit. However, the PSO algorithm showed better performance than the GA approach used in this study. The optimisation design of the grid-connected PV system is introduced in [7]. The decision variables of the proposed methodology are the type of PV modules, inverter, and tilt angle. The study supports the mathematical models of the PV array, inverter and solar irradiance on tilt PV modules surface. The optimisation process considered three types of inverter, four types of PV modules and seven values of tilt angle, as well as the hourly solar irradiance and ambient temperature. As a result, the optimal design of the system is selected based on maximum efficiency.

In 2012 [8], Sulaiman, S.I., et al. proposed a sizing methodology by using an evolutionary programming sizing algorithm. The optimisation procedure supports all possible combinations of PV modules and inverters considering different types of PV modules and inverters. The technical and economic aspects are included in this method, and both the maximum yield factor and the net present value of the PV system were calculated. Chen et al. have proposed an iterative method for the optimal size of inverter for PV systems with maximum savings in nine locations in the USA [9]. The optimisation procedure has selected the gainful inverter size for each location. Additionally, optimum inverter size lower than or the same as that of PV array rated size can be installed, due to the inverter intrinsic parameters, economic and weather considerations. In 2014, Perez-Gallardo proposed an optimal configuration of the grid-connected PV power plant of different PV technologies by using the GA technique, by considering economic, technical and environmental criteria [10]. This study aims to maximise annual energy generation. Another methodology was proposed in [11] to design a PV plant for the self-consumption mechanism for different capacities in the range of 450–1250 kWp for the university campus. The simulation was performed using PV\*SOL software.

A study in [12] investigated the selection and configuration of inverter and PV modules for a PV system for minimising costs. The purchasing costs can be reduced by 16.45% of 10 kW by using this model. However, this evaluation model is applicable only at the lowest price and cannot be applied to achieve the highest efficiency in power production. A mathematical procedure is presented in [13–15] to determine the optimal number of rows and a PV module tilt angle for maximising the profit during PV plant lifetime, by considering the effect of shading on the PV module output power. A work in [16] investigated the design of PV systems grid-connected, considering the PV module degradation rate, to select the optimum inverter size for increased energy and reduced cost. Actual inverters with high efficiency offer a wider range than inverter with low-efficiency for sizing factor to increase the energy generation. Research presented in [17] proposed an eco-design for grid-connected PV systems, on the basis of the combination of multi-objective optimisation and other software. The techno-economic and environmental criteria were optimised simultaneously. The installation of thin-film PV modules in PV systems show an advantage over crystalline silicon ones. A methodology for achieving the optimal configuration of large-scale PV power plants to improve its performance is presented in [18]. The optimisation process was performed using different algorithms and is considered to minimise the LCOE by using crystalline silicon and thin-film cadmium telluride PV module technology. According to this study, the proposed technique of grey wolf optimiser showed improved results compared with the other methods in solving the optimal design of the PV power plant. The PV plant LCOE with the thin film had a lower value than crystalline silicon and is more productive. The work reported in [19] proposed a method to convert the design of PV power plants to binary linear programming to achieve an economical design. However, in this method, only the number of inverters and PV modules connected in series and parallel were considered as the

design variables. Some other methods have also been employed and published by researchers in this topic to propose a suitable configuration and determine the best solution that considers the environment, economic and technical aspects of the PV system [20–36]. Additionally, references [37–41] reviewed the grid-connected PV system optimisation and challenges.

The average time for the input meteorological data is an essential factor in PV system design, because the monthly and daily average time of the meteorological data fails to determine an optimum design, resulting in the oversizing system and high energy losses and increasing the financial risk of the PV plant. Additionally, the geographic latitude of the PV plant installation site can lead to a significant variation of the PV module optimal tilt angle from one location to another, to convert maximum solar irradiance into electricity and make the PV system more profitable [42].

This paper intends to present a methodology for designing PV power plants by considering semi-hourly time-resolution (i.e., 30 min-average) to address the accuracy of the meteorological data variation, and thus determine the PV plant optimal design and increase its performance. The procedure considers the detailed specifications of the different alternatives of PV modules and inverters to determine the optimum component and system topology for the location under study. Three meteorological parameters of solar irradiation, wind speed, and ambient temperature were measured for 1 year at the installation field are considered. Hybrid grey wolf optimiser-sine cosine algorithm (HGWOSCA) [43] and sine cosine algorithm (SCA) [44] were applied as optimisation techniques to solve the PV plant design problem for two different objectives, including minimum levelised cost of energy (LCOE) and maximum annual energy, while considering many design variables for improving the system performance. The contributions of this article to the book of knowledge in this research field are described below.


This paper is organised as follows: Section 2 presents an overview of the renewable energy potential of Algeria, and Section 3 presents the work methodology, including the formulation of the design problem, the PV system description and meteorological data and, the proposed design optimization. In Section 4, the HGWOSCA algorithm is described. Section 5 presents the obtained results with the sensitivity study. Finally, Section 6 presents the conclusions of the paper.

#### **2. The Renewable Energy Potential of Algeria**

Algeria has an important potential for electricity generation from renewable energy sources, as performed in several recent studies. However, according to reference [45], approximately 0.415% of electricity in Algeria is generated from renewable energy sources in 2014. The diesel generator is the dominant energy source in rural and Saharan regions in Algeria [46].

#### *2.1. Solar Energy*

The potential of solar energy in southern Algeria is the largest in all Mediterranean basins, with 1,787,000 km<sup>2</sup> of Sahara desert, according to the German Aerospace Centre (DLR). The insolation time of almost all the national territory exceeds 2000 h annually and reaches 3900, as shown in Figure 1 (high plains and Sahara) [47]. Over most of the country and during the day, the energy obtained on a horizontal surface of 1 m2 is nearly 5 kWh or about 2263 kWh/m2/year in the south and

1700 kWh/m2/year in the north [48]. This great potential in solar energy compels Algeria to go towards the exploitation of solar energy for power generation, rather than oil and gas. Table 1 shows the rate of sunshine for each region of Algeria.

**Figure 1.** Horizontal Irradiation of Algeria [50].


