1. Introduction
As the global population continues to expand, and the use of electrical technologies becomes more prevalent, the power sector is experiencing mounting challenges to satisfy the rising global energy demands. Yet depending solely on conventional energy sources exacerbates harm to the environment and jeopardizes human welfare. As a result, scholars are shifting their attention toward renewable energy sources like geothermal, solar, biomass, tidal, and wind energy extraction. This shift aims to address the urgent requirement for sustainable energy options. This shift in focus aims to explore and harness the potential of these renewable resources as alternatives to mitigate environmental harm and promote long-term sustainability [
1,
2,
3].
Solar energy has become widely popular because of its numerous advantages, which encompass its eco-friendly nature, minimal need for maintenance, quiet operation, simple execution, and plentiful availability in the natural environment [
3]. Due to the benefits of renewable energy, the Indian government announced plans to increase its renewable energy capacity by 50 GW yearly over the next five years to reach the goal of 500 GW by 2030. India presently has 168.96 GW of total renewable energy capacity (as of February 28, 2023), with roughly 82 GW of that capacity being implemented at various phases and over 41 GW being in the bidding stage. As seen in
Figure 1, this comprises 64.38 GW of solar power, 51.79 GW of hydropower, 42.02 GW of wind power, and 10.77 GW of biopower [
4].
Moreover, the costs associated with establishing photovoltaic (PV) systems are rapidly decreasing because of advancements in PV technologies. The cost of producing one unit of energy has significantly decreased in recent decades due to PV technology advancements. However, there are still several issues with and limits of solar PV technology, including relatively poor power efficiency, non-linearity, a short lifespan, and reliance on environmental variables like insolation and temperature for the best power output [
3,
5]. Nevertheless, despite these limitations, solar PV technology has a lot of potential as a sustainable energy source.
The power output of a photovoltaic (PV) array can be significantly affected by partial shading (PS) resulting from various factors including shading from buildings, bird droppings, and cloud cover. Moreover, PS has the potential to create hotspots, which can lead to localized damage within the PV module as a result of overheating at specific points. To address the effects of PS, one common approach is to incorporate bypass diodes into the system, although this can sometimes result in multiple peaks on the power and voltage (P-V) curve [
6,
7]. Numerous optimization techniques, including the use of the maximum power point tracking (MPPT) methodology, were developed to overcome this difficulty. These methods tried to separate the many peaks in the power output that exist from the global maximum power point. The authors of [
3] divide MPPT techniques into two main categories: firmware-based methods and hardware-based reconfiguration techniques. Differential power processing (DPP) [
8], solar array reconfiguration [
9], and DC optimizer techniques [
10] are only a few examples of hardware-based reconfiguration systems that have demonstrated excellent performance in tackling this issue. However, these techniques do have some drawbacks. For instance, extra switching mechanisms, skilled staff, and long connections are required to prevent PV module crossover [
8]. The usage of numerous DC-to-DC converters is also required by DPP and DC optimizer techniques to regulate power output and handle power level variations across different PV modules [
11]. In contrast, firmware-based MPPT methods are gaining notable traction and acceptance. This is primarily due to their capability to identify the GMPP without necessitating extra switching elements or sensors [
3]. These methods make use of MPPT controllers that run the PV array at its GMPP by using optimization algorithms. This strategy requires no extra equipment, is simple to apply, and lowers the cost of power generation [
12]. Deterministic approaches, intelligent-based techniques, and metaheuristic algorithms are the three primary categories into which MPPT control techniques are divided in the extant literature [
12].
Traditional or deterministic MPPT techniques, including Perturb and Observe (P&O) [
13], incremental conductance (INC) [
14], and Fractional Open Circuit Voltage (FOCV) [
15] algorithms, suffer from several limitations. These limitations include inadequate convergence, a tendency to converge to the LMPP under shaded conditions, and fluctuations around the maximum power point. To overcome these challenges, researchers developed artificial intelligence (AI)-based techniques that aim to achieve higher efficiency in dynamic weather conditions. These techniques include artificial neural networks (ANNs) [
16], fuzzy logic controllers (FLCs) [
17], and evolutionary algorithms (EAs) [
18]. Although these techniques have excellent tracking speeds and efficiency, they frequently need intricate control circuits and a lot of data processing for system training. A fuzzy logic controller (FLC) distinguishes itself by enabling MPPT implementation without the need for prior system knowledge. In contrast, artificial neural networks (ANNs) are highly efficient tracking methods that require substantial amounts of training data to enhance accuracy. ANN-based techniques utilize dynamic inputs such as insolation and temperature, which are archived as datasets [
19]. The algorithms mentioned above alleviate the storage and computational load on the microprocessor. They also mitigate inaccurate outcomes arising from data gaps or irregularities, which are difficulties associated with vast training data and are relevant to MPPT applications.
Bio-inspired MPPT algorithms like the cuckoo search algorithm (CSA) [
20], Flying Squirrel Search Optimization Strategy (FSSO) [
21], and owl search algorithm (OSA) [
22]. Overall, the recent Bio-Inspired Algorithm for MPPT brings significant advantages in terms of global optimization, adaptability, robustness, and parallel processing. However, it is essential to consider the computational complexity, parameter tuning requirements, and convergence speed when applying these algorithms in practice.
Numerous metaheuristic algorithms were put forward for the optimization of boost converter parameters in the context of MPPT for photovoltaic (PV) systems. These algorithms encompass particle swarm optimization (PSO) [
23], the Dandelion Optimizer (DO) [
24], driving training-based optimization (DTBO) [
25], the emperor penguin optimizer (EPO) [
26], Adaptive JAYA (AJAYA) [
5], the Giant Trevally Optimizer (GTO) [
27], Artificial Rabbits Optimization (ARO) [
28], and the Liver Cancer Algorithm (LCA) [
29]. These algorithms are designed to increase the likelihood of reaching the GMPP. However, there are certain restrictions on how metaheuristic algorithms may be utilized in MPPT for PV systems. The lack of theoretical guarantees for convergence to the global optimum solution is one of them. Furthermore, there are other factors to consider, such as the gradual rate of convergence, the requirement to adjust multiple parameters for the optimal results, the susceptibility of the initial conditions causing less-than-ideal solutions, the intricacy of implementation demanding substantial computational capacity, and the susceptibility to the initial conditions. Given its quick calculation, straightforward design, parallel processing capabilities, resilience, simplicity, and ease of implementation, the PSO algorithm is one of these metaheuristic algorithms that is frequently used in MPPT applications [
30]. It also has a high likelihood of discovering the best overall solution. It is particularly desirable for applications involving multiple-peak functions, making it suitable for MPPT in PV systems [
31,
32].
Even while PSO increases the likelihood of locating the global peak under shaded circumstances, it does not ensure convergence to the ideal operating zone [
33,
34]. The PSO-based MPPT algorithm has some limitations. Firstly, it can be time-consuming to track the MPP when dealing with large search spaces [
34]. Secondly, it can be challenging to choose the right values for the social and cognitive criteria (c1 and c2), and the inertia factor denoted by
w is required to precisely follow the global maxima because the conventional PSO formulation uses random integers, which can reduce search efficiency.
The emperor penguin optimizer (EPO) is used in [
26] to assess a novel MPPT control approach under varying irradiance levels and partial shade situations. The movement of penguin agents is principally responsible for EPO. The progress made by this algorithm can be comparatively slower when compared to other optimization methods. The relatively slower convergence speed can negatively affect performance, especially in scenarios where the rapid and accurate identification of the GMPP is critical. This is particularly relevant in situations characterized by rapidly changing irradiance conditions or partial shading, where the need for efficient MPPT is of the utmost importance.
Metaheuristic algorithms, such as PSO, JAYA, and CSA, often face challenges in striking the right balance between exploration and exploitation. These algorithms are intended to find the best answers in challenging search spaces, but they frequently prioritize exploitation, which causes early convergence and traps users in local optima. However, their exploration abilities are frequently poor, which hinders their capacity to find possibly better answers in uncharted portions of the search space [
25,
35]. The success of an optimization algorithm often depends on its ability to strike the right balance between exploration and exploitation. This balance is crucial because too much exploration can lead to a slow convergence rate, as the algorithm spends too much time searching for new solutions and not enough time exploiting promising ones. Too much exploitation can result in premature convergence to a suboptimal solution, as the algorithm may get stuck in a local optimum without exploring other, potentially better regions of the solution space. Challenges faced by metaheuristic algorithms can arise due to algorithm-specific characteristics, parameter settings, and the nature of the optimization problem being solved. While these algorithms have their strengths, they may not always optimally perform in all scenarios.
Therefore, it becomes essential to achieve a good trade-off between exploration and exploitation when designing and putting these metaheuristic algorithms for MPPT into practice. Their performance and resilience under various environmental situations must be improved. Achieving an optimal balance between exploring new areas of the search space and effectively exploiting the discovered solutions necessitates careful consideration and adjustments.
This paper presents a novel metaheuristic algorithm, named the energy valley optimizer (EVO) algorithm [
36], for maximum power point tracking in PV systems. This paper specifically focuses on its application in addressing the partial-shading-conditions problem in solar PV systems, particularly in the context of maximum power point tracking. The development of EVO is inspired by the principles of stability and particle decay found in advanced physics concepts [
36]. By utilizing the fundamental principles of particle decay in physics, this study brings a unique perspective to the field. Although this paper presents various examples, it is essential to remember that confirming or denying the superiority of each algorithm requires comprehensive comparisons. In addition, a simulation is conducted to evaluate the effectiveness of the EVO algorithm in various scenarios in the PV system.
Utilizing the EVO algorithm for MPPT in PV systems offers several notable benefits. EVO stands out as a superior choice when compared to other metaheuristic algorithms like PSO, CSA, and the AJAYA algorithm in the following ways:
Enhanced Tracking Accuracy: EVO excels at accurately tracking the MPP under varying environmental conditions, including partial shading scenarios. It leverages its unique exploration and exploitation capabilities to swiftly adapt to changing conditions, resulting in a higher tracking accuracy.
Improved Convergence Speed: EVO converges to the MPP more quickly than some other algorithms like PSO or CSA. This quicker convergence is crucial for maintaining system efficiency, especially when environmental conditions rapidly change.
Robustness in Partial Shading: EVO exhibits robustness when confronted with partial shading conditions. Unlike PSO, which may struggle with premature convergence to local optima in such scenarios, EVO effectively navigates through these challenges, minimizing the impact of shading on energy production.
The arrangement of this research paper is as follows. The formulation and modeling of the photovoltaic (PV) framework are presented in
Section 2 of this study. By examining the I-V and P-V curves,
Section 3 looks at how changes in solar irradiance affect the output performance of the PV system.
Section 4 of this paper outlines the concept of partial shading, while
Section 5 offers a description of the framework employed in this study, encompassing maximum power point tracking (MPPT) and a model of the photovoltaic (PV) system.
Section 6 provides a detailed explanation of how the proposed EVO-based maximum power point tracking (MPPT) and other metaheuristic algorithms used in this study were implemented. The MATLAB/Simulink results are evaluated and compared in
Section 7 and
Section 8. In
Section 9 and
Section 10, the proposed EVO-based MPPT algorithm’s findings are presented together with a comparison with CSA algorithms for the real-time hardware-in-the-loop (HIL) implementation of MPPT. While
Section 11 lists potential uses for the recommended method in the future,
Section 12 brings this paper to a close.