Editorial of Maximum Power Point Tracking Methods for PV Systems in Micro-Grids

1. Introduction

In recent years, renewable energy has become one of the most critical pillars for addressing the dual challenges of global energy shortages and environmental degradation. Among renewable sources, photovoltaic (PV) systems have gained particular attention for their sustainability, scalability, and zero on-site emissions [1,2,3,4].

The photovoltaic (PV) system converts sunlight directly into electricity through the photovoltaic effect, enabling clean energy generation without combustion or mechanical components [5,6]. Consequently, PV systems require minimal maintenance and exhibit long operating lifespans. However, their performance is strongly influenced by environmental factors such as irradiance and temperature, resulting in nonlinear current–voltage (I–V) and power–voltage (P–V) characteristics that complicate energy harvesting [7,8,9].

Recent studies have revealed that the goal of improving the conversion efficiency and methods of Maximum Power Point Tracking (MPPT) remains one of the main topics of research in modern PV systems [10,11,12,13]. Conventional MPPT techniques such as the perturb-and-observe (P&O) method and the incremental conductance (INC) method were the first techniques introduced in commercial inverters [14,15]. However, these methods suffer from oscillations and slow dynamic responses under partial shading (PS) conditions [16,17], and therefore hybrid and bio-inspired optimization algorithms, such as particle swarm optimization (PSO), ant colony optimization (ACO), grey wolf optimizer (GWO) and Harris Hawk Optimization (HHO), have been developed to remedy these conditions [18,19,20,21].

A representative example is the hybrid Harris Hawk Optimization (HHO) and P&O algorithm proposed for complex partial shading environments [22,23,24]. This hybrid model proved to be superior both in speed of tracking and in steady-state ripple losses compared with conventional MPPT schemes. Likewise, fuzzy logic and artificial neural network (ANN) controllers combined with incremental conductance and perturb-and-observe methods have proven more adaptive to the transient conditions of irradiance [25,26]. The comparative experiments undertaken using these adaptive controllers confirmed gains in energy harvesting of the order of 3–5% in distributed PV arrays [27,28].

On the level of converters, differential power processing (DPP) types and switched-capacitor–inductor (SCL) types have been developed to prevent the losses that arise from differences in the PV module output level [29,30,31]. Converter developments such as partial power processing converters (PPPCs) have enabled isolated module optimization and power decoupling [32,33,34]. Efficiencies exceeding 98% have been exhibited in various simulations, while a compact circuit design while maintaining a relatively simple control architecture [35,36]. Similar research has been reported in Energies journal [2,9], with reference to the subject of converter integration (simplified bidirectional DC–DC structures) where backflow power in isolated systems has been eliminated.

PV management on the microgrid scale has been revolutionized by advances in data analytics and monitoring associated with the IoT [12,37,38,39]. The use of real-time data from IoT sensors related to irradiance, temperature, and load is sent to cloud-based platforms for adaptive scheduling and fault identification. AI-based predictive methods have improved the accuracy of fault diagnosis by as much as 15–20% when compared with surrogate methods based on conventional thresholds [40,41,42].

Such systems have also been successfully employed in intelligent agricultural factories where interlinked servers manipulate environmental controls, irrigation, and lighting fed by IoT [3,43]. AI-supported decision management can support energy efficiency and safety in renewable energy infrastructures.

The convergence of converter-level innovation, intelligent control, and IoT-based adaptive management characterizes the technological basis of the new photovoltaic microgrid [44,45,46]. This basis supports the transition to cyber–physical PV ecosystems, leading to flexible, resilient and sustainable energy networks [47,48,49].

2. Review Papers

In recent articles, Thi Thu Em Vo et al. conducted one of the first reviews synthesizing floating photovoltaic (FPV) applications within the context of aquaculture and offshore industry, emphasizing structural design and adaptation to environmental conditions [4,5,50].

The cited reviews showed that the aim of FPV is to lower module temperature and evaporation and increase the energy yield when compared to land-based PV systems [51,52]. They also emphasize the importance of FPV for sustainable coastal economies and hybrid solar and tidal systems [4,5].

Subsequent studies extended these discussions to desert and inland aquaculture contexts. They found that FPV installations had the potential to achieve a balance between land and water use [6,19,20]. Recent techno-economic analyses showed that FPV deployment could result in a roughly 10–13% increase in annually generated energy while reducing evaporation loss from water reservoirs [21,22,23]. Environmental simulations showed that the cooling effect of the water surface improved the efficiency of solar PV modules and their longevity [24,25].

Nguyen and Bhattacharyya conducted design optimization assessments of mooring configuration, hydrodynamic stability, and energy production performance in large-scale FPV systems [26,27]. Their results indicated that optimized anchor angles and flexible mooring materials could improve energy capture by 2–4% for varying wind conditions [28,29]. Additional analyses focusing on renewable energy and applied energy indicated that lightweight composite materials reduce installation costs and environmental footprint [30,31,32]. Such results are consistent with previous analyses published in Energies journal on FPV–aquaculture integration, which also confirmed the mutual benefits of energy and ecological sustainability [4,5,6].

In addition, the incorporation of predictive maintenance and AI-based monitoring in FPV systems is perceived as an emergent trend. The possibility of real-time optimization of floating structures under variable conditions of weather and load is provided through smart sensors, edge computing, and data-based anomaly detection [33,34,35]. These innovations further reinforce the change of FPV systems into intelligent, self-learning, and eco-adaptive infrastructures aiding sustainable aquaculture and the development of an offshore industry.

Li et al. and Santos et al. surveyed AI-based MPPTs, which included comparative assessments of deep, fuzzy, and bio-inspired hybrid controller access in photovoltaic systems [36,37,38,39]. These meta-analyses concluded that intelligent controllers have higher performance compared to traditional algorithms in convergence speed, adaptability, and energy yield. The authors also compared the problems encountered in their hardware realization, including data generalization and the cross-platform application of learning-based controllers [12,40].

The importance of cybersecurity and privacy is rapidly expanding in distributed energy systems. In particular, recent studies emphasize privacy-preserving authentication, secure data-sharing protocols, and blockchain-based intrusion-detection paradigms as key elements of resilient AI-enabled energy systems [7,42,43,44,45,46].

3. Research Papers

The work presented by Muhammad Shahid Niazi et al. [2], “A Simple Mismatch Mitigating Partial Power Processing Converter for Solar PV Modules”, introduces a switched-capacitor–inductor (SCL)-based differential power processing (DPP) converter that was proven to be capable of minimizing the mismatch losses occurring in PV modules due to partial shadowing effects. Experimental results showed conversion efficiencies greater than 98% for this converter, indicating that it brings greater reliability and simplicity for use in high performance PV systems.

In a proposed study by Kyung-Yong Kim et al. [3], titled “Research on Crop Growing Factory: Focusing on Lighting and Environmental Control with Technological Proposal,” an IoT-based control framework was developed to control the lighting intensity, temperature, and humidity in plant factories. The proposed system achieved many advantages in terms of energy efficiency and operational stability, which validated its applicability for microgrid-based distributed energy management.

In the paper by Muhammad Annas Hafeez et al. [8], “A Novel Hybrid MPPT Technique Based on Harris Hawk Optimization (HHO) and Perturb and Observe (P&O) under Partial and Complex Partial Shading Conditions”, the hybrid technique adopted blended classical and bio-inspired optimization techniques to obtain better results in tracking performance. The simulation results showed that the efficiency obtained by the HHO-P&O technique was 99.8% and the oscillations in the steady-state condition were almost negligible; their technique was considered to have the best performance compared to the existing MPPT PSO and ACS techniques.

In their study titled “Simulations for the Elimination of Back Flow Power in a Three-Port Isolated Bidirectional DC–DC Converter”, Norbert Njuanyi Koneh et al. [9] propose a modified dual phase-shift (DPS) control law to eliminate backflow power losses through the synchronized internal and external phase displacement angles. The simulation results confirm no backflow power losses and a close to 20% increase in efficiency, which represents a theoretical solution for stable power control in microgrids based on photovoltaic energy conversion.

These scholarly articles cover a wide variety of subjects related to the optimization of photovoltaic systems, converter design, efficient energy management, hybrid MPPT algorithms, and secure power conversion control. Collectively, they give empirical and simulation results for improving energy efficiency, system stability, and intelligent PV-integrated microgrids, hence providing valuable information towards improving renewable, intelligent, and sustainable energy technology.

4. Conclusions and Future Work

As the importance of sustainability in global society has been forging ahead in the wake of advances in artificial intelligence, renewable sources of energy, and digitalization, the application of intelligence in energy systems must be explored globally to achieve this goal. This special publication, “Editorial of Maximum Power Point Tracking Methods for PV Systems in Micro-Grids”, showcases the latest advances in intelligent energy conversion, intelligent energy management, and secure means of communication technology, directly contributing to the development of modern energy systems.

In detail, the eight representative papers contained in this Special Issue give wide-ranging views of the different aspects of PV and microgrid research. They deal with converter-level efficiency optimization [2], IoT-based smart energy management [3], floating photovoltaic (FPV) applications for sustainable aquaculture and offshore systems [4,5], PV−aquaculture integration in desert environments [6], secure mechanisms for smart grid authentication [7], hybrid intelligent MPPT algorithms [8], and high-efficiency dual phase-shift (DPS) power conversion control [9]. Overall, such works present the technological advances required for the intelligent, secure, and efficient energy operation of microgrids in Industry 4.0.

This Special Issue provides a holistic overview of the MPPT methods used for PV systems employed in microgrid environments, in terms of principles, comparative performance, and applicability within the framework of Industry 4.0 energy management. To provide a complete view of these interconnected developments, the conceptual framework of the development of microgrid PV systems is illustrated in Figure 1 illustrates five important research dimensions that jointly enable intelligent, secure and sustainable energy management.

This Special Issue is not only concerned with increasing energy output or reducing costs, but rather emphasizes technologies that express the values of humanity, support the sustainable utilization of energy, and ensure for the safety and security of our systems. One of its aims is to demonstrate how each renewable energy technology can coexist with artificial intelligence and intelligent control to create clean and sustainable energy systems that will also be stable and reliable.

A total of eight academic contributions are published, including review papers and research papers. The continuation of this theme will be expanded in future Special Issues under the title “Intelligent Energy Systems and AI-Based Microgrid Optimization, Volume 2” comprising themes on new topics such as energy–data integration, secure decentralized power networks, artificial intelligence, ethical implications of energy systems, etc. The next volume will investigate the intelligent, adaptive and human-centered energy solutions aligned to global commercial objectives for sustainability, safety, and innovation in technology.