Search Results (783)

The increasing penetration of rooftop photovoltaic (RTPV) systems in low-voltage (LV) distribution networks introduces challenges such as voltage rises, reverse power flow, and reduced hosting capacity, thereby necessitating effective active power regulation (APR) in module-level micro inverters. This paper proposes a dual-layer control framework for a 250 watt-peak (Wp) three switch rooftop PV micro-inverter, integrating quantum-behaved particle swarm optimization with reinforcement learning (QPSO-RL) for accurate maximum power point tracking (MPPT) and a linear quadratic regulator (LQR) for reserve- aware APR. The QPSO-RL algorithm improves available-power estimation under varying irradiance, temperature, and partial-shading conditions, while the LQR-based controller ensures fast, well-damped, and grid-compliant power regulation. The proposed framework was developed and validated using MATLAB/Simulink 2024 for simulation studies and LabVIEW with NI myRIO 2022 for real-time hardware implementation. Both simulation and experimental results confirm that the proposed method achieves 99.5% MPPT accuracy, convergence within 20 ms, grid-injected current total harmonic distortion (THD) below 3%, and a near-unity power factor. In addition, the reserve-based regulation strategy improves feeder compliance and reduces converter stress, thereby supporting reliable rooftop PV integration. These results demonstrate that the proposed QPSO-RL + LQR framework offers a practical and intelligent solution for high-performance, grid-supportive rooftop PV micro-inverter applications. Full article

The integration of photovoltaic (PV) generation with bidirectional electric vehicle (EV) fast-charging systems offers a promising pathway toward sustainable transportation and grid support. However, the dynamic coupling between maximum power point tracking (MPPT) perturbations and grid-side power quality presents a fundamental challenge in such multi-converter architectures. This paper addresses this challenge through a coordinated design and optimization framework for a grid-connected, PV-assisted bidirectional off-board EV fast charger. The system integrates a 184.695 kW PV array via a DC-DC boost converter, a common DC link, a three-phase bidirectional active front-end rectifier with an LCL filter, and a four-phase interleaved bidirectional DC-DC converter for the EV battery interface. A comparative evaluation of three MPPT algorithms establishes the Fuzzy Logic Variable Step-Size Perturb & Observe (Fuzzy VSS-P&O) as the optimal strategy, achieving 99.7% tracking efficiency with 46 μs settling time. However, initial integration of this high-performance MPPT reveals system-level harmonic distortion, with grid current total harmonic distortion (THD) reaching 4.02% during charging. To resolve this coupling, an Artificial Bee Colony (ABC) metaheuristic algorithm performs coordinated optimization of all critical PI controller gains. The optimized system reduces grid current THD to 1.40% during charging, improves DC-link transient response by 43%, and enhances Phase-Locked Loop (PLL) synchronization accuracy. Comprehensive validation confirms robust bidirectional operation with seamless mode transitions and compliant power quality. The results demonstrate that system-wide intelligent optimization is essential for reconciling advanced energy harvesting with stringent grid requirements in next-generation EV fast-charging infrastructure. Full article

In this paper, the dynamic modeling and integrated simulation of a ship microgrid system designed to enhance power quality and energy efficiency in electric propulsion vessels are proposed. The proposed system consists of a photovoltaic (PV) array, a battery energy storage system (BESS), a diesel generator, and a propulsion system, all of which are organically integrated through power conversion devices. To compensate for the intermittent nature of solar power, a control strategy featuring Maximum Power Point Tracking (MPPT) for the PV system and bidirectional DC/DC converter control for the battery was implemented. Specifically, a control logic to stabilize the system output in response to the fluctuating loads of the electric propulsion system was developed using PSCAD (v50) software. The simulation results demonstrate that the proposed control strategy maintains DC-link voltage deviation within ±1.8% and achieves a settling time of less than 0.8 s while optimizing propulsion efficiency (peak-shaving ratio 25–30%) under both constant and variable speed operating conditions. Battery SOC variation is limited to 18–88%, preventing overcharge or discharge. This research provides a foundational framework for the design of energy management systems (EMSs) and grid stability assessments for future eco-friendly electric propulsion ships. Full article

This study develops and applies an integrated modeling framework to assess the solar-to-hydrogen-to-power potential across Romania’s five hydrogen ecosystems defined in the National Hydrogen Strategy. The methodology couples PVGIS-based photovoltaic yield simulations, based on hourly solar irradiation data and including system losses, with MHOGA-based electrolysis simulation, enabling a quantitative-energetic-environmental (Q-E-E) system-level assessment. A 1 MW photovoltaic plant was simulated under three mounting configurations (15° fixed tilt, optimal tilt, and solar tracking) and interfaced with alkaline (AEL) and proton exchange membrane electrolysers (PEMEL). Specific photovoltaic yields reach up to 360 kWh/m²_PV·year under tracking conditions, producing up to 7.5 kg/m²_PV·year (AEL) and 6.8 kg/m²_PV·year (PEMEL), expressed per unit of photovoltaic surface area to enable consistent comparison across the configurations considered. The modeled round-trip efficiency of the full solar–electricity–hydrogen–electricity chain is 38.32% for AEL and 34.57% for PEMEL. Life-cycle-based emission modeling yields 0.92 kg CO₂/kg H₂ (AEL) and 1.03 kg CO₂/kg H₂ (PEMEL), while avoided emissions exceed 250 g CO₂/kWh relative to grid intensity. Land-use modeling indicates area requirements between 9402 and 18,804 m²/MW, depending on the Ground Coverage Ratio. Results demonstrate that system configuration exerts a stronger influence than regional solar variability in determining hydrogen yield, highlighting the need for integrated techno-environmental optimization for large-scale deployment. Full article

Traditional virtual synchronous generator (VSG) control in photovoltaic–storage systems struggles with severe dynamic deterioration under high-impedance weak grid conditions. Through small-signal modeling, this paper analytically reveals that increased grid inductance forces the system’s dominant poles to migrate significantly toward the real axis, inducing a critical “overdamped hysteresis” that degrades transient tracking speed and oscillation attenuation. To break these physical constraints, an improved exponential synergistic adaptive control strategy is proposed. By establishing a synergistic optimization mechanism between the virtual inertia and damping coefficients via a square-root coupled exponential function, the proposed method achieves precise multi-parameter coordination. During the initial phase of disturbances, it triggers an explosive parameter surge to provide “stiff” transient support, strictly limiting frequency deviations and the rate of change of frequency (RoCoF). During the recovery phase, it drives a precipitous parameter decay to actively neutralize the overdamped coupling effect, forcibly pulling the migrated poles back to the ideal underdamped region. Rigorous switching-model simulations demonstrate that, compared to conventional fixed-parameter and power function-based adaptive methods, the proposed synergistic strategy significantly improves transient performance. Quantitatively, during load steps, it restricts the frequency nadir to 49.85 Hz (compared to 49.73 Hz for fixed parameters). During extreme grid stiffness transitions (SCR drops), it completely eliminates active power tracking hysteresis by reducing the settling time to just 0.26 s and aggressively clamps AC overcurrent peaks from 38 A down to 31 A. Supported by coordinated PV–storage energy management, the proposed method offers a highly robust grid-forming framework for renewable-dominated weak power grids. Supported by coordinated PV–storage energy management, the proposed method offers a highly robust grid-forming framework for renewable-dominated weak power grids. Full article

The intermittent nature of photovoltaic (PV) energy, especially under nonlinear and unbalanced loading situations, has made it more difficult to ensure steady operation as it is increasingly integrated into modern power systems. The Power Quality (PQ) issues cause severe degradation of both system performance and device lifetime. A novel Neural Power Quality Network (NeuPQ-Net) controlled Unified Power Quality Conditioner (UPQC) combined with a Quasi Z-Source Lift (QZSL) converter for PV applications is presented in this research as a novel solution for addressing these issues. The QZSL converter, which is controlled by a Maximum Power Point Tracking (MPPT) algorithm based on Perturb and Observe (P&O), increases the PV source voltage to the necessary DC-link level. A Zebra Optimisation Algorithm tuned PI (ZOA-PI) controller continually adjusts PI gains for quick and accurate regulation, ensuring steady DC-link voltage. Unlike conventional Synchronous Reference Frame (SRF) or Decoupled Double Synchronous Reference Frame (DDSRF)-based reference generation, the proposed NeuPQ-Net operates directly in the abc domain, eliminating Phase-Locked Loop (PLL) dependency and reducing computational complexity. Simulation and hardware prototype validations demonstrate that the proposed system achieves significant improvements in PQ indices, including reduced Total Harmonic Distortion (THD), faster response to transients, and enhanced voltage regulation, while complying with IEEE-519 standards. Full article

The fast growth of electric vehicles (EVs) and renewable energy motivates reliable charging infrastructure with balanced energy management and good power quality. However, conventional converter controllers like proportional and integral (PI) and fuzzy logic controllers (FLCs) exhibit slow dynamic response, poor adaptability to varying solar conditions, unbalanced energy management, low power quality, and higher total harmonic distortion (THD). To overcome these limitations, this work proposes an adaptive neuro-fuzzy inference system (ANFIS) controller for balanced energy management and improved power quality in EV charging stations. The ANFIS controller is a combination of a fuzzy inference system (FIS) and a neural network (NN). The FIS provides the best maximum power point tracking and robust control during changing solar PV conditions. The NN optimally controls the flow of power between the solar PV system, energy storage battery (ESB), EV, and utility grid. The entire system is simulated in MATLAB/Simulink. It consists of a PV system with a capacity of 2 kW, an ESB with a capacity of 10 kWh and an EV battery with a capacity of 4 kWh, which are linked by bidirectional DC/DC converters. A 30 kVA bidirectional inverter, along with an LCL filter, is connected between the 500 V DC bus and 440 V utility grid, allowing for both directions. The results validate the effectiveness of the proposed ANFIS controller in terms of DC bus voltage stability, faster dynamic response, enhanced renewable energy utilization, improved efficiency to 98.86%, reduced voltage and current THD to 4.65% and 2.15% respectively, reduced utility grid stress, and enhanced energy management compared to conventional PI and FLCs. Full article

This study introduces a control strategy that integrates a photovoltaic (PV) array reconfiguration approach into a Three-Level Neutral Point Clamped (NPC) inverter with LCL filtering and Space Vector Pulse Width Modulation (SVPWM) control. The control strategy eliminates multiple local Maximum Power Points (MPP) caused by partial shading in PV systems, thereby reducing mismatch losses and preventing the Maximum Power Point Tracking (MPPT) algorithm from becoming stuck at a local maximum. To achieve this, it utilizes an electrical reconfiguration strategy that dynamically shifts the PV array interconnections. Furthermore, this strategy reduces the system’s Total Harmonic Distortion (THD) by adjusting the DC bus voltage. Consequently, simulation evaluations across four different weather conditions have shown that this control strategy achieves significant power improvements: up to 54.8% in Case 1, 39.4% in Case 2 and 3, 21.3% in Case 4. Furthermore, the proposed approach suppressed DC bus voltage changes (<8.8 V) even under the worst conditions and reduced the THD in the grid current from 10.1% to 3.7%. Full article

In light of the low efficiency and unstable power transmission capacity of grid-connected energy storage in photovoltaic (PV) systems, this paper proposes a maximum power point tracking (MPPT) control strategy based on a novel intelligent fusion algorithm. We begin by highlighting that improving the power generation efficiency of PV systems under non-ideal conditions—such as partial shading—is a key challenge for increasing the utilization rate of renewable energy and promoting the sustainability of energy systems. The proposed strategy integrates two complementary search algorithms: the Whale Optimization Algorithm (WOA) for global exploration and the Sparrow Search Algorithm (SSA) for local exploitation. These are combined through a weighted superposition mechanism to enhance the overall search balance. First, a Chaotic Map is used to initialize the populations of both WOA and SSA, while population weights are restructured to improve diversity. Subsequently, a weighted superposition mechanism reorganizes the initialized populations to generate a fused WOSSA population, enabling a global search that produces and evolves a set of optimal solutions across the entire search space, further enhancing search diversity. Then, a local search is applied to selected high-quality individuals to prevent premature convergence and rapidly exploit promising regions. Finally, boundary-handling functions and a power restart mechanism are introduced during the population position update phase to refine position updates in the WOSSA algorithm. This optimizes the iterative process, accelerates convergence, and strengthens the algorithm’s ability to escape local optima. The proposed algorithm is simulated in MATLAB. Simulation results demonstrate that, compared with the SSA algorithm, the convergence speed is improved by approximately 55%, the maximum power tracking performance is enhanced by about 70%, and the voltage of the energy storage unit remains above 380 V. Experimental validation further shows that the PV system achieves an average daily output of 425.5 V and 4.3 A, a grid frequency of 49.9 Hz, a daily energy yield of 0.5 kWh, and a power generation efficiency per unit installation area of 2 kW. The method also exhibits good performance in improving the quality of grid-connected power. Full article

Photovoltaic (PV) system monitoring, optimization, and control have completely changed as a result of the convergence of internet of things (IoT) and machine learning (ML) technologies. While IoT makes it possible to gather, transmit, and store electrical and environmental data, ML offers intelligent data analysis for prediction and adaptive decision-making. This review provides a comprehensive analysis of recent advances in the application of IoT as well as ML for improving PV performance and efficiency. It examines the IoT hardware and communication architectures and highlights their roles in achieving high-resolution and real-time monitoring. In addition, this paper explores the application of ML in PV systems, including power forecasting, maximum power point tracking (MPPT), fault detection, and energy management. Moreover, it analyzes the benefits and performance improvements as well as challenges and limitations of the combined IoT–ML framework with PV systems. It outlines the future directions, such as federated learning, edge intelligence, and digital-twin integration. This combination enhances the system performance by improving tracking efficiency, reducing forecasting error, and decreasing operational cost, which makes these technologies key parts of the next generation of PV systems. Full article

Renewable energy sources have been widely utilized in many applications worldwide in recent years, particularly to support sustainable and energy-efficient systems. One of the most vital applications of these sources is the photovoltaic (PV) traffic light system (TF-LS), which represents a sustainable alternative to conventional grid-powered traffic infrastructure. This paper presents the design and experimental implementation of a stand-alone PV TF-LS, consisting of a PV power system and an integrated TF-LS that operate autonomously while ensuring reliable and efficient energy utilization. The proposed control of the PV power system accomplishes two main functions: maximum power point tracking (MPPT) of the PV module and battery charging management. The MPPT system is implemented using a perturb and observe (P&O)-based PI algorithm with reduced step size and is experimentally validated using a dSPACE 1104 real-time control platform. In addition, this paper proposes and experimentally implements a novel intelligent control strategy for the TF-Ls that relies on vehicle counting and real-time comparison of traffic densities at road intersections instead of the traditional fixed-time scheduling approach, using LabVIEW software and an Arduino microcontroller. Experimental results demonstrate the effectiveness of the proposed control techniques for MPPT, battery charging, and traffic signal operation. Moreover, the proposed TF-LS control demonstrates fast and efficient operation under real-time traffic conditions, providing a simple and practical solution for mitigating traffic congestion. Full article

Against the backdrop of the global low-carbon energy transition, the volatility of photovoltaic (PV) output has been found to significantly affect the safe and stable operation of hydrogen production systems. To ensure efficient hydrogen production while enhancing system safety, a coordinated optimal operation strategy is proposed for a hybrid hydrogen production system that accounts for the dynamic characteristics and state transitions of electrolyzers. By leveraging the power-efficiency curves and historical operating time of electrolyzers, together with a state tracking and feedback mechanism, the proposed strategy is able to capture state transition processes and the real-time operational status of electrolyzers. Subsequently, by accounting for the operating characteristics of different electrolyzers and dividing reasonable power intervals, the optimal power allocation among hybrid electrolyzers is achieved. A comparative analysis of four different schemes was conducted via simulation experiments, and the results demonstrate that compared with a single electrolyzer hydrogen production system, the hybrid system achieves a better trade-off between efficiency and economic performance while effectively enhancing PV absorption capacity. Furthermore, relative to a traditional operation strategy, the proposed strategy increases hydrogen production capacity, efficiency, and profit by 25.75%, 8.90%, and 36.00%, respectively, while reducing the unit hydrogen production cost by 23.13%. Full article

As the global energy transition accelerates toward low-emission and sustainable industrial energy systems, green hydrogen produced from renewable sources has emerged as a promising alternative to natural gas in energy-intensive sectors. This study presents the design, implementation, and experimental validation of a rooftop photovoltaic–proton exchange membrane (PV–PEM) hydrogen energy system developed as a proof-of-concept for textile industry applications. The proposed system integrates monocrystalline photovoltaic panels with east–west solar tracking, a 4 kW inverter, and a PEM electrolyzer with a hydrogen production capacity of 3.6 L/h, enabling on-site solar-to-hydrogen conversion. Produced hydrogen is stored in a high-pressure metal tank and utilized for downstream energy applications, demonstrating a complete renewable energy pathway. System performance is monitored in real time and evaluated using an experimental methodology supported by GUM-based and Monte Carlo uncertainty analysis. A carbon reduction assessment is conducted under representative industrial operating scenarios, including uncertainty quantification. The results indicate that the prototype system achieves an energy output corresponding to an average monthly emission reduction of approximately 222 kg CO₂e. The modular and scalable architecture allows flexible expansion to support gradual natural gas substitution in textile processes such as drying, heating, and steam generation. Overall, the study demonstrates the technical feasibility and environmental potential of integrating rooftop PV–PEM hydrogen systems into textile manufacturing, providing a transferable framework for industrial decarbonization. Full article

The high expansion rate of industrial-scale photovoltaic (PV) systems in emerging economies requires proper performance prediction models that consider particular climatic variabilities. Although the theoretical potential of solar energy in South Asia is well documented, there still exists a gap in the validation of simulation models to operational data over long periods in subtropical monsoon climates. Unlike prior studies, this work combines multi-year operational data with dynamic TRNSYS simulations to quantify both technical and environmental performance of a 1 MW PV system under subtropical monsoon conditions. This paper provides a detailed performance evaluation of a 1 MW grid-connected PV system located in Punjab, Pakistan. The actual performance of the system is compared with a dynamic simulation model that is created in the Transient System Simulation Tool (TRNSYS) using three years of operational data. Four different scenarios are analyzed: (1) Ideal Theoretical Operation, (2) Actual Field Data, (3) Simulated Operation with Maximum Power Point Tracking (MPPT), and (4) Simulated Operation without MPPT. The results reveal that the real system produced an average of 1342 MWh/year, whereas the MPPT-enabled simulation predicted 1664 MWh/year, indicating a performance difference of 19.3%. Statistical validation revealed a strong correlation ($R^2=0.84$) between the model and reality, yet identified a normalized Root Mean Square Error (nRMSE) of 26.8%. This deviation represents a performance gap which is deconvoluted into agricultural soiling losses and grid curtailment. The research work quantifies the technical effect of MPPT where a 27% operational advantage is realized in comparison to fixed-voltage cases, proving its necessity in climates with high diffuse radiation during monsoon seasons. Economic analysis demonstrates a Levelized Cost of Energy (LCOE) of $0.0378/kWh of the existing system, and a Simple Payback Time (SPBT) of 4.74 years at the current industrial tariffs. Sensitivity analysis also indicates that in case of an increase in grid tariffs to 50 PKR/kWh, Internal Rate of Return (IRR) increases to 18.8%. Environmental analysis confirms a carbon emission reduction of 765 tons/year. These results validate the techno-economic feasibility of large-scale PV in the area and provide an important understanding of the critical yield losses in monsoon seasons, which offers an effective robust benchmark for future industrial energy policy in developing economies. Full article

Agrivoltaic (AV) systems offer a promising solution to global challenges, such as land scarcity, food insecurity, and increasing energy demand, by enabling the simultaneous production of photovoltaic (PV) electricity and agricultural outputs on the same land. This review synthesizes more than two decades of interdisciplinary research on solar–agriculture integration, including agrivoltaic systems, biomass-based approaches, and greenhouse-integrated photovoltaic technologies, with particular emphasis on their relevance to arid and semi-arid environments, such as those found in the Middle East. The impacts of different PV configurations (such as semi-transparent, bifacial, vertical, and sun-tracking modules) on crop productivity, microclimatic conditions, and land-use efficiency are critically examined. The findings indicate that AV systems, particularly in water-scarce, high-irradiance regions, can enhance climate resilience, reduce competition for land, and improve both energy and water-use efficiency. Recent advances in crop selection strategies, adaptive PV system designs, and smart irrigation technologies further strengthen the feasibility of these systems for Middle Eastern agricultural systems. Nevertheless, key challenges remain, including the need for region-specific design optimization, improved understanding of crop light requirements, and robust assessments of economic viability under diverse policy and market conditions. Overall, life cycle assessments and techno-economic analyses confirm the environmental and economic benefits of AV systems, especially for sustainable irrigation, agricultural productivity, and rural development in the Middle East context. This review provides strategic insights to support the sustainable deployment and scaling of agrivoltaic systems across Middle Eastern agricultural landscapes, informed by global experience. A dedicated regional assessment summarizes existing agrivoltaic pilots and feasibility studies across the Middle East and North Africa, highlighting technology choices, crop compatibility, and policy drivers. Full article