Search Results (875)

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

The increasing penetration of inverter-based resources (IBRs) has been reducing system inertia and intensifying frequency stability challenges. Hence, various grid demands have been imposed on grid-connected systems, e.g., requiring the provision of an auxiliary service to the grid. In this context, this paper investigates the provision of synthesized inertia from the DC-link capacitors in grid-connected photovoltaic (PV) systems. For this configuration, the PV converter adopts a frequency–voltage droop control (FVDC) strategy, while a virtual synchronous generator (VSG) is employed on the grid side to emulate a synchronous generator, to enable the DC-link energy to contribute to primary frequency support. To quantify the virtual inertia and evaluate the closed-loop stability, a small-signal model of the inverter system is established. An eigenvalue analysis reveals that while increasing the DC-link voltage or capacitance enhances the achievable virtual inertia, it simultaneously narrows the stability margin. As such, comparative stability assessments under different parameter settings are performed, highlighting the distinct impacts of the DC-link voltages and capacitances on the emulated inertia and stability margins. The study provides insights into the maximum virtual inertia achievable via DC-link capacitors and offers practical guidelines for coordinating the controller and DC-link design to enhance frequency robustness in low-inertia power systems. Real-time hardware-in-the-loop (RT-HIL) tests validate the analytical findings. Full article

With the increasing penetration of inverter-based photovoltaic (PV) generation, utility-scale grid-connected PV plants are frequently exposed to voltage regulation and voltage stability challenges driven by intermittent irradiance and limited reactive power flexibility under operating constraints. Conventional static Volt/VAR control schemes are typically designed for quasi-steady conditions and therefore struggle to respond to fast variations in PV output and network states. This paper presents a digital twin (DT)-enabled framework for dynamic Volt/VAR optimization in large PV plants. A four-layer DT architecture is developed to achieve real-time cyber-physical synchronization through multi-source data acquisition, secure transmission, fusion, and quality control. To balance model fidelity and computational efficiency, a hybrid physics–data-driven model is constructed, and a local voltage stability L-index is incorporated as an explicit security constraint. A multi-objective optimization problem is formulated to minimize node voltage deviations and reactive power losses while maximizing the static voltage stability margin. The problem is solved using an adaptive parameter particle swarm optimization (AP-PSO) algorithm with dynamic inertia and learning coefficients. Case studies on modified IEEE 33-bus and 53-bus systems demonstrate that the proposed method reduces the voltage profile index by up to 68.9%, improves the static voltage stability margin by 76.5%, and shortens optimization time by up to 30.3% compared with conventional control and representative meta-heuristic or learning-based baselines. The framework further shows good scalability and robustness under practical uncertainties, including irradiance forecast errors and measurement noise. Overall, the proposed approach provides a feasible pathway to enhance operational security and efficiency of grid-connected PV plants under high-penetration scenarios. Full article

The integration of renewable energy sources, including photovoltaic (PV) and fuel cell (FC) systems, into AC grids has attracted immense research interest in recent times. Furthermore, incorporating these renewable sources of energy into medium-voltage grids is garnering increased attention because of the obvious benefits of medium-voltage integration at elevated power levels. Photovoltaic applications entail the arrangement of solar panels capable of outputting voltages up to 1.5 kV; nonetheless, fuel cells display restricted output voltage, with a maximum market range of 400 to 700 V. Hence, the efficient integration of renewable energy sources into low-voltage or medium-voltage grids demands the utilization of a step-up direct current (DC–DC) inverter and a converter for connection to the alternating current (AC) grid, in which an efficient step-up converter is critical for the medium-voltage grid. Therefore, this study presents a three-phase buck-boost split-source inverter (BSSI) that resolves the constrained output voltage of the fuel cells. This study focuses on modifying the configuration of a conventional three-phase split-source inverter (SSI) circuit by adding a few components while maintaining the inverter’s modulation. This novel circuit design enables the reduction in voltage strains on the inverter switch components and improves DC-link use in relation to a traditional SSI configuration. For an 800 bus, maximal voltage stress on the primary inverter switches is lowered when compared with the standard SSI that delivers entire DC-bus voltage to switches. A rectifier-based model is employed to simulate the behavior of a renewable energy source. Combining these advantages with the conventional modulation of the inverter offers a more effective design. The buck-boost split-source inverter (BSSI) was analyzed using three distinct modulation techniques: the sinusoidal pulse-width modulation scheme (SPWM), the third-harmonic injected pulse-width modulation (THPWM) scheme, and space vector modulation (SVM). The proposed analysis was validated through MATLAB-SIMULINK and practical outcomes on a 5.0 kW model. The practical and SIMULINK data were found to be closely aligned with the analysis. The circuit developed in this study also ensures efficient DC-to-AC conversion, specifically with regard to low-voltage sources, like fuel cells or photovoltaic (PV) systems. Full article

Although solar photovoltaic (PV) technology is a well-known sustainable energy source, as glass-on-glass bifacial modules have dominated the market during the rapid scaling of the PV industry, glass breakage has become an environmental concern. This study explores the reuse of broken modules to further improve PV sustainability through case studies of 3.45 MWp and 1.4 MWp solar farms, comprising over 12,700 modules. After cleaning and testing, 1.45% of PV modules were physically damaged due to glass breakage during transport or handling at the solar farms in Brazil. Recycling of PV modules is infrequent; therefore, the reuse of modules under these conditions was explored. Nine modules with glass damage were connected to the electricity grid using a carport-type structure, and 36 diagnostic current–voltage (I-V) tests were carried out over an 18-month experimental period. Tests to detect faults and low system insulation resistance indicated that the implementation of non-invasive, remote, online, and periodic monitoring enabled the system to operate and enabled the reuse of damaged modules. Although the electrical results were promising, future work is needed to evaluate methods to ensure the broken glass is sealed to prevent electric shock hazards and to maintain long-term safe performance. 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

Photovoltaic (PV) cells play an important role in the development of green energy. However, in practical photovoltaic systems, shunting-related defects and hotspot phenomena may originate not only from manufacturing imperfections, but also from mechanical stress and environmental factors during transportation, installation, and long-term field operation. Such hotspots not only reduce the power-generation efficiency and service life of PV cells but may also pose safety risks to grid-connected photovoltaic power stations. To address this problem, a squared even-order derivative (SEOD) method based on surface temperature analysis is introduced to enable the quantitative detection of thermal defects in PV cells. In this study, typical faults in PV cells, including low-resistance defects and silicon-based deep scratches, are analyzed. A simulation model is established to correlate typical faults with their equivalent volumetric heat sources, followed by experimental validation for low-resistance defects. Based on this framework, the SEOD algorithm is developed and applied to achieve high-precision localization and quantitative characterization of thermal defects in both simulation models and experimental samples. Full article

Integrating renewable energy into agricultural systems has emerged as a critical strategy for reducing the sector’s greenhouse gas emissions. However, limited research has examined how farm-specific operational patterns influence the techno-economic performance of solar photovoltaic (PV) systems. This study presents a comprehensive techno-economic and environmental assessment of grid-connected solar PV systems for two types of dairy farm operations: spring-calving and winter-calving. Using detailed farm-specific energy consumption profiles and solar irradiance data, system performance was evaluated under Ireland’s policy framework, including the Targeted Agricultural Modernisation Scheme grant and the Clean-Export tariff. The spring-calving operation achieved superior economic performance (payback period: 3.25 years; levelised cost of electricity: EUR 0.091/kWh) compared to the winter-calving operation (3.83 years; EUR 0.099/kWh). This superior performance is due to better seasonal alignment between solar generation and electricity demand. Sensitivity analysis reveals solar irradiance, grid electricity cost, and grant funding as main economic viability influencing factors. Environmental analysis demonstrates CO₂ emission reductions of 77% for spring-calving and 61% for winter-calving operations. The findings demonstrate that solar PV systems are both economically viable and environmentally beneficial for dairy farms. These results provide actionable insights for farmers and policymakers seeking to promote clean energy adoption and emission reduction in agriculture. Full article

The increasing integration of solar energy into the power grid necessitates robust fault detection and diagnosis (FDD) guidelines to ensure energy continuity and optimize the performance of grid-connected photovoltaic (GCPV) systems. This research addresses a gap in the literature by systematically evaluating machine learning (ML) algorithms for the detection and classification of AC-side faults (inverter and grid faults) in GCPV systems. We utilized three commonly employed algorithms, namely K-Nearest Neighbors (KNN), Logistic Regression (LR), and Artificial Neural Networks (ANNs), to develop fault detection models. These models were trained using a monthly electrical dataset obtained from the AYCEM-GES-GCPV power plant in Giresun, Turkiye, and their performance was rigorously evaluated using classification accuracy, Area Under the Curve (AUC), and Receiver Operating Characteristic (ROC) analyses. The results demonstrate that the algorithms are highly effective in fault detection, with AUC values consistently exceeding the critical threshold. The obtained accuracies for KNN, LR, and ANN were 0.9826, 0.782, and 0.7096, respectively. These findings emphasize the high effectiveness of ML algorithms, with KNN exhibiting the best performance, for identifying AC-side faults in GCPV installations. While the study focused on AC-side fault detection, subsequent work developed a smart card module to identify complex DC side electrical faults and built a PV array for experimental testing. Full article

The global transition towards decentralized, decarbonized energy systems worldwide must include robust methods for controlling hybrid AC/DC microgrids to integrate diverse renewables and storage technologies effectively. This paper presents a Finite Control Set–Model Predictive Control (FCS-MPC) architecture for coordinated control of a hybrid microgrid comprising photovoltaic and wind generation, along with an energy storage system and MATLAB/Simulink component-level modeling. The islanded and grid-connected modes of operation are seamlessly simulated at the component level, ensuring maximum power point tracking and stability. The method has been experimentally validated through dynamic simulations across a range of operating conditions, demonstrating good performance: PV and wind MPPT efficiency > 99%, DC-link voltage control with <2% overshoot, AC voltage THD < 3%, and efficient grid synchronization. It is superior to conventional PID and sliding mode control in terms of dynamic response, voltage deviation (reduced compared to before), and power quality. The proposed FCS-MPC is an all-in-one solution to enhance the stability, reliability, and efficiency of modern hybrid microgrids. Full article

Transportation hubs serve as critical convergence points for traffic, information, and energy flows. However, their energy systems are characterized by high consumption randomness, significant power flow fluctuations, and geographically dispersed source and load nodes. These features pose challenges for integrating distributed renewable energy and often lead to high energy cost issues. Additionally, accommodating distributed photovoltaic (PV) is further complicated by grid corridors and high investment expenditure. To address these issues, this paper proposes a two-stage optimization model for a multi-node interconnected architecture for transportation hubs. In the first stage, a greedy algorithm determines a fixed connection topology, considering distance constraints and connection port limits to ensure engineering feasibility. The second employs linear programming to optimize real-time power allocation. This approach precomputes connection relationships, significantly reducing evaluation time and enabling efficient processing of operational data from multiple nodes. A case study confirms that the proposed method can increase PV consumption by 38.71%, with optimization evaluated on a millisecond scale. By inputting node generation, load, and distance data in prescribed format, the model outputs actionable planning results for flexible interconnection projects. Full article

This paper presents a PSCAD-based analysis of short-circuit faults and protection characteristics in a real distribution-level microgrid that integrates a 1 MWh battery energy storage system (BESS) with a 500 kW power conversion system (PCS) and a 500 kW photovoltaic (PV) plant connected to a 22.9 kV feeder. While previous studies often rely on simplified inverter models, this paper addresses the critical gap by integrating actual manufacturer-defined control parameters and cable impedances. This allows for a precise analysis of sub-millisecond transient behaviors, which is essential for developing robust protection schemes in inverter-dominated microgrids. The PSCAD model is first verified under grid-connected steady-state operation by examining PV output, BESS power, and grid voltage at the point of common coupling. Based on the validated model, DC pole-to-pole faults at the PV and ESS DC links and a three-phase short-circuit fault at the low-voltage bus are simulated to characterize the fault current behavior of the grid, BESS and PV converters. The DC fault studies confirm that current peaks are dominated by DC-link capacitor discharge and are strongly limited by converter controls, while the AC three-phase fault is mainly supplied by the upstream grid. As an initial application of the model, an instantaneous current change rate (ICCR) algorithm is implemented as a dedicated DC-side protection function. For a pole-to-pole fault, the ICCR index exceeds the 100 A/ms threshold and issues a trip command within 0.342 ms, demonstrating the feasibility of sub-millisecond DC fault detection in converter-dominated systems. Beyond this example, the validated PSCAD model and associated data set provide a practical platform for future research on advanced DC/AC protection techniques and protection coordination schemes in real BESS–PV microgrids. Full article

With the widespread adoption of renewable energy, distribution grids face increasing challenges in efficiency, safety, and economic performance due to stochastic generation and fluctuating load demand. Traditional operational models often exhibit limited adaptability, weak coordination, and insufficient holistic optimization, particularly in early-/mid-stage distribution planning where feeder-level network information may be incomplete. Accordingly, this study adopts a planning-oriented formulation and proposes a distributed energy storage system (DESS) planning strategy to enhance distribution network resilience under high uncertainty. First, representative wind and photovoltaic (PV) scenarios are generated using an improved Gaussian Mixture Model (GMM) to characterize source-side uncertainty. Based on a grid-based network partition, a priority index model is developed to quantify regional storage demand using quality- and efficiency-oriented indicators, enabling the screening and ranking of candidate DESS locations. A mixed-integer linear multi-objective optimization model is then formulated to coordinate lifecycle economics, operational benefits, and technical constraints, and a sequential connection strategy is employed to align storage deployment with load-balancing requirements. Furthermore, a node–block–grid multi-dimensional evaluation framework is introduced to assess resilience enhancement from node-, block-, and grid-level perspectives. A case study on a Zhejiang Province distribution grid—selected for its diversified load characteristics and the availability of detailed historical wind/PV and load-category data—validates the proposed method. The planning and optimization process is implemented in Python and solved using the Gurobi optimizer. Results demonstrate that, with only a 4% increase in investment cost, the proposed strategy improves critical-node stability by 27%, enhances block-level matching by 88%, increases quality-demand satisfaction by 68%, and improves grid-wide coordination uniformity by 324%. The proposed framework provides a practical and systematic approach to strengthening resilient operation in distribution networks. Full article

The high rate of photovoltaic integration poses significant challenges in terms of violations of voltage limits in power grids. Additionally, the operational behavior of PV systems under fault conditions requires thorough investigation in receiving-end grids. This paper analyzes the dynamic coupling characteristics between reactive power and transient voltage in a receiving-end grid with high PV penetration and multiple HVDC infeeds, considering typical AC and DC fault scenarios. Voltage adaptability issues in PV generation systems are also examined. Through an enhanced sensitivity analysis method, the suppression capabilities of transient voltage peaks are quantified in the control parameters of low-voltage ride-through (LVRT) and high-voltage ride-through (HVRT) photovoltaic inverters. On this basis, a hierarchical optimization strategy for PV inverter control parameters is proposed to mitigate post-fault transient voltage peaks and improve the transient voltage response both during and after faults. The feasibility of the proposed method has been verified through simulation on a revised 10-generator 39-bus power system. Following optimization, the transient voltage peak is reduced from 1.263 to 1.098. This validation offers support for the reliable grid connection of the Henan Power Grid. In the events of the N-2 fault at 500 kV and Tian-zhong HVDC monopolar block fault, the post-fault voltage at each node remains below 1.1 p.u. This serves as evidence of a significant enhancement in transient voltage stability within the Henan Power Grid, demonstrating effective improvements in power supply reliability and operational performance. Full article

The integration of solar energy into smart grids requires high-efficiency power conversion to support grid stability. However, Partial Shading Conditions (PSCs) remain a primary obstacle by inducing multiple local maxima on P–V characteristic curves. This paper presents a hardware-aware and memory-enhanced Maximum Power Point Tracking (MPPT) approach based on a modified Dragonfly Algorithm (DA) for grid-connected microinverter-based photovoltaic (PV) systems. The proposed method utilizes a quasi-switched Boost-Switched Capacitor (qSB-SC) topology, where the DA is specifically tailored by combining Lévy-flight exploration with a dynamic damping factor to suppress steady-state oscillations within the qSB-SC ripple constraints. Coupling the MPPT stage to a seven-level Packed-U-Cell (PUC) microinverter ensures that each PV module operates at its independent Global Maximum Power Point (GMPP). A ZigBee-based Wireless Sensor Network (WSN) facilitates rapid data exchange and supports ‘swarm-memory’ initialization, matching current shading patterns with historical data to seed the population near the most probable GMPP region. This integration reduces the overall response time to 0.026 s. Hardware-in-the-loop experiments validated the approach, attaining a tracking accuracy of 99.32%. Compared to current state-of-the-art benchmarks, the proposed model demonstrated a significant improvement in tracking speed, outperforming the most recent 2025 GWO implementation (0.0603 s) by approximately 56% and conventional metaheuristic variants such as GWO-Beta (0.46 s) by over 94%.These results confirmed that the modified DA-based MPPT substantially enhanced the microinverter efficiency under PSC through cross-layer parameter adaptation. Full article