Search Results (849)

This paper presents an economic–environmental power dispatch approach for a grid-connected microgrid (MG) with photovoltaic (PV) generation and battery energy storage systems (BESSs). The problem was formulated as a multiobjective optimization problem with functions such as minimizing fixed and variable generation costs, power losses, and CO${}_2$ emissions. This study addresses the problem of intelligent energy management in microgrids with PV generation and BESSs to optimize their performance based on multiple criteria. This study focuses on optimizing the Energy Management System (EMS) with metaheuristic algorithms to achieve practical implementation with simpler algorithms to solve a complex optimization problem. This study employs four multiobjective optimization algorithms: Nondominated Sorting Genetic Algorithm II (NSGA-II), Harris Hawks Optimization (HHO), multiverse optimizer (MVO), and Salp Swarm Algorithm (SSA), which are classified as robust techniques for obtaining Pareto fronts. The computational resources employed to simulate the problem are presented. The optimal dispatch obtained from the Pareto front achieved reductions of 0.067% in fixed costs, 0.288% in variable costs, 3.930% in power losses, and 0.067% in CO${}_2$ emissions, demonstrating the effectiveness of the proposed approach in optimizing both economic and environmental performance. The SSA stood out for its stability and computational efficiency, establishing itself as a promising method for energy management in urban and rural microgrids (MGs) and providing a solid framework for optimization in alternating current systems. Full article

The article analyses the hosting capacity of low-voltage (LV) power grids for connecting distributed energy sources (DER), mainly photovoltaic installations (PV), considering technical limitations imposed by power system operating conditions. The main objective of the research was to develop a simple equation that enables the quick estimation of the maximum power of an energy source that can be safely connected at a given point in the network without causing excessive voltage rise or overloading the transformer and line cable. The analysis was performed on the basis of relevant calculation formulas and simulations carried out in DIgSILENT PowerFactory, where a representative low-voltage grid model was developed. The network model included four transformer power ratings (40, 63, 100, and 160 kVA) and four cable cross-sections (25, 35, 50, and 70 mm²), which made it possible to assess the impact of these parameters on grid hosting capacity as a function of the distance from the transformer station. Based on this, the P_HCI indicator was developed to determine the hosting capacity of a low-voltage network, using only the transformer rating and the length and cross-section of the line for the calculations. A comparison of the results obtained using the proposed equation with detailed calculations showed that the approximation error does not exceed 15%, which confirms the high accuracy and practical applicability of the proposed approach. Full article

Leakage current is a prevalent issue in non-isolated photovoltaic (PV) energy storage inverter systems, which not only induces additional power losses but also poses potential safety hazards and degrades system operational efficiency. To address this critical problem, this paper proposes an improved three-phase four-leg PV energy storage inverter topology integrated with independent split capacitors, based on the traditional three-level topology. First, an in-depth analysis of the leakage current generation mechanism is conducted, focusing on the impacts of common-mode voltage fluctuations and parasitic capacitance on leakage current paths. By establishing an equivalent mathematical model, a systematic comparative analysis is performed between the proposed topology and the traditional topology regarding key performance indicators, including leakage current suppression capability, DC-side neutral point potential stability, and power quality. Notably, the improved topology requires no additional control strategy design; under the same carrier modulation strategy and parameter configuration as the traditional topology, it can stably constrain the DC-side neutral point potential to fluctuate within an acceptable range. Experimental results demonstrate that the proposed topology reduces the peak leakage current to within 200 mA while maintaining the total harmonic distortion (THD) of the load-side current at a low level. These performance metrics comply with the relevant national and industry power quality standards for PV grid-connected systems, endowing the topology with high engineering practical value. Full article

The rapid adoption of electric vehicles (EVs) has increased the need for sustainable charging infrastructure supported by renewable energy. This study presents a comprehensive techno-economic and environmental analysis of private EV charging stations in Kuwait powered by grid-connected solar and wind systems using the HOMER Pro 3.18.4 optimization software. Four configurations—grid-only, grid–solar, grid–wind, and grid–solar–wind—were modelled and evaluated in terms of energy output, cost performance, and carbon emission reduction under Kuwait’s climatic conditions. HOMER simulated 484 systems, of which 244 were technically feasible. The optimal configuration, combining grid, 5 kW photovoltaic (PV) (BEIJIAYI 600 W panels), and a 5.1 kW AWS wind turbine, achieved a renewable fraction of 78%, reducing grid dependency by 78.1% and annual CO₂ emissions by approximately 7027 kg. Although the hybrid system required a higher initial investment (USD 7662) than the grid-only setup (USD 1765), it achieved the lowest Levelized Cost of Energy (LCOE = USD 0.017/kWh) and long-term cost competitiveness through reduced operating expenses. Sensitivity analysis confirmed the hybrid system’s robustness against ±15% variations in wind speed and ±10% changes in solar irradiance. The results highlight that hybrid solar–wind systems can effectively mitigate intermittency through diurnal complementarity, where daytime solar generation and nighttime wind activity ensure continuous supply. The findings demonstrate that integrating renewables into Kuwait’s EV charging infrastructure enhances economic viability, energy security, and environmental sustainability. The study provides practical insights to guide renewable policy development, pilot deployment, and smart grid integration under Kuwait Vision 2030’s clean-energy framework. Full article

The high proportion of renewable energy connected to the grid poses new challenges to the safe and economic operation of active distribution networks (ADNs). However, most of the existing research focuses on single-objective optimization or ignores the influence of the uncertainty of renewable energy output and the demand response mechanism, and lacks verification of the scalability of models in large-scale systems. For an active distribution network system with distributed wind power and photovoltaic access, this paper establishes a multi-objective day-ahead optimal dispatching model that takes into account economy, reliability, and safety. The research adopts a scenario-based method and chance-constrained programming (CCP) to handle the uncertainty of wind and solar output. It combines the quasi-Monte Carlo (QMC) method and Kantorovich distance to achieve scenario generation and reduction, and introduces price-based and incentivized demand response mechanisms to form four combined optimization models. The multi-objective optimization solution was carried out based on the multi-objective evolutionary algorithm based on decomposition (MOEA/D), verifying the effectiveness of the proposed method in terms of operation cost, load shedding expectation, and node voltage limit control. The case study is based on the improved IEEE 30-node and 200-node 49-generator systems. The results indicate that this method can effectively balance multiple objectives such as operation costs, load shedding expectations, and node voltage limit; can significantly enhance the renewable energy consumption capacity of active distribution networks; and can provide an effective solution for the optimal dispatching of active distribution networks with a high proportion of renewable energy. Full article

Early detection of performance degradation and prevention of critical failures in photovoltaic (PV) arrays are essential for ensuring system reliability and efficiency. This study presents an intelligent fault detection and classification framework based on a Multi-Layer Neural Network (MLNN). The model was developed and validated using a simulated 250 kW grid-connected PV system tested under five operating scenarios: normal operation, open-circuit fault, partial short-circuit, partial shading, and string-to-string fault. Unlike conventional diagnostic approaches, the proposed model directly processes raw electrical measurements (current, voltage, power, irradiance, and temperature) under varying environmental conditions, thus emulating real-world operational variability. The MLNN achieved 98% test accuracy and outperformed benchmark classifiers Support Vector Machine (SVM) and Random Forest (RF) across multiple metrics. Performance was evaluated using the confusion matrix, precision, recall (sensitivity) and F1-score. The framework is designed for scalability and can be integrated into predictive maintenance platforms to enable early fault detection and improve long-term PV system availability and efficiency. Full article

This work proposes an intelligent strategy for the coordinated management of active and reactive power in Battery Energy Storage Systems (BESSs) within AC microgrids operating under both grid-connected (GCM) and islanded (IM) modes to minimize daily operational costs. The problem is formulated as a mixed-variable optimization model that explicitly leverages the control capabilities of BESS power converters. To solve it, a Parallel Particle Swarm Optimization (PPSO) algorithm is employed, coupled with a Successive Approximation (SA) power flow solver. The proposed approach was benchmarked against parallel implementations of the Crow Search Algorithm (PCSA) and the JAYA algorithm (PJAYA), both in parallel, using a realistic 33-node AC microgrid test system based on real demand and photovoltaic generation profiles from Medellín, Colombia. The strategy was evaluated under both deterministic conditions (average daily profiles) and stochastic scenarios (100 daily profiles with uncertainty). The proposed framework is evaluated on a 33-bus AC microgrid that operates in both grid-connected and islanded modes, with a battery energy storage system dispatched at both active and reactive power levels subject to network, state-of-charge, and power-rating constraints. Three population-based optimization algorithms are used to coordinate BESS schedules, and their performance is compared based on daily operating cost, BESS cycling, and voltage profile quality. Quantitatively, the PPSO strategy achieved cost reductions of 2.39% in GCM and 1.62% in IM under deterministic conditions, with a standard deviation of only 0.0200% in GCM and 0.2962% in IM. In stochastic scenarios with 100 uncertainty profiles, PPSO maintained its robustness, reaching average reductions of 2.77% in GCM and 1.53% in IM. PPSO exhibited consistent robustness and efficient performance, reaching the highest average cost reductions with low variability and short execution times in both operating modes. These findings indicate that the method is well-suited for real-time implementation and contributes to improving economic outcomes and operational reliability in grid-connected and islanded microgrid configurations. The case study results show that the different strategies yield distinct trade-offs between economic performance and computational effort, while all solutions satisfy the technical limits of the microgrid. Full article

This article assesses the use of solar photovoltaic radiation as a renewable resource for a region of the Isthmus of Tehuantepec in Mexico, where a 163.2 kW grid-connected photovoltaic system is located. The study aims to understand the system’s performance under the specific location conditions and to demonstrate the feasibility of installing photovoltaic systems in the Isthmus region. System monitoring was conducted for one year, with monthly and daily averages of normalized performance parameters determined. A three-month study of the power quality was conducted to assess compliance with interconnection and power quality requirements for power plants with a rated power Pn ≤ 500 kW. Results show higher energy production in the spring–summer months (138.946 MWh) than in autumn–winter (136.500 MWh), while the best overall performance occurred in autumn–winter (PR = 85% vs. 79.5% in spring–summer), probably due to cooler photovoltaic module temperatures. The final yield and PR indicate stable and predictable operation, even without maintenance, with PR = 82.3%. This supports the feasibility of photovoltaic installations in the southwestern region of Mexico. The present work is particularly relevant as it advances understanding of photovoltaic performance in understudied regions with substantial solar potential, such as the Isthmus of Tehuantepec, where policy prioritizes wind resource exploitation over solar energy. Full article

This study proposes an unsupervised anomaly detection method to identify the performance degradation in grid-connected photovoltaic (PV) inverters under multitask operation. Principal Component Analysis (PCA) and One-Class Support Vector Machine (OCSVM) were integrated to build a detection model using routine operational data. The key features include DC input, AC output, AC/DC ratio, and AC power variation, which are reduced to two principal components for anomaly boundary construction. The inverters were flagged as degraded if the AC/DC ratio was <0.96, the power fluctuation exceeded 20%, or the data fell outside the OCSVM-defined boundary. Compared with the Isolation Forest, the proposed method showed higher sensitivity. When applied to a 120 MW PV plant in Taiwan with 1292 inverters, including 55 PV-STATCOM units at night, the framework detected degradation in 5.4% of them. These results support their use in intelligent monitoring and predictive maintenance. In addition, through early fault detection and maintenance prioritization, the proposed framework contributes to enhancing reliability, reducing maintenance costs, and promoting the sustainable operation of utility-scale photovoltaic power plants. Full article

Grid-connected PV inverters (GCPI) are key components that enable photovoltaic (PV) power generation to interface with the grid. Their control performance directly influences system stability and grid connection quality. However, as PV penetration increases, conventional controllers encounter difficulties in managing nonlinear dynamics and weak-grid conditions. This paper reviews both conventional and artificial intelligence (AI)-based control methods for GCPI. It compares their performance characteristics, application scenarios, and limitations and summarizes current research progress and remaining challenges. The potential and issues of applying AI to enhance system intelligence are also highlighted. Finally, future development trends are discussed, emphasizing high efficiency, strong adaptability, and intelligent integration in GCPI technologies. Full article

The integration of large-scale photovoltaic (PV) systems requires advanced converter architectures capable of ensuring both high efficiency and fast dynamic response. Leveraging the inherent modularity and low harmonic distortion of Modular Multilevel Converters (MMCs), this paper presents a novel control and modulation framework for grid-connected PV applications. The key innovation lies in the implementation of distributed, string-level Maximum Power Point Tracking (MPPT), enabling optimal energy extraction even under non-uniform (shaded) irradiance conditions. The proposed method operates within a dual time-scale control architecture: an outer Perturb and Observe (P&O) loop assigns independent power references, while the inner modulation stage employs an innovative switching strategy that activates only one module per sampling period. Unlike conventional MPPT-based schemes, where submodules are driven by voltage references, the proposed approach directly regulates the power of each MMC submodule, eliminating the need for PV-side current measurement. Full article

Grid-connected photovoltaic (PV)–battery systems require advanced control to maintain stable operation, efficient energy exchange, and minimal conversion losses under variable generation and load conditions. This study proposes a dual-loop Energy Management System (EMS) integrated with a Hybrid Grey Wolf Optimizer–Particle Swarm Optimization (GWO–PSO) algorithm for coordinated control of a low-voltage PV–battery–grid system (380 V AC, ≈800 V DC bus). The hybrid optimizer was chosen due to the limitations of standalone GWO and PSO methods, which frequently experience slow convergence and local stagnation; the integrated GWO–PSO strategy enhances both exploration and exploitation during the real-time adjustment of PI controller gains. The rapid inner loop effectively balances instantaneous power among the PV, battery, and grid, while the outer optimization loop aims to minimize the ITAE criterion to enhance transient response. Simulation outcomes validate stable DC-bus voltage regulation, quicker transitions between power import and export, and prompt power balance with deviations maintained below 2.5%, signifying reduced converter losses and improved power-sharing efficiency. The battery’s state of charge is sustained within the range of 20–80%, ensuring safe operational conditions. The proposed hybrid EMS offers faster convergence, smoother power regulation, and enhanced dynamic stability compared to standalone metaheuristic controllers, establishing it as an effective and reliable solution for grid-connected PV–battery systems. Full article

With the vigorous development of contemporary clean energy, the participation rate of photovoltaic (PV) power generation in the whole power system is increasing day by day, and accurate PV power prediction technology is crucial for the optimal scheduling of the power system. However, the frequent occurrence of extreme climate in recent years has caused greater disturbance to PV power generation, which greatly increases the degree of difficulty in accurately predicting PV power generation and thus affects the security, economy, reliability and stability of grid system operation. In order to predict PV power under extreme climatic conditions, we firstly elaborate the PV power prediction methods and their respective advantages and disadvantages for sand, dust, rainstorm and snowfall in existing studies, and on this basis, we propose the Gray Wolf Optimization for Short-Term Forecasting Models of the Long and Short-Term Memory Model based on K-Means clustering, which ensures the accuracy of PV power prediction under extreme climatic conditions. power prediction accuracy under extreme climate conditions. Firstly, the K-means clustering algorithm is utilized to perform weather typing, which is divided into four weather categories, namely, dusty weather, heavy rain, heavy snow and normal weather. Then, for the weather typing results, the prediction effects of the Gray Wolf Optimization Long Short-Term Memory Network (GWO-LSTM) Model, Random Forest (RF) Model, Multilayer Feedforward Neural Network (BP) Model, and Long and Short-Term Memory Network (LSTM) Model are compared, respectively. The prediction results indicate that GWO-LSTM achieves the highest forecasting accuracy, with a mean root mean square error (RMSE) of 0.6235 across all four weather scenarios. Its prediction accuracy reaches approximately 95%, providing effective data support for the safe and stable operation of new power systems featuring high proportions of grid-connected photovoltaic generation. Full article

The rapid growth of renewable energy integration in modern power systems brings new challenges in terms of stability and quality of electricity supply. Hybrid AC/DC microgrids represent a promising solution to integrate photovoltaic panels (PV), wind turbines, fuel cells, and storage units with flexibility and efficiency. However, maintaining adequate power quality (PQ) under variable conditions of generation, load, and grid connection remains a critical issue. This paper presents the modelling, implementation, and validation of a hybrid AC/DC microgrid equipped with a fuzzy-logic-based energy management system (EMS). The study combines PQ assessment, measurement architecture, and supervisory control for technical compliance and economic efficiency. The microgrid integrates a combination of PV array, wind turbine, proton exchange membrane fuel cell (PEMFC), battery storage system, and heterogeneous AC/DC loads, all modelled in MATLAB/Simulink using a physical-network approach. The fuzzy EMS coordinates distributed energy resources by considering power imbalance, battery state of charge (SOC), and dynamic tariffs. Results demonstrate that the proposed controller maintains PQ indices within IEC/IEEE standards while eliminating short-term continuity events. The proposed EMS prevents harmful deep battery cycles, maintaining SOC within 30–90%, and optimises fuel cell activation, reducing hydrogen consumption by 14%. Economically, daily operating costs decrease by 10–15%, grid imports are reduced by 18%, and renewable self-consumption increases by approximately 16%. These findings confirm that fuzzy logic provides an effective, computationally light, and uncertainty-resilient solution for hybrid AC/DC microgrid EMS, balancing technical reliability with economic optimisation. Future work will extend the framework toward predictive algorithms, reactive power management, and hardware-in-the-loop validation for real-world deployment. Full article

This article presents the development and implementation of an IoT-enabled, off-grid solar power supply prototype designed to power a range of electrical devices. The developed system comprises a Photovoltaic panel, a Maximum Power Point Tracking (MPPT) charger, a 2.5 kWh/24 V high-performance LiFePO4 battery bank with a Battery Management System, an embedded controller with IoT connectivity, and DC/DC and DC/AC converters. The PV panel serves as the primary energy source, with the MPPT controller optimizing battery charging, while the DC/DC and DC/AC converters supply power to the connected electrical devices. The article includes a case study of a developed platform for powering an information and advertising system. The system features a predictive energy management algorithm, which optimizes the appliance operation based on daily solar irradiance forecasts and real-time battery State-of-Charge monitoring. The IoT-enabled controller obtains solar irradiance forecasts from an online meteorological service via API calls and uses these data to estimate energy availability for the next day. Using this prediction, the system schedules and prioritizes the operations of connected electrical devices dynamically to optimize the performance and prevent critical battery discharge. The IoT-based controller is equipped with both Wi-Fi and an LTE modem, enabling communication with online services via wireless or cellular networks. Full article