Search Results (5,298)

Photovoltaic (PV) pumping systems offer a sustainable alternative to diesel solutions, yet their nonlinearity, intermittent irradiation, and complex motor-pump dynamics challenge energy extraction and reliability. Currently, these systems predominantly rely on classical Maximum Power Point Tracking (MPPT) algorithms such as Perturb and Observe (P&O) and Incremental Conductance (INC), which suffer from slow convergence, steady-state oscillations, and an inability to track Global MPP (GMPP) under uniform irradiance variation conditions. Furthermore, existing studies typically address MPPT optimization and motor control in isolation, without considering their coupled interaction, and rarely incorporate economic viability assessments. To address these limitations, this paper proposes an innovative control architecture integrating four advanced metaheuristic MPPT techniques, namely the Genetic Algorithm (GA), Gray Wolf Optimizer (GWO), Cuckoo Search (CS) algorithm, and Horse Herd Optimization Algorithm (HOA), with Model Predictive Control (MPC) for a Brushless DC (BLDC) motor-driven pumping system, supplemented by battery storage. Comprehensive simulations were conducted under both constant and variable irradiance profiles (1000 to 500 to 1000 W/m²) to evaluate dynamic performance, tracking accuracy, and system robustness. The results demonstrate that HOA and GWO significantly outperform GA and CS, achieving superior DC bus voltage stability with ripple values below 2.4 V, faster convergence times, reduced electromagnetic torque oscillations, and enhanced MPPT efficiency exceeding 99%. Under variable irradiance, HOA exhibits the fastest stabilization with minimal overshoot and superior disturbance rejection, while GA suffers from severe oscillations and CS displays sawtooth ripple patterns. A techno-economic analysis further confirms the economic viability of the proposed system, with HOA and GWO strategies yielding lower lifecycle costs, extended converter lifespans from 5 to over 12 years, and improved return on investment compared to conventional approaches. This integrated framework offers a robust, efficient, and economically sustainable solution for autonomous PV water pumping applications. Full article

The growing share of photovoltaic generation in power grids intensifies the need for converter architectures capable of combining efficient energy conversion, DC-bus stability, and ancillary service provision at the grid coupling point. This paper presents the modeling, implementation, and simulation-based evaluation of a 91 kWp three-phase photovoltaic (PV) system integrated with a battery energy storage system (BESS), developed in the PLECS environment. The proposed architecture comprises three interleaved Boost stages for maximum power point tracking (MPPT), a DC bus regulated at 600 V, three independent bidirectional buck–boost converters for LiFePO${}_4$ bank management, and a two-level three-phase voltage source inverter (VSI) with an LC output filter. The control is organized in cascade voltage–current loops for the DC–DC stages and in vector control within the synchronous reference frame (SRF) for the inverter, with synchronization via SRF-PLL. A C-Script supervisory block integrates the Perturb and Observe (P&O) MPPT algorithm, independent state of charge (SOC) estimation per bank via coulomb counting, and Volt–VAR reactive power reference generation with a dead band of 0.90–1.10 pu. Five scenarios are analyzed for validation: DC-bus regulation under irradiance transients; reactive power support during undervoltage and overvoltage events (0.80–0.85 pu and 1.15–1.20 pu); BESS operation as an active DC-link support element; and PV curtailment with fully charged banks. All five scenarios were additionally corroborated on a Typhoon HIL402 Pro 2 hardware-in-the-loop platform, reproducing the PLECS waveforms within the amplitude and timing resolution of the oscilloscope captures. Across all scenarios, the DC bus is held within $\pm 15$ V (2.5%) of the 600 V reference, with the worst-case transient recovering in 80–100 ms; under a sustained 9 s bidirectional disturbance, redirecting PV surplus to BESS charging in both the undervoltage and overvoltage segments—with no externally imposed active-current limit—keeps the current-vector magnitude $\sqrt{i_d^2+i_q^2}$ below the 335 A rating throughout (≈271 A and ≈242 A, respectively), while the available reactive margin $Q_{disp}$ reaches ≈78– 80 kVAr in both segments and the bank SOC advances by ≈$0.03$ pu; and supervisory curtailment under a sustained overvoltage ride-through with a saturated bank keeps the per-bank SOC dispersion within $4\times {10}^{-5}$ pu while expanding the available reactive margin $Q_{disp}$ from ≈50 to ≈90 kVAr. Full article

Accurate identification of customer transformer relationships in distribution sub-zones is a fundamental prerequisite for the refined management of low-voltage distribution networks and the integration of distributed generation sources. Addressing current issues such as missing records, non-standard wiring and unclear boundaries between multiple sub-zones, this paper proposes an identification method based on the Power Admittance Regression Algorithm (PARA). Based on the fundamental laws of electrical circuits, this method constructs a regressible model of the linear relationship between the total admittance at the transformer end and the admittances at each consumer end. By utilising electrical data collected simultaneously from smart metres and distribution transformer terminals, it formulates the identification of consumer transformer relationships as a problem of minimising regression residuals. For three typical operating conditions—pure residential load, mixed residential and commercial load, and photovoltaic connection at the feeder terminus—constrained least-squares regression models and binary regression models incorporating PV variables were established respectively; ridge regression regularisation was introduced to suppress multicollinearity and enhance model robustness. Simulation tests were conducted using a dataset comprising 150 consecutive time sections and 70 test nodes (of which 60 were customers within the local substation area and 10 were interference nodes from other substation areas) for validation. The results indicate that, under the three conditions described above, in engineering simulations accounting for three-phase imbalance, random perturbations in line parameters and measurement noise, the average accuracy of this method, as determined by 100 Monte Carlo simulations, was 86.2 percent, 92.8 percent and 93.1 percent respectively, with standard deviations ranging from 1.6% to 1.9%, thereby validating its effectiveness and superiority in scenarios involving complex load structures and the integration of renewable energy. As this work is based on simulation data, further online validation using actual feeder data from electricity consumption data acquisition systems is required. Full article

High photovoltaic (PV) penetration can reduce grid-energy imports but may worsen voltage performance in radial distribution feeders. This paper develops a unified mixed-discrete PV–BESS planning framework that jointly optimizes siting, sizing, hourly storage operation, and a common inverter voltage-support gain subject to inverter capability limits. Five metaheuristic algorithms are compared over 30 independent runs on the 33-bus system using an explicitly converged under-relaxed fixed-point iteration. Adaptive Differential Evolution achieves the lowest best and median fitness values. Its best solution reduces active-loss energy by 38.0%, grid-energy import by 14.9%, peak grid power by 12.5%, voltage deviation by 53.7%, voltage-violation duration by 100%, and annual cost by 8.4% relative to the no-PV/no-BESS case. A separately optimized comparison shows substantial additional voltage and loss improvements from inverter reactive-power support. Objective-weight, voltage-penalty, BESS energy-CAPEX, and representative-day analyses clarify key planning trade-offs. On the 69-bus feeder, the 30-run ADE validation reduces active-loss energy by 51.2%, grid-energy import by 19.5%, peak grid power by 10.5%, voltage deviation by 39.5%, voltage-violation duration by 98.8%, and annual cost by 12.2%. A supplementary analysis with a higher PV upper bound further clarifies the influence of the adopted planning limit. Full article

With the large-scale integration of distributed photovoltaic generation (PV) into modern distribution networks, the inherent stochasticity and volatility of renewable energy outputs have imposed non-negligible impacts on the secure and economic operation of power systems. Conventional probabilistic power flow (PPF) methods are limited in accurately modeling source–load uncertainty and, more importantly, in capturing complex nonlinear and time-varying dependence among multiple renewable energy sources. To address these issues, this paper proposes a novel PPF calculation framework based on advanced source-load modeling and time-varying D-vine Copula. Firstly, an enhanced finite mixture Beta model and a Gaussian cluster mixture model are developed to characterize the uncertainty of PV output and load demand, respectively. Secondly, a time-varying D-vine Copula model based on the generalized autoregressive score framework is constructed. And a two-stage regularized profile likelihood estimation method is proposed to estimate correlation parameters, capturing the dynamic nonlinear dependence among multiple PV generators. Finally, the de-randomized Sobol sequence-based Quasi-Monte Carlo method is adopted to perform stochastic power flow calculation. Simulation results on a real-world 129-bus distribution system in East China verify the accuracy and effectiveness of the proposed method. Full article

Accurate prediction of photovoltaic (PV) cell power from electroluminescence (EL) images is a key enabler for automated quality assessment and performance estimation in PV manufacturing and diagnostics. However, most existing image-based deep learning models are developed and evaluated for a single PV cell technology, limiting their ability to generalize across the wide variety of cell types used in practice. This work investigates the impact of PV cell technology on power prediction accuracy and proposes a novel Convolutional Hierarchical Mixture of Experts (CHME) architecture to overcome these generalization limitations. First, convolutional neural networks and feature-based machine learning models are evaluated on multiple PV cell technologies. While technology-specific convolutional models achieve low mean absolute errors (MAEs) of 0.02–0.04 when tested on the same cell type, their performance deteriorates substantially (MAEs of 0.08–0.23) when applied to different technologies. Feature-based models exhibit greater robustness across technologies but at the cost of lower prediction accuracy. To address these limitations, the proposed CHME model combines multiple pretrained technology-specific convolutional experts with a discriminator network that automatically identifies the PV cell technology and selects the most appropriate expert for power prediction. Experimental results demonstrate that CHME achieves the best overall performance, reducing the MAE to 0.0262 compared with 0.0298 for the best standalone convolutional model, while preserving adaptability to heterogeneous datasets. These results demonstrate that explicitly accounting for PV cell technology significantly improves image-based power prediction and that the proposed hierarchical mixture-of-experts framework provides an accurate, scalable, and easily retrainable solution for real-world PV diagnostic systems. Full article

Agriculture is increasingly challenged by water scarcity, climate change, and rising energy demands, requiring more efficient and sustainable irrigation solutions. Conventional irrigation systems often lack the capability to adapt their operation to changing soil conditions and renewable energy availability, resulting in inefficient water and energy use. This study proposes a digital twin-based framework for energy management and irrigation optimization in photovoltaic (PV)-powered smart agriculture systems using Internet of Things (IoT) soil monitoring. The proposed system integrates a physical irrigation infrastructure, an IoT monitoring network, a fuzzy logic control layer, and a digital twin environment that periodically synchronizes the virtual model with IoT measurements to support the system representation and decision-making. The digital twin models soil moisture, temperature, nutrient levels, PV energy generation, battery state of charge, and irrigation water consumption. The virtual representation was periodically aligned with the physical system using measurements transmitted through the long-range (LoRa)-based network. An energy-aware irrigation scheduling strategy was developed to optimize irrigation timing based on soil conditions, battery status, and solar energy availability. The framework was evaluated using field data collected in a real apple orchard through an ESP32-based IoT platform and a standalone PV-powered irrigation system; quantitative experimental validation was performed for the soil twin. The results demonstrate high soil twin synchronization accuracy, with an overall RMSE of 1.47 percentage points and R² of 0.981, based on experimental field measurements. The energy twin and irrigation twin were evaluated using experimentally acquired sensor data together with model-based performance assessment, demonstrating the potential of the proposed digital twin framework for integrated water–energy management in smart agriculture. Full article

Driven by low-carbon mandates, hybrid power vessels integrating diesel, battery, shore-power, and photovoltaic vessel (PV) systems are emerging as a key green shipping pathway. However, operation scheduling is essentially complex due to the integration between supply-side routing and load-side energy dispatch, which is compounded by multi-dimensional uncertainties in PV generation, port-grid loads, and feeder delays. To address this, we formulate a unified two-stage robust optimization model. The objective is to simultaneously minimize operating costs and enhance port-grid friendliness by coordinating on-board energy management and shore-power interactions. In detail, the first stage determines routing and sailing speeds, while the second stage allocates multi-source power under a worst-case budgeted polyhedral uncertainty set. A piecewise-linearization scheme handles the cubic speed–power relation, rendering a tractable mixed-integer linear programming problem. The problem is efficiently solved via a tailored Benders decomposition algorithm, utilizing a genetic-algorithm warm-start to substantially accelerate convergence. Validated on three real-world networks via 1000 Monte Carlo scenarios, the proposed model reduces mean operating costs by 20.2–23.5% and suppresses cost variance by over 60% compared to deterministic approaches. Full article

Driven by the rapid expansion of the new energy industry and the growing demand for photovoltaic (PV) power plant construction, optimizing site selection to ensure operational efficiency and stability has emerged as a critical imperative. Addressing the lack of precise zonation assessment systems for PV development in China’s arid and semi-arid regions, this study introduces a multi-dimensional, five-tier suitability evaluation framework. By integrating Boolean logic, the Bayesian Best–Worst Method (B-BWM), and Equal Interval classification, we developed a multi-resolution integrated assessment framework where spatial boundaries are mainly constrained by 30 m topographic and land-cover data. Results indicate that the candidate PV construction space (the upper two suitability classes, PDSI ≥ 3.40) spans 0.85 million km² concentrated in central-western Inner Mongolia and southeastern Xinjiang, and model validation achieves an Area Under the Curve (AUC) of 0.79. The annual technical potential reaches 19,069 TWh, equivalent to approximately 207% of China’s total electricity consumption in 2023. If fully developed, this potential offers a theoretical annual CO₂ emission reduction ranging from 12.69 to 21.48 billion tons under different conversion efficiency scenarios, with a baseline estimate of 14.65 billion tons. These multi-resolution integrated findings provide a useful spatial reference for preliminary site screening in arid and semi-arid regions, support China’s “Dual Carbon” goals, and offer a practical methodological approach for renewable energy planning on marginal lands. Full article

High photovoltaic (PV) penetration in distribution networks can cause significant voltage deviations, making effective voltage regulation a critical issue for distribution system operators. Step voltage regulators (SVRs) provide a cost-effective solution; however, their performance strongly depends on control parameter settings that should account for PV-induced voltage fluctuations. Because installing high-resolution sensors at all PV sites is impractical, this paper proposes a method for determining SVR control parameters using short-term PV output estimation. The proposed approach assumes that only large-capacity PV systems are equipped with high-resolution sensors and estimates the outputs of other PV systems using the inverse distance weighting (IDW) method. Based on historical power flow data and estimated PV outputs, the method determines 48 sets of SVR control parameters for the following day while capturing rapid voltage rises caused by PV fluctuations. Numerical simulations using a practical distribution network model developed from an actual Chubu Electric Power Grid Co., Inc. system demonstrate the effectiveness of the proposed method. The results show that the allowable PV hosting capacity increases by 25% compared with a method without short-term PV output estimation, while the increase in SVR tap operations remains within an acceptable range. Full article

The problem of load frequency control (LFC) becomes more severe with the extensive integration of photovoltaic (PV) generation owing to the intermittent nature of the source. In this study, a machine learning approach is developed to design the proportional–integral–derivative (PID) controller of a two-area PV–thermal LFC system, extending a prior proportional–integral (PI) benchmark to full PID action. A Random Forest model is trained to predict the relationship between the six PID gains and the closed-loop integral of time-multiplied absolute error (ITAE), yielding an accurate performance model (test R² = 0.933) that is subsequently searched by a metaheuristic optimizer to determine the controller gains; the resulting controller is termed ML-PID. The novelty of the approach lies in employing the learned model not as a controller or a physical-quantity predictor, as in existing ML-based LFC studies, but as a reusable performance model that maps the controller gains directly to the closed-loop index and guides the PID design. To isolate and quantify the contribution of the learned model, the same three optimizers, namely the Cheetah Optimizer (CO), the Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO), are also applied directly to the plant, yielding purely metaheuristic controllers (CO-PID, GWO-PID, and PSO-PID) that are compared against the machine learning-assisted designs under identical algorithms and computational budget, with CO selected on the basis of the Friedman and Wilcoxon tests. The proposed ML-PID-CO controller attains the minimum ITAE under a step-load disturbance, approximately 70% lower than that of the reference SCHO-PI controller and comparable to the directly optimized controllers, with reduced control effort. Under a simultaneous variation in the plant time constants, it is the most robust of all controllers, exhibiting the smallest Δf₁ undershoot and a performance that degrades about 4.2 times less than that of the reference. The results show that a learned performance model provides a good and reusable basis for PID design. It can be searched over repeatedly once built and reduces the per-design simulation burden relative to direct metaheuristic tuning, while the design is largely independent of the optimizer used. Full article

This study considers the multi-objective optimization of a standalone hybrid nanogrid system (HNGS) providing electricity power to a residential load in an off-grid area of Djelfa Province, Algeria. The focus of this study is to obtain the optimum design of a standalone hybrid nanogrid system consisting of photovoltaic (PV) panels, wind turbines (WTs), battery storage (BT), diesel generators (DGs), and power converters to satisfy the energy demand of residential consumers in Djelfa Province, Algeria. In this context, four multi-objective optimization algorithms (MOPs), NSGA-II, MOPSO, MOSSA, and MODE, are used to solve the optimal sizing problem of the proposed system. The formulated multi-objective optimization problem takes into account multiple performance criteria such as cost of energy (COE), loss of power supply probability (LPSP), renewable energy penetration, and diesel generator usage reduction, balancing economic, reliability, and sustainability aspects. The optimization process optimizes critical design parameters, including the size of the PV system, the number of wind turbines, and the size of the battery storage system, for a realistic operating scenario. The optimization algorithms are combined with an energy management strategy (EMS) that helps to coordinate the power flow distribution between various parts of the system to achieve optimum system performance. The effectiveness of each of the proposed approaches is analyzed based on the obtained results, where it was found that the MODE algorithm provides the best compromise solution, with a COE of 0.167 USD/kWh and an LPSP of 6.372%, and the lowest carbon dioxide emissions of 205.1 kg/year compared to MOPSO, NSGA-II, and MOSSA. Moreover, the results obtained from this process will provide a set of feasible design solutions, which will allow decision-makers to choose the most suitable design solution based on technical and economic specifications. Full article

With the advancement of China’s “dual carbon” goals and the green transformation of the railway sector, railway stations, as key energy-consuming nodes, require integrated energy systems that support low-carbon and renewable energy utilization. This review focuses on zero-carbon railway station integrated energy systems incorporating photovoltaic (PV) generation, energy storage, hydrogen systems, and charging facilities. Based on existing studies, the paper systematically reviews system configuration methods, operational strategies, and capacity optimization approaches. It first summarizes the roles of photovoltaic, energy storage, and hydrogen systems in railway station energy supply and outlines representative integration frameworks. It then compares standalone operation and coordinated multi-energy complementary operation, with particular attention to technical challenges in renewable energy accommodation, energy storage coordination, and hydrogen utilization. Mainstream capacity optimization approaches are further reviewed according to different energy configurations, including photovoltaic systems, energy storage systems (ESSs), hydrogen systems, and multi-energy complementary systems, with emphasis on optimization objectives, constraint formulations, and solution methodologies. The review shows that existing studies have gradually shifted from single-energy configurations toward coordinated multi-energy planning, but limitations remain in load forecasting accuracy, dynamic operational optimization, and large-scale engineering validation. Existing uncertainty management methods mainly include stochastic programming, robust optimization, chance-constrained optimization, and scenario-based approaches, which are used to address renewable energy fluctuations and load uncertainties. Future research should strengthen uncertainty modeling, real-time scheduling, and case study platforms considering diverse meteorological and load scenarios. This review provides a theoretical reference for planning and optimizing zero-carbon railway station integrated energy systems. Full article

Rural houses in cold regions of China usually have high energy demands, particularly for space heating and domestic hot water. Passive design can reduce building energy demand, but additional renewable energy systems are still needed to improve on-site energy supply. This study evaluates the performance and limitations of photovoltaic (PV) and solar water heating (SWH) systems in a passive-designed rural house in Xi’an, China. Hourly simulations were conducted for PV-only and PV–battery configurations with different south-facing roof coverage ratios and battery capacities, together with an evacuated-tube SWH system. The results show that PV electricity supply was limited by the mismatch between household electricity demand and PV generation. Household demand mainly occurred in the morning and evening, whereas PV generation was concentrated around noon. The 13 m² PV case achieved approximately 11% electricity supply capacity with a utilization ratio of 62%, while increasing the PV area to 50 m² raised the supply capacity to only 15% and reduced the utilization ratio to 23%. With battery storage, the largest PV–battery configuration supplied 48% of annual household electricity demand, while the overall electricity utilization ratio was 73%, indicating a trade-off between household electricity self-supply and system utilization. The SWH system showed better applicability for domestic hot water supply, with an annual average hot water supply capacity of 60.2% and an average device efficiency of 43.5%, but its winter performance remained weak. These results indicate that PV and SWH are useful but insufficient solar energy strategies for passive-designed rural houses. PV is mainly constrained by daily time mismatch, while SWH is mainly constrained by seasonal climate variation. Full article

The digitalization of photovoltaic (PV) systems can support sustainable energy deployment by improving operational efficiency, system visibility, and energy extraction. However, many existing Internet of Things (IoT)-enabled solutions address monitoring and maximum power point tracking (MPPT) separately or depend on proprietary platforms, remote cloud services, and relatively costly hardware, which may restrict their accessibility and replication in small-scale and resource-constrained applications. This study presents the implementation and laboratory-scale experimental evaluation of an edge-IoT architecture that integrates real-time PV monitoring, embedded adaptive MPPT control, local data management, and visualization using low-cost hardware and open-source software. The proposed architecture combines an ESP32 microcontroller with a Raspberry Pi (RPi) local server to enable environmental and electrical sensing, edge-based control, message queuing telemetry transport (MQTT) communication, local data storage, and interactive visualization through the open-source Node-RED, InfluxDB, and Grafana platforms. An adaptive perturb-and-observe (AP&O) algorithm is implemented on the ESP32 to dynamically adjust the duty cycle of a DC–DC boost converter in response to changing operating conditions. The system is experimentally evaluated using a PV test bench equipped with a custom boost converter and sensing modules measuring eleven electrical and environmental parameters. The architecture achieved an average communication latency of 193 ± 23 ms and an average MPPT efficiency of 97.3 ± 0.54%. It also provided a power gain of 0.7 ± 0.5% compared with the conventional fixed-step perturb-and-observe method. By combining local processing, open-source software, low-cost components, and integrated monitoring and control, the proposed system reduces dependence on external cloud infrastructure while supporting responsive and accessible PV energy management. These results demonstrate its potential as a replicable technological framework for improving the operational sustainability and digital management of small-scale PV installations. Full article