Search Results (97)

Among the various renewable-powered pathways for green hydrogen production, solar photovoltaic (PV) technology represents a particularly promising option due to its environmental sustainability, widespread availability, and declining costs. However, the inherent intermittency of solar irradiance presents operational challenges for electrolyzers, particularly in terms of stability and efficiency. This study presents a MATLAB-based dynamic model of an off-grid, DC-coupled solar PV-Anion Exchange Membrane (AEM) electrolyzer system, with a specific focus on realistically estimating hydrogen output. The model incorporates thermal energy management strategies, including electrolyte pre-heating during startup, and accounts for performance degradation due to load cycling. The model is designed for a comprehensive analysis of hydrogen production by employing a 10-year time series of irradiance and ambient temperature profiles as inputs. The results are compared with two simplified scenarios: one that does not consider the equipment response time to variable supply and another that assumes a fixed start temperature to evaluate their impact on productivity. Furthermore, to limit the effects of degradation, the algorithm has been modified to allow the non-sequential activation of the stacks, resulting in an improvement of the single stack efficiency over the lifetime and a slight increase in overall hydrogen production. Full article

Agrivoltaic (Agri-PV) systems face the critical challenge of balancing photovoltaic energy generation with crop productivity, yet systematic approaches to quantifying the trade-offs between these objectives remain scarce. In this study, we identify nine essential design indicators: panel tilt angle, elevation, photovoltaic coverage ratio, shading factor, land equivalent ratio, photosynthetically active radiation (PAR) utilization, crop yield stability index, water use efficiency, and return on investment. We introduce a novel dual matrix Analytic Hierarchy Process (AHP) to evaluate their relative significance. An international panel of eighteen Agri-PV experts, encompassing academia, industry, and policy, provided pairwise comparisons of these indicators under two objectives: maximizing annual energy yield and sustaining crop output. The high consistency observed in expert responses allowed for the derivation of normalized weight vectors, which form the basis of two Weighted Influence Matrices. Analysis of Total Weighted Influence scores from these matrices reveal distinct priority sets: panel tilt, coverage ratio, and elevation are most influential for energy optimization, while PAR utilization, yield stability, and elevation are prioritized for crop productivity. This methodology translates qualitative expert knowledge into quantitative, actionable guidance, clearly delineating both synergies, such as the mutual benefit of increased elevation for energy and crop outcomes, and trade-offs, exemplified by the negative impact of high photovoltaic coverage on crop yield despite gains in energy output. By offering a transparent, expert-driven decision-support tool, this framework enables practitioners to customize Agri-PV system configurations according to local climatic, agronomic, and economic contexts. Ultimately, this approach advances the optimization of the food energy nexus and supports integrated sustainability outcomes in Agri-PV deployment. Full article

Precisely predicting photovoltaic (PV) output is crucial for reliable grid integration; so far, most models rely on site-specific sensor data or treat large meteorological datasets as black boxes. This study proposes an explainable machine-learning framework that simultaneously ranks the most informative weather parameters and reveals their physical relevance to PV generation. Starting from 27 local and plant-level variables recorded at 15 min resolution for a 1 MW array in Çanakkale region, Türkiye (1 August 2022–3 August 2024), we apply a three-stage feature-selection pipeline: (i) variance filtering, (ii) hierarchical correlation clustering with Ward linkage, and (iii) a meta-heuristic optimizer that maximizes a neural-network R² while penalizing poor or redundant inputs. The resulting subset, dominated by apparent temperature and diffuse, direct, global-tilted, and terrestrial irradiance, reduces dimensionality without significantly degrading accuracy. Feature importance is then quantified through two complementary aspects: (a) tree-based permutation scores extracted from a set of ensemble models and (b) information gain computed over random feature combinations. Both views converge on shortwave, direct, and global-tilted irradiance as the primary drivers of active power. Using only the selected features, the best model attains an average R² ≅ 0.91 on unseen data. By utilizing transparent feature-reduction techniques and explainable importance metrics, the proposed approach delivers compact, more generalized, and reliable PV forecasts that generalize to sites lacking embedded sensor networks, and it provides actionable insights for plant siting, sensor prioritization, and grid-operation strategies. Full article

To combat the catastrophic effects of climate change, the usage of renewable energy sources (RESs) has increased dramatically in recent years. The main drivers of the increase in solar photovoltaic (PV) system grid integrations in recent years have been lowering energy costs and pollution. Active and reactive powers are controlled by a proportional–integral controller, whereas energy storage batteries improve the quality of energy by storing both current and voltage, which have an impact on steady-state error. Since traditional controllers are unable to maximize the energy output of solar systems, artificial intelligence (AI) is essential for enhancing the energy generation of PV systems under a variety of climatic conditions. Nevertheless, variations in the weather can have an impact on how well photovoltaic systems function. This paper presents an intelligent power management controller (IPMC) for obtaining power management with load and electric-vehicle applications. The architecture combines the solar PV, battery with electric-vehicle load, and grid system. Initially, the PV architecture is utilized to generate power from the irradiance. The generated power is utilized to compensate for the required load demand on the grid side. The remaining PV power generated is utilized to charge the batteries of electric vehicles. The power management of the PV is obtained by considering the proposed control strategy. The power management controller is a combination of the twisting sliding-mode controller (TSMC) and Modified Pufferfish Optimization Algorithm (MPOA). The proposed method is implemented, and the application results are matched with the Mountain Gazelle Optimizer (MSO) and Beluga Whale Optimization (BWO) Algorithm by evaluating the PV power output, EV power, battery-power and battery-energy utilization, grid power, and grid price to show the merits of the proposed work. Full article

Aiming to solve optimization scheduling difficulties caused by the double uncertainty of source-side photovoltaic (PV) output and load-side demand response in active distribution networks, this paper proposes a two-stage distribution robust optimization method. First, the first-stage model with the objective of minimizing power purchase cost and the second-stage model with the co-optimization of active loss, distributed power generation cost, PV abandonment penalty, and load compensation cost under the worst probability distribution are constructed, and multiple constraints such as distribution network currents, node voltages, equipment outputs, and demand responses are comprehensively considered. Secondly, the second-order cone relaxation and linearization technique is adopted to deal with the nonlinear constraints, and the inexact column and constraint generation (iCCG) algorithm is designed to accelerate the solution process. The solution efficiency and accuracy are balanced by dynamically adjusting the convergence gap of the main problem. The simulation results based on the improved IEEE33 bus system show that the proposed method reduces the operation cost by 5.7% compared with the traditional robust optimization, and the cut-load capacity is significantly reduced at a confidence level of 0.95. The iCCG algorithm improves the computational efficiency by 35.2% compared with the traditional CCG algorithm, which verifies the effectiveness of the model in coping with the uncertainties and improving the economy and robustness. Full article

The proactive involvement of photovoltaic (PV) smart inverters (PVSIs) in grid management facilitates voltage regulation and enhances the integration of distributed energy resources (DERs) within distribution networks. However, to fully exploit the capabilities of PVSIs, it is essential to achieve optimal control of their operations and effective coordination with voltage-regulating devices in the distribution network. This study developed a dual strategy approach to forecast the optimal setpoints of onload tap changers (OLTCs), PVSIs, and distribution static synchronous compensators (DSTATCOMs) to improve the voltage profiles in power distribution systems. The study began by running a centralized AC optimal power flow (CACOPF) and using the hourly PV output power and the load demand to determine the optimal active and reactive power of the PVSIs, the setpoint of the DSTATCOM, and the optimal tap setting of the OLTC. Furthermore, Machine Learning (ML) models were trained as controllers to determine the reactive-power setpoints for the PVSIs and DSTATCOMs as well as the optimal OLTC tap position required for voltage stability in the network. To assess the effectiveness of the method, comprehensive evaluations were carried out on a modified IEEE 33 bus with a high penetration of PV energy. The results showed that deep neural networks (DNNs) outperformed other ML models used to mimic the coordination method based on CACOPF. Furthermore, when the DNN-based controller was tested and compared with the optimizer approach under different loading and PV conditions, the DNN-based controller was found to outperform the optimizer in terms of computational time. This approach allows predictive control in power systems, helping system operators determine the action to be initiated under uncertain PV energy and loading conditions. The approach also addresses the computational inefficiency arising from contingencies in the power system that may occur when optimal power flow (OPF) is run multiple times. Full article

The integration of battery energy storage systems (BESSs) within active distribution networks (ADNs) entails optimized day-ahead charge/discharge scheduling to achieve effective peak shaving.The primary objective is to reduce peak demand and mitigate power deviations caused by intermittent photovoltaic (PV) output. Quasi-static time-series (QSTS) co-simulations for determining optimal heuristic solutions at each time interval are computationally intensive, particularly for large-scale systems. To address this, a two-stage intelligent BESS scheduling approach implemented in a MATLAB–OpenDSS environment with parallel processing is proposed in this paper. In the first stage, a rule-based decision tree generates initial charge/discharge setpoints for community BESS units. These setpoints are refined in the second stage using an optimization algorithm aimed at minimizing community net load power deviations and reducing peak demand. By assigning each ADN community to a dedicated CPU core, the proposed approach utilizes parallel processing to significantly reduce the execution time. Performance evaluations on an IEEE 8500-node test feeder demonstrate that the approach enhances peak shaving while reducing QSTS co-simulation execution time, utility peak demand, distribution network losses, and point of interconnection (POI) nodal voltage deviations. In addition, the use of smart inverter functions improves BESS operations by mitigating voltage violations and active power curtailment, thereby increasing the amount of energy shaved during peak demand periods. Full article

This study explores the optimization of solar energy harvesting in Truro City in the UK using PVSyst simulations integrated with real-time meteorological data. Focusing on panel orientation, tilt angle, shading, and albedo, the research aimed to enhance both energy efficiency and economic viability of photovoltaic (PV) systems in green buildings. A 100 kWp rooftop solar installation served as the case study. Energy outputs derived from spreadsheet-based models and PVSyst simulations were compared to validate results. Optimal tilt angles were identified between 35° and 39°, and the azimuth angle of 0° yielded the highest energy gain without requiring solar tracking. Fixed configurations with a 5 m pitch showed only a 10% shading loss, requiring 1680 m² of space and generating an average of 646.83 kWh/m² monthly. Compared to recent works, our integration of real-time climate data improved simulation accuracy by 6–9%, refining operational planning and decision-making processes. This included better timing of high-load activities and enhanced prediction for grid feedback. The study demonstrates that data-driven optimization significantly improves performance reliability and system design, offering practical insights for solar infrastructure in similar temperate climates. These results provide a benchmark for urban energy planners seeking to balance performance and spatial constraints in PV deployment. Full article

The increasing integration of distributed photovoltaics (PVs) has intensified voltage violations in active distribution networks (ADNs). Traditional centralized voltage regulation approaches face substantial challenges in terms of communication and computation. Distributed control methods can help mitigate these issues through distributed algorithms but struggle to track real-time fluctuations in PV generation. Local control offers fast voltage adjustments but lacks coordination among different PV units. This paper presents a hierarchical distributed and local voltage control strategy for PV clusters. First, the alternating direction method of multipliers (ADMM) algorithm is adopted to coordinate the reactive power outputs of PV inverters across clusters, providing reference values for local control. Then, in the local control phase, a Q-P control strategy is utilized to address real-time PV fluctuations. The flexibility of the local control strategy is enhanced using the lifted linear decision rule, enabling a rapid response to PV power fluctuations. Finally, the proposed strategy is tested on both the modified IEEE 33-node distribution system and a practical 53-node distribution system to evaluate its performance. The results demonstrate that the proposed method effectively mitigates voltage issues, reducing the average voltage deviation by 53.93% while improving flexibility and adaptability to real-time changes in PV output. Full article

This study presents an integrated methodology to assess and reduce greenhouse gas (GHG) emissions in institutional buildings by combining organizational carbon footprint (CFO) analysis with rooftop photovoltaic (PV) system simulation. The HM Building at King Mongkut’s Institute of Technology Ladkrabang (KMITL), Thailand, was selected as a case study to evaluate carbon emissions and the feasibility of solar-based mitigation strategies. The CFO assessment, conducted in accordance with ISO 14064-1:2018 and the Thailand Greenhouse Gas Management Organization (TGO) guidelines, identified total emissions of 1841.04 tCO₂e/year, with Scope 2 electricity-related emissions accounting for 442.00 tCO₂e/year. Appliance-level audits revealed that classroom activities represent 36.7% of the building’s electricity demand. These findings were validated using utility data totaling 850,000 kWh/year. A rooftop PV system with a capacity of 207 kWp was simulated using PVsyst software (version 7.1), incorporating site-specific solar irradiance and technical loss parameters. Monocrystalline modules produced the highest energy output of 292,000 kWh/year, capable of offsetting 151.84 tCO₂e/year, equivalent to 34.4% of Scope 2 emissions. Economic evaluation indicated a 7.4-year payback period, with a net present value (NPV) of THB 12.49 million and an internal rate of return (IRR) of 12.79%. The integration of verified CFO data with empirical load modeling and derated PV performance projections provides a robust, scalable framework for institutional carbon mitigation. This approach supports data-driven Net Zero campus planning aligned with Thailand’s Nationally Determined Contributions (NDCs) and carbon neutrality policies. Full article

Optimizing the operation of active distribution networks (ADNs) has become more challenging because of the uncertainty created by the high penetration level of distributed photovoltaic (PV). From the convex optimization perspective, this paper proposes a two-layer optimization model to simplify the solution of the ADN optimal operation problem. Firstly, to pick out the ADN “key” nodes, a “key” nodes selection approach that used improved K-means clustering algorithm and two indexes (integrated voltage sensitivity and reactive power-balance degree) is introduced. Then, a two-layer ADN optimization model is built using various time scales. The upper layer is a long-time-scale model with on-load tap-changer transformer (OLTC) and capacitor bank (CB), and the lower layer is a short-time-scale optimization model with PV inverters and distributed energy storages (ESs). To take into account the PV users’ interests, maximizing PV active power output is added to the objective. Afterwards, under the application of the second-order cone programming (SOCP) power-flow model, a linearization method of OLTC model and its tap change frequency constraints are proposed. The linear OLTC model, together with the linear models of the other equipment, constructs a mixed-integer second-order cone convex optimization (MISOCP) model. Finally, the effectiveness of the proposed method is verified by solving the IEEE33 node system using the CPLEX solver. Full article

This research proposes a novel adaptive virtual synchronous generator (VSG) control strategy for a photovoltaic-energy storage (PV-storage) hybrid system. In comparison to the traditional VSG control approach, the adaptive control strategy presented in this research markedly diminishes the fluctuations in output power. This improvement is accomplished through the dynamic adjustment of virtual inertia (J) and damping coefficient (D), which enables real-time responsiveness to variations in light intensity, converter power, and load power factors that traditional VSG controls are unable to address promptly. Initially, a small signal model of VSG’s active power closed-loop system is established and analyzed for a grid-connected converter in a PV-storage hybrid system. The influence of these parameters on the response speed and stability of the PV-storage system is discussed by analyzing the step response and root locus corresponding to varying J and D conditions. Then, this study employs the power angle and frequency oscillation characteristics of synchronous generators (SGs) to formulate criteria for selecting the J and D. Based on the established criteria, a parameter-adaptive VSG control strategy is proposed. Ultimately, the efficacy of the proposed strategy is validated in MATLAB/Simulink under three distinct conditions: abrupt changes in light intensity, converter power, and load power. The results indicate that the strategy is capable of diminishing power oscillation amplitude, effectively mitigating instantaneous impulse current, and notably alleviating frequency overshoot. Full article

Due to the integration of distributed photovoltaic (PV) into distribution networks, significant challenges have affected voltage regulation and power quality maintenance. To improve the flexibility and stability of system operation, a synergetic optimization operation method based on PV and a flexible interconnection device (FID) is proposed. Both PV and FID hold the capability of controlling active power and reactive power. Besides the active power output of PV, three reactive power output schemes of power factor controlling, direct reactive power output, and night static var generator scheme are defined and analyzed. By adopting different schemes during the day or night, five reactive power output modes were built. FID with four-quadrant power control ability was used to coordinate with PV in system power balance. Different port numbers of FIDs are discussed. An optimization model with the aim of reducing voltage deviation, network loss, and the ratio of PV abandonment was constructed. Three algorithms were used for solving the multi-objective optimization model. Simulation results verify that the proposed synergetic optimization method can obviously improve power quality and decrease network loss. The optimal performance is obtained when PV operates in mode 5 and FID holds four ports. The proposed method shows potential in the coordinated operation of various resources and the flexible interconnection of the distribution network. Full article

Flexible resources (FRs) have significant potential in ensuring the dynamic balance between supply and demand as well as enhancing the security of active distribution networks (ADNs). However, determining the optimal FR capacity in an economically reasonable manner remains a challenging task. This paper addresses the lack of representativeness of wind turbine (WT) and photovoltaic (PV) power output scenarios in the planning stage by generating a basic set of joint WT-PV output scenarios using random sampling. Subsequently, a Wasserstein confidence set (WCS) is established based on data-driven technology to better represent the unknown distribution of the actual WT-PV joint fluctuations. This provides a more detailed description of the scenario set, enabling the precise quantification of the risk of resource allocation scenarios and enhancing the flexibility and rigor of the subsequent optimal configuration model (OCM). To improve the coordination of active–reactive FRs, a bi-level OCM with multi-timescale considerations is developed. Compared to traditional configuration methods, the proposed model not only improves economic efficiency but also ensures that system voltage remains within safe limits after configuration. The effectiveness and superiority of the proposed optimal configuration method are demonstrated through simulations on an improved 33-bus test system, where the model achieved a 9.208% reduction in annual cost compared to robust methods while maintaining voltage quality and avoiding overvoltage or equipment overloads. Full article

With the continuous advancement of urbanization, rural areas are facing increasingly severe environmental pollution, excessive energy consumption, and high carbonization resulting from both daily living and production activities. This study, which is aligned with the low-carbon objectives of “carbon sequestration increase and emissions reduction”, explores the optimization strategies for ecological benefits through the combined application of rooftop photovoltaics and rooftop greening in rural residences. Three design approaches are proposed for integrating rooftop photovoltaics with green roofing: singular arrangement, distributed arrangement, and combined arrangement. Using PVsyst (7.4.7) software, this study simulates the effects of roof inclination, system output, and installation formats on the performance of photovoltaic systems, providing a comprehensive analysis of carbon reduction benefits in ecological rooftop construction. A rural area in East China was selected as a sample for adaptive exploration of ecological roof applications. The results of our research indicate that the optimal tilt angle for rooftop photovoltaic (PV) installations in the sample rural area is 17°. Based on simulations combining the region’s annual solar path and the solar parameters on the winter solstice, the minimum spacing for PV arrays is calculated to be 1.925 m. The carbon reduction benefits of the three arrangement methods are ranked, from highest to lowest, as follows: combined arrangement $14530.470{\mathrm{t}\mathrm{C}\mathrm{O}}_{2\mathrm{e}}$ > singular arrangement $11950.761{\mathrm{t}\mathrm{C}\mathrm{O}}_{2\mathrm{e}}$ > distributed arrangement $7444.819{\mathrm{t}\mathrm{C}\mathrm{O}}_{2\mathrm{e}}$. The integrated design of rooftop PV systems and green roofing not only meets the energy demands of buildings but also significantly reduces their carbon footprint, achieving the dual objectives of energy conservation and sustainable development. Therefore, the combined application of rooftop PV systems and green roofing in rural spaces can provide data support and strategic guidance for advancing green transformation and ecological civilization in East China, offering significant practical value for promoting low-carbon rural development. Full article