Search Results (429)

Decarbonizing the transportation sector requires quick adoption of low-carbon energy carriers, with green hydrogen becoming a promising option for zero/low-emission mobility. Hydrogen refueling stations powered by renewable energy sources present a practical way to cut down lifecycle greenhouse gases and ease grid congestion. Nonetheless, most existing photovoltaic (PV)-based hydrogen production systems focus solely on electrical aspects, overlooking thermal energy flows and temperature effects that greatly impact PV and Electrolyzer performance. This study provides a thorough techno-economic evaluation of a hybrid PV/photovoltaic-thermal (PVT) green hydrogen system for refueling stations. The simulation framework models the combined electrical, thermal, and hydrogen subsystems under realistic conditions, incorporating rooftop PV/PVT collectors, battery storage, a water Electrolyzer, and hydrogen storage. Thermal energy from the PVT is used to pre-heat Electrolyzer feedwater, lowering electricity demand for hydrogen production and boosting PV efficiency via active cooling. Hydrogen production follows a demand-driven control strategy based on randomly generated stochastic daily refueling events. Three configurations are compared: (i) grid-only electrolysis, (ii) PV-only assisted electrolysis, and (iii) fully integrated PV/PVT-assisted electrolysis. The results show that the integrated PV/PVT setup significantly increases self-consumption, autarky rate, and overall efficiency, while lowering reliance on grid electricity and hydrogen production costs. Developed case studies highlight the economic feasibility and real-world viability of PV/PVT-assisted (decentralized) hydrogen refueling infrastructure. Full article

Bhutan’s hydropower-reliant electricity supply faces seasonal imbalances, with a winter deficit prompting costly imports from India at tariffs of up to $0.09/kWh. Despite the estimated solar potential of 12 GW, PV deployment remains limited. This study presents a demand-driven techno-economic assessment of a 150.8 kWp rooftop PV system for the Ministry of Infrastructure and Transport using high-resolution hourly load data and PVsyst simulation. Three operational configurations are evaluated: self-consumption without export, self-consumption with export, and a battery energy storage system (BESS) introduced to mitigate curtailed energy. The system is expected to generate 252 MWh annually, achieving self-sufficiency and Self-Consumption Ratios of around 60%. Without export, the performance ratio (PR) is reduced to 51% due to significant curtailment, resulting in a negative Net Present Value (NPV) of −$33,687.5 and a Levelized Cost of Electricity (LCOE) of $0.0682/kWh. Enabling export raises the PR to 85.62%, improving the NPV to $27,965.42, the Internal Rate of Return (IRR) to 8.07%, and the LCOE to $0.0405/kWh. A 200 kWh BESS, sized based on surplus energy and nighttime demand, increases self-consumption and self-sufficiency to 75% and 73%, respectively. However, the LCOE rises to $0.0841/kWh, limiting economic viability under current tariff structures. The results reveal a structural mismatch between prosumer-level economics and system-level benefits, underscoring a need for improved compensation and targeted policy support in Bhutan and similar hydropower-dependent systems. Full article

This study proposes a three-layer gateway control framework for a behind-the-meter virtual power plant (VPP) comprising vehicle-to-grid (V2G)-capable electric vehicle (EV) chargers, battery energy storage systems (BESS), and rooftop photovoltaic (PV) generation in multi-dwelling residential developments, creating a sustainable future through maximising distributed energy resource (DER) utilisation. In particular, the first layer performs day-ahead scheduling to determine the hourly grid import baseline and frequency regulation ancillary service capacity for the following day. In the second layer, real-time regulation dispatch is performed by following the dynamic regulation signal from the grid operator, wherein V2G-capable EVs are coordinated alongside BESS as active demand-side participants in frequency regulation ancillary services, enabling the aggregated behind-the-meter fleet to respond to regulation signals in real time. The third layer performs per-minute three-phase load balancing to maintain network power quality compliance across the multi-dwelling site. The overall goal is to coordinate distributed energy resources behind a single network connection point to simultaneously reduce peak demand, maximise renewable self-consumption, and provide demand-side frequency regulation as a dispatchable VPP asset. Full article

Against the backdrop of the global energy structure accelerating its transition towards a clean and low-carbon model, rooftop-distributed photovoltaic (PV) systems are playing an increasingly prominent strategic role in urban energy supply systems, owing to their notable advantages such as environmental friendliness and high spatial utilization efficiency. Consequently, they are becoming a critical pillar in advancing urban energy transformation and enhancing sustainable development. This paper aims to explore deep learning-based techniques for assessing the potential of large-scale distributed PV installations. To accurately evaluate their dynamic power generation capability, a hybrid prediction model integrating variational mode decomposition (VMD), the mutual information (MI) method, and a cascaded xLSTM-Informer network is proposed. Firstly, the model preprocesses key meteorological sequences using VMD, decomposing them into modal components of different frequencies. Subsequently, the MI method is employed to extract critical sequences. Then, the xLSTM module is utilized to learn the long-term complex dependencies between meteorological conditions and PV power output, while the Informer network captures key global temporal patterns, achieving high-precision time-series forecasting of PV generation. Finally, employing the forecasted time-series power curve as the core input, a comprehensive analytical framework for PV installation potential is constructed, integrating assessments of technical feasibility, economic viability, and environmental performance. This framework aims to scientifically estimate the admissible installed capacity and system value of distributed PV systems, thereby providing a dynamic basis for decision-making in urban planning. Full article

In modern society, distributed photovoltaics are widely used, and overhead photovoltaic roofs are favored for their many advantages; however, they are vulnerable to failure during high-wind events. Parapets are common auxiliary structures on building rooftops. Wind tunnel testing was employed to investigate the effects of parapet configurations on wind pressures acting on overhead photovoltaic (PV) roofs. Results show that wind suction dominates, with maximum negative pressure consistently at the windward corner leading edge. A solid parapet significantly increases the maximum mean pressure coefficient, whereas perforated parapets have little effect. In most cases, parapets reduce fluctuating pressure coefficients. Extreme pressure distribution exhibits significant regional characteristics, with the most unfavorable area at the roof corner. The solid parapet increases unfavorable extreme values at the corner. Horizontal and rectangular grid parapets reduce extreme pressure coefficients at the high-eave corner with minimal impact on the low-eave corner, while the vertical parapet increases values at the low-eave corner. Under the conditions of this experiment, among the four parapet types, the horizontal and rectangular grid parapets have little effect on the mean wind pressure and significantly reduce the peak wind pressure, thereby helping to ensure the wind resistance safety of the photovoltaic roof. Full article

This work uses battery-coupled power electronic converter systems and distributed backstepping controllers to improve the reliability of electrical distribution networks. The motivation is to prevent blackouts such as the 28 April 2025 outage in Spain, Portugal, and the south of France. It is now accepted that a rapid rise in solar power injections caused AC overvoltage above grid code limits, triggering photovoltaic (PV) park disconnections as overvoltage self-protection. This case study considers near-Zero-Energy Buildings (nZEBs) connected to the Madeira Island isolated microgrid, where PV power installation is increasing excessively. The main university facility will be upgraded as an nZEB, using roughly 3000 m² of unshaded rooftops plus coverable parking areas to install PV panels. Optimizing the profits/energy cost ratio, a PV power system of around 560 kW can be planned, and the Battery Storage System (BSS) energy capacity can be estimated. The BSS is connected to the university nZEB via backstepping-controlled multilevel converters to manage PV and BSS, enabling the building to contribute to voltage and frequency regulation. Distributed multilevel converters inject renewable energy into the medium-voltage network, regulating active and reactive power to prevent overvoltages shutting down the PV inverters. This removes sustained overvoltage and maximizes PV penetration while augmenting AC grid reliability and resilience. When there is excess solar power and reactive power is insufficient to reduce voltage, controllers slightly curtail PV active power to eliminate overvoltage, maintaining operation with minimal revenue loss while preventing long interruptions, thereby improving grid reliability and power quality. Full article

Due to the high energy demand of buildings, especially educational buildings, it is crucial to improve total building energy consumption. The proposed methodology is the integration of a photovoltaic (PV) system with a smart control plan for educational buildings. The main aim is to improve energy consumption in an educational building (Electrical Engineering Department, Assiut University, Egypt) using photovoltaic integration and a smart control plan to regulate energy and boost indoor comfort without requiring a significant change in the building architecture. This study was conducted in two main phases: field measurements for annual energy consumption in Assiut University over a five-year period from 2009 to 2014, and an analysis of energy consumption for the Electrical Engineering Department. Then, integration of PV panels on the roof to generate electricity was considered, with the calculation of the shading factor and tilt angle to ensure a realistic estimation of energy yield and to improve energy efficiency using smart control plans. The findings indicate that the average annual peak consumption reached about 30 GWh in Assiut University during the academic years 2009 to 2014. The maximum energy consumption for a typical occupied day in the educational building is 47 kWh. An improvement in building energy consumption was achieved using PV, producing 33–35 MWh annually with an effective smart control plan and without installing sensor-based systems. The results of this study will help improve energy consumption for educational buildings in hot arid climates without building modifications. This study highlights that unoccupied periods—when human activity is absent in classrooms and other rooms—account for up to 40% of the scheduled energy consumption. Using PV panels will result in a shading factor of 0.562 from the total roof area. Full article

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

This study evaluates the rooftop solar photovoltaic (PV) potential at the building level in the tourism-rich districts of Northern Khyber Pakhtunkhwa (KPK), Pakistan, using advanced geospatial analysis to support renewable energy planning. By combining the Area Solar Radiation tool with detailed building footprint data, the study identified solar energy potential and prioritized areas for PV system installations. Results show that approximately 35% of the 1.29 million buildings analyzed are suitable for solar panels, with energy generation capacity varying by building size and district. Spatial analysis further highlighted Union Councils (UCs) where over 50% of buildings are solar-suitable, enabling precise targeting of renewable energy initiatives. The study underscores the importance of integrating local geographical and socio-economic data to enhance the feasibility and scalability of solar energy solutions in rural and urban settings and can be used to guide policy prioritization and funding decisions. This research demonstrates how geospatial analysis and open data can drive localized clean energy adoption, directly contributing to Sustainable Development Goal 7 by advancing affordable and sustainable energy solutions. Full article

The deployment of solar photovoltaic (PV) systems on rooftops in urban environments is to reduce land area required for electricity generation. These deployments may encounter shading and masking on the PV collectors from surrounding building walls, thus reducing the generated electricity. The present article proposes a novel anisotropic diffuse radiation model and investigates the diffuse masking losses stemming from obscuring part of the visible sky to the PV collectors by front rows and by walls erected near the collectors. Monthly and annually collected energies of the anisotropic and the isotropic diffuse radiation models are compared for four different simulated configurations of PV systems deployed near walls. The proposed novel modified model uses the original Klucher (1979) analytical diffuse radiation model for comparing the energies. The anisotropic model predicts a diffuse energy between 4.5% and 13% higher than the isotropic model for a site with 30% diffuse radiation, and nearly 30% higher diffuse energy for a site with 50% diffuse radiation, depending on system configuration. Applying the proposed anisotropic model allows us to assess more accurately the contribution of the diffuse radiation to the generated electric energy of PV systems. Full article

The electrical characteristics of distribution networks (DNs) are drastically changing, which is mainly due to widespread adoption of small-scale distributed energy resources (DERs) by end-users. In these cases, conventional planning models may lead to overinvestment choices. This paper presents a planning model for utility companies that explicitly incorporates a model of end-users’ energy-related decisions, considering a neighborhood energy trading scheme (NETS). The model is formulated based on the Stackelberg game (SG) approach, which guarantees the optimality of the final solution for each user and the utility. The proposed mixed-integer second-order cone programming (MISOCP) problem finds the optimal investment plan for transformers, lines, distributed generators (DGs), and energy storage systems (ESSs) for the utility, considering the scenarios of end-users’ investments in rooftop photovoltaic (PV) and battery systems that maximize their benefits. Additionally, a dynamic network charge (NC) scheme is designed to rationalize the network use. Also, Benders decomposition (BD) is used to improve the convergence of the solution algorithm. The numerical studies on a real 23-bus low voltage (LV) network in Perth, Australia, using real-world data reveals that the proposed planning model offers the lowest total cost and the highest penetration of DERs in comparison with conventional models. Full article

This study presents a performance and techno-economic evaluation of a 24 kWp grid-connected rooftop photovoltaic system in Yeşilova, Burdur, Türkiye, based on measured operational data from 2024. Beyond conventional software comparisons, this research establishes a validated benchmarking protocol for medium-scale rooftop PV systems by quantifying the divergence between measured data and predictive modeling under fluctuating seasonal conditions. Measured results were compared with energy yield predictions from PVsyst and HelioScope. Key performance indicators, including final yield, performance ratio (PR), and capacity factor, were evaluated alongside main loss components. The system produced an annual energy output of 33,977.5 kWh, corresponding to an average PR of 75.7% and a capacity factor of 16.99%. Simulation results show deviations from measured values, with PVsyst moderately overestimating and HelioScope underestimating the annual yield. Thermal effects were identified as the dominant contributor to performance losses, particularly during elevated summer temperatures. A techno-economic assessment indicates a payback period of 8.4 years, a levelized cost of electricity (LCOE) of 0.0485 US$/kWh, and an internal rate of return (IRR) of 15.58%. These findings underline the importance of validating simulation-based assessments with site-specific measurements to improve the reliability of photovoltaic system performance and investment evaluations. Full article

Regional energy self-sufficiency based on microgeneration from clean, local energy sources is essential and strategic for meeting growing electricity demand. In this context, initiatives driven by local governments are decisive in achieving such progress. This study proposes a methodology for sizing photovoltaic (PV) capacity at the parish level, which is the basic political–administrative unit in Ecuador. Rooftop-based microgeneration and self-supply are considered to entail minimal environmental impact while offering significant potential to meet the basic energy demands of buildings in the Andean equatorial climate. The results demonstrate that, using accessible tools such as drones, computer-aided design software, and Agisoft Metashape, and through low-labour processes, it is feasible to estimate the PV potential of buildings at the parish scale. A total of 1698 rooftops were surveyed, and after discarding those with precarious construction materials, the estimated solar potential was found to be between ten and twenty-three times higher than the electrical demand of the analysed parishes. The estimated annual generation potential reaches 28,101 MWh, compared to an annual demand of 1827 MWh for both parishes combined. The proposed process enables the incorporation of rooftop-based technological capacity, relying on a low-technology, affordable methodological approach and instruments for low-income parish governance offices, with low-density populated areas as the main novelty, providing clear information to both authorities and the local population. Full article

Accurate time-domain photovoltaic (PV) models are needed to evaluate performance under outdoor variability beyond STC datasheet conditions. This paper presents a traceable modeling workflow based on the standard single-diode formulation, implemented in MATLAB/Simulink (R2023a) as a modular white-box architecture that explicitly resolves photocurrent generation and loss mechanisms (diode recombination, shunt leakage, and series resistance effects) with temperature-consistent propagation through $V_T\left(T\right)$ and saturation-current terms. The method couples optical boundary conditions to the electrical model by embedding plane-of-array (POA) excitation via the incidence angle $\theta \left(t\right)$ and roof albedo directly into the photocurrent source term, preserving the causal chain from mounting geometry to electrical response. Calibration is separated from prediction by initializing key parameters using the standard Simulink PV block and then freezing them for time-domain evaluation. The workflow is validated on a 395 W rooftop prototype using 1 min resolved POA irradiance (ISO 9060:2018 Class A radiometric chain) and module temperature (IEC 60751 Class A Pt100), synchronized with electrical measurements. Over a multi-week campaign, the model exhibits high fidelity, with a worst-case relative current error of ~1.1% and a consistently low bias and dispersion, quantified by ME, MAE, RMSE, ${\sigma }_e$, and thresholded MAPE. Full article

This study introduces a multi-stage, multi-objective optimization framework based on the Gravitational Search Algorithm (GSA) for determining the optimal sizing and placement of distributed energy resources (DERs) and associated infrastructure. The proposed approach considers solar distributed generation (DG) units with battery storage systems (BSSs), wind DGs, shunt capacitors (SCs) and electric vehicle charging stations (EVCSs). With the rapid adoption of electric vehicles as part of global decarbonization efforts, integrating EVCSs into already stressed distribution networks poses significant operational challenges, often requiring system reinforcement supported by renewable-based DGs. The uncoordinated deployment of EVCSs and DGs can exacerbate power losses and deteriorate voltage profiles. To address these issues, the first stage of the methodology employs GSA to optimally allocate solar DGs with BSSs, wind DGs and SCs, targeting objectives such as minimizing power losses, enhancing voltage stability and alleviating substation loading. The second stage identifies optimal locations and maximum feasible capacities for EVCS integration. Finally, the third stage upgrades the network to mitigate the impacts of EVCS integration. The effectiveness of the proposed approach is validated through simulations on a practical 52-bus, 11 kV distribution network under hourly varying load, solar irradiance and wind velocity conditions for all seasons. The simulation results show an 85% reduction in power losses during peak hours, with nodal voltages maintained above 0.95 p.u. under all scenarios. Additionally, net-zero grid power exchange during peak periods confirms the full islanded operation. Full article