Search Results (9,730)

With the rapid development of sustainable photovoltaic power generation, energy-storage-coordinated grid-connected photovoltaic systems have been widely adopted to stabilize power output and enhance grid adaptability. Aiming at the low fault tolerance of conventional photovoltaic grid-connected systems under grid voltage swell disturbances, this paper designs a single-stage power management system for grid-connected photovoltaic generation and studies its energy-storage-coordinated high-voltage ride-through (HVRT) technology. The single-stage topology boasts simple structure, low cost and high conversion efficiency, yet faces prominent stability risks under voltage swell faults. The system integrates photovoltaic units, energy storage modules and grid-connected interfaces to implement flexible bidirectional power dispatching. A three-phase AC/DC converter realizes photovoltaic maximum power point tracking (MPPT), and the energy storage module connects to the DC bus via a dual half-bridge (DHB) converter to restrain power fluctuations. Under HVRT faults, the energy storage coordination strategy elevates DC bus voltage to maintain stable grid-tied operation without disconnection. Different from schemes requiring extra hardware or complicated control optimization, the proposed method realizes stable bus voltage regulation and flexible energy scheduling with zero additional hardware cost. Simulations and experiments validate the rationality, feasibility and outstanding fault-ride-through performance of the designed system. Full article

In an agrivoltaic plot, the hourly panel tracking angle governs electricity, on-site economics, avoided carbon dioxide, irrigation demand and crop yield. Operating for photovoltaic output alone discards that space, and does so when a midday kilowatt-hour has lost its value: 715 h of the studied season carried a non-positive export price, 517 of them at midday. This work formalizes the electrical operating layer of a mechanistic hourly simulator as nine panel-orientation modes, each a constrained program solved deterministically by sequential quadratic programming, a dense angular scan, or both. Two modes keep an energy-proportional objective non-degenerate through an adaptive clean-grid switch and an economics–carbon weighting anchored on the crop. The catalog is a framework, demonstrated over a 153-day season on a 66 kWp single-axis sage (Salvia officinalis) plot near Valencia, Spain. Seasonal photovoltaic energy ranged from 57.0 to 31.9 MWh, plot-mean relative yield from 95% to 109%, and worst-plant protection from 61% to 94%. Energy maximization sits at a flat extreme of the frontier: the Pareto mode gained 11.4 yield points and 31 worst-plant points for 1.1% less energy. Avoided carbon dioxide varied by 2.1% across the nine modes, so a carbon objective is degenerate with energy without an interior term. Full article

Reliable rooftop photovoltaic planning requires design-stage energy predictions to be verified against actual system performance. The novelty of this study is the integration of a multi-year assessment of agreement with PV*SOL design estimates with an independent assessment of normalized productivity, interannual variability, seasonality, and meteorological effects for several rooftop systems operating under the same regional conditions. PV*SOL, a commercial photovoltaic simulation software used to estimate system energy production during the design stage, was evaluated using three years (2023–2025) of monitored data from three rooftop photovoltaic (PV) systems (17.60–75.40 kWp) in Rzeszów, south-eastern Poland. The analysis comprised 108 installation-month observations and included final yield, capacity factor, annual prediction errors, seasonal variability, Pearson correlations, and hierarchical regression. Mean annual final yield ranged from 907.4 to 966.2 kWh/kWp, while annual deviations from PV*SOL design estimates ranged from −0.80% to +7.41%. Monthly final yield was strongly associated with solar irradiation, and the final hierarchical regression model explained 96.4% of its variability. The results indicate that PV*SOL provides a useful annual design reference, but operational monitoring and local benchmark data remain essential for reliable performance assessment. The study supports United Nations Sustainable Development Goal 7 (Affordable and Clean Energy) by improving the evidence base for rooftop photovoltaic planning and monitoring. Full article

Hourly angle optimization for photovoltaic (PV) shading is conducted by selecting values from independent fixed-angle simulations, although the selected hours do not share a consistent building thermal history. This study develops a database-based constant-action rolling horizon look-ahead heuristic that minimizes net electricity for a PV louver in a Beijing office while correcting thermal-history inconsistency. Nineteen angles from 0° to 90° were simulated to construct hourly thermal and PV maps. Schedules were generated for horizons of 1–48 h under a 10° h⁻¹ movement limit and evaluated by continuous EnergyPlus replay. The independently optimized sequence predicted −310.90 kWh of annual net energy, whereas continuous replay yielded −101.61 kWh, revealing a 209.29 kWh state-inheritance error dominated by HVAC electricity. The 12 h horizon minimized net energy at −119.11 kWh. A 24 h horizon incurred a 5.89 kWh penalty while reducing angle changes and total rotation by approximately 65%, indicating a favorable energy–movement compromise. Requiring an angle of at least 15° during occupancy eliminated full closure at a net-energy cost of 35.79 kWh. Continuous replay and operating constraints are therefore necessary when translating fixed-angle databases into implementable façade controls. Full article

Reliable electricity supply is essential for healthcare institutions, particularly where grid instability can disrupt service continuity, compromise patient safety, and affect the operation of critical medical equipment. In this context, selecting an appropriate photovoltaic (PV) proposal is a complex decision problem that requires assessing technical, economic, environmental, and regulatory factors jointly. This study develops a hybrid multi-criteria decision-making framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP), the Decision-Making Trial and Evaluation Laboratory (DEMATEL), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to support PV proposal selection in healthcare institutions. The framework was applied to four competing proposals for a hospital case study in Barranquilla, Colombia. After integrating FAHP and DEMATEL, the economic, technical, and environmental criteria received balanced interdependence-adjusted weights of 0.324, 0.337, and 0.338, respectively. At the same time, DEMATEL identified the technical dimension as the main net influencing dimension within the expert-elicited influence network. The final ranking placed Proposal 1 first, followed by Proposal 4, Proposal 2, and Proposal 3, with closeness coefficients of 0.530, 0.518, 0.498, and 0.492, respectively. Additional comparative analysis showed that omitting DEMATEL changed the winning alternative, whereas preserving the FAHP–DEMATEL weighting structure and replacing TOPSIS with MARCOS yielded the same ranking. Robustness analyses further showed that the ranking remained stable in most supplier-exclusion and leave-one-expert-out scenarios. In contrast, bootstrap-based probabilistic sensitivity analysis showed that Proposal 1 ranked first in 96.2% of the replications. These results support the practical usefulness of the proposed framework for decision-making in healthcare energy planning. Full article

Water scarcity, rising energy costs, and limited technology adoption challenge Mediterranean greenhouse horticulture. Although irrigation automation and digital decision-support tools have been widely studied, adoption barriers and integrated water–energy decision support are often addressed separately, particularly under farmers’ real operational constraints. This study combines a survey of greenhouse farmers in Almería, southeastern Spain, with an interpretable Mamdani fuzzy logic model to assess irrigation automation and support water–energy management. Survey results show that 61% of farms use basic irrigation systems, 33% use semi-automated systems, and only 6% use fully automated irrigation; 87% rely exclusively on grid electricity. The main barriers were high initial investment, reliability and configuration concerns, and limited willingness to undertake specialized training. Automation was positively associated with cultivated area and educational attainment, but not with age or crop type. The fuzzy model integrates reservoir level, irrigation demand, irrigation mode, tariff period, and energy-supply configuration to estimate irrigation cost and feasible operating time. Scenario-based validation differentiated grid-connected, hybrid photovoltaic, and off-grid configurations and highlighted the potential of renewable integration and time-sensitive irrigation management to reduce dependence on conventional energy. By linking farmer adoption constraints with interpretable operational decision support, the framework provides a practical basis for gradual irrigation modernization and supports water–energy–food nexus management in Mediterranean greenhouse horticulture. Full article

Building facades and construction materials are continuously exposed to airborne particulate matter, organic pollutants, and microbial colonization, which cause progressive soiling, aesthetic degradation, and structural deterioration while imposing high maintenance and energy burdens. Photocatalytic titanium dioxide (TiO₂) has emerged as the most widely studied material for imparting self-cleaning functionality to building surfaces, owing to its ability to mineralize adsorbed contaminants under solar irradiation and to modulate surface wettability. This narrative review provides a structured account of TiO₂-based self-cleaning coatings for building materials, organized around three complementary themes: surface mechanisms, performance metrics, and outdoor durability. We first rationalize the two intertwined self-cleaning mechanisms—photocatalytic oxidative degradation and photoinduced superhydrophilicity—and their combination with physically repellent (superhydrophobic/superamphiphobic) wetting states. We then survey the principal coating-design strategies, including morphology and facet engineering, SiO₂-TiO₂ composites, metal/non-metal doping and heterojunction construction for visible-light activation, and dual-functional photocatalytic–superhydrophobic systems, and their integration into cementitious substrates, natural stone and cultural heritage, and transparent glass/photovoltaic surfaces. The quantitative metrics used to benchmark self-cleaning performance—water contact angle, dye photodegradation, NO_x and VOC abatement, and antimicrobial activity—are critically discussed together with the limitations of standardized laboratory tests. Finally, we analyze the weathering-induced deactivation pathways (photocatalyst leaching, surface contamination by soluble salts, and UV aging of organic matrices) and the emerging strategies for durable coatings, including inorganic binders, light-driven hydration, and defect- and heterojunction-engineered photocatalysts. The review concludes with an outlook on the open challenges that must be addressed to translate these coatings from laboratory demonstrations to long-lived, large-scale building applications. Full article

The increasing integration of distributed energy resources and flexible loads has transformed university campuses into complex energy systems that require coordinated operational strategies capable of managing renewable uncertainty while maintaining economic and environmental performance. This paper proposes a two-stage stochastic mixed-integer linear programming (MILP) framework for the optimal day-ahead energy management of a smart university campus. The proposed model jointly coordinates photovoltaic generation, battery energy storage systems, electric vehicle charging, HVAC operation, and demand response under uncertainties associated with solar generation, electricity demand, energy prices, and ambient temperature. Unlike previous campus energy management approaches, the proposed framework explicitly distinguishes first-stage scheduling decisions from second-stage recourse actions, enabling adaptive operation while preserving decision consistency across uncertainty scenarios. A realistic case study based on the operational characteristics of the Universidad Politécnica Salesiana campus in Ecuador is used to evaluate the proposed methodology. The results demonstrate that the coordinated stochastic scheduling strategy reduces daily operating costs by 36.37%, decreases CO₂ emissions by 42.81%, and lowers peak grid demand by 37.99% compared with conventional operation. In addition, photovoltaic self-consumption reaches 91.7%, while renewable energy utilization increases to 93.4% without compromising occupant thermal comfort. The proposed framework provides a scalable pathway toward low-carbon, resilient, and energy-efficient smart campus operation. Full article

With the rapid development of the electric vehicle (EV) industry, large-scale integration of EVs into the power grid has led to increasingly prominent problems such as low charging efficiency, intensified load fluctuations, and reduced economic benefits for users. To address these issues, an optimization model is constructed with charging time, load fluctuation, and user charging cost as the objectives, comprehensively considering uncertainties including renewable energy output, user charging behavior, and electricity price fluctuations. An uncertainty-aware multi-objective scheduling strategy based on an improved chaotic Lévy flight multi-objective particle swarm optimization (CLM-MOPSO) algorithm is proposed. Specifically, Weibull and Beta distributions are adopted to generate scenarios for wind and photovoltaic power output, while Poisson and normal distributions are used to characterize the uncertainty of user charging behavior. In addition, a stochastic electricity price process and load uncertainty sets are introduced to establish a robust optimization framework based on multi-scenario stochastic programming. On this basis, an improved CLM-MOPSO algorithm is designed, in which Tent chaotic mapping is utilized for high-quality population initialization, Lévy flight mutation is introduced to enhance the global search capability, and adaptive parameter adjustment together with an external archive mechanism is incorporated to improve the search efficiency while maintaining good convergence and diversity of the Pareto solution set. Finally, simulation studies based on real road network and power grid operation data are conducted, and the results verify the effectiveness of the proposed method. The results demonstrate that the proposed method significantly reduces charging time, mitigates load fluctuations, and lowers user charging costs, while also exhibiting strong robustness and potential for practical engineering applications. Full article

In grid-connected smart integrated energy systems with high shares of renewable generation, source-side variability and inadequate coordination among battery storage, other energy carriers, and the external grid limit local renewable-electricity utilization and impede deep decarbonization. This study proposes a machine-learning-assisted, renewable-driven framework for multi-energy coupling and scenario-based multi-objective optimization of electricity–heat–hydrogen–storage systems. Historical meteorological and load data are processed using K-means clustering and Latin hypercube sampling to construct representative operating scenarios across multiple volatility regimes and characterize source–load uncertainty. The equipment model includes photovoltaic arrays, wind turbines, heat pumps, electrolyzers, fuel cells, grid-interactive battery energy storage, thermal storage, and hydrogen storage; cross-carrier conversion dynamics and emissions from purchased electricity and natural gas are embedded in the energy-balance constraints. A mixed-integer linear programming formulation then co-optimizes battery charging and discharging, grid exchange, and other multi-energy flows with respect to operating cost, carbon emissions, and renewable-energy curtailment. At 95% renewable-energy penetration, the proposed method achieves a renewable-energy absorption rate of 91.6% and a curtailment rate of 8.4%. Across the carbon-price cases, annualized operating cost ranges from 126.5 × 10⁴ to 141.2 × 10⁴ USD yr⁻¹, while carbon-emission intensity ranges from 26.4 to 38.5 gCO₂/kWh_eq. Under the specified high-risk grid disturbances, the coordinated strategy limits load shedding to 1.8%—73% below deterministic scheduling and 79% below the heuristic benchmark—and maintains 92.6% hydrogen self-sufficiency. These results provide a data-driven modeling and decision framework for battery–grid coordination and deep decarbonization in smart integrated energy systems. Full article

Continuous operation is increasingly expected of Internet of Things (IoT) and wireless sensor network (WSN) nodes, yet practical solar front ends must account for source variability, intermediate storage, conversion losses, sensing overhead, and battery-management constraints. This article develops and evaluates a discrete, two-stage photovoltaic front end for such nodes: a perturb-and-observe (P&O) buck stage tracks the maximum power point of a 20 W Solarland SLP020-12U module (rated 17.2 V, 1.16 A) and feeds an intermediate storage bus, while a PI-compensated SEPIC stage regulates the IoT rail to 3.2 V independently of that bus voltage. Closed-loop MATLAB/Simulink simulations are reported at 1000, 800, and 600 W/m². The reported conversion figures originate from an idealized switching model and should therefore be interpreted as simulation-only values rather than measured prototype efficiency. A low-cost Arduino-based prototype confirms correct switching behavior and a 20.0048 kHz PWM signal, but the available captures lack synchronized, calibrated input/output power logging; consequently, no hardware efficiency, MPPT tracking efficiency, regulation error, ripple, or settling-time figure is claimed. The revised manuscript makes this simulation-to-hardware boundary explicit, adds the power cost of sensing and data conversion to the loss discussion, strengthens the battery-management and deployment caveats, and defines the measurements required for full quantitative validation. Full article

Public buildings play a critical role in national decarbonization strategies and green energy transitions due to their high energy consumption densities, large occupant capacities, and potential to drive public awareness. Optimizing energy efficiency in these structures not only alleviates the financial burden on public budgets but also serves as a benchmark for sustainable urban development. To investigate the energy-saving potentials, thermal comfort dynamics, and financial feasibilities within this sector, this study selects two university buildings from two different countries with distinct climatic, structural, and operational profiles as comparative case studies: university buildings in Türkiye (TR) and Italy (IT), respectively. A total of seven tailored retrofitting scenarios were developed based on country-specific legislative frameworks and subsidy mechanisms: the Minimum Environmental Criteria (CAM) and Conto Termico 3.0 for Italy, and the Public Buildings Energy Efficiency Project (KABEV), Energy Performance Contracting (EPC), and Nearly Zero Energy Buildings (NSEB) mandates for Türkiye. The scenarios evaluate deep building envelope insulation, high-efficiency window replacements, lighting automation (LED with daylighting controls), mechanical ventilation with heat recovery units (HRV), air-to-water heat pump integrations, and rooftop photovoltaic (PV) installations using calibrated DesignBuilder simulation models. The quantitative results demonstrate that country-specific green building incentives drastically enhance both the energy performance and financial viability of deep retrofits. For the Turkish case study, the comprehensive near-zero energy building (nZEB) retrofitting package (TR-4) successfully reduced annual primary energy consumption by 75% (from 284 to 71 kWh/m²·year) and cut annual thermal comfort discomfort hours by 72% (from 3147 to 880 h), yielding a Subsidized Net Present Value (NPV) of +310,600 €. Similarly, for the Italian case study, the holistic retrofit combined with rooftop photovoltaic integration (IT-4) achieved an 80% energy reduction (dropping from 128 to 25.6 kWh/m²·year), minimized annual discomfort hours to 45 h, and generated a Subsidized NPV of +425,500 €. Furthermore, national incentive mechanisms shortened simple payback periods by more than half, establishing that targeted public policy is vital to accelerate public sector building decarbonization while ensuring long-term fiscal profitability. Full article

High penetrations of wind and photovoltaic generation create simultaneous challenges for battery energy storage system (BESS) planning in distribution networks, including differences in nodal regulation value, power-energy configuration, and day-ahead operation under forecast uncertainty. This study develops a sequential planning-to-operation workflow comprising candidate-bus generation, siting and sizing within the candidate set, and finite-scenario day-ahead scheduling for a fixed configuration. First, nodal net-injection sensitivities, Jacobian-assisted pre-screening, and deterministic topology/support safeguards are used to generate the main candidate set, and alternating-current (AC) finite-difference refinement is performed only for the sensitivity-led fast set; in the IEEE-33 system, this refinement reduces the number of AC power-flow calls from 65 for full-node analysis to 17. Next, Sensitivity-Guided Envelope-Based Nonanticipative Adjustable Recourse Optimal Power Flow (SG-ENAR-OPF) is solved separately for each bus in the main candidate set; the BESS location and power/energy capacities are jointly determined subject to the P-Q LinDistFlow model, BESS duration constraints, a shared affine response, and finite-scenario constraints. The full-node audit serves only as an independent paper-level validation benchmark and is not part of the deployable workflow. After the configuration is fixed, interval forecasts for load, photovoltaic (PV) output, and wind-turbine (WT) output at // are used to construct 25 static load-renewable disturbance points and seven temporal stress paths, over which a shared finite-scenario day-ahead policy is optimized. The IEEE-33 MAIN case selects Bus 30, with BESS capacities of approximately 10.66 MW/10.66 MWh. Using scaled public time-series data, the final policy is replayed over 46 consecutive 24 h execution windows, comprising 1104 h actual trajectories; under the 0.002 MW/MWh storage-engineering criterion, all 46/46 windows pass storage engineering validation, and energy continuity is maintained across all 45/45 interday boundaries. Further nonlinear AC post-validation converges at all 1104/1104 operating points, of which 1058/1104 satisfy the complete voltage and branch-capacity constraints. Supplementary results for IEEE-69 show that the workflow can be executed on a second radial test feeder. However, the conclusions are strictly limited to the tested feeders, finite scenarios, scaled public-data settings, and stated engineering tolerances and do not constitute a formal guarantee over a continuous uncertainty domain or of general cross-system applicability. Full article

Seawater reverse osmosis (SWRO) desalination is electricity-intensive, making its environmental performance highly dependent on the power supply. This study couples site-specific energy-system modelling with life cycle assessment to examine how renewable integration changes both total impacts and life-cycle hotspots. A modelled SWRO plant with a specific electricity consumption of 3.4 kWh m⁻³, derived from a process model for Mediterranean feedwater at 48% recovery with energy recovery devices, was assessed in Gela and Trapani, two grid-connected coastal sites with contrasting wind resources, and Lipari, a non-interconnected island with carbon-intensive backup generation. Grid-only, photovoltaic (PV)-grid, wind-grid, and PV-wind-grid configurations were modelled in HOMER Pro without storage and with excess electricity limited to 13%, then evaluated in SimaPro using Environmental Footprint 3.1. Hybrid configurations supplied 46.7%, 64.4%, and 53.3% renewable electricity in Gela, Trapani, and Lipari, reducing climate-change impacts by 30%, 42%, and 45%, respectively. Renewable integration also lowered fossil resource use, whereas PV-containing scenarios increased land and mineral/metal resource use. As electricity-related impacts declined, chemical consumption became the main non-energy hotspot, particularly for ecotoxicity, freshwater and eutrophication. Environmental performance therefore depends not only on renewable penetration, but also on technology choice and the residual electricity supply. Comprehensive system boundaries are essential when planning lower-carbon desalination for coastal and island water security. Full article

Hydrogen is acknowledged as a clean and sustainable energy source due to the increasing demand for renewable energy sources. Photoelectrochemical (PEC) water splitting presents a viable approach for directly producing hydrogen from solar energy with negligible implications for the environment. However, regardless of the intensive studies over several years, a major hurdle to translating impressive laboratory-scale efficiency into robust, dependable, large-scale production of hydrogen is increasing competition from quickly advancing photovoltaic (PV)–based electrolysis technology. In parallel, Z-scheme or S-scheme artificial leaf catalyst systems mimicking photosynthesis are gaining ground in the research community. The performance and reliability of photo-electrocatalytic large-scale hydrogen production should be evaluated via pilot-scale and field studies, along with life cycle and economic studies. In the present manuscript, a comprehensive overview of technologies related to scalability is presented, with a focus on semiconductor materials and reactor design. In conclusion, problems and opportunities for future research on large-scale production technologies are presented. Full article