Search Results (1,771)

Wind power, photovoltaic (PV), hydropower, and energy storage, along with other multi-type clean energy sources, transmitted via islanded voltage source converter based high voltage direct current (VSC-HVDC) systems, will become an important form of delivery for renewable energy bases. However, due to the volatility and uncertainty of renewable energy, its high-proportion integration significantly exacerbates system frequency fluctuations and voltage violation risks, posing severe challenges to the stable operation of the system. To strike a balance between maximizing clean energy integration and maintaining the stability of the islanded system, this paper presents a capacity optimization approach for multiple types of clean energy within an islanded VSC-HVDC transmission system. First, typical wind power and PV output scenarios are obtained via Monte Carlo simulation, and a virtual slack bus is introduced to establish a power flow calculation model for the islanded VSC-HVDC transmission system. Second, the active power is regulated through fast VSC-HVDC support and droop control mechanisms, while a quadratic programming model for voltage is established based on the relationship between reactive power and voltage, aiming to drive the virtual slack bus power to zero, thereby improving system frequency and voltage stability. Finally, a genetic algorithm (GA) is employed to achieve optimal capacity configuration for maximizing renewable energy integration, and the corresponding optimal energy storage capacity is determined accordingly. Simulation results demonstrate that, while satisfying operational constraints, the proposed method identifies the maximum installable capacities of wind power and PV while simultaneously reducing the required energy storage capacity. Full article

High levels of photovoltaic (PV) generation in distribution networks create substantial uncertainty and voltage variability, which limits the effectiveness of conventional deterministic distribution network reconfiguration (DNR) strategies. In PV-dominated feeders, rare but severe operating conditions may considerably influence active power losses and voltage stability. To address this challenge, this paper proposes a risk-informed optimization framework for DNR that combines reinforcement learning with probabilistic performance assessment. A Deep Q-Network (DQN) agent is designed to support the selection of feasible radial switching configurations by interacting with the distribution network environment. Throughout the learning process, candidate network topologies are evaluated through radial load flow calculations, while a composite objective function incorporating active power losses and voltage deviation steers the agent toward improved configurations. The training stage is based on deterministic performance indices; however, the final reconfiguration solution is assessed under uncertainty to examine its operational robustness. For this purpose, extensive Monte Carlo simulations are performed to capture the stochastic behavior of PV generation and load demand. Tail-based risk metrics, including Value at Risk (VaR) and Conditional Value at Risk (CVaR), are computed for both loss and voltage deviation indices, providing insight into the performance of the selected configuration under unfavorable operating scenarios. The proposed framework is first validated on the IEEE 33-bus distribution system and then further investigated on the IEEE 69-bus network. The obtained results demonstrate that the proposed DQN-based reconfiguration approach can enhance voltage profiles and reduce power losses under high PV penetration. In addition, the probabilistic analysis identifies meaningful trade-offs between efficiency and voltage robustness, highlighting the importance of considering uncertainty-driven risk assessment in computational decision-making for modern active distribution networks. Full article

This research assesses the practical synthesis of Building Information Modeling (BIM), Autodesk Forma and Dalux to support LEED-oriented sustainable design for a higher education building. Autodesk Revit 2025 functioned as the central BIM platform, while Autodesk Forma enabled early-stage simulations of solar exposure, daylight potential, wind conditions, microclimate, noise and solar-energy potential. Dalux supported model coordination and information management in accordance with ISO 19650 principles. The workflow links simulation outputs to BIM elements through project-defined parameters, allowing performance evidence to inform design refinement. Quantitative indicators were consolidated for daylight exposure, wind comfort, outdoor thermal stress, acoustic exposure and photovoltaic potential. The solar-energy analysis considered an area of approximately 970 m², an annual potential of 1030 kWh/m² and a theoretical yield of approximately 999,100 kWh/year. Assuming 70% roof coverage and 18% panel efficiency, the estimated photovoltaic the expected output is approximately 125,113 kWh/year. These findings demonstrate the value of combining BIM, cloud-based analysis and CDE-based coordination for early-stage sustainable design, while LEED certification, operational energy modelling and lifecycle assessment require additional specialist validation. The proposed workflow provides a consistent approach for aligning design development with sustainability objectives and can be applied to similar building types. Full article

With the high-proportion integration of renewable energy, integrated energy systems face greater demands regarding renewable energy utilisation, power balancing, and operational efficiency. By aggregating distributed generation, energy storage and load resources, integrated energy systems can provide effective support for multi-energy coordinated scheduling. This paper proposes a 24 h day-ahead multi-objective optimal scheduling framework for an integrated hydro–wind–photovoltaic–storage energy system based on multi-objective particle swarm optimisation (MOPSO). Firstly, this paper establishes mathematical models for wind power, photovoltaic (PV), hydropower, and energy storage units. Subsequently, it incorporates the outputs of hydropower, wind power, PV, and storage, along with the charging and discharging of energy storage and the process of purchasing electricity from and selling electricity to the main grid, into a unified optimisation model. The objectives are to maximise economic benefit and variable renewable energy utilisation while minimising the peak-to-valley difference in residual load. To address the conflicts between these multiple objectives, a MOPSO algorithm combined with a normalised weighted scoring method is employed to select a compromise optimal solution. Results from case studies based on typical days of the four seasons and various operational strategies demonstrate that the proposed method can rationally allocate the outputs of different energy sources, reduce the system’s dependence on the main grid, and improve variable renewable energy utilisation, thereby providing a reference for the optimal scheduling of integrated energy systems. Full article

Off-grid photovoltaic (PV) hydrogen production systems must coordinate rapidly varying PV power, battery energy, and the operating states of multiple alkaline water electrolyzers. Inappropriate coordination may lead to PV curtailment, frequent unit switching, and persistent workload concentration on a small number of electrolyzers. This paper develops an efficiency- and load-balanced operation (ELBO) scheme as an improved rule-based supervisory strategy rather than an online optimization method. ELBO adopts a two-level decision structure. A planned number of online electrolyzers is first determined from the moving-average PV power and the reference power associated with high single-unit efficiency. This planned count is then corrected using real-time PV power, battery state of charge, and the previous electrolyzer states. The controller adjusts the powers of the online units before changing their number, uses the battery to bridge temporary power deficits, and distributes the remaining adjustable power under the operating and ramp-rate constraints. Five representative PV profiles selected from one year of measured data were used to compare ELBO with PV-following operation (PFO), multi-electrolyzer coordinated operation (MECO), and an offline mixed-integer linear programming (MILP) benchmark. ELBO produced 1328 kg of hydrogen, which was 8.85% and 6.07% higher than PFO and MECO, respectively. Its overall PV-to-hydrogen efficiency and PV utilization reached 65.2% and 94.9%, respectively, with 36 start–stop events. MILP produced 1345 kg of hydrogen, only 1.28% more than ELBO, but required the complete future PV sequence. Ablation analysis further shows that the planned-count layer, moving-average filtering, battery-supported retention, and load-balancing allocation contribute to different and complementary aspects of capacity matching, operating continuity, and workload distribution. The results indicate that the benefit of ELBO arises from the ordered coordination of these supervisory functions and that it provides a practical compromise between operating performance, workload distribution, information requirements, and computational complexity under the representative conditions considered. Full article

Convolutional Neural Networks (CNNs) are fundamental tools for image analysis and recognition in monitoring systems, particularly in photovoltaic (PV) installations, where visual inspection and thermal imaging play crucial roles in anomaly detection and energy optimization. This publication presents a Hybrid Fuzzy Convolutional Neural Network (HFCNN) that integrates a fuzzy dense layer utilizing Ordered Fuzzy Numbers (OFNs) into the CNN architecture. The architecture is additionally validated on the public ELPV benchmark of 2624 electroluminescence images of photovoltaic cells, where the HFCNN with Mean of Maxima defuzzification attains classification quality statistically indistinguishable from a CNN baseline while using a four times smaller dense layer and training two to three times faster. The methodology combines the feature extraction capabilities of traditional CNNs with the uncertainty handling properties of fuzzy logic. Experiments using the MNIST dataset demonstrate that HFCNN with Mean of Maxima (MOM) defuzzification achieves comparable accuracy to standard CNNs while using significantly fewer parameters (75% reduction in the dense layer). This efficiency gain is advantageous for deployment on edge computing devices. This work constitutes a methodological contribution—establishing, for the first time, the feasibility of integrating Ordered Fuzzy Numbers into CNN architectures without requiring expert membership function design. While the current study validates this approach on MNIST, actual photovoltaic applications require dedicated future research on real PV thermal imagery. Nevertheless, the proposed HFCNN framework could potentially support practical photovoltaic energy system applications in detecting panel degradation, performance anomalies, and autonomous decision-making in large-scale PV installations. Full article

The decarbonization of maritime transportation requires shipboard energy systems to coordinate conventional generators, renewable energy sources, energy storage devices, and thermal energy units under voyage-dependent operating constraints. This paper develops a configurable hybrid multienergy ship system for coordinated electrical and thermal energy scheduling. The proposed framework functionally separates the propulsion subsystem from the service and thermal subsystem while retaining system-level coordination among photovoltaic generation, wind generation, diesel generators, micro gas turbines, energy storage batteries, and thermal energy units. A convolutional neural network is employed to provide short-term photovoltaic power forecasts for day-ahead scheduling. The resulting scheduling problem simultaneously considers voyage completion, power balance, equipment operating limits, ramp-rate constraints, battery charging and discharging restrictions, operating costs, and pollutant emission treatment costs. The nonlinear operating logic is reformulated as a mixed-integer optimization problem and solved using CPLEX. A representative coastal voyage case study is used to evaluate the proposed framework. The results demonstrate that the method can coordinate multiple shipboard energy sources, satisfy the prescribed electrical and thermal demands, and provide a set of Pareto-optimal solutions describing the trade-off between operating cost and emission-related cost. The proposed framework provides a system-level scheduling approach for supporting the economic and low-carbon operation of hybrid multienergy ships under increasingly stringent maritime emission reduction requirements. Full article

Fresnel high-concentration photovoltaic/thermal (HCPV/T) systems operating in sandy environments are susceptible to natural lens soiling, which attenuates the effective concentrated solar input and alters electrical and thermal performance. Natural-soiling comparison tests were conducted over 0–28 d, and an SD-CNN-BiLSTM-Attention model was developed to forecast cell-center temperature and electrical power 5, 10, and 20 min ahead. At a surface soiling density of 10.760 g·m⁻², current and electrical power decreased by 38.37% and 39.28%, respectively, relative to the concurrently operated clean-reference unit; cell-center temperature and water-tank temperature rise decreased by 7.74% and 15.13%. Thermal power also showed an overall downward trend, although the magnitude was affected by relatively large measurement uncertainty. Under grouped cross-validation, temperature RMSEs were 0.473, 0.515, and 0.555 °C at 5, 10, and 20 min, corresponding to reductions of 8.34%, 21.68%, and 44.07% relative to Persistence. Electrical-power RMSEs were 0.661, 0.618, and 0.640 W, with an 18.24% reduction relative to Persistence at 20 min. Ablation analysis showed a limited contribution from surface soiling density at 5 and 10 min but a clearer contribution at 20 min. These results support electrical and thermal performance assessment and short-term operational forecasting of Fresnel HCPV/T systems in sandy environments. Full article

Extreme weather couples spatially correlated component damage with photovoltaic (PV) derating, changing electric-vehicle (EV) demand, repair delay, and time-varying network topology. This paper develops a coordinated restoration architecture for multi-area feeders containing PV, battery energy storage, and charging stations. Its weather-facing layer constructs joint outage-risk, renewable-error, charging-demand, and voltage-vulnerability descriptors. Those descriptors parameterize a two-stage mixed-integer second-order-cone program with a finite-support optimal-transport ambiguity set that remains well defined for discontinuous mixed-integer recourse. Regional actor–critic agents propose five-minute corrections around the hourly robust schedule; constrained projection, non-linear AC power-flow screening, emergency fallback, and margin-tightened re-optimization retain the authority to accept or reject each proposal. The evaluation uses public 33-node and 123-node feeders together with synthetic 240-node and 850-node stress networks. A pre-fit manifest allocates 240 records to training, 80 to validation, and 320 to final testing, while aggregate operational outcomes cover 50 random streams. Within this controlled benchmark, accepted schedules restore 93.6% of critical-load energy (SD 2.1 percentage points), serve 96.7% of total demand (SD 1.8 percentage points), retain 82–86% of EV service across hazard classes, and reduce the modeled 24 h objective by 25.8% relative to deterministic dispatch. The full pipeline records two to four candidate-stage voltage-limit events by hazard, and 4.9% of candidates undergo tightened re-optimization before accepted schedules reach zero reported AC voltage-limit violations. Between-method comparisons are descriptive and unpaired; the larger synthetic cases are structural stress tests rather than feeder-transfer tests. Full article

As global photovoltaic (PV) installation capacity grows rapidly, the environmental pollution and resource waste from the large-scale end-of-life (EOL) wave have drawn increasing attention. Traditional disposal methods such as landfilling and incineration are no longer viable, making green recycling a logical path for the PV industry. This paper reviews recent progress in the disassembly and recycling of EOL crystalline silicon (c-Si) PV modules. It first describes the structural material composition of c-Si PV modules and summarizes global recycling policies and regulatory frameworks. It then analyzes the mechanisms and process parameters of major delamination technologies, including mechanical crushing, pyrolysis, thermal cutting, high-voltage pulse fragmentation, solvent-based approaches, and laser peeling. Methods for purifying silicon and recovering precious metals such as silver and copper are also covered. Finally, key challenges in the recycling field and future development trends are discussed, with the aim of supporting the advancement of c-Si PV recycling technologies and the sustainable development of related industrial chains. Full article

Photovoltaic parks support climate change mitigation by generating low-emission electricity. However, their environmental impacts strongly depend on site features and proximity to environmentally sensitive areas. The aim of the present paper is to develop and apply an innovative methodological framework that integrates an environment-based Rapid Impact Assessment Matrix with selected indicators. Six environmental components are linked to ten measurable indicators. Each indicator is documented through an inventory sheet and evaluated using adapted RIAM criteria. The proposed methodology is applied to the existing photovoltaic farm installations located in the Regional Unit of Messinia, Greece. The results show that most indicators produced positive Environmental Scores across the examined installations. The negative impacts are mainly associated with the proximity of the projects to wildlife refuges. The largest variations were associated with distance from wildlife refuges and forested areas, with standard deviations of 67.9 and 58.4, respectively. PV7 received major negative Environmental Scores of −96 and −72 because it is located within a wildlife refuge and forested land, while PV8 received a score of −72 due to its overlap with sclerophyllous vegetation. On the other hand, the positive impacts are mainly reflected in CO₂ reduction across all photovoltaic parks in the study area. These findings confirm that appropriate site selection of photovoltaic parks is a decisive factor in either reducing or eliminating environmental impacts. The proposed methodology can be applied to any photovoltaic farm and study area, enabling quantification of environmental impacts and facilitating comparative assessment of these projects. Full article

Existing industrial buildings represent a critical but under-addressed field for operational carbon emission reduction, as retrofit decisions are constrained by production continuity, limited investment capacity, and heterogeneous technical options. This study developed a decision support framework integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods to evaluate and prioritize operational phase decarbonization retrofit schemes for existing industrial buildings. The framework was applied to the Thyssenkrupp S Plant in eastern China, where ten candidate schemes were identified through an energy audit, on-site investigation, and expert consultation. The results show that heating, ventilation, and air conditioning (HVAC) operational control and temperature set-point optimization ranked first, followed by lighting operational management and automatic control. These management-based measures offer strong near-term applicability because of their low investment, short payback periods, limited implementation disturbance, and immediate emission reduction benefits. Their sustained effectiveness, however, requires standardized procedures, staff education, energy monitoring, and appropriate automation. Rooftop photovoltaics provide the largest annual carbon reduction but have a lower short-term priority because of their high upfront investment. Expert-consistency testing and sensitivity analyses, including criterion weight perturbation, preference scenarios, and Monte Carlo simulation, support the robustness of the leading ranking pattern. The findings support staged retrofit planning that prioritizes durable management measures in the short term, equipment-level efficiency improvements in the medium term, and renewable energy deployment in the long term. Full article

The rapid expansion of residential photovoltaics (PV) increases the need for long-term evidence on system performance under real operating conditions. This study compared four grid-connected rooftop PV microinstallations (4.69–5.04 kWp) located in the Sandomierz Basin, southeastern Poland, over 2022–2025. Monthly alternating-current production from SolarEdge monitoring was combined with regional sunshine duration and mean air temperature from the IMGW Sandomierz station, providing 192 installation–month observations. Specific yield, capacity factor, a model-based estimate of the performance ratio (PR_AP), Pearson correlations, ordinary least-squares regression, and sensitivity analysis of a documented failure were applied. Mean annual specific yields were 1077.7, 1061.7, 908.6, and 779.6 kWh/kWp for PV-I–PV-IV, respectively, while mean PR_AP estimates were 82.2%, 82.5%, 70.6%, and 65.5%. Sunshine duration was strongly correlated with monthly specific yield (r = 0.830–0.983; p < 0.001), and the combined model explained 88.8% of its variability. Excluding the zero-output failure month of PV-IV increased R² for the sunshine–yield relationship from 0.689 to 0.812 and improved the combined-model fit from 0.888 to 0.921. Greater nominal capacity did not guarantee higher normalized productivity. Regional solar-resource information should therefore be complemented by monitored operational data to support design, benchmarking, fault detection, and local distributed-energy planning. The findings also support SDG 7 by providing evidence for more reliable, locally adapted planning and operation of household photovoltaic systems. Full article

Indonesia has significant fisheries potential; however, limited cold chain infrastructure at small-scale fishing ports contributes to post-harvest losses and quality degradation of fishery products. This study presents the design and techno-economic assessment of a modular portable cold storage unit for tuna fisheries integrated with renewable energy systems. The system (7 × 3 × 5 m) uses polyurethane sandwich panels and requires a maximum cooling load of 6.14 kW with peak power consumption of 7.93 kW. The estimated capital cost is approximately USD 34,100, while the operational cost is about USD 198 per cycle. A comparative analysis using the Levelized Cost of Energy (LCOE) method indicates that diesel generators provide the lowest cost at approximately USD 0.56/kWh, whereas standalone photovoltaic (PV) systems exhibit the highest cost at around USD 0.89/kWh. However, hybrid PV systems offer the best balance between cost efficiency and environmental performance by reducing carbon emissions. The results demonstrate that integrating hybrid renewable energy into modular cold storage enhances cold chain reliability, reduces fish losses, and supports sustainable coastal development. This approach contributes to low-carbon fisheries infrastructure and aligns with global sustainability and renewable energy transition goals. Full article

This study evaluates a disturbance-informed, planning-level workflow for assessing steady-state feeder performance and post-disturbance frequency security in renewable-rich distribution networks. The workflow links feeder operation in DIgSILENT PowerFactory to reduced-order frequency-security screening in OpenModelica and PSAT, with Pandapower used as an independent steady-state cross-check. The IEEE 33-bus feeder includes distributed photovoltaic units, DFIG-based wind generation, and a grid-forming battery energy storage system (BESS). Hourly feeder time-series results are used to identify renewable-output deficits, and each deficit is converted from MW to the common 10 MVA dynamic-system base before being applied as a conservative step disturbance. The steady-state validation gives a maximum voltage mismatch of 0.0155 pu, within the adopted 2% screening limit. The cross-tool frequency benchmark shows close agreement for nadir and settling time, while RoCoF is interpreted conservatively because of its sensitivity to numerical differentiation and event implementation. As renewable penetration increases from 0% to 100%, the minimum bus voltage remains close to 1.0 pu (0.9999–0.9991 pu), the maximum bus voltage rises from 1.0295 pu to 1.0826 pu, and feeder losses increase from 0.0246 MW to 0.1531 MW. The 24 h assessment gives a maximum daily voltage of 1.0755 pu at 100% penetration, while loading remains below thermal limits. For the corrected 75% severe event (0.3048 MW; −0.0305 pu on the 10 MVA base), the 2 MW droop-plus-FFR case improves the nadir from 49.9695 Hz without support to 49.9924 Hz. Voltage therefore becomes the earliest binding screening constraint from 50% penetration onward, whereas thermal loading and supported frequency nadir remain non-binding under the studied conditions. Full article