Search Results (1,749)

Against the background of the dual carbon goals and the high proportion of distributed energy access, the distribution network presents the characteristics of source–network–load–storage two-way interaction. Traditional centralized control struggles to cope with voltage fluctuation, new-energy consumption difficulties and control dimension explosion. This paper focuses on the study of flexible resource aggregation modeling and cloud-side collaborative control, constructs the control constraint model of distributed Photovoltaic, energy storage, electric vehicle and flexible load constraints, proposes a resource aggregation method based on weight-improved K-means clustering, and includes voltage sensitivity to achieve accurate evaluation of adjustable capacity. A cloud–edge–end three-level collaborative control framework is built, and a two-layer scheduling model is established with the goal of peak shaving and valley filling so as to realize global optimization and local rapid response. The simulation results based on the improved IEEE 33-node distribution network show that the proposed method can effectively cluster flexible resources and quantify the adjustable potential. The cloud–edge coordination strategy can effectively reduce the load peak–valley difference, improve new-energy consumption rate and voltage stability, and provide a feasible technical path for the efficient regulation of the active distribution network. Full article

This study addresses the power dispatching of a DC-coupled three-phase grid-connected photovoltaic (PV) and energy storage-integrated system by proposing a peak shaving and valley-filling control architecture based on time-of-use (TOU) pricing. This research involves achieving maximum power-point tracking (MPPT) for PVMAs using a boost converter combined with the perturb and observe (P&O) method. A lithium-iron phosphate battery pack is integrated into the DC link via a bidirectional buck-boost converter, where charging and discharging control is executed according to peak and off-peak periods to regulate and stabilize the DC link voltage. Furthermore, bidirectional power flow control for peak and off-peak electricity consumption is realized using hysteresis current control and sinusoidal pulse-width modulation (SPWM) technologies within a smart inverter. By integrating the aforementioned power control architecture, the grid system can store energy from the utility during off-peak hours and release the stored energy during peak hours to reduce the load demand on the utility side. Initially, a simulation environment was established using Matlab/Simulink (2024b version) software, followed by control verification of the proposed system on a physical platform. The simulation and experimental results confirm that the integrated control architecture can precisely control the system’s DC link voltage at 800 V and stabilize the grid-connected AC voltage at an effective value (RMS) of 380 V. Moreover, under conditions of peak/off-peak switching and load variations, the system effectively demonstrates its stability and efficacy in performing valley filling and peak shaving. The proposed strategy achieves a power factor above 0.99 and a total harmonic distortion (THD) below 5%, regulates the DC-link voltage at 800 V with a steady-state error within 1.75%, and prevents up to 66.4 kWh of over-contract energy consumption per day under a 35 kW contract capacity, thereby contributing to sustainable energy management and economic savings. Full article

Highway service-area buildings are characterized by long operating hours, diverse functional spaces, and considerable energy consumption, resulting in significant life-cycle carbon emissions. This study quantifies life-cycle carbon emissions of the buildings in highway service areas. A life-cycle accounting framework was established, and net emissions were further evaluated by considering the contributions of photovoltaic (PV) electricity and vegetation carbon sinks. Five representative service areas covering hot-summer/cold-winter, severe-cold, cold, temperate, and hot-summer/warm-winter zones were investigated through field surveys and indoor thermal environment measurements to obtain envelope properties, equipment configurations, and operating profiles. Results revealed that life-cycle carbon emissions vary substantially across climatic regions, ranging from 4.31 × 10³ to 3.06 × 10⁴ tCO₂e. The operational stage accounts for the largest share of total emissions, approximately 61–84%. Heating demand dominates operational emissions in severe-cold and cold regions, whereas cooling and lighting loads become increasingly important in warm and temperate climates. The orthogonal analysis reveals significant differences in the sensitivity of design parameters across climatic regions. After implementing climate-adaptive optimization measures, life-cycle carbon emissions are reduced by 40.35–87.94% in four service areas. In the hot-summer/warm-winter region, the combined effects of PV electricity generation and vegetation carbon sinks maintain a net-negative carbon balance. The findings provide evidence for sustainable highway service-area design by linking life-cycle accounting, climate-specific design priorities, and renewable-energy substitution. Full article

Sustainable pavement maintenance increasingly requires coordinated management of infrastructure condition, renewable-energy availability, carbon emissions, financial resources, and operational capacity. This study proposes a renewable-energy resource management framework for low-carbon network-level pavement maintenance using simulation-based pavement-energy modeling and multi-agent deep reinforcement learning. The proposed framework develops an AnyLogic-based pavement-energy simulation environment in which road sections, deterioration states, work zones, maintenance crews, equipment resources, photovoltaic generation, battery storage, grid support, diesel backup, carbon tracking, and budget consumption are represented within one integrated decision environment. To support adaptive maintenance control, pavement sections are modeled as interacting agents, while road connectivity, dispatch dependency, traffic interaction, and maintenance-route relationships are encoded through graph structures. A graph-based multi-agent deep reinforcement learning model, named Graph-MAPPO, is then used as the decision controller. The model integrates multi-head graph attention for spatial dependency learning, GRU-based temporal memory for deterioration-history representation, finite-element-assisted structural-risk indicators for hidden damage characterization, and constraint-aware action masking to prevent infeasible decisions under budget, carbon, energy, crew, and equipment constraints. Two calibrated datasets were generated to support the framework: a pavement network and maintenance dataset containing 4437 records and 55 features, and a renewable energy-carbon-budget dataset containing 9875 records and 38 features. The decision controller jointly selects the pavement section, treatment type, intervention timing, crew, equipment, and energy mode. Results from 20 experimental configurations show that the balanced Graph-MAPPO policy improves average $PCI$ from 69.4 to 78.9, achieves an $RSL$ gain of 6.8 years, reduces emissions to 58.3 tCO₂e, maintains a renewable-energy share of 74.6%, and limits the constraint-violation rate to 1.8%. Under high renewable-energy availability, the framework achieves the best overall performance, with an average $PCI$ of 80.2, renewable-energy share of 84.6%, emissions of 50.8 tCO₂e, and reward of 0.90. These findings demonstrate that integrating pavement-energy simulation, renewable-energy resource allocation, carbon-aware maintenance planning, structural-risk awareness, and multi-agent decision control can support more adaptive, low-carbon, and resource-efficient pavement maintenance management. Full article

This study presents a technical assessment of solar energy systems for integrated agricultural use and rural electrification. A model village comprising 30 households was considered, and high-resolution hourly load profiles were developed to characterize consumption dynamics, including peak demand and sectoral distribution across residential, agricultural, public, healthcare, and commercial users. A 60 kW photovoltaic (PV) system was designed in conjunction with an independent solar thermal installation for hot water supply. The system configuration was established through component sizing and numerical modeling, incorporating heat transfer mechanisms and operational constraints. Time-dependent simulations performed in MATLAB (R2022b) evaluated PV power output, battery storage cycling, and thermal system performance over a 24-h horizon. A comparative analysis of standalone PV, hybrid PV/T, and decoupled PV–thermal configurations was conducted based on performance and operational criteria. The results indicate that separated electrical and thermal subsystems achieve improved cost-effectiveness, enhanced reliability, and reduced maintenance requirements. The proposed approach demonstrates the technical viability of solar-based energy systems for rural applications, supporting energy autonomy, reduced fossil fuel dependence, and sustainable agricultural development. Full article

In the context of the global transition toward sustainability and energy efficiency, the retrofitting of existing service buildings has become a strategic priority. With the increasing adoption of electric vehicles (EVs) and the need to reduce greenhouse gas emissions, adapting these buildings is essential to achieving low-carbon urban environments. This paper presents a numerical tool developed to simulate the energy performance of a service building and to evaluate the impact of multiple energy efficiency measures on energy consumption and CO₂ emissions. The assessed measures include the installation of photovoltaic panels on roofs and facades, optimization of Heating, Ventilation, and Air Conditioning (HVAC) systems through temperature set-point adjustments, improvements to the building envelope and integration of electric mobility infrastructure. The analysis focuses on an existing building in Lisbon, Portugal, considering both individual and combined effects of these strategies. The results indicate that combined implementation of all measures, including EV integration, can reduce energy demand and CO₂ emissions by up to approximately 50%. However, regulatory uncertainty regarding EV accounting remains a challenge, highlighting the need for clearer policies to support sustainable urban transformation. Full article

Restaurants combine long opening hours, catering demand, kitchen ventilation, DHW, and mixed-fuel cooking loads, making their decarbonisation different from generic commercial retrofit. For small- and medium-sized enterprise (SME) hospitality premises, this makes the transition to net-zero operation a distinct sustainability challenge because a large, process-driven share of demand lies outside conventional building-fabric and building-services retrofit. This single-case study develops a whole-building utility-calibrated OpenStudio/EnergyPlus model for Beit El Zaytoun, a 655.82 m² restaurant in Park Royal, London. Monthly electricity and gas data for June 2024–May 2025 were used to calibrate the baseline at whole-building level. Standalone and cumulative scenarios tested insulation, low-emissivity double glazing, LED lighting and controls, ASHP service scenarios, and an 11 kWp PV array. Baseline demand was 413,895 kWh/yr, equivalent to 631.1 kWh/m²·yr and 75,020 kgCO₂e/yr. The lowest-net-energy analytical package reduced net imported energy to 314,734 kWh/yr and operational carbon to 56,700 kgCO₂e/yr, a retained 24.0% reduction on the source reporting basis; this package is treated as an analytical bound rather than as a final design recommendation because it excludes cooling. The model-derived residual process load, kitchen and catering gas plus kitchen, and back-of-house electricity remained 233,920 kWh/yr across building-focused scenarios. The Residual-Load Index (RLI) rose from 0.57 to 0.74; with ±15% process-load allocation uncertainty, the optimised RLI range was 0.63–0.85, so the post-retrofit balance remained process-load dominated. The case demonstrates a practical decarbonisation ceiling likely to recur in similar high-process-load hospitality premises: fabric, lighting, heat electrification, and PV are necessary but insufficient without catering-equipment, cooking-fuel, kitchen-ventilation, refrigeration-control, sub-metering, and demand-response strategies. The paper contributes whole-building utility-calibrated quantitative evidence and a transferable RLI metric for sub-sector-specific sustainable retrofit policy, and the net-zero transition of SME food-service premises. Full article

Under carbon peaking and neutrality goals, building integrated with photovoltaics and energy storage system clusters (BIPECs) enable efficient on-site renewable energy use and can act as dispatch units for the public grid. However, BIPECs face significant uncertainties and are still under development. This study proposes a decentralized cooperative power dispatch model coupling a multi-agent proximal policy optimization (MAPPO) algorithm and offline digital twin (ODT) technology to optimize the photovoltaic (PV) power consumption of clusters despite limited data availability. An integrated BIPEC energy system model is established, and by leveraging the multi-agent system model of the BIPEC, the decentralized dispatch problem is converted into a fully cooperative multi-agent reinforcement learning (MARL) problem. A simulation-assisted ODT framework constructs a digital environment for MAPPO to augment data, conduct MAPPO training, and optimize the reward function, thereby obtaining power dispatch strategies. The results show that the proposed optimization model can obtain dispatch strategies that reflect a high degree of collaboration, reducing the cumulative power supply from the public grid by 0.55–2.56% per month compared to the non-cooperative self-generating and self-using strategy. This study presents the application of MARL in BIPECs by introducing a decentralized collaborative power dispatch methodology for building clusters, enhancing building energy efficiency and facilitating flexible collaborative power dispatch. Full article

Ports combine clusters of operational buildings, shared energy infrastructure, and structurally critical assets requiring coordinated management to ensure safety and efficiency. Nevertheless, existing Digital Twin (DT) frameworks for building energy management rarely integrate Structural Health Monitoring (SHM) with energy performance assessment, while port-specific implementations remain scarce. This paper presents a pre-operational energy-aware DT architecture for port building clusters, structured in a unified five-layer framework integrating three capabilities: (i) EGMS/InSAR-based SHM screening with planned in situ sensing and computer-vision inspection workflows; (ii) smart metering and measurement and verification (M&V) protocols aligned with ISO 50001/50015 and IPMVP standards; and (iii) weighted multi-criteria prioritization considering structural condition, energy saving potential, service continuity, and cost. The framework is applied to the Port of Formia (Italy), a brownfield district comprising nine buildings (3371 m²), 16 high-mast lighting towers, shore power infrastructure, and 90 kWp of planned photovoltaics. In the absence of operational metering, energy and carbon values are reported as bounded ex-ante scenario estimates, not as verified performance outcomes. The analysis estimates photovoltaic generation of 116–137 MWh/year and lighting retrofit savings of 31.5–36.8 MWh/year; the related carbon values are treated as gross grid-displacement upper bounds pending measured self-consumption and export data. A four-phase validation roadmap with quantitative acceptance criteria supports the transition from feasibility assessment to verified performance. Full article

Port operators must now reduce emissions without weakening the reliability of cargo-handling and logistics services. Two load groups are especially important in this setting: vessels connected to shore-side facilities during berthing and heavy-duty vehicles working inside the terminal area. Their energy-use patterns shape both dispatch stability and the carbon intensity of the port energy system. This paper therefore proposes an integrated port energy management model that jointly schedules wind power, photovoltaic generation, hydrogen production and storage, shore power, conventional purchases, berthed-vessel demand, and low-carbon heavy-duty transport demand. The model combines price-based demand response with a tiered carbon-trading penalty so that flexible electricity consumption and emission costs are reflected in the dispatch decision. Numerical simulations show that the joint use of demand response and the carbon-penalty mechanism lowers total economic dispatch cost by about $11.05\%$ and reduces carbon emissions by $24.52\%$. The results indicate that coordinated renewable-energy and logistics-aware scheduling can improve the economic and environmental performance of port operations. Full article

Buildings account for 40% of global energy consumption and one-third of greenhouse gas emissions. Renewable energy systems (RESs), such as solar photovoltaic (PV) and geothermal heat pumps, are critical technological solutions for decarbonization. Despite the growing literature, existing reviews lack a comprehensive synthesis integrating machine learning (ML), Internet of Things (IoT), and Building Information Modeling (BIM). Following the PRISMA protocol, this paper presents a systematic review of 41 studies published between 2012 and 2025. The review evaluates four primary domains: RES performance, building energy prediction, HVAC optimization, and occupancy-aware management. Quantitative findings reveal that solar PV-integrated buildings achieve electricity cost reductions of 35–64%, while ML-enhanced energy prediction models attain accuracies up to R² = 0.989. Critical research gaps are identified, including the scarcity of real-time sensor integration and geographically inclusive multi-climate datasets. Ultimately, this review contributes a structured synthesis of effective technologies, a comparative analysis of methodological approaches (ML, simulation, hybrid), and actionable future directions. It provides practical guidance for researchers and policymakers toward achieving net-zero energy buildings. This study serves as a definitive reference for the development of sustainable, low-energy built environments. Full article

In the context of rural revitalization and carbon neutrality, this study addresses energy inefficiency and thermal discomfort in existing rural housing by optimizing passive design strategies for the “SunnyInside” sunroom model. Using parametric simulation with Ladybug and Honeybee, a dynamic light-thermal coupling model was developed to evaluate climate-adaptive performance in two distinct Chinese climates: the cold climate of Datong and the hot-summer-cold-winter climate of Wuhan. Multi-objective optimization focused on orientation, overhang depth, and photovoltaic (PV) tilt angles to enhance ventilation, shading, and daylighting. Key findings include: (1) Optimal building orientations of 15° west of south (Datong) and 16° east of south (Wuhan); (2) A 1.5m overhang depth in Wuhan improved summer shading efficiency by 28.6% and extended thermal comfort duration by 15%; (3) PV tilt ranges of 29–36° (Datong) and 13–23° (Wuhan) maximized energy performance. These optimizations achieved a 19.3–24.7% improvement in comprehensive performance coefficients and reduced air conditioning energy consumption by 17.8–21.4 kWh/m² (with ≥82% photovoltaic conversion efficiency). The study demonstrates the effectiveness of parametric simulation and intelligent algorithms in refining climate-responsive rural housing renovations, providing quantitative guidelines for PV shading systems across diverse climatic zones. Full article

Harvesting remains one of the main bottlenecks in microalgae-based technologies. Although microalgae hold great promise for industrial biotechnology, their growth in dilute suspensions makes biomass recovery challenging. Conventional harvesting methods are often energy-intensive and costly, limiting large-scale implementation. This study applies a life cycle assessment (LCA) to evaluate the environmental performance of a laboratory-scale magnetic harvesting process of Chlorella vulgaris (C. vulgaris) using Fe₃O₄ microparticles in combination with polyaluminum chloride (PAC) and polyacrylamide (PAM), followed by magnetic oscillation for particle detachment and subsequent reuse. Electricity consumption was identified as the dominant environmental hotspot across most impact categories, with the detachment step accounting for nearly two-thirds of the total energy demand, a step often overlooked in previous LCA studies. The global warming potential (GWP) is consistent with typical laboratory-scale assessments and is mainly driven by energy inefficiencies associated with small processing volumes. The values obtained and the scale-up literature indicate that further optimization and future industrial-scale production will decrease these values into a realistic and competitive range. Sensitivity analysis showed that replacing grid electricity with photovoltaic power significantly reduces environmental impacts. The use of NaOH as a reagent also contributed substantially to environmental impacts. Reusing magnetic particles (4 cycles) reduced material resource depletion by up to fourfold, which is a very relevant result bearing in mind the principles of sustainability and circularity. Full article

This study presents a hybrid controller that integrates fuzzy logic control and the Incremental Conductance method. This controller optimizes maximum power point tracking in a 330 W photovoltaic system by designing a DC-DC converter. The study evaluates how the number and distribution of membership functions, specifically three-, five-, and seven-function configurations, affect system performance using the Integral Square Error (ISE) and Integral Absolute Error (IAE) indices. The empirical results demonstrate that the seven-function architecture yields optimal performance, minimizing ISE and IAE to 0.1155 and $7.365\times {10}^{-4}$, respectively. Furthermore, this optimal configuration attains an energy efficiency of 99.7%, notably outperforming the baseline three-function configuration, which exhibited a worst-case efficiency of 98.9 %. To assess robustness against dynamic environmental variations, this study subjects the optimal configuration to fluctuating irradiance and temperature profiles. Additionally, an analysis of computational resource consumption reveals that the proposed hybrid controller incurs a lower computational load for rule evaluation than three controllers reported in the recent literature. These findings demonstrate the system’s structural efficiency and superior optimization capability, achieving maximized photovoltaic energy harvesting at a low computational cost. Full article

This study investigates an integrated ecological strategy to reduce electricity consumption in semi-collective housing located in the hot–arid climate of Biskra, Algeria, a region with high solar potential. The research combines photovoltaic (PV) electricity generation with passive thermal insulation using a locally sourced bio-based material derived from date palm fibers. The case study includes 104 dwellings within a residential complex of 350 units. Results show that monocrystalline PV panels (350 W) can produce approximately 479 kWh/panel/year. To meet the total annual electricity demand (504,712 kWh), around 1052 panels are required, corresponding to 1714 m² (13.8%) of the available building envelope. This installation area demonstrates the significant photovoltaic potential of the residential complex under hot–arid climatic conditions. Thermal analysis indicates that integrating a 5 cm palm fiber insulation layer increases thermal resistance from 2.06 to 2.62 m²·°C/W and reduces heat flux from 2.18 to 1.72 W/m². This improvement decreases conductive heat transfer through the envelope by approximately 21%, while numerical simulations indicate indoor temperature reductions of 4–8 °C during summer conditions. These findings demonstrate that combining PV systems with bio-based insulation significantly enhances energy efficiency and thermal comfort in residential buildings under desert climatic conditions. Full article