Search Results (6,140)

A key challenge in port–road logistics is the need to align operational priorities with efficient and sustainable freight transportation decisions. This study develops an Analytic Hierarchy Process (AHP)-based decision-support system for the Multi-Objective Electric Vehicle Routing Problem (MOEVRP) in port–road logistics. Initially, the AHP method is used to determine the relative importance of cost-efficient route planning, vehicle and port management, environmental impact management, energy efficiency, and operational efficiency and well-being based on expert judgment. Next, the resulting priority weights are then used to inform the decision-making framework for the MOEVRP, which determines routing decisions by minimizing total cost, carbon emissions, and maximum vehicle working time while accounting for electric vehicle constraints and charging behavior. This issue is critical in rapidly developing industrial corridors such as Thailand’s Eastern Economic Corridor, where growing freight demand, energy constraints, and environmental pressures must be managed simultaneously. By linking port–road operational priorities with the routing model, the proposed framework provides a structured approach for evaluating trade-offs between economic, environmental, and operational considerations during the transition toward low-carbon freight transportation. A case study in Chonburi–Rayong provinces demonstrates the applicability of the integrated system in a real-world maritime–land logistics corridor. The findings contribute to the design of more sustainable supply chain systems that support renewable and decarbonized logistics. Full article

The increasing penetration of renewable generation and the rapid growth of data-center demand require coordinated use of heterogeneous flexibility while respecting network operating limits. This study proposes a three-objective electricity–computing–carbon dispatch method that minimizes physical system cost, direct carbon dioxide emissions, and unutilized renewable energy. Data centers are represented through task arrivals, deadlines, server capacity, and facility efficiency, whereas pumped-storage plants retain independent power limits, reservoir states, terminal conditions, and connection locations. A diversity-archive disturbance–multi-objective particle swarm optimizer (DAD-MOPSO) couples archive diversity and stagnation feedback with resource-group temporal-block disturbance. In 30 paired runs, DAD-MOPSO produced feasible final solutions in all runs and reduced mean spacing by 51.8% relative to conventional MOPSO, with a Holm-adjusted p-value of 0.0071. Repeated evaluation of 12 workload-resource cases showed that pumped storage provided the dominant improvement in renewable-energy utilization, while workload rescheduling provided a smaller marginal contribution. Factorial analysis indicated partially overlapping rather than universally superadditive flexibility. Fixed-total-capacity tests further revealed pronounced siting dependence: the same 1200 MW/7200 MWh storage capacity was feasible in 30/30 runs at Bus16 but only 16/30 runs at Bus27 because of undervoltage. The proposed framework therefore provides a unified basis for coordinating heterogeneous flexibility and distinguishing marginal, interaction, siting, and network-limited effects. Full article

High penetration of renewable energy imposes greater demands on the scheduling flexibility of demand-side resources in virtual power plant (VPP) dispatch. Nevertheless, heterogeneous resources exhibit remarkable differences in response reliability, and electric vehicles (EVs) in particular show distinct execution performance between orderly charging and vehicle-to-grid (V2G) modes. To tackle this issue, this paper proposes a two-stage optimal scheduling strategy for multi-agent VPPs incorporating scheduling success probability. A quantitative model for the effective dispatch contribution coefficient is constructed from two dimensions, i.e., relative capacity weight and dispatch execution reliability, with differentiated parameters tailored for EV charging and V2G modes. The two-stage leader–follower game problem is decoupled via backward induction, and the optimal dispatch price is rigorously derived through Karush–Kuhn–Tucker conditions. A 24 h case study covering wind power, photovoltaics, energy storage, EVs, and air-conditioning loads validates the proposed method. Results indicate that the strategy boosts total VPP revenue by 7.43% compared with independent operation, lifts the renewable energy accommodation rate from 88.3% to 94.6%, and reduces the average operating cost by 19 CNY/MWh. Through dual-mode differentiated scheduling, EVs achieve 5.10% revenue growth and serve as a key flexible resource for VPP economic operation. Full article

The optimal placement and power injection of photovoltaic (PV), wind, and battery energy storage system (BESS) units are critical for reliable microgrid operation under renewable-generation uncertainty. This study proposes a two-level planning-and-operation framework for a grid-connected microgrid. At the planning stage, PV and wind uncertainty is represented using correlated Latin Hypercube Sampling (LHS) with scenario reduction; candidate nodes for placing DG units are identified through Active Power Loss Sensitivity Factor (APLSF) screening and optimal DG power injection is obtained using a Multi-Objective Jellyfish Search Algorithm (MOJSA) that jointly minimizes power loss and pollutant emissions. At the operational stage, a rule-based energy management system (EMS) coordinated with demand-side elastic load shifting with an aim to minimize the 24 h operating cost. The two-level optimization framework is evaluated on 33-node and 118-node microgrid test systems across five demand-side management (DSM) participation levels. Relative to the no-DSM baseline, results show the operating cost reductions of up to 3.42% (33-node, 30% DSM) and 10.35% (118-node, 40% DSM), with peak-hour pollutant emission reductions of 52.43% and 21.556%, respectively. Full article

The intensification of beef cattle feedlot systems generates large volumes of manure, whose inadequate management can lead to negative environmental impacts and nutrient losses. Anaerobic digestion is an effective strategy for mitigating greenhouse gas emissions while producing renewable energy and nutrient-rich by-products. However, the high solids content of beef cattle manure may limit process performance and operational stability. This study evaluated an integrated management approach combining solid–liquid separation and digestate recycling during semi-continuous anaerobic digestion of beef cattle manure, followed by the agronomic use of the resulting by-products in wheat cultivation. Four treatments were evaluated in 60 L horizontal tubular digesters operated under mesophilic conditions and a hydraulic retention time of 30 days: solid–liquid separation followed by water dilution; solid–liquid separation combined with 100% digestate recycling; raw manure diluted with water; and raw manure diluted with water and digestate, with 60% recycling. Biogas and methane production were monitored, and the digestate and separated solid fractions were chemically characterised. A greenhouse experiment was subsequently conducted to compare wheat growth and grain yield under mineral, digestate-based, and organomineral fertilisation strategies. Solid–liquid separation combined with full digestate recycling produced the highest specific methane yields, whereas treatments without separation showed higher volumetric methane production due to their higher organic loading rates. Digestate-based fertilisation resulted in wheat grain yields comparable to or higher than those obtained with mineral fertilisation. These findings suggest that integrating solid–liquid separation and digestate recycling has potential to reduce freshwater demand, promote nutrient recovery, and support the agronomic reuse of anaerobic digestion by-products. Full article

This paper develops a stochastic approach for designing and operating a hybrid renewable energy system (HRES) through stochastic co-optimization. A hybrid renewable energy system consisting of solar photovoltaic power, a battery, and a grid is considered as the energy source to supply irrigation demands. Wind generation is retained in the formulation as a candidate technology, and the optimization returns an effectively zero optimal wind capacity under the conditions examined. A multi-scenario approach addresses uncertainty in solar output and demand. The methodology optimizes HRES components for future uncertain climate scenarios. Irrigation demand has a strong daily cycle. For example, irrigation demand rises from 4–6 kW at night and early morning, reaching a maximum of 88 kW at hour 13. The PV system’s peak power matches the maximum irrigation demand. In total, the PV system provides around 430–440 kWh of energy. The grid imports 35–40 kWh of energy, yielding a PV-to-grid ratio of more than 10:1. Therefore, solar-dominant systems can reliably supply energy to support agricultural activities, particularly irrigation. This methodology can serve as a basis for sustainable irrigation planning under uncertainty. Full article

Achieving net-zero energy and zero-emission buildings is a critical pathway toward decarbonizing the built environment, particularly in cooling-dominated regions where operational energy demand remains exceptionally high. Existing university buildings in hot–humid climates face significant challenges due to intensive cooling requirements, limited passive cooling potential, and the economic burden associated with large-scale renewable energy deployment. This study develops and evaluates a climate-responsive retrofit framework that integrates sequential energy optimization, CFD-based passive-cooling analysis, and on-site renewable energy systems to transform an operational university building in Jeddah, Saudi Arabia, into a nearly net-zero energy building (NZEB). A high-fidelity DesignBuilder–EnergyPlus model was calibrated using three years of monthly measured electricity consumption data, achieving strong agreement with utility records (NMBE = 2.19%, CV(RMSE) = 7.93%). The proposed framework prioritizes demand-side load reduction through optimized HVAC operation, envelope enhancement, daylight-responsive lighting control, natural ventilation, and Passive Downdraught Evaporative Cooling (PDEC) before renewable energy integration. The baseline building exhibited an Energy Use Intensity (EUI) of 613 kWh/m²·year, with cooling accounting for approximately 70% of total electricity consumption. Sequential optimization reduced annual energy demand by 58%, while CFD-supported passive cooling strategies provided an additional 17% reduction in cooling energy and improved indoor airflow performance. Crucially, nearly 80% of total energy savings were realized prior to photovoltaic (PV) deployment. A 1586-kW rooftop photovoltaic system subsequently offset the residual annual demand, achieving a nearly net-zero annual energy balance. Over 25 years, the proposed retrofit pathway reduced life-cycle costs from 7.51 million SAR to 3.13 million SAR. The findings demonstrate that climate-responsive demand reduction is the primary enabler of NZEBs in hot–humid regions, substantially reducing renewable energy requirements and long-term economic costs while providing a scalable pathway to decarbonize existing campus infrastructure. Full article

The growing demand for flexible and wearable electronics has stimulated the development of sustainable energy-storage materials capable of maintaining electrochemical performance under mechanical deformation. Natural polymers are attractive candidates owing to their renewability, low toxicity, biodegradability, and structural versatility; however, their limited electrical conductivity and electrochemical activity often require functional reinforcement. This review critically examines natural polymer–ceramic nanocomposites developed for flexible batteries and supercapacitors, considering their use as electrodes, electrolytes, and separators. A systematic literature search identified 41 studies in which the natural polymer remained a constituent of the final functional composite, while systems employing natural polymers solely as sacrificial templates were excluded. Cellulose emerges as the most widely explored matrix, combined with a broad range of ceramic materials, including MnO₂, ZnO, SnO₂, TiO₂, Fe₃O₄, BaTiO₃, and other functional oxides and inorganic compounds. Ceramic incorporation generally improves electrochemical activity, ion transport, thermal stability, or mechanical integrity, although the reported performance strongly depends on composition, architecture, and device configuration. A major limitation across the literature is the lack of standardized mechanical and flexibility testing, which hinders meaningful comparison between systems. Future progress will require the development of multifunctional, genuinely sustainable architectures together with standardized electrochemical–mechanical testing, biodegradability and end-of-life assessments, and scalable manufacturing strategies. Full article

Heritage buildings sit outside most national energy codes, yet Palestine alone hosts over 3000 historic structures whose 80–120 cm two-leaf stone construction is absent from any published, measurement-grounded retrofit dataset. A 19th-century three-storey heritage building (Qaser Morcos, Bethlehem; net floor area 406 m², volume 2100 m³) was investigated through (i) in situ measurements (heat-flux meter to ISO 9869-1; infrared thermography; illuminance survey); (ii) a Heritage Building Information Model (H-BIM) built in DesignBuilder v7 with EnergyPlus 25.1 and the Jerusalem-centre typical meteorological year weather file, with end-uses other than heating, ventilation and air conditioning (HVAC) calibrated to three years of measured electricity records and the HVAC baseline comfort-normalised; (iii) a Heritage Impact Assessment per EN 16883:2017; and (iv) a 5000-run Monte Carlo uncertainty analysis on five envelope and system inputs. Two retrofit stages were evaluated: Stage 1 (passive and renewable measures—roof insulation, shading elements, LED lighting with motion sensors, solar water heating, and a 20 kWp photovoltaic system) and Stage 2 (deep envelope and system upgrade—Stage 1 plus triple-glazing, Variable Refrigerant Flow system, and a 17.5 kWp photovoltaic system). Predicted final (delivered) electricity intensity falls from 94.5 to 71.2 kWh/m²·y under Stage 1 (−24.7%) and to 63.3 kWh/m²·y under Stage 2 (−33.0%; 90% Monte Carlo uncertainty interval 59.0–70.4 kWh/m²·y), both well below the Palestinian Energy Building Code limit of 120 kWh/m²·y. On-site generation exceeds post-retrofit electrical demand under both stages, yielding a net-positive electrical balance. Direct carbon dioxide emissions fall by 6.84 t/y (−33%) at Stage 2. Discounted paybacks are 4.6 y (Stage 1) and 10.4 y (Stage 2), with internal rates of return of 24.7% and 11.7%. All proposed interventions received project-team consensus scores of ≥3/5 against EN 16883:2017 heritage-impact criteria. This study delivers a replicable simulation-based workflow for the energy assessment and heritage-compatible retrofit of thick-walled Palestinian stone buildings and one of the first field-informed heritage-retrofit datasets from the Levant. Full article

In this work, Intelligent Bidirectional Long Short-Term Memory with Temporal Fusion Transformer-based prediction and Proximal Policy Optimization (IBT-PPO) is proposed in response to the challenges of uncertain renewable generation, fluctuating demand, and inefficient energy scheduling in hybrid renewable energy systems. The algorithm is based on a dual-stage framework that integrates machine learning forecasting with reinforcement learning-based planning. Initially, a hybrid Bi-LSTM-TFT model is employed to generate accurate short-term forecasts of wind power, solar power, and demand, which employs temporal dependencies and multi-horizon patterns. After that, the PPO strategy is designed to optimize scheduling decisions, adaptively balancing battery usage, grid reliance, and renewable dispatch. To enhance robustness, adaptive feature weighting and temporal gating strategies are incorporated, ensuring stable convergence and reduced planning redundancy. Subsequently, the energy allocation is refined through iterative learning to minimize operational cost and maximize renewable penetration. The proposed framework is evaluated as an offline/post hoc forecasting and scheduling approach, with the Bi-LSTM–TFT module exploiting historical temporal representations and the PPO agent optimizing energy-management decisions based on the resulting forecasts. The experimental evaluation is carried out using the Open Power System Data (OPSD) dataset, which provides realistic time-series data for wind, solar, demand, and electricity prices. Thus, the IBT-PPO system integrates multi-horizon probabilistic forecasting and adaptive feature weighting for better prediction and planning accuracy and achieves a 24.1% cost reduction and 95.5% renewable utilization, thereby advancing efficient and intelligent energy prediction and planning. Full article

The growing penetration of variable renewable energy (VRE) is increasing the frequency of very low and negative prices, although these events also depend on demand, transmission capacity, price-regime persistence and flexibility resources. This study examines which pre-auction and diagnostic variables are associated with negative day-ahead prices across European bidding zones, and whether these relationships remain stable over time and transferable across markets. More than 736,000 observations from 12 bidding zones in 2019–2025 were analysed, with sample coverage varying by data completeness. A gate-closure-audited Extreme Gradient Boosting (XGBoost) model achieved moderate risk-ranking performance and positive probabilistic skill in 2024–2025. The strongest pre-auction signals were completed-auction price history, calendar features and structural load relationships. Leave-one-market-out validation showed partial and heterogeneous transferability, supporting local calibration. A separate diagnostic layer revealed market-specific, non-linear associations involving VRE forecasts, residual load and cross-border exchange. Negative prices are therefore interpreted as screening signals for market configurations potentially associated with limited surplus absorption, rather than direct evidence of a flexibility shortfall. The framework separates operational pre-auction prediction from later market diagnosis and provides a reproducible basis for local early-warning applications. All SHAP, ALE and scenario results are interpreted as predictive associations and model diagnostics, not as causal effects or direct measures of physical flexibility. Full article

Electric-vehicle (EV) charging can intensify voltage drops, feeder currents, and technical losses in radial distribution networks, particularly when charging coincides with periods of high residential demand. This paper investigates how the adopted load representation affects reported EV–DER feeder performance when rule-based vehicle-to-grid (V2G) support and renewable distributed energy resources are considered. Particle swarm optimization (PSO) is used for DG siting, sizing, and power-factor selection at the full-load planning condition, after which the selected DG plans are evaluated using a 24 h, 15 min backward/forward sweep (BFS) time-series simulation. Two load representations are compared using the same EV, V2G, and DER models: a constant full-load representation in which the nominal base demand is maintained over all 96 intervals, and a daily load-curve (DLC) representation in which the base demand varies over time. Across the three examined scenarios, the DLC representation reduces daily loss energy by approximately 29.2–36.0% relative to the constant full-load case, while changes in peak real-power loss and peak source current remain comparatively smaller. The results demonstrate that load representation has a substantial influence on energy-based conclusions even when the feeder, DG plan, and operational models are unchanged. The study therefore supports time-series DLC assessment for energy-based reporting and transparent comparison of EV–DER distribution-feeder performance. Full article

The integration of bioactive compound extraction with anaerobic digestion (AD) represents a promising strategy for maximizing the valorization of agro-industrial residues within circular biorefinery systems. However, the extraction of valuable compounds may reduce or enhance the methane potential of the remaining biomass, making it important to evaluate the feasibility of subsequent energy recovery. This study investigates AD and anaerobic co-digestion (AcoD) of tomato pomace after supercritical CO₂ extraction (TPCO₂) and green walnut shell after ethanolic extraction (GWSe), aiming to identify synergistic interactions capable of enhancing biomethane production. Batch biochemical methane potential (BMP) assays were performed under mesophilic conditions using laboratory-scale (500 mL) reactors, followed by experiments in 5 L reactors. The mono-digestion of TPCO₂ and GWSe resulted in low methane yields of approximately 169 and 105 NmL CH₄ g⁻¹ VS, respectively. In contrast, AcoD enhanced methane production, reaching 361 NmL CH₄ g⁻¹ VS for the 0.50:0.50 mixture, with 409 NmL CH₄ g⁻¹ VS obtained in 5 L reactors (scale-up assay), confirming that the synergistic effect was maintained at larger reactor volumes. Kinetic modeling using a first-order model with a lag phase adequately described methane production (R²adj = 0.88–0.99) and indicated rapid microbial adaptation under all experimental conditions. Based on the BMP results obtained in the 5 L reactor, the estimated availability of the residues in Portugal, and assuming an electrical conversion efficiency of 35%, the 0.75:0.25 mixture could theoretically generate 5.50 × 10⁷ kWh year⁻¹ of electricity, equivalent to the annual electricity demand of approximately 40,400 inhabitants. These findings demonstrate that residual biomasses generated after supercritical CO₂ and ethanolic extraction remain suitable feedstocks for biomethane production and highlight AcoD as an effective strategy for integrating renewable energy generation into agro-industrial biorefineries. Full article

Long-term distribution planning requires weather sequences that capture not only changes in temperature and precipitation, but also the dependence and persistence among heat, humidity, wind, solar radiation, and rainfall. This paper develops multivariate weather scenario generation for Shaanxi Province, China, using daily data from six NEX-GDDP-CMIP6 models for 1985–2014 and 2031–2060 under SSP2-4.5 and SSP5-8.5 at four locations. Two generators are compared: a vector autoregressive model with seven-day residual blocks, and a season-conditioned multivariate nearest-neighbor analog. The comparison leaves out complete five-year periods within 24 climate-model–location units and tests whether the future-minus-historical changes in 16 weather and compound-event indices are preserved. The two methods each obtain the lower overall loss in 12 units. The vector autoregressive method better preserves most marginal and correlation signals, whereas the nearest-neighbor method better preserves RX3day, hot–dry–low-wind days, dry-spell duration, and three-day compound stress. Both are retained to generate 960 30-year paths, which drive a fixed IEEE 33-node resilience example showing that generator differences propagate to demand, renewable availability, repair duration, and unserved energy. The resulting conditional scenario sets provide an auditable weather basis for climate-resilient and sustainable long-term planning of distribution systems, which is a prerequisite for a sustainable energy transition under a changing climate. Full article

The increasing penetration of converter-interfaced renewable energy sources poses significant challenges to power quality in modern microgrids, especially under nonlinear and unbalanced load conditions. Conventional virtual-impedance strategies can improve converter-grid interaction; however, the use of fixed parameters or adaptation based on a single electrical variable may provide limited performance under dynamically changing power-quality conditions. This article proposes a multi-objective power-quality-index-based adaptive virtual impedance (PQI-AVI) strategy for grid-connected hybrid microgrids with high renewable-energy penetration. The supervisory PQI combines normalized voltage total harmonic distortion (THDv), the voltage unbalance factor (VUF), and voltage-magnitude deviation to continuously adjust the virtual resistance and reactance. To prevent excessive impedance adaptation, the setpoint generated by the PQI is further constrained by a grid-strength-dependent stability limit derived from a small-signal analysis that includes pulse-width modulation (PWM) delay dynamics. The proposed strategy is evaluated in MATLAB-Simulink^® using a modified IEEE 14-bus hybrid microgrid with an aggregate operating demand of 10 MW under nonlinear and unbalanced load conditions. Compared with the uncompensated condition, the proposed controller reduces THDv from 7.84% to 2.11%, THDi from 15.8% to 4.8%, and VUF from 2.50% to 0.80%, while simultaneously improving the power factor from 0.86 to 0.97. The stability analysis further demonstrates that the admissible virtual-impedance adaptation depends on grid strength when PWM dynamics are explicitly considered. These results show that the proposed stability-constrained PQI-AVI framework enables coordinated power-quality improvement while restricting the virtual-impedance command to the numerically identified small-signal stable adaptation region for the grid-strength conditions considered. Full article