Search Results (4,396)

This study presents a gate-to-gate life cycle assessment (LCA) of an industrial sweet cherry sorting and packing facility in Greece, directly addressing environmental sustainability in agri-food supply chains through data-driven impact quantification and improvement pathways in post-harvest operations. The assessment focuses on a gate-to-gate system boundary encompassing all processes inside the cherry sorting and packing facility, while upstream cherry production and downstream waste management are modeled and reported separately to provide system-level context. Core-stage hotspots are then analyzed in detail in the Results section, highlighting the dominant role of electricity use compared with packaging materials. The functional unit is defined as 1 kg of packed, market-ready cherries at the factory gate. Primary data are obtained from high-resolution, batch-level measurements of mass flows, energy use, water consumption, packaging materials and waste streams over a full processing season, structured as virtual sensor outputs. These sensor-informed operational data are combined with secondary life cycle inventory information from established databases to quantify climate change impacts and identify environmental hotspots across materials, energy, water, and waste, thereby delivering a quantified picture of environmental performance in the post-harvest stage. The results show that corrugated cardboard and associated packaging components are among the main contributors within the facility-level, gate-to-gate system, while the Core stage accounts for 28.43% of total GWP100. Upstream cherry production dominates the overall Upstream–Core–Downstream climate footprint with 70.61% of total impacts. Moreover, practical mitigation scenarios are modeled, including packaging optimization, partial substitution of grid electricity with photovoltaic generation, and increased water recirculation. Ιn the combined mitigation scenario, where packaging optimization, low-carbon electricity and improved water management are implemented simultaneously, total GWP100 decreases from 114,207.32 to 92,500.27 kg CO₂-eq (−19.0%) relative to the baseline, providing actionable sustainability improvements for industry stakeholders and supporting Sustainable Development Goals (SDGs) related to climate action and resource efficiency. In addition, the proposed virtual sensor architecture and data workflow support continuous monitoring, eco-efficiency management and near-real-time LCA implementation in post-harvest agri-food systems, enabling operational sustainability. Full article

The rapid electrification of urban freight transport requires new optimization approaches that jointly consider logistics operations and energy system constraints. The problem is formulated as a mixed-integer linear programming (MILP) model that captures the interdependencies between vehicle operations, battery constraints, charging infrastructure availability and the temporal variability of photovoltaic energy. A multi-objective structure is adopted to minimize total energy costs and CO₂ emissions while maximizing the utilization of locally generated renewable energy. The model is evaluated using scenario-based simulations under three solar integration levels (0%, 30% and 60%). The results demonstrate that integrating solar energy into routing and charging decisions significantly reduces grid dependency, lowers emissions and improves overall system efficiency. Three types of charging stations are considered in the study (S1, S2, and S3), differing in photovoltaic (PV) energy penetration levels, ranging from conventional grid-based charging (S1) to high renewable integration stations (S3). The quantitative analysis reveals a clear resource and emission structure across the simulated scenarios. Incorporating charging stops grid-wide increases the total distance from theoretical routes to real tracks with stops to overcome the 120 kW battery limit. However, the integration of solar energy significantly alters the system’s environmental costs: total CO₂ emissions drop non-linearly by 33.4%, decreasing from 364.64 kg in the ‘Low Sun’ scenario to 243 kg in the ‘High Sun’ scenario. Furthermore, the localized impact shows that utilizing pure grid energy (S1) results in 405 kg of CO₂, while maximizing solar integration up to 60% (S3) reduces emissions to 162 kg. The sensitivity analysis showed how varying the share of solar energy at the two main stations (S2 and S3) affects the total CO₂ emissions, while maintaining the same routes. Three scenarios were examined: low (10% and 30%), base (30% and 60%) and high (50% and 90%) solar energy shares. As the share of solar energy in the system increases, a clear effect of emission reduction and energy cost optimization is observed. Full article

In electricity–hydrogen coupling systems (EHCSs), the uncertainty of renewable energy generation (REG) tends to impact electrolyzers (ELs) in the following ways: (1) input powers of ELs are prone to fluctuations; (2) ELs are forced to operate under variable load states. Consequently, both impacts will reduce the service life of ELs. In this paper, considering the start–stop characteristics and combined operation modes of multiple ELs, a two-stage multi-time-scale energy allocation strategy (MSEAS) is proposed to mitigate the impacts of REG uncertainty and optimize the energy allocation for EHCSs. First, five refined operating states of ELs, such as shutdown, cold standby, low-load, variable-load and overload, are formulated as mixed-integer constraints and embedded into the system-level energy optimization model. Second, to mitigate power fluctuations caused by REG, a day-ahead optimization is employed to plan the power allocations of ELs, lithium batteries, fuel cells, and the grid with a 1 h time step; and then an intra-day rolling optimization is employed to adjust the operating states and power outputs of the above units with a 4 h window and 15 min step. Third, by enabling multiple ELs to flexibly operate in a combined mode, power-sharing mode and switching mode, the proposed MSEAS can refine the operation powers of ELs and reduce their start-up frequency. Comparative case studies are conducted in the off-grid and grid-connected operation tests, and the relevant results verify that the proposed MSEAS can effectively prevent the frequent start–stop of ELs, which contributes to extending the service life of ELs and reducing the system operating cost. Full article

With the increasing integration of grid-friendly concentrated solar power (CSP) plants into high-proportion new energy power systems, the system is confronted with challenges such as insufficient regulation capability and power balance difficulties. To address these issues, this paper proposes a multi-stage optimal regulation strategy for CSP–wind power systems based on adaptive step-size model predictive control (ASMPC), from the perspectives of tapping transmission line current-carrying capacity and coordinating system regulation resources. This strategy first establishes an electro–thermal–mechanical coupling dynamic line rating (DLR) model to characterize line safety margins, then constructs an optimization decision-making model aiming at minimizing the total multi-stage coordinated scheduling cost and adopts ASMPC to dynamically adjust the control step size, effectively improving scheduling accuracy and real-time correction capability. Simulation results based on the modified IEEE 39-bus system show that the proposed method reduces the total system cost by 26.8% (nearly 30%), increases the CSP unit output ratio by 27.9%, and decreases the average grid load rate by 12.6 percentage points. The proposed strategy can effectively mitigate the impact of source-load uncertain fluctuations and significantly improve the economic operation level of the CSP–wind power combined system. Full article

Anaerobic digestion is a viable pathway to mitigate environmental impacts from swine manure in tropical regions while contributing to circular economy strategies. However, no standardized or integrated framework currently exists that simultaneously quantifies the closure of energy, material, carbon, nutrient, and water loops at the farm scale. This research presents the techno-economic design and environmental assessment of a covered, mechanically agitated lagoon biodigester for a 10,000-head swine fattening module located in Matanzas, Cuba. The system is sized by integrating hydraulic, thermal, and structural parameters, and its economic viability is assessed through Net Present Value (NPV = $1.09 million), Internal Rate of Return (IRR = 32%), and a payback period of approximately three years. A comparative screening-level life cycle assessment shows that biogas-based electricity generation substantially reduces impacts on climate change, air quality, and fossil fuel scarcity compared with conventional diesel-based generation, with trade-offs in eutrophication and ecotoxicity. As a key methodological contribution, five quantitative circular economy indicators are proposed and calculated: the Energy Self-Sufficiency Ratio (ESSR = 1.71), the Waste Valorization Index (WVI = 0.91), the Decarbonization Index (DCI = 6.7), the Fertilizer Substitution Rate (FSR = 16.3 t N year⁻¹), and the Water Closure Factor (WCF = 1.30). These indicators show that the system achieves a 71% net energy surplus, valorizes over 90% of the input mass, avoids 6.7 times more emissions than it generates, replaces synthetic fertilizers, and returns more water than it consumes. The findings provide quantitative evidence that the convergence of mesophilic operation without auxiliary heating, high carbon intensity of the power grid, and availability of agricultural land enhances circularity performance in tropical covered lagoon bioreactors, and the proposed integrated indicator framework, aligned with ISO 59020:2024, provides a reproducible and transferable methodological basis for the comparative assessment of anaerobic digestion systems for livestock waste. Full article

The deployment of rural electrification actions through off-grid mini-grid solutions is one of the most effective approaches to achieving universal access to electricity in an affordable, reliable, and sustainable way. To assess the sustainability of three mini-grid projects (Sembezea, Mawayela, and Dongane), this study applied a framework that integrates different methods (HOMER, LCA based on SimaPro, and Input–Output) and indicators under the economic, environmental, and social dimensions. Data for the analysis were obtained through site visits in the case study areas, a literature review, and the HOMER and ecoinvent databases. Sembezea and Mawayela were assessed based on their operational experience, whereas the Dongane biogas system is analyzed based on a projected household biodigester experience. The results of this study revealed the considerable benefits of biogas in generating local employment (506 employees) compared to wind/solar PV (98 employees) and hydro/solar PV (91 employees), as it is expected to require a considerable number of employees for feedstock collection for the digester, under the assumed scale and conditions. Additionally, in the long term, biogas would present the lowest cost of electricity at $0.22/kWh compared to wind/solar PV ($0.28/kWh) and hydro/solar PV ($0.60/kWh), thereby improving the ability of the local community to pay for electricity. In contrast, this study concluded that, in terms of environmental impact—particularly CO₂ emissions—biogas has relatively poor environmental performance (4.58 × 10⁻² kg CO₂ eq) compared to wind/solar PV (8.50 × 10⁻⁴ kg CO₂ eq) and hydro/solar PV (3.94 × 10⁻⁴ kg CO₂ eq) in the long term. Nevertheless, biogas presents carbon neutrality as an advantage, in the sense that the CO₂ released during its combustion is assumed to be carbon-neutral. By applying the framework to the aforementioned case studies, the extent to which it is possible to provide an integrated overview of the economic, environmental, and social aspects, as well as the impacts of different HRES options in line with the SDGs, is demonstrated. Full article

The utilization of polyethylene terephthalate (PET) waste from single-use packaging offers potential for sustainable manufacturing. This study evaluates recycled PET (rPET) from bottles as an FDM filament by varying infill architectures (honeycomb, gyroid, grid, and triangles) and layer thicknesses (0.20, 0.25, and 0.30 mm), with commercial PETG as a benchmark. Compared with previous rPET FDM studies, which were limited to reporting mechanical strength, the novelty of this study lies in the fact that it not only reports mechanical strength performance, but also compares printing time requirements and material efficiency. Efficiency calculations are obtained by comparing the weight of the filament to the weight of the printed specimen, which then correlates with optimizing processing time and costs. Overall, rPET produced densities of 1.11–1.22 g/cm³, tensile strengths of 12.5–22.5 MPa, flexural strengths of 12.5–30 MPa, impact strengths of 0.032–0.060 J/mm², and surface roughnesses of Ra 5.2–7.1 μm, while PETG showed higher mechanical performance (tensile 30–39.5 MPa, flexural 30–50 MPa, impact 0.037–0.065 J/mm²) and comparable density (1.15–1.27 g/cm³). Within rPET, gyroid provided the best optimal performance; the gyroid (0.20 mm) variation achieved the highest impact response (0.060 J/mm²) and the lowest Ra (5.2 μm) and the gyroid (0.25 mm) variation maximized flexural strength (30 MPa) and the gyroid (0.30 mm) variation maximized tensile strength (22.5 MPa). Material utilization efficiency was consistently higher for rPET (65–68%) than for PETG (46–56%). These results provide an integrated rPET-specific assessment and practical parameter recommendations for functional 3D printing, while also aligning with SDG 12 by pro-moting resource-efficient circular-economy practices through the utilization of waste materials in additive manufacturing. Full article

Although critical to modern logistics, heavy-duty commercial vehicles face mounting pressure to improve energy efficiency and reduce emissions. The aim of this study was to evaluate the techno-economic and environmental performance of four vehicle configurations: internal combustion engine (ICE) tractors and battery electric tractors (BETs), each respectively paired with either a conventional or an electrified trailer. To optimize energy utilization while proactively mitigating the longitudinal impact risks that trigger vehicle instability, a coordinated control strategy based on power decoupling and a real-time, efficiency-oriented torque distribution strategy were designed. Simulations under C-WTVC and CHTC-TT cycles revealed that electrified trailers substantially improved the system efficiency. Under fully loaded conditions, BETs paired with electrified trailers reduced the direct energy expenditures by 76.5% compared to conventional ICE vehicles. Notably, compared to pure electric tractors with conventional trailers, the addition of electrified trailers further reduced the energy consumption by 29.1%. Meanwhile, ICE tractors paired with electrified trailers achieved a 35.6% energy cost reduction. Furthermore, a fuel-cycle well-to-wheels (WTW) assessment of the use phase, based on a specified regional grid emission factor, demonstrated that the BETs and hybrid configurations reduced the operational greenhouse gas emissions by 64.9% and 29.3%, respectively, compared to the baseline. These findings indicate that trailer electrification offers consistent economic and environmental benefits under the simulated scenarios, thereby providing a robust theoretical foundation for the low-carbon transition, transportation sustainability, and selection of sustainable technologies in road freight. Full article

The aim of this paper is to study the impact of the variability of inertia of interconnected electrical systems on transient stability. In the first part of the paper, after formulating the transient stability problem as a boundary value problem, we demonstrate how to evaluate the transient stability margin, considering the impact of the randomness of inertia, with reference to a single area connected to an infinite power grid. In the second part of the paper, we determine the distributional properties of the transient stability margin for two interconnected areas, considering the correlation of the area’s inertias, described as random variables. To demonstrate the robustness of the procedure, two case studies are analyzed. In the first case, the random variables are described as correlated lognormal random variables, while in the second they are considered as correlated gamma random variables. The numerical analyses reported in the final part of the paper show an almost linear dependence of transient stability margin from inertia while correlation coefficient affects mainly the transient stability random variable’s dispersion rather than its magnitude. Some useful considerations are performed regarding the applicability and validity of the linearized probabilistic method instead of the Monte Carlo method. The Monte Carlo method allows considering the non-linearity of the model, which is more pronounced in the tails. This could affect, in some critical cases, the priority of access to production in the logic of the free market. In such a case, greater accuracy allows for a more transparent mechanism of access to production. Full article

The convergence of Software-Defined Networking (SDN) and Blockchain (BC) creates a symbiotic relationship in which SDN’s programmable global visibility complements BC’s decentralized, immutable trust model to address critical cybersecurity vulnerabilities and cyber attacks. Addressing the fragmentation in the current literature, this study rigorously investigates BC and SDN (B-SDN) integration with the primary objectives of: (1) differentiating impacts across varied sectors, including the Internet of Things (IoT), Smart Grids, and Vehicular Ad Hoc Networks (VANETs) and more; (2) analyzing critical performance metrics such as energy efficiency and scalability; (3) classifying mitigation, detection, and prevention schemes for specific threats; (4) examining novel Artificial Intelligence (AI) methods; and (5) identifying open challenges and future research directions. Methodologically, this study conducts a survey of state-of-the-art B-SDN studies to investigate six key areas: Industry-specific applications, security mechanisms, defense strategies, defenses against specific attacks, AI integration, and implementation performance. The findings demonstrate that B-SDN integration shows strong potential in simulated and prototype environments to mitigate specific high-impact threats, such as Distributed Denial of Service (DDoS), Man-in-the-Middle (MiTM), and spoofing, across various domains, including IoT, 5G/6G, VANETS, and Smart Grid. Despite the benefits and advantages promised by B-SDN, several limitations continue to exist, including the latency–security trade-off inherent to consensus protocols and scalability constraints in large-scale deployments. Finally, open research challenges persist in AI-driven automation, particularly in Federated Learning (FL) and in the development of standardized interoperability protocols required to enable the transition from conceptual models to operational systems. Full article

Jordan is rapidly adopting renewable energy and electric vehicles (EVs), positioning itself as a leader in the Middle East’s energy transition. However, challenges in maintaining grid stability are rising. Time-of-Use (ToU) electricity tariffs hold promise in promoting demand-side flexibility; however, their impact in developing countries remains underexplored. This study investigates the effects of ToU tariffs on Jordan’s residential and transport sectors using historical data under a static demand assumption to isolate the direct tariff-design effect. Our results reveal that ToU tariffs may disproportionately burden low-income households, with electricity bills rising by 67% to 158%. In the transport sector, even grid-friendly EV charging results in a significant rise in bills, up to 130%. These findings raise equity concerns and highlight the need for tailored ToU structures. We conclude our study by discussing the policy implications of our findings and offer actionable insights for policymakers to ensure equitable access to affordable energy in Jordan and other developing countries facing similar challenges. Full article

The displacement of synchronous generators by inverter-based renewable energy sources (RES) has eroded system inertia, weakening frequency stability even as voltage stability improves. This paradox poses a major challenge for modern grids. Battery Energy Storage Systems (BESS) offer synthetic inertia and rapid frequency response, but their stabilising impact depends critically on placement. Using dynamic simulations on the IEEE 9-bus system, this study demonstrates the voltage–frequency paradox across increasing RES penetration. Results show that strategic siting prevents frequency collapse while enhancing voltage recovery, providing a unified mitigation strategy for high-renewable systems. Full article

As declared in the European Green Deal, the decarbonization of the EU energy system is essential for achieving Europe’s climate neutrality targets, demanding a substantial expansion of renewable energy sources and the rapid phase-out of coal and gas. It is therefore essential that newly installed PV products within the EU are designed to avoid creating additional environmental burdens due to environmental impacts during production and at the end of life (EOL) of photovoltaic (PV) modules. This study presents a life cycle assessment (LCA) of sustainable/green PV module designs in terms of recyclability using advanced high-quality recycling technologies. It compares two product systems both based on mono c-Si PV technology and the glass–glass (G–G) module design: 1. Passivated Emitter and Rear Contact (PERC) and 2. Tunnel Oxide Passivated Contact (TOPCon) cell technologies, which are assessed under production scenarios in China and Germany, and two recycling scenarios (hypothetical high-recovery recycling and partial recycling) using inventory data from eco-invent and literature sources. The results across most impact categories show that the PERC and TOPCon module designs produced in Germany with high-recovery recycling as the end-of-life strategy exhibit lower impacts than those produced in China with partial recycling as the end-of-life strategy under the adopted assumptions such as electricity mix and end-of-life modelling choices for module-only impacts (excluding BOS components). The climate change results show that TOPCon cell design under high-recovery recycling yields 10.4% lower emissions than the PERC cell design under partial recycling in Germany and 9.7% lower in China. However, both module designs emit 26.6% and 27.2% less GHG emissions when produced in Germany compared to production in China, respectively, which is line with earlier studies. With the exception of human toxicity, both PERC and TOPCon cell technologies perform better in this study than previously reported in reviewed LCA studies, reflecting the use of more recent state-of-the-art industry data concerning manufacturing requirements. The sensitivity analysis carried out on the design changes and electricity grid mix available shows that any improvements in the design process and increases in renewable energy penetration into the grid corresponds to a proportional reduction in environmental impacts across all impact categories. Full article

Cable routing is a pivotal design component for electrical systems and safety-critical engineering fields, such as nuclear propulsion systems, nuclear power plants and aircraft. Scientific and optimized routing schemes are essential for efficient and safe power and signal transmission and for mitigating system failure risks. Previous studies have adopted heuristic search and swarm intelligence optimization algorithms for cable path planning; however, these methods tend to converge to local optima under complex constraints and cannot theoretically guarantee global optimality, failing to address multi-constraint, high-dimensional optimization challenges of confined-space cable routing. This paper proposes a mathematical programming-based systematic optimization model: it first discretizes continuous three-dimensional space into a grid coordinate system and constructs a composite cost field integrating geometric distance and thermal interference, then formulates a multi-objective optimization model considering path length, thermal impact and routing feasibility, which is converted into a single-objective problem via normalized weighting coefficients and solved by exact mathematical programming techniques, yielding a best feasible solution together with a provable lower bound and an optimality gap. When the solver converges within the time limit, global optimality for the discretized model can be certified. Simulation results show the proposed method reduces overall path cost by an average of 31.8% compared with classical algorithms like the A* algorithm, Dijkstra’s algorithm, Rapidly-exploring Random Tree (RRT), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Furthermore, it cuts decision variables by an average of 70% (up to 82% in complex scenarios) against the 0–1 Integer Linear Programming (ILP) model and the graph-theoretic Multi-Commodity Flow (MCF) model with multi-cost considerations. These results preliminarily validate the favorable solution quality, computational efficiency and engineering applicability of the proposed model for confined-space cable routing optimization. Full article

Electricity purchase price variation can influence the economic feasibility of residential distributed generation, particularly in regulated markets where grid electricity prices and export compensation conditions affect investment decisions. This study evaluates the impact of flat electricity purchase price scenarios on the techno-economic viability of residential grid-connected energy systems in Santo Domingo Tehuantepec, Oaxaca, Mexico, using HOMER Pro. The analysis considers PV, wind generation, diesel generation, converter, and grid connection as candidate components, while evaluating three residential demand profiles of 11.26, 30.00, and 83.30 kWh/day and 10 electricity purchase price scenarios ranging from 3.45 to 5.00 MXN/kWh. The objective is to identify the electricity purchase price values at which the optimal architecture changes from conventional grid-only supply to PV/converter/grid adoption under the evaluated case study assumptions. The results show that grid-only supply remains the least-cost option from 3.45 to 4.20 MXN/kWh for all demand profiles. At 4.25 MXN/kWh, HOMER Pro selects PV/converter/grid configurations for the medium- and high-demand profiles, while the low-demand profile remains grid-only. At 4.30 MXN/kWh, PV/converter/grid also becomes optimal for the low-demand profile. At 5.00 MXN/kWh, ROI reaches 11.0% for the three residential demand profiles, while payback decreases to 6.5 years for the low- and medium-demand profiles and 6.4 years for the high-demand profile. The wind turbine and diesel generator were not selected in the optimal configurations, despite being included as candidate technologies. These findings provide a practical case study indicator of the electricity purchase price levels at which residential PV-grid adoption becomes economically competitive under flat purchase price scenarios and zero export compensation. Full article