Search Results (2,970)

“Power-to-ammonia” is widely regarded as a viable solution for large-scale consumption of wind and solar power, as well as for deep decarbonization in the energy and chemical sectors. However, the intermittent nature of renewable energy requires ammonia synthesis systems to operate across a wide and varying range of loads, posing challenges to their economic viability. To address this, we develop a simulation and optimization methodology for ammonia reactor operation under varying loads. Firstly, a high-fidelity reactor model is developed based on the reactor’s structural characteristics by incorporating reaction kinetics and thermodynamic mechanisms. This reactor model is then integrated with compression and separation units. To ensure computational efficiency, surrogate models are developed to approximate the ammonia synthesis and flash separation units. A case study of an ammonia plant with a nominal production rate of 100,000 tons/year is conducted to demonstrate the effectiveness of the proposed method. The results indicate that the feasible operation region of the reactor narrows significantly as the system production load decreases. System operation parameters, including reactor inlet temperature, reactor pressure, and ammonia separation temperature, are optimized for the ammonia synthesis loop over a wide operating window from 30% to 100% of nominal capacity. It is recommended to increase the system inlet temperature as the production load decreases, thereby compensating for the reduced heat release per unit product resulting from the decreased system pressure. Full article

This study addresses the multi-objective optimization of characterizing a flat glass processing plant. To assess the operational conditions required for a flat glass processing small and medium-sized enterprise (SME) to become a prosumer compatible with renewable energy community (REC) participation. This work is motivated by the presence of more than 300 SMEs in Italy, like this, where RECs represent one of the few viable strategies for achieving the European Union’s 2050 decarbonization targets. The research is carried out in two scenarios; Scenario-I includes Stage-i and Stage-ii with the mutual goal of forecasting and optimizing. Forecasting is used in Stage-i to optimize the factory load, and in Stage-ii to shift and curtail energy loads based on the forecast, considering the Italian national energy price and the regional price bands (“fasce orarie”) F1, F2, and F3. Forecasting and the indicators of environmental and social performance are the means to ensure the best energy utilization and management, as they prove that the reduction in CO${}_2$ emissions and benefits on the community level can be both obtainable. Subsequently, the techno-economic analysis and evaluation of prosumer-readiness conditions are carried out through the optimization of industrial energy demand: three optimization objectives are assessed in this study (i) energy cost, (ii) carbon emission, and (iii) load curtailment. Four algorithms are put into effect to solve the tri-objective optimization: multi-objective particle swarm optimization (MOPSO), multi-objective ant nesting algorithm (MOANA), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective grey wolf optimization (MOGWO). The algorithms are validated in Stage-ii to find the desired optimum in the cost of energy, reduce peak formation, and carbon emissions. To achieve this goal, a stochastic approach based on Monte Carlo simulations and VIKOR is used to optimally select the results. The findings show that the NSGA-II, MOPSO, and MOANA are more effective in solving the problem, while the MOGWO algorithm more quickly finds the optimal solution. Based on the defined objectives, a new configuration for the energy community is introduced, together with a community well-being index and an evaluation of the resulting benefits for the factory. In Scenario-II, the PV plants’ installation on the factory is sized, and the excess energy shared with the grid is evaluated. The Scenario-II results show that 497.184 MWh (33.9%) of energy is shared with the grid. Both results suggest how optimized industrial demand profiles improve SME participation in future RECs. Full article

The move to sustainable energy systems has increased the requirement for comprehensive decision support frameworks that are uncertainty-aware to guide the selection of solar power plant sites. The rapid growth of investments in solar energy has increased the demand for systematic and accurate decision-support tools to choose the best sites for photovoltaic (PV) power facilities. The selection of solar power plant sites is a complicated multi-criteria decision-making (MCDM) problem that involves technical, economic, environmental, social, and technological aspects. The process is typically associated with ambiguity and incomplete knowledge of experts. To overcome these problems, this paper offers an interval-valued neutrosophic fuzzy TOPSIS (IVN-TOPSIS) method, which extends the standard TOPSIS methodology by including truth, indeterminacy, and falsity membership degrees as interval values. The methodology is utilized in a real case study in the Mediterranean region of Türkiye, comprising three provinces with great potential: Antalya, Mersin, and Adana. An assessment of a complete set of environmental, economic, social, and technological criteria is performed using expert judgments stated in interval-valued neutrosophic language assessments. They were incorporated into a Geographic Information System (GIS) to produce a suitability map indicating the most suitable sites for the facility. The suggested approach is different from the traditional crisp or fuzzy MCDM techniques since it clearly models the degrees of truth, indeterminacy, and falsehood, thus providing a more detailed representation of the expert evaluations. According to the data, Mersin is the most ideal site for the construction of a solar power plant, followed by Antalya, and the least suitable site is Adana. The results suggest that sustainable solar energy planning must go beyond technical resource potential and include integrated and uncertainty-aware assessments. The suggested IVN-TOPSIS framework can serve as a powerful decision-support tool to policymakers, planners, and investors that wish to encourage regionally balanced and sustainable renewable energy development. Full article

Hydrogen production and storage constitute a promising technology in the path towards a global energy scenario featured by renewable energy penetration, decarbonization, sustainable development and resilience. In particular, so-called green hydrogen is generated from renewable energy sources, generally produced in an electrolyzer by means of Proton Exchange Membrane (PEM) water electrolysis. To make these expectations reality, experimental and real-world facilities are required, dealing with challenging aspects such as new technologies and integration of equipment. Thus, this paper presents the implementation and operation of a pilot plant for green hydrogen generation and storage based on a commercial 30 kW PEM electrolyzer. The renewable source is a photovoltaic generator of 60.6 kW which supplies the hydrogen generator through an inverter. Furthermore, the deployment of a supervisory system entirely based on open-source technologies is reported. The equipment employed and the supervisory system developed in this work exhibit a level of complexity and scale that is uncommon in the literature. Therefore, this article is a novelty in the literature and aims to contribute to the advancement of green hydrogen production and storage by providing experimental data and descriptions of a fully functional plant operating under real-world conditions. The achieved results under real operation conditions prove the successful implementation of the pilot plant as well as the suitability of the supervisory system to effectively track the most relevant variables. Full article

At present, treated wastewater may still contain residual nutrients and micropollutants, including heavy metals, pharmaceuticals, and dyes, which can negatively affect receiving water bodies. Increasingly stringent environmental regulations, including Directive (EU) 2024/3019, require both enhanced removal of these contaminants and greater integration of renewable energy sources in wastewater treatment plants. This paper presents a review of biomass-based wastewater polishing technologies employing biological agents such as microalgae, fungi, bacteria, co-cultures and duckweed for the removal of residual contaminants from treated effluents. The compiled data indicate that while optimal conditions can drive pollutant removal efficiencies beyond 90%, system performance varies widely depending on species selection, wastewater characteristics, and operational conditions (e.g., pH, temperature, salinity, nutrient availability, and light intensity). In addition to effluent polishing, the produced biomass can be valorized for bioenergy generation, contributing to renewable energy production and supporting circular economy principles in wastewater treatment plants. Despite these benefits, biomass harvesting remains a major technical and economic bottleneck, often representing a significant share of operational costs and limiting large-scale implementation. Overall, biomass-based treatment technologies are a promising approach for improving effluent quality and supporting renewable energy objectives; however, further advances in biomass recovery are required for broader application. Full article

Solar power plants are among the most widely used renewable energy sources today. Varying radiation levels from region to region, and similarly varying consumption depending on the user within a given region, make the optimal sizing of these plants challenging. In this study, a machine learning-based surrogate model for the real-time sizing optimization of solar power plants, trained with a completely original dataset, has been developed. In the first stage, 500 different solar power plant installation scenarios were synthetically generated and evaluated in HOMER, and the obtained optimal sizing outputs were used as training targets for the proposed surrogate model rather than real operational data. The results obtained by applying various machine learning methods to the generated dataset are presented comparatively. Among 7 different machine learning models, XGBoost, Gradient Boosting, and LightGBM demonstrated the best performance. The developed model achieved an average R² score of 0.9425 for a total of 3 targets, while target-specific performance showed R² scores of 0.9747 for inverters, 0.9365 for PV panels, and 0.9165 for batteries. This model serves as a computationally efficient surrogate of the HOMER optimization process, enabling high-accuracy real-time predictions while significantly reducing the computational burden associated with intensive mathematical calculations, iterative procedures, and complex search spaces. Full article

A short-term performance test of blended biodiesel (FAME), green diesel (HVO), and diesel was experimentally assessed in a 100 kW Cummins 6BTAA5.9-G12 diesel engine under multiple load conditions. The objective was to determine the technical feasibility, operational trade-offs, and optimal blend formulations for renewable energy deployment in diesel power plants. All tested blends operated stably without engine modification, confirming the “drop-in capability” of FAME–HVO mixtures for existing diesel engines. Specific fuel consumption (SFC) increased notably at high loads, with penalties up to 15.15% for B30D20 and B35D15 relative to neat diesel, although overall efficiency improved with load. Among the ternary fuels, B30D10 and B30D20 provided the most balanced compromise between combustion reactivity and flow properties. Exhaust gas temperatures rose with load for all fuels, with FAME-rich blends exhibiting higher temperatures than neat diesel, while coolant-side analysis showed D100 and D50 as thermally favorable and B50–B100 imposing the highest cooling demand. The results emphasize the need for injection system recalibration on an energy basis for HVO-rich fuels, and for strengthened filtration and maintenance practices for FAME-rich blends to avoid filter clogging and injection instability. Considering performance, operability, and system stability up to 100 kW, B30D10 and B35D15 are identified as optimal compromise blends. The study highlights the necessity of future work on long-term durability, fuel system compatibility, supply chain robustness, and techno-economic viability to safely scale green diesel use in Indonesian stationary power generation. Full article

The study presents the results of research aimed at developing digital models for determining the operating parameters of railway power supply systems equipped with distributed generation plants based on renewable energy sources (RESs). RESs can be used in railway transport to increase the reliability of power supply to facilities located in areas with insufficiently developed power grids. This primarily applies to consumers, for whom a power failure can lead to significant damage, accidents, and a threat to human life. RES can serve as independent power sources for special-group consumers and can increase energy conversion efficiency. Furthermore, large-scale implementation of renewable energy sources can significantly reduce energy supply costs and improve power quality. The study employs phase-coordinate modeling, which is characterized by the following features: a systems approach, which implies determining operating conditions while considering the properties and characteristics of complex traction and supply networks; versatility, which enables modeling of power supply systems of various structures and designs; and comprehensiveness, which involves calculating normal, emergency, and special operating parameters—crucial for scenarios such as ice melting on catenary wires. The modeling results obtained using the Fazonord AC-DC software (ver. 5.3.5.2) show that RES-based distributed generation plants provide a variety of beneficial effects: reduction in electricity consumption from power system networks; decrease in voltage unbalance and harmonic distortion on the busbars of regional windings of traction substations; and stabilization of voltage levels on current collectors of electric locomotives. Full article

Large-scale drinking water treatment plants contribute to environmental burdens through energy consumption, chemical use, and sludge generation. However, Life Cycle Assessment applications to full-scale drinking water treatment plants remain limited in Morocco and other Global South contexts, where site-specific operational data are often scarce. This study assesses the environmental performance of an existing conventional drinking water treatment plant in Al-Hoceima, northern Morocco, using full-scale operational data and a Life Cycle Assessment (LCA) approach based on the ISO 14040/14044 framework. The assessment was performed using OpenLCA v1.11 and the ReCiPe 2016 Midpoint (H) method, with a functional unit of 1 m³ of treated drinking water. The results show that the operational phase dominates the environmental impacts, mainly due to sludge generation and electricity consumption. Two improvement scenarios were therefore evaluated: sludge recycling and the integration of a hydroelectric turbine as an on-site renewable energy option. Both scenarios showed potential to reduce environmental impacts while improving resource efficiency and long-term economic performance. By integrating environmental and techno-economic analyses, this study provides a practical decision-support framework for the sustainable transformation of conventional drinking water treatment plants in Morocco and comparable developing regions. Full article

While rapid digital transformation has significantly optimized sectors such as finance and e-commerce, maintenance management in industrial environments has historically received lower levels of technological and capital investment. This lag creates critical gaps in operational efficiency and asset longevity, particularly within renewable energy infrastructures where sustainability and resilience are paramount. Addressing this technological disparity is essential for minimizing ecological footprints and maximizing the viability of net-zero systems. This paper introduces an advanced multi-platform digital solution designed to optimize the operation and maintenance of renewable energy systems and smart infrastructures. The platform addresses traditional management gaps by implementing standardized protocols that integrate real-time remote monitoring, sensor networks, and cloud-based data acquisition. By centralizing historical and real-time data from solar, wind, and hybrid grids, it facilitates advanced analytics, such as predictive modeling of component degradation. Real-world validation across photovoltaic plants and wind farms demonstrates significant impacts: a 30% reduction in unplanned outages and a 20% to 25% decrease in operational and maintenance costs. The results confirm that digitalizing maintenance processes is a strategic pillar for the energy transition, aligning industrial performance with global low-carbon pathways. Full article

To improve the flexibility and carbon reduction performance of coal-fired power plants, a solar-aided power generation (SAPG) system integrated with parabolic trough collectors and thermal energy storage (TES) was proposed and investigated using a combined Aspen Plus and System Advisor Model (SAM) framework. Two different integration schemes, namely SAPG-1 and SAPG-2, were evaluated under 100%, 75%, and 50% load conditions with a solar multiple of 2 and a TES duration of 6 h. The thermodynamic, economic, and environmental performances of the systems were comprehensively analyzed. The results show that TES significantly improves solar energy utilization, annual solar contribution, and system dispatchability. Compared with SAPG-2, SAPG-1 demonstrates superior thermodynamic and economic performance due to its lower boiler heat demand and more effective feedwater integration. At full load, the solar contribution of SAPG-1 with TES reaches 16.04%, while the annual solar energy production increases to 190.35 GWh with a capacity factor of 21.75%. In addition, TES integration effectively reduces the levelized cost of electricity and shortens the payback period under both CO₂ pricing and non-CO₂ pricing scenarios. The proposed SAPG framework demonstrates considerable potential for enhancing renewable energy utilization, operational flexibility, and economic feasibility in large-scale solar–coal hybrid power generation systems. Full article

To address the issues of high energy consumption, high carbon emissions, and the waste of associated energy (AE) in coal mine production, which severely hinder global sustainable development goals, this paper proposes a novel low-carbon economic collaborative optimal scheduling model for a coal mine integrated energy system (CMIES) oriented towards sustainable energy transitions. First, a refined utilization model for AE encompassing coal mine gas, ventilation air methane (VAM), and mine groundwater (GW) is constructed, and a tiered carbon emission trading mechanism (TCET) is introduced to constrain carbon emissions and promote ecological sustainability. Second, a concentrated solar power (CSP) plant is integrated to break the rigid “power determined by heat” constraint of a traditional combined heat and power (CHP) unit, thereby enhancing the system’s scheduling flexibility and renewable energy integration. Meanwhile, abandoned mines are retrofitted into solvent storage tanks to construct an integrated flexible carbon capture system (IFCCS), achieving sustainable reuse of mining wastelands. Finally, to tackle the multi-source, heterogeneous uncertainties on both the source and load sides, a hybrid risk assessment method combining information gap decision theory (IGDT) and conditional value at risk (CVaR) is proposed. Case study results demonstrate that, compared to traditional energy supply modes, the proposed model reduces carbon emissions and total costs in the mining area by 66.04% and 15.97%, respectively. This significantly improves resource utilization efficiency and ecological benefits, providing a highly viable pathway for the sustainable development and clean transition of coal mine operations. Furthermore, the proposed hybrid assessment method can effectively assist decision-makers in achieving a refined trade-off between operating costs and system robustness under varying risk preferences. Full article

Digital twins (DTs) in the energy sector are operational-data-driven models of assets, installations, and networks. Their value grows alongside renewable expansion, electronic communications, and stricter resilience requirements for critical infrastructure. This study evaluates DT applications in Poland’s energy transition, identifying regulatory and cybersecurity determinants required for safe, scalable use. The methodology combines an international literature review, regulatory assessment, and qualitative desk research focusing on DT projects across four Polish energy groups: Enea, Energa, PGE, and Tauron. Each case is assessed using a DT maturity and governance framework covering scope, data coupling, decision support, and security posture. The study identifies four primary deployment types: (1) operational network twins for distribution system operators leveraging SCADA/ADMS, GIS, and state estimation; (2) AI-driven asset performance twins for wind turbines and CHP plants; (3) flexibility twins for hydropower system services; and (4) immersive training twins for the offshore wind sector. Main constraints include data quality, interoperability, fragmented data access regulations, and expanded cyber-attack surfaces from OT/IT convergence. DTs aid spatial planning, mitigating location and land use conflicts. Recommendations emphasize harmonized data governance, cybersecurity-by-design, special determinants, and the creation of regulatory sandboxes to support DT implementation within critical energy infrastructure. Full article

Against the backdrop of global green and low-carbon energy structural transition, renewable energy represented by photovoltaic power has emerged as a critical strategy for safeguarding energy security and mitigating climate change. As typical energy-intensive infrastructures, wastewater treatment plants (WWTPs) suffer from excessive energy consumption and substantial carbon emissions. In this study, a distributed photovoltaic power generation system is deployed at WWTPs to alleviate on-site power demand, and its economic and environmental benefits are quantitatively analyzed via PVsyst software simulation. The simulation results indicate that the overall system efficiency reaches 83.3%, with an annual average power generation capacity of 825,500 kW·h. Annually, the proposed system can save 275.17 tons of standard coal, and correspondingly reduce carbon dioxide emissions by 687.92 tons, sulfur dioxide emissions by 20.64 tons and nitrogen oxide emissions by 10.32 tons, thereby realizing synergistic enhancement of economic and environmental performances. This work offers a feasible engineering reference for promoting the modernized transformation of WWTPs toward energy self-sufficiency and low-carbon operational modes. Full article

This study presents the design, development, and evaluation of an integrated solar-powered seed sowing and fertilizer-watering system to enhance planting efficiency, improve resource utilization, and reduce labor in small-scale agriculture. The prototype features a 600-watt photovoltaic panel, DC motors, and a manual mechanical dispensing mechanism, enabling automated seed placement, water distribution, and fertilizer application in off-grid farm environments. Development was guided by a product-based design approach using locally sourced materials to ensure cost-effectiveness, maintainability, and accessibility for rural users. Field simulations and performance trials assessed charging efficiency, seed sowing accuracy, irrigation flow rate, and fertilizer dispensing precision. Results showed high consistency in operational performance, including up to 99% seed placement accuracy, efficient water delivery, and reliable fertilizer timing, with solar energy providing adequate power storage during periods of peak irradiance. Expert evaluations using a standardized instrument demonstrated strong agreement on the system’s usability, material availability, ergonomic features, modularity, and overall functional design. Findings indicate that the system can minimize manual labor, reduce operational costs, and offer a practical transition toward clean-energy–assisted mechanization in agriculture. The study concludes that integrating renewable energy into essential farm operations can contribute to sustainable productivity and recommends future enhancements through sensor integration, increased battery capacity, and adaptive control mechanisms to support wider agricultural adoption. Full article