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Abstract In this study, batch tests were performed to evaluate the effects of different thermal pretreatment temperatures (55–160 °C) and durations (15–120 min) on the anaerobic digestion of kitchen waste (KW). Two commonly used approaches, namely the modified Gompertz model and the approach developed by Koch and Drewes, were applied to assess the effects of the different pretreatment parameters on the biomethane yield, lag time and hydrolysis rate constant via data fitting. The subsequent anaerobic digestion of KW pretreated at 55–120 °C presented greater efficiency, and longer treatment durations resulted in increased methane production and higher hydrolysis rate constants. These findings were obtained due to the lower nutrient loss observed in KW treated at lower temperature treatments compared with that found with higher temperature treatments. In general, the effects of thermal pretreatment on the lag phase and hydrolysis rate differed depending on the treatment parameters leading to the variations in the KW compositions. The soundness of the two model results was evaluated, and higher statistical indicators (R2) were found with the modified Gompertz model than with the approach developed by Koch and Drewes.

Abstract A coiled-pipe exchanger was employed to extract heat rapidly at relatively high temperatures from a 90-litre hot-water charged tank, the water being initially at a temperature of approximately 80°C. The free-convective movements of the water around the outside of the coiled pipe (immersed in the store) were due to buoyancy forces induced by colder water being forced through the heat-exchanger's pipe. For the heat-exchanger orientations tested, the maximum effectiveness, with respect to the quality of the heat extracted was achieved (i) by having the axis of the coiled heat-exchanger arranged horizontally with its inlet at the lowest level; and (ii) with the lower rate tested (=6·6 litre/min) of water being passed through the heat-exchanger's pipe, partly because this led to a lower rate of disruption of the stratification of the water within the store.

Abstract As the increasing penetration of distributed renewable energy sources, the inherent fluctuation of their output power aggravates more control burden for the power system. Based on the direct load control, the regulation potential of distributed flexible loads can be developed to relieve this pressure. This paper proposes a real-time two-stage optimization model for multiple thermostatically controlled load groups to smooth the power fluctuation in distribution network. The upper-level evaluates the load regulation capacity and suppresses the net exchange power fluctuation; the lower-level further minimizes the regulation costs and makes the target demand curves for each load group, which can guide the subsequent load power tracking. Besides, within the load group, a proportion aggregation method is applied to reflect its external power regulation characteristics, while the decomposition program assigns the regulation task to each terminal user. For the complex problem established above, a hierarchical and iterative solving strategy is proposed to obtain the approximate optimal solution. The simulation results show that the proposed approach can effectively decrease the net exchange power fluctuation as well as regulation costs.

A performance analysis and optimization of a open-cycle regenerator gas-turbine power-plant is performed in this paper. The analytical formulae about the relation between power output and cycle overall pressure-ratio are derived taking into account the eight pressure-drop losses in the intake, compression, regeneration, combustion, expansion and discharge processes and flow process in the piping, the heat-transfer loss to the ambient environment, the irreversible compression and expansion losses in the compressor and the turbine, and the irreversible combustion loss in the combustion chamber. The power output is optimized by adjusting the mass-flow rate and the distribution of pressure losses along the flow path. Also, it is shown that the power output has a maximum with respect to the fuel-flow rate or any of the overall pressure-drops and the maximized power output has an additional maximum with respect to the overall pressure-ratio. The numerical example shows the effects of design parameters on the power output and heat-conversion efficiency.

Enhancing the reliability of energy networks and minimizing downtime is crucial, making self-healing smart grids indispensable for ensuring a continuous power supply and fortifying resilience. As smart grids increasingly incorporate decentralized prosumers, innovative coordination strategies are essential to fully exploit their potential and improve system self-healing capabilities. To address this need, this paper presents a novel bi-level strategy for managing the self-healing process within a smart grid influenced by Hydrogen Refueling Stations (HRSs), Electric Vehicle Charging Stations (EVCSs), and energy hubs. This approach taps into the combined potential of these prosumers to boost system self-healing speed and reliability. In the initial stage, the Smart Grid Operator (SGO) conducts self-healing planning during emergencies, communicating required nodal capacities to prevent forced load shedding and outlining incentives for smart prosumers. Subsequently, prosumers schedule their activities and contribute flexible capacities to the SGO. Bridging the first and second stages, an adaptive Alternating Direction Method of Multipliers (ADMM) algorithm ensures convergence between the SGO and prosumer schedules within a decentralized framework. This strategy underwent implementation on a 118-node distribution system using GAMS. Results demonstrate that the proposed concept reduces Forced Load Shedding (FLS) by 32.04% and self-healing costs by 17.48% through effective utilization of smart prosumers' flexible capacities. Furthermore, outcomes indicate that the SGO reduces FLS by 6.69% by deploying Mobile Electrical Energy Storages (MEESs) and Mobile Fuel Cell Trucks (MFCTs) to critical nodes. This research has benefited from the funding of the program Strategic projects oriented to the ecological transition and digital transition with NextGenerationEU/PRTR funds of the Spanish Ministry of Science and Innovation/State Research Agency (project ‘‘Local markets for energy communities: designing efficient markets and assessing the integration from the electricity system perspective (OptiREC)’’, with reference number TED2021-131365B-C43. The research has also supported by the Natural Science Foundation of Universities of Anhui Province (KJ2020A0009)

Under the development of information and communication technologies, this paper discusses a cloud-based user-side integrated energy management to improve the energy utilization efficiency in a smart community, for which a two-level model is proposed that relies on an energy hub and load aggregators. Furthermore, to address the issue of preserving confidentiality for the cloud service, an information-masking mechanism is designed based on linear mapping functions, whose information-masking requirements and processes are specified accordingly. Numerical results demonstrate the effectiveness of the proposed model and of the method for cloud computing.

Abstract Energy storage is needed for the renewable energy power, and it is highly promising to convert CO2 to fuels utilizing the surplus renewable energy. Solid oxide cell (SOC) is an efficient energy conversation device, and it can realize energy storage by reversible operation. However, the CO2 electrolysis process is limited by the intensive energy input and low efficiency. To improve the electrolysis efficiency and realize syngas production, in this work, nickel-yttria stabilized zirconia (Ni/YSZ) supported solid oxide cell has been used for CH4 assisted CO2 electrolysis (CH4-SOEC). Moreover, a novel energy storage concept by cycling operating the cell in CH4-SOFC mode (discharge) and CH4-SOEC mode (charge) has been proposed, called CH4 assisted solid oxide cell (CH4-SOC). Besides energy storage, the CH4-SOC technology can realize syngas production as well. In the experiments, CH4 is continuously supplied to the Gd-doped ceria (GDC) modified Ni/YSZ anode, while air/CO2 are supplied to the YSZ-La0.6Sr0.4FeO3−δ (LSF)/GDC cathode in discharge/charge cycles, respectively. Experiments demonstrate that the cell presents well electrochemical performance and durability for 400 h under different operation modes. Using distribution of relaxation times (DRT) method, the rate limiting steps of the fundamental electrochemical processes in the electrodes have been identified. Moreover, thermodynamic calculations have been performed to evaluate the cell efficiency. Results suggest that the cell could realize an energy conversion efficiency of 84% in case of fuel utilization of 0.9, where the ratio of the discharge electric energy to the charge electric energy is about 0.95. Both experiments and calculations demonstrate the feasibility of technology, and using a conventional SOC is much important for the real application of it.

Digital twins have emerged as a promising concept for improving building energy efficiency, but their implementation faces challenges in interoperability and adaptability. This paper presents a large-scale field demonstration of an interoperable energy modeling framework for building digital twins, using ontology-based semantic models as data sources for automated model generation and calibration of data-driven component models. The study focuses on a single floor of a hospital building, comprising 12 conditioned zones and data from 45 measuring devices. Across the 45 sensors, the model achieved average mean absolute errors of 0.40∘C for temperature, 32 ppm for CO2 concentration, 0.06 for valve position, and 0.04 for damper position predictions. These results demonstrate the framework's ability to generate and calibrate accurate and flexible building energy models with reduced effort. The paper also showcases the framework's practical application in exploring system modifications to improve indoor comfort, highlighting its potential for scenario analysis and decision support. The proposed approach significantly streamlines the process of creating and maintaining accurate, up-to-date energy models, offering a robust foundation for digital twin applications in the built environment.

Presentation des resultats des etudes realisees par Mandix pour the Welsh Development Agency concernant l'impact sur l'environnement de l'utilisation de l'energie et sur la necessite economique et ecologique d'une utilisation rationnelle de l'energie.Analyse de l'impact sur le plan d'occupation des sols et l'infrastructure des villes et des regions.Application a la vallee de Taff (Pays de Galles).