A hybrid air-conditioning (A/C) system has been proposed as an alternative energy-efficient solution for marine A/C application using waste-heat-driven dehumidification and a seawater-based evaporative cooler. This paper establishes a mathematical model of a hybrid thermal wheel and evaporative cooling A/C system, by taking the energy consumption characteristics, dehumidification capacity and cooling capacity as the evaluation indexes. The influence of the outdoor temperature and humidity, regeneration air temperature on the overall performance of the system is studied. It is found that the hybrid A/C system has excellent dehumidification and cooling capabilities at high inlet air temperature and humidity as well as high regeneration air temperature. The coefficient of performance (COP) of the hybrid system can reach 13.
Short-term load forecasting is a fundamental task in reliable and secure power system operation, particularly in the current landscape marked by increased integration of renewable energy sources and electric vehicles, which introduces stochasticity and raises uncertainty. To express uncertainty in load predictions in the form of a probabilistic forecast, prediction intervals are generated. The variability in load values exhibits higher volatility during the day due to increased human activities, contrasting with lower variability at night. Classic methods for constructing prediction intervals cannot correctly model the variability in uncertainty leading to overly conservative prediction intervals. In this paper, we propose a novel approach – conformalized quantile regression – to create more informative, variable-length prediction intervals. Experimental results, based on a real load dataset from the Croatian Transmission System, showcase the method’s superior performance in capturing adaptive-length prediction intervals. This translates to achieving higher coverage with shorter prediction intervals compared to conventional methods.
The electrification of transportation modes such as cars, buses, and boats offers the potential of providing vehicle-to-X services during idle times. Pools of vehicles can provide balancing power, trade on the electricity market, or be used for load peak shaving. In this work, the usage patterns of electric cars, electric buses, and electric boats are investigated, and the provision of vehicle-to-X with these vehicles is simulated using an open-source simulation tool. A data analysis and a vehicle usage pattern assessment show that especially private electric cars behave predictably at night. It also reveals that the vehicle-to-X availability varies over the week for all vehicle types and is highest at night for cars and buses. During the day on weekdays, private cars are available for vehicle-to-X 30 to 70% of the time, the analyzed buses 5 to 50% of the time, and the availability of the boats depends on their primary use as ferries or private boats. If the three transportation modes provide vehicle-to-X during idle times, the equivalent full cycles that the lithium-ion batteries complete increase at different rates depending on the vehicle pool size, while the mean charging rates decrease. Furthermore, an exemplary aging analysis shows that the additional load of vehicle-to-X provision slightly increases the capacity loss of the car batteries compared to a paused unidirectional charging strategy.
The Air Handling Unit (AHU) system is influenced by various types of errors, which can cause thermal discomfort of occupants and energy waste in building. Therefore, an early and accurate Fault Detection and Diagnosis (FDD) is important for optimal control of building heating/cooling systems and increasing occupant productivity. The data-driven FDD is promising because it is convenient compared to the first principles-based rule set that demands in-depth expertise. However, in order to realize the data-driven FDD for real-life cases, the data imbalance problem in FDD must be solved. In this study, the authors suggest a novel approach that generates synthetic data from an entire building system simulation tool, HVACsim+ and then use them as a source model for applying transfer learning to a target AHU system. For the transfer learning, only the normal operational data from the existing target system was used. It is found that the transfer learning approach is satisfactory, confirming that the proposed method will be effective in mitigating the data imbalance issue in developing the data-driven FDD.
Building Integrated Photovoltaics (BIPV) with Energy Storage Systems (ESS) enables buildings to play a crucial role in on-site PV consumption. However, due to the uncontrollability of PV, buildings often struggle to fully utilize it in real time. This paper proposes a decentralized cooperative power dispatch approach based on multi-agent proximal policy optimization (MAPPO) for cluster consisting of multiple BIPV with ESS. To acquire reliable strategies, a digital twin (DT) is employed as a sample and training environment for MAPPO to minimize cumulative grid power replenishment. An example of a small-scale building cluster is used to demonstrate the coupling of MAPPO and DT. The decentralized dispatch strategy is obtained with a one-hour time step. Verification results indicate a 9.85 MWh boost in PV self-absorption compared to a self-generating self-using strategy. Leveraging DT opens up further possibilities for applying MAPPO to power dispatch challenges.
At present, facing the dual problems of world energy crisis and carbon emission pollution, the cavitation phenomenon formed in the nozzle due to the high pressure difference and whether the physical property difference of clean alternative fuels methanol and ammonia can be successfully applied and replace the traditional fuel has become the focus of current research. In this paper, the Schnerr-Sauer cavitation model considering the thermal effect of cavitation and the physical property parameters of fuel varying with temperature and pressure are embedded into the CFD software, and the cavitation characteristics of diesel, methanol and ammonia inside the nozzle are studied and compared. The results show that the influence of cavitation heat effect on diesel, methanol, and ammonia in the nozzle increases sequentially, manifested by the increase in the degree of cavitation in the nozzle; When the cavitation number is approximately 1.3 and 1.1, the fuel exhibits cavitation inception and supercavitation phenomena, respectively; The net temperature effect of fuel varies under different injection pressures, with diesel and methanol having a more significant heating effect in the nozzle flow, while ammonia has a more significant cooling effect when the injection pressure is less than 14MPa.
Energy storage plays a crucial role in the energy transition. Lithium-ion cell technology is the leading energy storage technology today across both the major pillars of the energy sector: mobility and electricity. Lithium-ion batteries are deployed in electric vehicles spanning all segments, and in stationary battery energy storage systems to provide a variety of both grid-connected and off-grid services. While there are no direct emissions due to the use of this technology, the carbon footprint of a Lithium-ion battery comprises of indirect emissions in its production, its operation, and recycling phases. Repurposing of decommissioned automotive batteries in â€˜second-lifeâ€™ stationary applications is a widely discussed concept to meaningfully extend the battery lifecycle before recycling. In this work, the lifecycle carbon footprint of Lithium-ion batteries operating in three overarching pathways is quantified simulatively with open-source python-based energy system and battery system simulation programs. These pathways are â€“ i) automotive application (A), ii) stationary application (S), and iii) automotive application followed by a second-life stationary application (AS). From the dual perspective of decarbonization and resource efficiency, it is essential to identify the most effective lifecycle pathways for battery system applications. The metric â€˜Levelized Emissions of Energy Supplyâ€™, LEES, is used to compare the scenarios. It is found that under the considered assumptions and simulation conditions, the S pathway performs the best, followed by the cascaded AS pathway. The automotive pathway A has the highest LEES value.
Parametric inversion is recognized to be an effective method for evaluation of hydraulic fracturing performance. Based on the unsteady seepage theory, the fracture parameters inversion method of deep coalbed methane (CBM) reservoir is established and solved semi-analytically considering the gas-water two-phase flow and the multiple nonlinear seepage mechanism of gas and water in the matrix and fractures. The numerical results from the proposed method are consistent with that from the existing numerical method and the computational speed is heightened greatly. The results show that the proposed method can accurately obtain half-length of fracture, permeability of fracture and stimulated reservoir volume (SRV) using the production data entering the boundary control flow stage.
Field observations, along with experimental laboratory, exhibit evidence that gas injection in tight oil reservoirs is technically feasible. However, there is a lack of understanding the effects of fracture aperture on the oil production behaviors, especially when the fracture aperture is less than 50Î¼m. In this study, CO2 flooding experiments were carried out using different fracture aperture core samples considering the confining pressure (11Î¼m, 15Î¼m, 20Î¼m, 25Î¼m). And the production characteristics and remaining oil distribution were evaluated based on nuclear magnetic resonance (NMR). The results showed that fracture aperture is of significance to the permeability. Specifically, with the increase of fracture aperture, permeability increases in power law while porosity increases linearly. It is worth noting that the recovery of CO2 flooding is the highest (17.60%) after water flooding when the fracture aperture is 20Î¼m. Moreover, the dissolution diffusion mechanism becomes more obvious when the fracture aperture increases (10~20Î¼m), but gas channeling will lead to a large amount of remaining oil when the fracture aperture exceeds a certain limit (20~25Î¼m) according to the results of nuclear Magnetic Resonance Imaging (MRI). The results provide a theoretical basis for further understanding the effect of fracture aperture on CO2 injection in tight reservoirs.
The need for cold storage is growing worldwide, especially for small-scale fisheries in tropical coastal regions where access to a continuous power supply and modern preservation methods is limited or nonexistent. This deficiency in adequate storage facilities significantly diminishes their catch quality. Novel renewable, grid-independent solutions are required to address this problem. This study proposes and demonstrates a solar-driven grid-independent cold storage unit through a dynamic model developed in TRNSYS simulation software. A detailed parametric study highlights the critical parameters for optimal sizing related to cold storage insulation, sizing of air conditioning systems, and photo-voltaic battery systems. Moreover, the optimal integration was compared with conventional (grid-based) cooling solutions for cold storage. The results show that grid-independent cold storage of 20 feet to store 1,285 kg of fish daily at -20Â°C requires 24.5 kW of PV array, 10 kW inverter and a battery storage of 151296 Wh to meet the annual electricity demand of 32.3 MWh. The parametric analysis shows that insulation of 150mm reduces thermal energy demand for cooling by 12.3% (from 74 MWh to 64.9 MWh), and a further increase in insulation does not provide significant benefits. The analysis shows how the cooling rate influences the sizing of the HVAC system, how the collector tilt angle adjustment frequency impacts PV performance, and how the battery backup time influences the battery size. The proposed system can save 23.3 tons of carbon emissions annually compared to grid-connected conventional cold storage. The paper discusses the design and implementation of the PV-driven grid-independent vapour compression cooling system, highlighting key influence design parameters, control strategies, and energy management techniques to minimise excessive cooling in the cold storage freezer room.