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The virtual energy hub (VEH), a combination of virtual power plant and energy hub concepts, faces many uncertainties due to its constituent distributed energy resources. This paper presents the deep learning-based scheduling of VEH for participation in electrical and thermal markets using bidirectional long short-term memory (BLSTM) network, which offers excellent accuracy in forecasting uncertain parameters by concurrent using past and future dependencies. In addition to applying learning methods, energy storage systems can also influence the optimal management of uncertainties. To provide the required electrical storage equipment, the VEH employs plug-in hybrid CNG-electric vehicles (PHGEVs) that can use both electrical energy and compressed natural gas (CNG) to fulfill their energy needs. The alternative fuel can tackle the limitations of prolonged charging of electric vehicles and excess load caused by these vehicles at peak hours. To supply the secondary fuel of PHGEVs, the modeled VEH includes a CNG station, which compresses the natural gas imported from the natural gas grid before delivering it to the vehicles. Furthermore, phase change material-based thermal energy storage (PCMTES) is considered in the VEH configuration, which unlike other common thermal energy storage systems, operates at a constant temperature during the charging and discharging period. Lastly, the simulation of the developed system illustrates that PHGEVs can reduce the imposed cost in unforeseen situations by up to 26 percent and increase the system's flexibility.

Abstract Microgrids integrating local renewable energy sources at low-voltage level show promising potentials in realizing a reliable, efficient, and clean supply of electricity. Further improvements are expected when such a microgrid is operated based on direct current (dc) instead of alternating current (ac) infrastructure for power distribution commonly in use today. Our study aims to systemically quantify the gap between environmental impacts of microgrids at building level using the case study of power distribution within office buildings. For this purpose, a scalable comparative life cycle assessment (LCA) is conducted based on a technical bottom-up analysis of differences between ac and dc microgrids. Particularly, our approach combines the micro-level assessment of required power electronic components with the macro-level requirements for daily operation derived from a generic grid model. The results indicate that the environmental impacts of employed power electronics are substantially reduced by operating a microgrid based on dc power distribution infrastructure. Our sensitivity analyses show that efficient dc microgrids particularly lead to savings in climate change impact emissions. In addition, our study shows that the state-of-the-art scaling rules of power electronics currently used in LCAs leads to inaccurate results. In contrast, the proposed methodology applies a more technical approach, which enables a detailed analysis of the environmental impacts of power electronic components at system level. Thus, it provides the foundation for an evaluation criterion for a comprehensive assessment of technological changes within the framework of energy policy objectives.

This paper proposes a technique for the probabilistic wind power forecasting (WPF) of a newly built wind farm (NWF) using a limited amount of historical data. First, the state-of-the-art Transformer network is employed to capture the power generation pattern of different wind farms (WFs) based on abundant historical training samples. Then, the Bayesian averaging regression method is applied to transfer the learned power generation pattern to the NWF by assigning proper weights to the WPF results of different WFs. This enables the proposed method to yield accurate NWF power predictions utilizing a limited amount of historical data. The Bayesian characteristics further enable the quantification of multiple uncertainties in forecasting results that may be essential for the NWF operator when the input is uncertain. Comprehensive tests were also performed by employing other deterministic and probabilistic WPF methods using field data. By comparing the results, the proposed method is demonstrated to produce accurate forecasting results with sparse historical data. Moreover, the uncertainties of outcomes are quantified, and acceptable performance is achieved.

This paper determines the energy consumption ratio and overall bio-energy production costs of microalgae cultivation, harvesting and conversion to secondary energy carriers, thus helping to clarify future perspectives of micro-algae production for energy purposes. A limitation growth model is developed, which determines the productivity of micro-algae for different climate profiles. Total direct and indirect energy consumption ratios for the production of heat, fuels and electricity derived from micro-algae are calculated. Overall direct energy consumption ratio for raceway ponds is 0.06 for the optimal case, indirect energy consumption ratio for that case is 0.74. Direct energy consumption ratio in horizontal tubular systems is 0.32 for the optimal case, indirect energy consumption ratio for that case is 117. The implementation of different improvement options could reduce the indirect energy consumption ratio by fifty percent for both raceway ponds and horizontal tubular systems in the optimistic scenario. Prominent elements of the energy consumption ratios are carbon dioxide supply for raceway ponds and circulation power consumption for horizontal tubular systems. The lower end of fuel production cost calculated for raceway ponds is 136 €2010/GJ and 153 €2010/GJ for horizontal tubular systems (non-renewable gasoline and diesel is about 5–20 €/GJ). Considering possible improvement options overall bio-energy production costs could be reduced by one-fourth. Current results suggest that micro-algae cultivation is not suitable for dedicated bio-energy production in considered cultivation, harvesting and conversion options. Coproduction of bio-energy with high-value products are more viable, but is not considered in this research. 2012 Elsevier Ltd. All rights reserved.

[EN] Fuel cell (FC) technology has been identified as a technically attractive solution to decarbonize the transportation sector, especially for heavy-duty vehicles. In this context, the industry and the scientific community are in need of advanced fuel cell systems (FCS) models that are able to replicate real-world operating conditions. Due to the scarcity of said models in the open literature, this study aimed to develop a comprehensive methodology to calibrate and validate multi-physics dynamic FCS models. Therefore, the key contribution of this paper is the detailed description of the calibration process for each component and the calibration order. The specific focus here was to accurately describe the behavior of the FC stack as well as the cathode, anode, and cooling circuits of the balance of plant. The model was calibrated with the aid of experimental data from a Toyota Mirai FC electric vehicle, which was predominantly retrieved from the vehicle¿s Controller Area Network (CAN) bus system thereby negating the need for major intrusion into the powertrain system. The validation process was deemed successful with the model being able to truthfully replicate the characteristics of the FC vehicle operated on the World-wide harmonized Light duty Test Cycle (WLTC) 3b and US06 driving cycle. The time-resolved physical parameters such as the cathode pressure, mass flow, or the FC stack temperature were captured with high fidelity, while the overall performance parameters such as the H2 consumption in the stack and the system, and the compressor energy consumption were predicted accurately with a deviation lower than 0.47%, 1.75% and 1.89% with respect to the experimental data, respectively. This research is part of the project TED2021-131463B-I00 (DI-VERGENT) funded by MCIN/AEI/10.13039/501100011033 and the European Union "NextGenerationEU"/PRTR. It has also been partially funded by the Spanish Ministry of Science, Innovation, and University through the University Faculty Training (FPU) program (FPU19/00550) . Toby Rockstroh and Ram Vijayagopal acknowledge support through the US DOE Vehicle Technologies Program. Argonne National Laboratory is operated by UChicago Argonne, LLC under Contract no. DE-AC02-06CH11357. The US Government retains for itself, and others acting on its behalf, a paid-up non-exclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly, by or on behalf of the Government. The authors would like to express their gratitude to Kevin Stutenberg from Argonne National Laboratory for the informative discussions surrounding the experimental test campaign.