Abstract

Liquid air energy storage system using Kapitza cycle is thermodynamically optimized with selected critical process variables by partial enumeration. With this method, the contour maps for the independent variables are illustrated, that give intuition to the behavior of the LAES systems. The Interaction between the variables can be found and thermodynamically analyzed. The optimized thermodynamic efficiency 40.0%, 48.8%, and 51.2% when compression pressure is set at 40 bar, 80 bar, and 120 bar, respectively.

Abstract

There has been growing concerns about climate change, and more environmentally friendly operation of energy systems is of high interest to energy systems owners. This study presents the optimal operation of the University of Warwick’s energy system considering both energy and carbon emission costs. The system that currently consists of gas-fired CHP units, heat and electricity networks would be upgraded to a heat pumpbased low-carbon energy system in 2050. A sensitivity analysis was conducted for both scenarios and provides insights into how sensitive the optimum solution is to different levels of demand, energy prices, and carbon price. The results show that a heat pump-based system is less sensitive to carbon emission price and has fewer carbon emissions than the existing CHP-based system.

Abstract

The collection and analysis of big data has become a ubiquitous process in numerous fields, including energy efficiency. The combination of smart meters that provide high-frequency readings of consumption and enhanced data analytics have enabled studies to uncover energy usage patterns of households. However, an accurate understanding of consumer behavior requires a more comprehensive approach. Henceforth, we present a novel data simulator that generates appliance-based datasets based on real data. By proper fusion of real smart meter data and periodic energy consumption habits, which are represented by micro-moments, we were able to simulate realistic domestic energy consumption scenarios. Pre-processing the aggregated readings and the application of a k-means clustering algorithm, user actions were detected (switch on/off) per appliance. Based on these records and using a apriori itemset extraction algorithm we obtained appliance usage patterns. A minute-averaged dataset was simulated for three rooms and nine appliances, monitored for a 47-month period. We managed to extract frequent usage patterns at appliance-level, taking into account contextual parameters such as room occupancy. Moreover, the simulated datasets can be expanded to any size to a variety of energy saving applications such as micro-moment classification and goal-based recommender systems for energy saving

Abstract

A deep learning based hierarchical predictive control is developed for regulating the oxygen stoichiometry of proton exchange membrane fuel cell (PEMFC) engine in this study. Firstly, a hierarchical predictive control scheme is proposed by designing the first-level predictor to determine the operation current of PEMFC engine, and then the second-level model predictive control (MPC) generating robust control input. BP neural network is selected to formulate the first-level prediction model and airflow model is linearized to design MPC with suitable prediction horizon and control horizon. A simulation test is carried out through operating in a mixed driving cycle MANHATTAN + (a part of) UDDS to verify the efficacy of the proposed method. The results indicate that the oxygen stoichiometry tracks the reference value well avoiding the starvation of the PEMFC engine.

Abstract

Photovoltaic plants appear as an option for a more environmentally friendly centralized generation. Solartracking is used for efficiency gain presenting better performance than fixed tilted plants. However, the system suffers generation and protection problems without the use of backtracking algorithms. Shading conditions can prejudice the energy generation and cause diode failure. This paper studies the influence of backtracking for protection and generation. For protection, it is shown that the bypass diode takes time to activate, generating hotspots. For efficiency, the production and plant size gains are highlighted.

Abstract

An indicative appraisal has been undertaken of a combined Anaerobic Digestion (AD) and Steam Methane Reforming (SMR) process to produce hydrogen (H2) from organic waste. The AD plant was based on the plant in Tilburg (The Netherlands), and was modelled from the kerbside organic waste collections through to methane production. The technical feasibility of H2 production of this combined process was assessed based on biogenic waste collected in Bath and North East Somerset (B&NES); a municipal area in the South West of England (UK). The B&NES collection trials yielded data that could be used to estimate the catchment area for an AD plant on a commercial scale. A thermodynamic evaluation of the combined process included energy and exergy analysis in order to determine the efficiency of each process, as well as to identify the areas that lead to inefficiencies. The main energy losses were associated with compressor inefficiencies. In contrast, the main exergy consumption was found to be due to the fermentation in the digestion tanks. The overall technical efficiency of the plant does not compare well with other processes when compost is considered as a waste product. However, the H2 is produced comes from entirely renewable sources and has the benefit of nearzero carbon emissions in contrast to fossil fuels. Finally, the case study included an indicative economic assessment of the collection to production chain. The use of high quality source sorted waste could yield of high levels of biogas and high quality compost could be produced. Should compost were employed as a useful by-product, then the adoption of the combined process would be economically attractive.

Abstract

Chemical looping combustion and reforming are promising technologies for converting fossil fuel into power and hydrogen (H2). However, the temperature limitations in the reactor reduce their attractiveness. In the current study, an additional combustor is added to increase the turbine inlet temperature. Furthermore, three different process integration between CLC plant and membrane-assisted CLR plant are evaluated. Direct integration results in an efficiency of 53.08%. With further process integration, the plant can produce both power and H2 with efficiencies 54.46% and 68.09%, respectively. The results show significant improvement in efficiency when compared to natural gas combined cycle and steam-methane reforming technologies.

Abstract

In the present study, we propose a new concept to capture carbon dioxide (CO2) from gas industries using a supersonic separator. We develop and validate a computational fluid dynamics model to evaluate the effect of a real gas model on the non-equilibrium condensation CO2 in high-pressure supersonic flows. The results show that the ideal gas equation of state underpredicts the CO2 condensation with the predicted liquid fraction of approximately 15% of the total mass. The Redlich-Kwong equation of state incorporating with the condensing flow model shifts the onset of the CO2 nucleation process upwards to the nozzle throat, while predicting a liquid fraction up to 28% of the total mass. The study indicates that the CO2 condensation in supersonic flows provides an efficient way to mitigate the CO2 emissions from gas industries.

Abstract

China nowadays faces comprehensive challenges on supplying modern clean space heating to a majority of its citizens. Various building space heating technologies are implemented throughout north and south China. However, investors and policy makers are suffering from a lack of a systematic assessment tool to evaluate which heating technology to choose based on unique local conditions from techno-economic and environmental perspectives. This paper fulfills such research gap by proposing a multi-criteria assessment infrastructure to assist relative stakeholders evaluate potentials of different space heating technologies. The proposed infrastructure is multi-disciplinary and requires to handle a large amount of data from various sources, which can well reflect the feasibility of building space heating technologies systematically.

Abstract

Long-term energy scenario modeling and dispatch simulations are two key stages in a methodology for cost-effective transition to a lowcarbon power system. Although these stages are equally important, they are often performed independently. This decoupled approach can lead to future investment trajectories decided by long-term energy models with no guarantee of generation adequacy. In this respect, TIMES-ANTARES is the result of linking a long-term energy model with a stochastic power system model. The aim of this study is to develop a general methodological framework using a multimodel approach to investigate optimal power mixes which meet generation adequacy requirements. The emphasis of this article is on the methodology, but we also include a case study of power generation planning applied to the French system for 2013-2050. The results show that using TIMES alone exposes the power generation mix for 2030 to a risk of insufficient supply. On the other hand, activating the iterative feed-back loops over capacity credit parameters has the potential to ensure both the economic effectiveness of the mix and the security of the electricity supply criterion set by the French public authorities.