This study assesses the overall sustainability level of the transport sector in all European Union member states and the UK using the composite sustainability index methodology for cross-country comparison. The composite transport sustainability index included 15 different indicators grouped into 4 main dimensions – mobility, sustainability, innovation, and environmental. Results show high potential for increasing transport sustainability in all countries included in the study. Sweden and the Netherlands can be seen as benchmarks for achieving higher long-term sustainability. Most countries are lagging behind in innovating transportation systems by providing the necessary infrastructure for electric vehicles and alternative fuel cars. More emphasis should be placed on increasing the share of public transport in total passenger transport.
With the goal of net-zero expected to be accomplished in recent decades, the development of a thermoelectric generator, one of the energy harvesting technologies, is important. Along with efforts to discover more cost-effective thermoelectric materials, geometric and structural optimization of thermoelectric generators is essential to maximize power and efficiency. This work demonstrates a segmented thermoelectric generator, one of the advanced structures of a thermoelectric generator, modeling using artificial neural networks. After training the artificial neural networks, we have achieved 98.9% accuracy compared to COMSOL simulation results under constant temperature difference while speeding up the computational speed over a few thousand times. This new approach illustrates the advantages of the modeling of segmented thermoelectric generators.
In this work, the biogas production of laboratory-scale anaerobic digestion reactors was adapted to the residual load of an electricity self-sufficient municipality. The adaptation of biogas production to such an irregular course, as specified by the residual load, has not yet been investigated. By using rapidly degrading sugar beet and medium-fast degrading maize silage, it was possible to achieve a high level of agreement. Compared to a continuously fed reactor, the gas yield was not negatively affected. Various parameters assessing process stability were continuously observed and indicate a stable operation even at high organic loading rates up to 5.9 kg VS m-3 d-1. This mode of operation can minimize the necessary gas storage capacity and prevent investments in gas storage expansions.
Worldwide electricity consumption is still increasing while there is the ambition to reduce greenhouse gas emissions. To ensure sufficient sustainable electricity resources for our society, an energy source diversification is necessary. Indeed, next to high potential but intermittent renewable energies like wind and solar, traditional thermal power production using renewable resources like syngas and biogas are good candidates to achieve these energy mix goals. However, given their specific properties, i.e. having a lower energy content, better characterize of non-conventional energy sources in their combustion behavior is needed.
In this work, we compare the combustion behavior of syngas with natural gas in the complex geometry of a typical mGT combustor, the Turbec T100. A first approach includes the development of a turbulent combustion model that allows to validate the temperature fields and species concentration gradients for natural gas, reference case in known conditions. In a second step, we aim to get some first insight on the effect of using biogas through injection in the main flame.
The results show the temperature fields for both natural and syngas as well as an accurate prediction on intermediate species and NOx, CO, CO2 and H2O in the flue gases. These obtained results will serve as benchmark for future characterization for a specific range of diluted inlet conditions of various syngases and biogases, which will allow to fully exploit its potential syngas in small-scale cogeneration application.
Due to the increasing penetration of renewable energy sources on the grid, the traditional power plants (PP) and the combined cycles, in particular, are increasingly forced to operate in discontinuous mode with continuous load changes. In the present work, two power-to-fuel-to-power processes are investigated as potential solutions to improve the Combined Cycle Power Plant (CCPP) flexibility by adsorbing and storing the electrical energy produced by the PP and not sold to the grid. The analysis was carried out on the Power-to-Hydrogen-to-Power (P2H2P) and Power-to-Ammonia-to-Power (P2A2P) systems investigating and comparing the process in terms of round-trip efficiency, storage energy density, and plant footprint. Despite the P2H system being more competitive from the efficiency point of view, it presents critical issues related to the energy storage density and system footprint as consequence. These problems can be overcome by ammonia which resulted in a much more effective energy storage medium.
Many businesses ceased working physically during the current pandemic and started working remotely. Therefore, launching home-based offices has now become popular among people ever before. One type of place where people are currently working is home-based offices. However, these residential places are not as standard as they are supposed to be. While working at their homes, workers consume more artificial lighting than natural lighting compared to normal conditions (their workplace). Thus, his research aims to study daylight and energy optimization used in home-based offices to achieve maximum natural light during working hours. This optimization works based on auto-extract-window-to-wall and automatic louvers for windows. The suggested research method for this study is the “Genetic Algorithm” to optimize the proportion, which can be achieved by using a special parametric algorithm in Grasshopper. The paper concludes that optimization can be conceived as a creative modeling method for increasing natural light and reducing energy consumption in home-based offices. The results of this study validate that with the use of optimization used for louvers, shelves, and other mentioned elements, spatial daylight autonomy have the potential to be increased up to 25%. The annual sunlight exposure can also be reduced up to 10%. This will lead to a reduction in artificial lighting consumption. This type of optimization develops the agenda of optimizing “daylight and energy retrofit.”
The principal objective of the paper is to study the techno-economic feasibility of developing the following types of Grid-Scale Solar Photo-Voltaic Energy Storage System (GSPV-ESS) Hybrids having capacity to deliver 350 MWh for one hour to shave the daily peak power demand contributed entirely from the state-owned installed generators of Assam, India:
i. Grid-Scale Solar Photo-Voltaic Lithium-Ion Battery Energy Storage System (GSPV-LiBESS) Hybrid
ii. Grid-Scale Solar Photo-Voltaic Pumped Hydro Energy Storage System (GSPV-PHESS) Hybrid
The amount of solar photovoltaic panels and the capacity of grid-scale energy storage systems required are well established in this paper. Further, the paper discusses the investment costs and the payback period required under the following two scenarios in Assam for Peak Power Shaving (PPS):
i. Scenario I: to shave the peak load, exclusively
ii. Scenario II: to shave the peak load and sell surplus electricity to consumers
The novelty of the paper lies in developing models of two different grid-scale solar photovoltaic energy storage system hybrids of capacity 350 MW each to shave daily peak power demand for one hour and contribute towards Demand Side Management (DSM) for India and abroad. The two models under two scenarios of Assam can be referred to as a template for similar grid-scale solar photovoltaic energy storage system hybrids, developed and erected for daily peak power shaving in other states of India and elsewhere in the world.
The air-conditioning systems served for high-tech cleanrooms, requiring strict space temperature, humidity and particle concentration controls, are usually energy-intensive, which consume the same magnitude of energy as that of data centers. Their system performance is strongly climate- and weather-dependent, which didn’t receive enough attention as data centers. Therefore, this study has evaluated the year-round energy performance of high-tech cleanroom air-conditioning systems in different climate conditions. A typical high-tech cleanroom air-conditioning system, i.e., MAU (make-up air unit) +DCC (dry cooling coil) +FFU (fan filter unit) system, is selected to examine the system performance using the EnergyPlus/Matlab platform. The results show that the monthly energy performance of the system could be modeled as a function of the weather condition. The system has the lowest energy consumption during the transition seasons throughout the year. It also shows better energy performance in hot and mild zones. The studied results could provide a general understanding of the energy performance of high-tech cleanroom air-conditioning systems in different climate and weather conditions.
Hydrogen is an alternative renewable energy resource. We report a new efficient formulation representation for the Gibbs free energy of hydrogen. The present prediction model is related to experimental data of three molecular structure constants of hydrogen. The developed prediction model is effective from a comparison of the theoretically calculated values with the data from the National Institute of Standards and Technology (NIST) database.
All numerical weather prediction models used for the wind industry need to produce their forecasts starting from the main synoptic hours 00, 06, 12, and 18 UTC, once analysis become available. The six-hour latency time between two consecutive model runs calls for strategies to fill the gap by providing new accurate predictions having, at least, hourly frequency. This is done to accommodate the request of frequent, accurate and fresh information from traders and system regulators to continuously adapt their work strategies. Here, we propose a strategy where quasi-real time observed wind speed and weather model predictions are combined by means of a novel Ensemble Model Output Statistics (EMOS) strategy. The success of our strategy is measured by comparisons against observed wind speed from SYNOP stations over Italy in the years 2018 and 2019.