An energy components analysis for stop-to-stop drive segments is provided. In particular, the components of rolling resistance, air drag, and kinetic energy are examined for conventional and optimal segments. It is shown that in energy-optimal driving, the maximum kinetic energy is reached early in the drive segment and then it is converted into work to overcome air drag and rolling resistance; in a sense, it is recovering kinetic energy. In conventional profiles, the maximum speed is attained late in the profile and thus cannot be used to cover much of the air drag and rolling resistance energy cost. The role of the initial acceleration is shown to play a key role, especially for short segments. These results are illustrated through several simulation examples.
Heat, cooling, and ventilation units are major energy consumers for commercial buildings, consuming as much as 50% of a building’s total annual power usage. Management of an air handling system’s energy is a key factor of reducing the energy costs and carbon dioxide (CO2) emissions that are associated with the demand when ventilating and conditioning the air in a building. One issue is that buildings are frequently over ventilated as a full assessment of the air handling unit (AHU) data is not evaluated by building operators. There are multiple variables that account for energy consumption of the AHU which need to be monitored by building operators. In order to assess the demand, it is required that the CO2 levels of the occupied zones be measured, and the outdoor air ventilation rate be adjusted based on real-time CO2. The concept of an energy management system and its characteristics are defined in respect to use with an AHU system. The prototype system used for the research is demonstrated and key data analyzed using real-time data collection. The goal of the research is to assess the number of CO2 sensors needed to accurately measure the demand-based needs for ventilation and provide review of the data required to monitor the AHU energy. Findings indicate that no more than one CO2 sensor would be required for a large lecture hall.
We describe a roadmap, based on a series of workshops and studies, to use base-load nuclear reactors to replace fossil fuels in a low-carbon world that integrates nuclear, wind, solar, hydro-electricity and biomass energy sources. Nuclear reactors with large-scale heat storage enable variable electricity to the grid with nuclear plants that both buy and sell electricity. The low-cost heat storage and assured generating capacity enables efficient use of largescale wind and solar. Nuclear hydrogen production facilities at the scale of global oil refineries produce hydrogen to replace natural gas as a heat source. Nuclear heat and hydrogen convert plant biomass into drop-in hydrocarbon biofuels to replace gasoline, diesel, jet fuel and hydrocarbon feed stocks for the chemical industry. The external heat and hydrogen greatly increases the quantities of biofuels that can be produced per unit of feedstock. The system can produce variable quantities of biofuels and sequestered carbon dioxide that enables negative carbon dioxide emissions and increases revenue if there is a market for removing carbon dioxide from the atmosphere.
This research firstly proposed a in situ hydrogen generation to convert glucose to sorbitol via Mg scarification. This process features the utilization of Mg powder as the hydrogen activator, water as the hydrogen source, commercial Ru/C as the catalyst, allowing the hydrogenation to occur under mild reaction conditions without the external supply of hydrogen. Efficient production of hydrogen inside the reaction solvent results in a glucose conversion rate of 96% as well as the sorbitol selectivity of 92% over Ru/C at 110 °C for 2 h. The hydrogenation behavior of this system is examined under the gradient Mg dosage, Ru/C loading, and reaction temperature. A wide application scope has been verified, which containing fructose, mannose, galactose, xylose, and cellobiose. Furthermore, the Ru/C applied in this process can be reutilized successfully without sacrificing its high catalytic activity and sorbitol selectivity. This hydrogenation system makes significant advances by removing the dependence on the external supply of high-pressure hydrogen. This is the first study of a self-propelling, highly active process to highly selective hydrogenation of sugars to polyol under mild conditions, thus providing an economical, safe, and sustainable alternative to conventional hydrogenation methods.
In its reports, the IPCC demands new prosperity ideas (i.e., new post-growth economic models (change of lifestyle, institutional innovations and networks of neighbourhood help
)) to solve the climate crisis [2, 3]. Therefore a Keynes sector covering non-market economic activity is modelled in an intertemporal dynamic multinational Computable General Equilibrium model (CGE model) . The CGE model consists of four countries (A, B, C, D) with three economic sectors (Food-Energy-Water (FEW)-sector, public and private service,
industry) each and country D has a derived Keynes sector representing the ideas of the post growth approach .
We discuss the economic effects of our CGE model in a scenario where the countries’ growth rates differ: Country A follows a zero-growth pathway, B and C grow by moderate rates, and country D is on a de-growth scenario. Using the CGE model, we discuss the implementation of a Keynes sector in an open economy with trade relations to the other three countries. This model approach reveals the possible socio-economic consequences and alterations of various growth models for the FEW Nexus sector, as well as the other economic sectors of the four countries.
The work to be presented is a comparative analysis of deep decarbonization strategies for the natural gas grid. The analysis is based on California supply and demand scenarios and unit costs, but results are broadly applicable to other markets seeking deep decarbonization. To achieve deep reductions in economy-wide GHG emissions, the fuel delivered over the natural gas system must be replaced by zero or near-zero-carbon substitutes. Electrification of many end uses will reduce the need for gaseous fuel over time. However, the least-cost approach to economy-wide decarbonization will likely include continued use of decarbonized forms of methane and expansion of the use of hydrogen for a range of applications. Low-carbon gaseous fuels are well suited for current uses of natural gas, those of conventional hydrogen (predominantly refining and ammonia production) and applications served by liquid fuels. Hydrogen and methane can be decarbonized through production pathways that use renewable energy sources and feedstocks, or through carbon capture and sequestration in geological formations or solid products. The presentation will compare the long-term costs of alternative strategies for decarbonizing the gas grid, including cost of potential transition from natural gas to pure hydrogen. Preliminary results show that decarbonized hydrogen is the most cost effective energy vector to serve zero-carbon gaseous fuel demand in the deeply decarbonized future economy.
As an important part of the energy system, energy storage system, especially with the increasing popularity of renewable energy, has become more and more important. Subsequently, the problems of supervision and control of the energy storage system have become increasingly prominent. Temperature regulation is an important part. Many methods have been proposed to predict the temperature of the battery energy storage system. At present, it is mainly divided into the method based on electrothermal model and the method based on data-driven. In this paper, firstly, a two-node electrothermal model is established. Then an integrated network with dual inputs and dual long and short-term memory networks is established. Finally, the adaptive boosting algorithm is used to modify the prediction results of the surface temperature of the battery energy storage system. The experimental results show that the proposed coupling model is effective and progressive.
The existing numerical and analytical models for fluid circulation in wellbores provide the necessary foundation for exploring geothermal energy prospects for generating power or hot water for various industrial usage. A steady-state fluid circulation rate in a closed-loop system provided insights into power generation’s efficacy in previous studies. Lately, the introduction of analytical modeling paved the way for exploring realistic scenarios for the time-variant geothermal gradient at a well’s proximity.
This article attempts to provide a roadmap for geothermal energy extraction by invoking the cyclical fluid circulation strategy for ensuring a stable surface fluid temperature or power. Both increasing and decreasing stepwise rate sequence provides the desired outcome. This rate-sequencing approach leads to assessing the value proposition of proposed thermal-energy extraction strategy in various North American basins. For a given depth, the overall thermal prospect depends on a well’s geographic location. Given the abundance of abandoned wells in oil fields, this study explores retrofitting such wells and drilling designed wells in geothermal-friendly areas to compare their relative economic value propositions.
This paper systematically investigates how the wind turbine relative position influences the power output of the wind farm. Firstly, an engineering three-dimensional (3-D) wind turbine wake model is introduced. The novel 3-D wake model is relatively accurate, and can quickly predict wind speed at any downstream position and at any height. Then, based on the wake model, how the hub heights and relative position of wind turbines affect the wind speed and power are deeply studied. The influence depends largely on the specific situation. Especially when decreasing the hub height of downstream wind turbine, the impact of wake can be reduced, but the equivalent wind speed will be decreased as well. Therefore, the influence should be evaluated according to the specific hub height change and the relative downstream distance.
The COVID-19 pandemic has accelerated and deepened crises in many parts of the world, while also raising questions of global equity in the context of vaccine distribution. However, it is only one of many daunting challenges faced daily by those in protracted crises. Refugee camps and other zones of humanitarian intervention serving displaced populations are among the hardest to plan for, given the operational complexities — both immediate and protracted — associated with infrastructure deployment and the maintenance that such forms of distribution require. ‘Containerized’ infrastructure solutions have the potential to power the needs of under-resourced communities at the Food/Water/Health nexus and have gained interest in recent years as a way to mediate the temporal and political uncertainties associated with basic needs provision for off-grid, underserved, or remote populations. Drawing from a uniquely large sample of identical containerized infrastructure deployments in Rwanda, we estimate the potential reach and impact that a massive scale-up of such a flexible, modular approach could entail for fast-growing yet resource-constrained communities around the world. We consider three separate use cases and find in optimistic scenarios that this containerized solution could provide for either 2,083 people’s daily drinking water needs, 1,674 people’s daily milk consumption, or 100% of a health clinic’s energy demand.