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Electric vehicle seems to go well together with the growing societal trend of becoming more self-supplying with renewable electricity produced in the household. However, aligning household electricity production and electric vehicle charging have received little attention in HCI although both areas have been pursued separately for a number of years. In this paper, we present findings from a qualitative study that explore the potential of aligning electric vehicle charging with times where renewable electricity is being produced in the household. We present an empirical qualitative study of 5 households (19 persons) that own electric vehicles and also produce their own renewable electricity. Our findings, described in five themes, reveal that aligning charging and electricity production can be a challenge and tension exist for aligning consumption such as motivation, roles, mobility patterns, and electricity producing technology. We further discuss our findings and possible directions for future HCI research in the field.

Climate change is driving compositional shifts in ecological communities directly by affecting species and indirectly through changes in species interactions. For example, competitive hierarchies can be inversed when competitive dominants are more susceptible to climate change. The brown seaweed Fucus vesiculosus is a foundation species in the Baltic Sea, experiencing novel interactions with the invasive red seaweed Gracilaria vermiculophylla, which is known for its high tolerance to environmental stress. We investigated the direct and interactive effects of warming and co-occurrence of the two algal species on their performance, by applying four climate change-relevant temperature scenarios: 1) cooling ) 2 °C below ambient - representing past conditions), 2) ambient summer temperature (18 °C), 3) IPCC RCP2.6 warming scenario (1 °C above ambient), and 4) RCP8.5 warming (3 °C above ambient) for 30 days and two compositional levels (mono and co-cultured algae) in a fully-crossed design. The RCP8.5 warming scenario increased photosynthesis, respiration, and nutrients' uptake rates of mono- and co-cultured G. vermiculophylla while growth was reduced. An increase in photosynthesis and essential nutrients' uptake and, at the same time, a growth reduction might result from increasing stress and energy demand of G. vermiculophylla under warming. In contrast, the growth of mono-cultured F. vesiculosus significantly increased in the highest warming treatment (+3 °C). The cooling treatment (-2 °C) exerted a slight negative effect only on co-cultured F. vesiculosus photosynthesis, compared to the ambient treatment. Interestingly, at ambient and warming (RCP2.6 and RCP8.5 scenarios) treatments, both F. vesiculosus and G. vermiculophylla appear to benefit from the presence of each other. Our results suggest that short exposure of F. vesiculosus to moderate or severe global warming scenarios may not directly affect or even slightly enhance its performance, while G. vermiculophylla net performance (growth) could be directly hampered by warming.

The advantages of real-time dataset with artificial intelligence-based machine learning algorithms can be utilized for improving energy management in Electric Vehicles (E-Vehicle). Here the machine learning algorithms such as classification, regression, reinforcement, and clustering are incorporated to identify the suitable profile pattern. Electric vehicle modelling can be developed from profile, outline, and formulation for energy efficiency can be done from vehicle state, specifications from acceleration and deceleration with necessary constraints. Each vehicle energy management stages are realized as kinetic energy, mechanical energy, and electrical energy for minimizing the losses within a boundary except proving the constraints. With a problem formulated in each stage for classifying the input profile, it is found that regression analysis matches the energy optimization, reinforcement technique fixes the boundary at every profile input and clustering of data ensures formation of data set. The proposed scheme is validated with MATLAB/Simulink environment and the results shows that the new methods improved the energy efficiency in every stage at the level of 10 to 30%. Also, among the energy efficient techniques, classification and regression schemes adds an error loss of 0.2 to 0.3%, the reinforcement method includes the data handling error of 0.3 to 0.4% and finally, the clustering method increases the error loss in the level of 0.4 to 0.5%.

Abstract Fuel Cell Vehicles (FCVs) has been introduced to the market around the world in recent years. As the largest automobile market of the world, China is also one of the potential FCVs market. However, a series of factors and barriers influence the willingness of China's customers to accept FCVs. By using Fishbone Diagram, field survey and workshop discussions, this paper proposes a group of factors that may affect customers' preferences on FCVs. Furthermore, Fuzzy AHP and Pareto Analysis are employed to prioritize these factors, and identify the critical ones. The results indicate that fuel availability, vehicle performance, and economic costs are the most important dimensions in affecting customers' attitude towards FCVs. More specifically, vehicle reliability and safety, purchasing cost, industry development, vehicle model and space contribute the most significance in customers' purchase decision. According to the results, some policy implications are proposed from the prospective of improving and demonstrating vehicle performance, government leading facility construction and operation, and costs reductions.

This chapter examines and deduces the essence from a series of studies of the large-scale integration of renewable energy sources into national energy systems. In addition to the presentation of these studies, this chapter presents a method for comparing different energy systems in terms of their ability to integrate renewable energy on a large scale. The question to be answered is how to design energy systems with a high capability of utilizing intermittent and fluctuating renewable energy sources, such as wind power. The chapter concludes with a series of recommendations concerning the most feasible technical measures, how to combine the measures, and when to use them considering the share of renewable energy in the system.

Energy is an essential ingredient of socio-economic development and economic growth. Renewable energy sources like wind energy is indigenous and can help in reducing the dependency on fossil fuels. Wind is the indirect form of solar energy and is always being replenished by the sun. Wind is caused by differential heating of the earth's surface by the sun. It has been estimated that roughly 10 million MW of energy are continuously available in the earth's wind. Wind energy provides a variable and environmental friendly option and national energy security at a time when decreasing global reserves of fossil fuels threatens the long-term sustainability of global economy. This paper reviews the wind resources assessment models, site selection models and aerodynamic models including wake effect. The different existing performance and reliability evaluation models, various problems related to wind turbine components (blade, gearbox, generator and transformer) and grid for wind energy system have been discussed. This paper also reviews different techniques and loads for design, control systems and economics of wind energy conversion system.

The Danish power system is characterized by a large penetration of wind power. As the nature of the wind power is unpredictable, more balancing power is desired for a stable and reliable operation of the power system. The present balancing power in Denmark is provided mostly by the large central power plants followed by a number of decentralised combined heat and power (CHP) units and connections from abroad. The future energy plans in Denmark aims for 50% wind power capacity integration which will replace many conventional large power plant units. The limited control and regulation power capabilities of large power plants in the future, demands for new balancing solutions like Vehicle-to-Grid systems. In this article, aggregated electric vehicle based battery storage representing a Vehicle-to-Grid system is modelled for the use in long term dynamic power system simulations. Further, it is analysed for power system regulation services for typical days with high and low wind production in the Western Danish power system. The results shows that the regulation needs from conventional generators and the power deviations between West Denmark and UCTE (Union for the Coordination of Electricity Transmission) control areas are significantly minimized by the faster up and down regulation characteristics of the electric vehicle battery storage.

This paper nuances the claim that electric vehicles offer a similar form of automobility as petrol cars through an analysis of the (im)mobility and circulations that enable them. With circulation a...