Abstract

Climate change and CO2 emissions is an important issue on the agenda of many politicians. Trying to decrease CO2 emissions, influences transportation, power production, etc. The power system is characterised by an increasing amount of renewables, with one of the most expanding renewable power sources being wind. Wind energy is fluctuating by nature, calling for increasing flexibility elsewhere in the energy system. For Denmark, hydro power from Norway help stabilizing the system, as does export of excess wind to Germany, although the latter is decreasing in use because of large correlations between high wind production in northern Germany and western Denmark. To decrease CO2 emissions through a decrease in the use of fossil fuelled plants, along with an increase the amount of renewable energy, the power system needs more flexibility such as flexible demands, storage etc. Flexibility could also come from the road transport system. Counting for 24% of the CO2 emissions in Denmark in 2009, the road transport system needs to move towards, e.g. electric drive vehicles. However, the electric drive vehicles are also demanding electricity from the power system. This brings both challenges and opportunities to the power system. One challenge is, that intelligence is needed unless peak-load is to increase drastically. With intelligent charging of the vehicles, though, the electric drive vehicles can be of great benefit providing flexible demand and charging at night time, instead of being regarded as yet another load and challenge for the energy system. Furthermore, discharging of vehicles can provide services to the power system. The batteries in the electric drive vehicles are batteries invested in anyway. Hence, why not use these actively for cheap storage by the energy system? Furthermore, the use of vehicles are opposite to the remaining demand for energy; e.g. while people are making dinner their vehicles will often be parked, being able to deliver back-up power - again, a great opportunity for the power system. In this PhD project I have focussed on modelling and analysis of a future integrated transport and power system. An integrated power and transport system enables analyses of the interactions between different parts of the energy system. The object of interest is an optimal configuration of an integrated power and transport system as well as I will be focussing on the drawbacks and benefits for the power system incorporating an electrified transport system. I have performed analyses in terms of integrating more renewable energy, for both Denmark as an isolated system and for the northern European countries including Denmark, Sweden, Norway, Finland, and Germany. The analyses are performed using the deterministic energy systems analysis model, Balmorel. Furthermore, analyses have been made for the Irish power system on the influence of introducing electric drive vehicles in a predefined power system, using the stochastic energy systems analysis model, Wilmar. Interesting is, that it turns out to be most profitable to invest in enough wind to more than cover the electrified transport in Denmark. This holds, both when modelling Denmark as an isolated country, and when including the interactions between the Nordic countries. Furthermore, analyses show that fuel cell electric vehicles are not yet ready for competing with the other vehicle types. This is, among other things, due the technologies not being cheap enough, thus, the development is not expected to have reached a competitive stage. Another interesting finding is the results showing that it is beneficial for Ireland to have electric drive vehicles in terms of both costs and CO2 emissions. However, introducing the electric drive vehicles in Ireland, imply an increase in both costs and CO2 in the Great British side, as most of the power for the vehicles is produced on British coal power plants. Thus, focusing nationally, Ireland should invest in the electric drive vehicles, although, on an international level, the investments are costly. Document type: Book

Abstract

n increasing number of municipalities are striving for energy autonomy. This study determines in which municipalities and at what additional cost energy autonomy is feasible for a case study of Germany. An existing municipal energy system optimization model is extended to include the personal transport, industrial and commercial sectors. A machine learning approach identifies a regression model among 19 methods, which is best suited for the transfer of individual optimization results to all municipalities. The resulting levelized cost of energy (LCOE) from the optimization of 15 case studies are transferred using a stepwise linear regression model. The regression model shows a mean absolute percentage error of 12.5%. The study demonstrates that energy autonomy is technically feasible in 6,314 (56%) municipalities. Thereby, the LCOEs increase in the autonomous case on average by 0.41 €/kWh compared to the minimum cost scenario. Apart from energy demand, base-load-capable bioenergy and deep geothermal energy appear to have the greatest influence on the LCOEs. This study represents a starting point for defining possible scenarios in studies of future national energy system or transmission grid expansion planning, which for the first time consider completely energy autonomous municipalities.

Abstract

Large-scale adoption of clean energy technologies, such as photovoltaic (PV) solar panels and electric vehicles (EVs), is key for ensuring a sustainable future. However, integrating clean energy technologies in the existing electricity grid is challenging for grid management. Aligning EV charging demand with PV power production via smart charging or using EVs as electricity storage in vehicle-to-grid (V2G) systems can reduce the impact of both technologies on the grid. Realizing the potential of such systems requires consumers to adopt these technologies, shift EV charging demand, and allow for discharging of EVs; a radical change from current energy behaviours. The extent to which consumers will do so is a major source of uncertainty in assessing the future of sustainable energy systems. This thesis analyses and qualifies this uncertainty by developing simulation models that incorporate technical as well as social aspects of smart charging and V2G. This multi-disciplinary approach allows to study how consumer behaviour could shape sustainable energy systems. Furthermore, the thesis analyses large datasets on actual diffusion and use of PV solar panels and EVs in the Netherlands. The results show that there can be great benefits for grid management in combining intermittent renewable energy sources with EVs via smart and/or V2G. However, this can only be a partial solution for load balancing in scenarios with high renewable energy uptake. Furthermore, consumer adoption and charging behaviour varies greatly, which influences the extent to which the envisioned benefits of smart charging and V2G can be realized in certain regions and neighbourhoods.

Abstract

This paper gives information about Renewable Energy Generation and its impacts on E-Mobility. By 2050, the two pillars of modern transformation are E-Mobility and Renewable Energy. This transformation requires adaptation to meet demographic and economic growth without increasing pollution and congestion. The modern world needs affordable, secure and inclusive, sustainable and integrated with customer-centric infrastructure and services. There is a great fundamental change needed in road transportation sector, which is to achieve its objective of a long term transition to a low-carbon economy. Electric Vehicles when charged with the electricity generated from Renewable Energy Sources can reduce future emissions of greenhouse gases and air pollutants from road transport. Electric Vehicles combined with renewable energy paint a different picture from the oversimplification of gas miles versus electric miles.Smart Mobility and Smart Grid technologies make people live more sustainable and efficiently.

Abstract

Charging infrastructure for electric vehicles (EV) will be the key factor for ensuring a smooth transition to e-mobility. This paper focuses on five technologies that will play a fundamental role in this regard: smart charging, vehicle-to-grid (V2G), charging of EVs from photovoltaic panels (PV), contactless charging and on-road charging of EVs. Smart charging of EVs is expected to enable larger penetration of EVs and renewable energy, lower the charging cost and offer better utilization of the grid infrastructure. Bidirectional EV chargers will pave the way for V2G technology where the EV can be used for energy arbitrage and demand-side management. Solar charging of EV will result in sustainable transportation and use of the EV battery as PV storage. On the other hand, stationary contactless charging and on-road inductive charging of EV will remove the necessity for any cables, eliminate range anxiety issues and pave the way for automated driving. The electromagnetic and power converter design for contactless power transfer systems for future highways is reviewed in this paper.

Abstract

This study attempts to fit daily travel distances (DTD) data collected from the Nagoya University (NU) car-sharing system for one year to several distribution functions, including a lognormal mixture model. It is deemed here that the lognormal distribution performs best among the five tested single-distribution functions based on their "i"p"/i"-values. Moreover, the lognormal mixture model can represent the driving pattern better overall with respect to the Akaike information criterion (AIC). Taking two types of electric vehicles (EVs) into consideration, the results show that 30 out of 48 vehicles can be substituted by the EV type with a larger battery capacity according to the observed DTD data and when a 95% confidence level is considered. In this exercise, the updated car-sharing system can have up to nine available vehicles at peak hour, which can reach the peak-shaving need and provides the possibility of contributing electricity for common use with the help of the vehicle-to-grid (V2G) system. Additionally, the updated system with a larger battery capacity can also reduce 24% of the CO"sub"2"/sub" emissions. These types of systems could be widely applied to other organizations or companies in the consideration of electricity consumption and emission reduction. Document type: Article

Abstract

Vehicle-to-building (V2B) technology permits bypassing the power grid in order to supply power to a building from electric vehicle (EV) batteries in the parking lot. This paper investigates the hypothesis stating that the increasing number of EVs on our roads can be also beneficial for making buildings sustainably greener on account of using V2B technology in conjunction with local photovoltaic (PV) generation. It is assumed that there is no local battery storage other than EVs and that the EV batteries are fully available for driving, so that the EVs batteries must be at the intended state of charge at the departure time announced by the EV driver. Our goal is to exploit the potential of the EV batteries capacity as much as possible in order to permit a large area of solar panels, so that even on sunny days all PV power can be used to supply the building needs or the EV charging at the parking lot. A system architecture and collaboration protocols that account for uncertainties in EV behaviour are proposed. The proposed approach is proven in simulation covering one year period for three locations in different climatic regions of the US, resulting in the electricity bill reductions of 15.8%, 9.1% and 4.9% for California, New Jersey and Alaska, respectively. These results are compared to state-of-the-art research in combining V2B with PV or wind power generation. It is concluded that the achieved electricity bill reductions are superior to the state-of-the-art, because previous work is based on problem formulations that exploit only a part of the potential EV battery capacity. Document type: Article

Abstract

Electric vehicles (EVs) are recognized as promising options, not only for the decarbonization of urban areas and greening of the transportation sector, but also for increasing power system flexibility through demand-side management. Large-scale uncoordinated charging of EVs can impose negative impacts on the existing power system infrastructure regarding stability and security of power system operation. One solution to the severe grid overload issues derived from high penetration of EVs is to integrate local renewable power generation units as distributed generation units to the power system or to the charging infrastructure. To reduce the uncertainties associated with renewable power generation and load as well as to improve the process of tracking Pareto front in each time sequence, a predictive double-layer optimal power flow based on support vector regression and one-step prediction is presented in this study. The results demonstrate that, through the proposed control approach, the rate of battery degradation is reduced by lowering the number of cycles in which EVs contribute to the services that can be offered to the grid via EVs. Moreover, vehicle to grid services are found to be profitable for electricity providers but not for plug-in electric vehicle owners, with the existing battery technology and its normal degradation. Document type: Article

Abstract

The uncertainties brought by intermittent renewable generation and uncoordinated charging behaviors of EVs pose great challenges to the reliable operation of power systems, which motivates us to explore the integration of robust optimization with energy scheduling in V2G networks. In this article, we first introduce V2G robust energy scheduling problems and review the stateof- the art contributions from the perspectives of renewable energy integration, ancillary service provision, and proactive demand-side participation in the electricity market. Second, for each category of V2G applications, the corresponding problem formulations, robust solution concepts, and design approaches are described in detail based on the characteristics of problem structures and uncertainty sets. Then, an adjustable robust energy scheduling solution is proposed to address the over-conservatism problem by exploring chance-constrained methods. Results demonstrate that the proposed algorithm not only can efficiently shift the peak load and reduce the total operation cost, but also provide great flexibility in adjusting the trade-off between economic performance and reliable operation. Finally, we present key research challenges and opportunities.

Abstract

The Parker project sought to experimentally validate that contemporary series-produced electric vehicles (EVs), capable of V2G, are ready to participate in a number of advanced grid services. In such services, the timing, size and direction of power and energy exchanged between the EV battery and grid is controlled as to support either a single building, the local neighborhood or the regional power system. Vehicles purposely designed for such services are referred to as grid integrated electric vehicles (GIVs). The field of research, describing how GIVs may be used to actively support the power system, is called Vehicle&ndash Grid Integration (VGI).The purpose of this paper is to present how the Danish Parker project has systematically categorized a range of grid services, collected in a service catalog, and then illustrate state-of-the-art EVs ability to support such services through experimental validation. Results are presented for three different tests performed in Parker marginal emission factor charging, frequency containment reserves and a performance test for controlling power setpoints. The ultimate aim of this paper, and the Parker project, is to promote the GIV concept so that it may inform the design and capabilities of present and future EVs, EV supply equipment (EVSE) and communication standards. Document type: Article