All articles published by are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by , including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https:///openaccess.
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.
Editor’s Choice articles are based on recommendations by the scientific editors of journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.
Papermaking! Vol.4 No.1 2018 By Pita.co.uk
Plug-in electric vehicles are the currently favoured option to decarbonize the passenger car sector. However, a decarbonisation is only possible with electricity from renewable energies and plug-in electric vehicles might cause peak loads if they started to charge at the same time. Both of these issues could be solved with coordinated load shifting (demand response). Previous studies analysed this research question by focusing on private vehicles with domestic and work charging infrastructure. This study additionally includes the important early adopter group of commercial fleet vehicles and reflects the impact of domestic, commercial, work, and public charging. For this purpose, two models are combined that capture the market diffusion of electric vehicles and their charging behaviour (ALADIN), as well as the load shifting potential of several new energy technologies (eLOAD). In a comparison of three different scenarios, we find that the charging of commercial vehicles does not inflict evening load peaks in the same magnitude as purely domestic charging of private cars does. Also, for private cars, charging at work occurs during the day and may reduce the necessity of load shifting while public charging plays a less important role in total charging demand as well as load shifting potential. Nonetheless, demand response reduces the system load by about 2.2 GW or 2.8% when domestic and work charging are considered when compared to a scenario with only domestic charging where a new peak might be created in the winter hours due to load shifting into the night.
To attain the climate targets, it is necessary to transform the energy system. Renewable energy sources (RES) can help to decrease greenhouse gas emissions in the electricity sector. In the transport sector, plug-in electric vehicles (PEVs) can be a means to reduce greenhouse gas emissions if powered by electricity from RES. However, in a significant number, they risk causing additional load peaks that have to be balanced to ensure a stable electricity system. Ideally, electricity demand of PEVs and electricity generation of RES are coordinated e.g., by demand response. However, for this purpose, a sufficient charging infrastructure is needed. While most studies focus on domestic charging facilities [1] or include additional charging at work of private passenger cars [2], this paper also considers commercial plug-in electric vehicles (PEV) and the use of public charging stations. The aim of this paper is to assess the extent to which additional charging facilities contribute to PEV market penetration in Germany and the shaving of peaks in the residual load (system load minus generation of fluctuating renewable energies).
For this purpose, we combine two models that have been developed and described earlier: The model ALADIN (Alternative Automobiles Diffusion and Infrastructure) is used to determine the market diffusion of plug-in electric vehicles and their charging infrastructure. Also, the use of several types of charging infrastructure (domestic, work, and public), as well as different vehicle user groups (private, commercial fleet vehicles, and company cars) can be analysed. Structure and results of the model have been described in several publications [3, 4, 5]. The results can be taken as an input into the eLOAD (energy LOad curve ADjustment) model, which aims to analyse the load shift potential of several (new) technologies of which electric vehicles are one important technology [6]. This combination permits to provide a new contribution in this field since the potential of public charge shifting as well as the inclusion of commercial fleet vehicles has, to the best of the authors’ knowledge, not been analysed. In a case study, we apply the model to Germany and make projections for 2030.
Pdf) Reliability And Validity Ofan Adapted Questionnaireassessing Occupationalexposures To Hazardouschemicals Among Healthcare Workers In Bhutan
The paper is structured as follows: First, we introduce the methods in Section 2 and data sources in Section 3. Thereafter, assumptions for a case study for Germany in 2030 are presented in Section 4. The results are shown in Section 5 before we summarize and draw conclusions for electricity suppliers and policy makers in Section 6.
The market diffusion model ALADIN (Alternative Automobiles Diffusion and Infrastructure, Figure 1) is an agent-based simulation model that is based on a large number of vehicle driving profiles of conventional vehicles. The model was introduced in [5] and PEV market diffusion results were published in [4]. When considering the individual driving behaviour, the replaceability by a battery electric vehicle (BEV) is analysed and what share of electric driving (often called utility factor) could be obtained by a plug-in hybrid electric vehicle (PHEV). Based on this technical feasibility, the utility of four drive trains (Gasoline, Diesel, BEV, and PHEV) is calculated and compared. This utility consists of the total cost of ownership for the vehicle, but also contains the cost for individual charging points (at home or a designated charging point at work) as an obstructing factor and a willingness to pay more for a plug-in electric vehicle as a favouring factor. The share of driving profiles with PEVs as utility maximizing option is considered as their market share for vehicle sales, which diffuse into the vehicle stock.
An enhancement for the integration of public charging infrastructure was introduced in [3]. After the PEV diffusion, the charging behaviour of the vehicle stock at public charging points is simulated and used to determine the energy that is charged in public. Based on this figure, the number of profitable public charging points is calculated and constructed in the most frequented areas. This might also lead to a reduction of public charging points if the amount of public energy charged is not sufficient to cover their cost. The new public charging stock is considered in the individual simulation and may lead to a higher utility of PEVs. For an illustration of the model, refer to Figure 1.
Pdf) Prevalence Of Noise Exposure In Australian Workplaces
The second part of the model is necessary if public charging infrastructure is considered, since PEVs can only be charged in public if these charging points are free at the time a PEV arrives. Thus, they interact at public charging points, which makes a joint simulation necessary. This requires that geographic vehicle movements are included in the analysis.
ALADIN’s modelling quality has been validated by forecasting market shares of diesel vehicles for commercial passenger cars and comparing the results to statistical data. More detailed information on the model validation can be found in [3, 5].
The driving, charging, and different parking profiles (domestic, work, public), as well as the total number of PEVs and their electricity demand from ALADIN serve as an input for the eLOAD (energy LOad curve ADjustment) model [6]. In this study, eLOAD is used to determine the least-cost scheduling of PEV-charging depending on an hourly price signal. It thereby simulates the potential contribution of demand side technologies residual load smoothing (also known as demand response, DR).
Weekly Information Pack
ELOAD consists of two modules, see the dark blue and grey areas in Figure 2. The first module addresses the long term evolution of the national system load curve, which is driven by structural changes on the demand side and the introduction of new appliances (such as PEVs). By using appliance specific load profiles, such as typical day profiles, regression based load profiles etc., a yearlong load curve can be generated for all of the considered appliances for the base year. The load curve is then scaled according to the respective annual demand in the base year. These load curves are deduced from the system load curve of the base year. The resulting remaining load curve and the appliance specific load curves are then scaled for all of the projection years, according to the yearly demand evolution. Reassembling the scaled remaining load and the scaled load curves gives the load curve of the projection year.
The main advantage of this approach is its ability to properly take structural changes in the load curve into consideration by explicitly modelling the main drivers for load deformation while preserving stochastic outliers and characteristic irregularities from historic load curves.
The second module of eLOAD addresses the active adjustment of the load curve by means of DR. In this study, eLOAD optimizes the
Papierschmiede® Typografie Poster, Werde Besser (elegant), Din A4 (21x30 Cm), Wanddeko Büro, Küche, Wohnzimmer, Schwarz Weiß Bild Mit Spruch Ohne Rahmen
The second part of the model is necessary if public charging infrastructure is considered, since PEVs can only be charged in public if these charging points are free at the time a PEV arrives. Thus, they interact at public charging points, which makes a joint simulation necessary. This requires that geographic vehicle movements are included in the analysis.
ALADIN’s modelling quality has been validated by forecasting market shares of diesel vehicles for commercial passenger cars and comparing the results to statistical data. More detailed information on the model validation can be found in [3, 5].
The driving, charging, and different parking profiles (domestic, work, public), as well as the total number of PEVs and their electricity demand from ALADIN serve as an input for the eLOAD (energy LOad curve ADjustment) model [6]. In this study, eLOAD is used to determine the least-cost scheduling of PEV-charging depending on an hourly price signal. It thereby simulates the potential contribution of demand side technologies residual load smoothing (also known as demand response, DR).
Weekly Information Pack
ELOAD consists of two modules, see the dark blue and grey areas in Figure 2. The first module addresses the long term evolution of the national system load curve, which is driven by structural changes on the demand side and the introduction of new appliances (such as PEVs). By using appliance specific load profiles, such as typical day profiles, regression based load profiles etc., a yearlong load curve can be generated for all of the considered appliances for the base year. The load curve is then scaled according to the respective annual demand in the base year. These load curves are deduced from the system load curve of the base year. The resulting remaining load curve and the appliance specific load curves are then scaled for all of the projection years, according to the yearly demand evolution. Reassembling the scaled remaining load and the scaled load curves gives the load curve of the projection year.
The main advantage of this approach is its ability to properly take structural changes in the load curve into consideration by explicitly modelling the main drivers for load deformation while preserving stochastic outliers and characteristic irregularities from historic load curves.
The second module of eLOAD addresses the active adjustment of the load curve by means of DR. In this study, eLOAD optimizes the
0 komentar
Posting Komentar