Impact of vehicle charging on Portugal's national electricity load profile in 2030

2021 ◽  
Vol 73 ◽  
pp. 101310
Author(s):  
Tiago Meintjes ◽  
Rui Castro ◽  
A.J. Pires
2016 ◽  
Vol 178 ◽  
pp. 647-659 ◽  
Author(s):  
Yue Xiang ◽  
Junyong Liu ◽  
Ran Li ◽  
Furong Li ◽  
Chenghong Gu ◽  
...  

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3543
Author(s):  
Angreine Kewo ◽  
Pinrolinvic D. K. Manembu ◽  
Per Sieverts Nielsen

It is important to understand residential energy use as it is a large energy consumption sector and the potential for change is of great importance for global energy sustainability. A large energy-saving potential and emission reduction potential can be achieved, among others, by understanding energy consumption patterns in more detail. However, existing studies show that it requires many input parameters or disaggregated individual end-uses input data to generate the load profiles. Therefore, we have developed a simplified approach, called weighted proportion (Wepro) model, to synthesise the residential electricity load profile by proportionally matching the city’s main characteristics: Age group, labour force and gender structure with the representative households profiles provided in the load profile generator. The findings indicate that the synthetic load profiles can represent the local electricity consumption characteristics in the case city of Amsterdam based on time variation analyses. The approach is in particular advantageous to tackle the drawbacks of the existing studies and the standard load model used by the utilities. Furthermore, the model is found to be more efficient in the computational process of the residential sector’s load profiles, given the number of households in the city that is represented in the local profile.


2011 ◽  
Vol 7 (2) ◽  
pp. 151-156
Author(s):  
A. M. Ihbal ◽  
H. S. Rajamani ◽  
R.A. Abd-Alhameed ◽  
M. K. Jalboub

This paper presents a method of generating realistic electricity load profile data for the UK domestic buildings. The domestic space features have been investigated excluding the heating and hot water systems. A questionnaire survey was conducted and the feedback were collected from a number of occupants at different intervals of times on daily bases in order to establish the probabilistic record of the estimated use of electrical appliances. The model concept of this study also considers the results of previous investigations such as that available in public reports and statistics as input data elements to predict the global domestic energy consumption. In addition, the daily load profile from individual dwelling to community can be predicted using this method. The result of the present method was compared to available published data and has shown reasonable agreement.


Data in Brief ◽  
2020 ◽  
Vol 30 ◽  
pp. 105531 ◽  
Author(s):  
Kevin Enongene Enongene ◽  
Fonbeyin Henry Abanda ◽  
Iduh Jonathan Joseph Otene ◽  
Sheila Ifeakarochukwu Obi ◽  
Chioma Okafor

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 4038 ◽  
Author(s):  
Alejandro Pena-Bello ◽  
Edward Barbour ◽  
Marta C. Gonzalez ◽  
Selin Yilmaz ◽  
Martin K. Patel ◽  
...  

Energy storage is a key solution to supply renewable electricity on demand and in particular batteries are becoming attractive for consumers who install PV panels. In order to minimize their electricity bill and keep the grid stable, batteries can combine applications. The daily match between PV supply and the electricity load profile is often considered as a determinant for the attractiveness of residential PV-coupled battery systems, however, the previous literature has so far mainly focused on the annual energy balance. In this paper, we analyze the techno-economic impact of adding a battery system to a new PV system that would otherwise be installed on its own, for different residential electricity load profiles in Geneva (Switzerland) and Austin (U.S.) using lithium-ion batteries performing various consumer applications, namely PV self-consumption, demand load-shifting, avoidance of PV curtailment, and demand peak shaving, individually and jointly. We employ clustering of the household’s load profile (with 15-minute resolution) for households with low, medium, and high annual electricity consumption in the two locations using a 1:1:1 sizing ratio. Our results show that with this simple sizing rule-of-thumb, the shape of the load profile has a small impact on the net present value of batteries. Overall, our analysis suggests that the effect of the load profile is small and differs across locations, whereas the combination of applications significantly increases profitability while marginally decreasing the share of self-consumption. Moreover, without the combination of applications, batteries are far from being economically viable.


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