Impact of electric vehicles charging demand on distribution transformers in an office area and determination of flexibility potential

Berg, M.A. van den; Lampropoulos, Ioannis; AlSkaif, Tarek

doi:10.1016/j.segan.2021.100452

Cited by 26 publications

(7 citation statements)

References 24 publications

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“…The EVs have a high potential to decrease greenhouse gas emissions and to attain other benefits such as promoting energy security and reduction in local air pollution [145] . The penetration of the EV appears to be a future reality in the global transportation sector, which comprises of substantial increment in electricity demand [146] .…”

Section: Results and Analysis Of The Electric Energy Demand In The Li...mentioning

confidence: 99%

The progress, impact analysis, challenges and new perceptions for electric power and energy sectors in the light of the COVID-19 pandemic

Çelík

Meral

Waseem

2022

Sustainable Energy, Grids and Networks

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Section: Results and Analysis Of The Electric Energy Demand In The Li...mentioning

confidence: 99%

The progress, impact analysis, challenges and new perceptions for electric power and energy sectors in the light of the COVID-19 pandemic

Çelík

Meral

Waseem

2022

Sustainable Energy, Grids and Networks

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“…The losses were eliminated by the correct sitting and sizing of DGs. Berget al [23] have suggested a method for calculating the future EV fleets demand and the flexibility in the office area. Compared to residential charging, office charging was different.…”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

Location Determination of Electric Vehicles Parking Lot With Distribution System by Mexican AXOLOTL Optimization and Wild Horse Optimizer

et al. 2022

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In this manuscript proposes a hybrid approach for locating and sizing Electric vehicle charging stations (EVCSs) optimally and managing the vehicle charging process. The proposed hybrid approach is to work in conjunction with Mexican Axolotl optimization (MAO) and Wild Horse Optimizer (WHO) hence it is named as MAOWHO approach. The major purposes of the proposed approach are to site and size of the electric vehicle parking lot (EVPL) and to improve the benefit of EVPL for the participation of the reserve market. In addition, power loss and voltage fluctuations occur due to the stochastic nature of renewable energy sources (RES) and electric vehicles (EV) demand load, which is reduced by the proposed approach. To optimally determine the size of the parking lot, the MAOWHO approach is adopted. The integration of the EV and PV systems, especially in parking lots, enhances the reliability and flexibility of the electrical system at critical moments. Multiple objective optimization problems are calculated to achieve objective variables to reduce power losses, voltage fluctuations, charging and supply costs, and EV costs. The location and capacity of the RES and EV charging stations in this optimization problem are objective variables. The MAOWHO approach enhances Solar Powered Electric Vehicle Parking Lot (EVSPL) participation in various energy and ancillary service markets that includes the effects of capacity payments. Besides, the implementation of MAOWHO approach is done by the MATLAB/Simulink platform and the performance of the MAOWHO approach is compared to the existing approaches. From the simulation outcome, it concludes that the proposed approach based performance provides a profit of 880 € compared to other approaches like SMO, CGO, SBLA.

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“…Generally, the annual time resolution studies the peak load during a selected day or week. In the case of HP studies, a typical cold winter day is generally used to analyse large network areas across a province or nation [23][24][25]. Top-down approaches using general HP properties are prevailing for long-term studies.…”

Section: Introductionmentioning

confidence: 99%

Medium‐term forecasting of daily aggregated peak loads from heat pumps using clustering‐based load duration curves to calculate the annual impact on medium to low voltage transformers

Rouwhorst,

Pondes,

Nguyen

et al. 2023

IET Smart Grid

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The energy transition drives the adoption of heat pumps (HPs). Their peak loads have a large impact on distribution networks. Therefore, the proposed methodology calculates the annual impact on medium to low voltage (MV/LV) transformers based on a medium‐term forecast of daily aggregated peak loads from HPs using the number, their rated power, a normalised load duration curve, and the daily average ambient temperature. The forecast differentiates between daily aggregated peak loads of HPs with different heating demands and the varying impact due to seasonal weather differences. First, 221 measured residential HPs over two years were randomly selected and clustered to identify different heating demands. These clusters were used to calculate four representation functions. Second, 108 other measured HPs were clustered and used to forecast the aggregated peak load with the calculated four representation functions. These forecasts were used to calculate the annual impact on an MV/LV transformer with a daily time resolution. These results indicate the periods over a year that the MV/LV transformer is at risk of congestion and it indicates the potential to mitigate congestion through demand‐side management.

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Impact of electric vehicles charging demand on distribution transformers in an office area and determination of flexibility potential

Cited by 26 publications

References 24 publications

The progress, impact analysis, challenges and new perceptions for electric power and energy sectors in the light of the COVID-19 pandemic

The progress, impact analysis, challenges and new perceptions for electric power and energy sectors in the light of the COVID-19 pandemic

Location Determination of Electric Vehicles Parking Lot With Distribution System by Mexican AXOLOTL Optimization and Wild Horse Optimizer

Medium‐term forecasting of daily aggregated peak loads from heat pumps using clustering‐based load duration curves to calculate the annual impact on medium to low voltage transformers

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