Reliable Dispatch of Renewable Generation via Charging of Time-Varying PEV Populations

Appino, Riccardo Remo; Munoz-Ortiz, Miguel; Ordiano, Jorge Ángel González; Mikut, Ralf; Hagenmeyer, Veit; Faulwasser, Timm

doi:10.1109/tpwrs.2018.2873409

Cited by 17 publications

(9 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the original charging time lasts less than 15 min, it is rounded up to a full 15-min interval. Otherwise, a modulo operation, mod, is performed and the modified charging time, t new , is calculated according to (7).…”

Section: F I G U R Ementioning

confidence: 99%

Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory: A case study from Finland

Unterluggauer

Rauma

Järventausta

et al. 2021

IET Electrical Syst in Trans

View full text Add to dashboard Cite

This study assesses the performance of a multivariate multi-step charging load prediction approach based on the long short-term memory (LSTM) and commercial charging data. The major contribution of this study is to provide a comparison of load prediction between various types of charging sites. Real charging data from shopping centres, residential, public, and workplace charging sites are gathered. Altogether, the data consists of 50,504 charging events measured at 37 different charging sites in Finland between January 2019 and January 2020. A forecast of the aggregated charging load is performed in 15min resolution for each type of charging site. The second contribution of the work is the extended short-term forecast horizon. A multi-step prediction of either four (i.e., one hour) or 96 (i.e., 24 h) time steps is carried out, enabling a comparison of both horizons. The findings reveal that all charging sites exhibit distinct charging characteristics, which affects the forecasting accuracy and suggests a differentiated analysis of the different charging categories. Furthermore, the results indicate that the forecasting accuracy strongly correlates with the forecast horizon. The 4-time step prediction yields considerably superior results compared with the 96-time step forecast.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: F I G U R Ementioning

confidence: 99%

Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory: A case study from Finland

Unterluggauer

Rauma

Järventausta

et al. 2021

IET Electrical Syst in Trans

View full text Add to dashboard Cite

show abstract

“…Research papers to model the energy demand caused by charging EVs already exist. They differentiate among approaches to predict and control the grid condition [11,34,35,14], and approaches for dimensioning and locating new charging stations [36,37]. Stochastic assumptions to describe the EV user behavior are a major deficiency of these approaches.…”

Section: Dynamic Charging Energy Mapmentioning

confidence: 99%

“…To avoid these issues, power suppliers may influence the EV users' affection to connect their vehicles via dynamic energy prices [9,10,12]. At the same time, multiple EVs connected to the grid-especially if bi-directional charging is enabled [13]-help power suppliers to level out imbalances due to renewable energy generation [14].…”

Section: Introductionmentioning

confidence: 99%

Data-driven charging strategies for grid-beneficial, customer-oriented and battery-preserving electric mobility

Schwenk¹,

Harr²,

Großmann³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

“…For weather forecasting methods we refer to [14]. How to integrate electrical vehicles into the electricity network under uncertainties is described in [15].…”

Section: Introductionmentioning

confidence: 99%

Surrogate models in bidirectional optimization of coupled microgrids

Baumann

Grundel

Sauerteig

et al. 2019

At - Automatisierungstechnik

View full text Add to dashboard Cite

The energy transition entails a rapid uptake of renewable energy sources. Besides physical changes within the grid infrastructure, energy storage devices and their smart operation are key measures to master the resulting challenges like, e. g., a highly fluctuating power generation. For the latter, optimization based control has demonstrated its potential on a microgrid level. However, if a network of coupled microgrids is considered, iterative optimization schemes including several communication rounds are typically used. Here, we propose to replace the optimization on the microgrid level by using surrogate models either derived from radial basis functions or neural networks to avoid this iterative procedure. We prove well-posedness of our approach and demonstrate its efficiency by numerical simulations based on real data provided by an Australian grid operator.

show abstract

Reliable Dispatch of Renewable Generation via Charging of Time-Varying PEV Populations

Cited by 17 publications

References 31 publications

Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory: A case study from Finland

Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory: A case study from Finland

Data-driven charging strategies for grid-beneficial, customer-oriented and battery-preserving electric mobility

Surrogate models in bidirectional optimization of coupled microgrids

Contact Info

Product

Resources

About