Analysis of residential EV energy flexibility potential based on real-world charging reports and smart meter data

Sørensen, Åse Lekang; Lindberg, Karen Byskov; Sartori, Igor; Andresen, Inger

doi:10.1016/j.enbuild.2021.110923

Cited by 49 publications

(20 citation statements)

References 31 publications

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“…Therefore, given the importance of a priori knowledge of the daily load profiles, in literature can be found many studies that try to assess the effects of EVs charging on the grid. Most of them focus on residential charging [15] [16] [17]. For instance, in [15], the authors compare time-series techniques and machine learning methods used to forecast the growth in building power consumption caused by the rising of EVs chargers.…”

Section: Load Power Demandmentioning

confidence: 99%

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Multi-Objective Optimization of PV and Energy Storage Systems for Ultra-Fast Charging Stations

et al. 2022

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The installation of ultra-fast charging stations (UFCSs) is essential to push the adoption of electric vehicles (EVs). Given the high amount of power required by this charging technology, the integration of renewable energy sources (RESs) and energy storage systems (ESSs) in the design of the station represents a valuable option to decrease its impact on the grid and the environment. Therefore, this paper proposes a multi-objective optimization problem for the optimal sizing of photovoltaic (PV) system and battery ESS (BESS) in a UFCS of EVs. The proposed multi-objective function aims to minimize, on one side, the annualized cost of the station, and on the other side, the produced pollutant emissions. The decision variables are the number of PV panels and the capacity of the ESS to be installed. The optimization problem is reduced to a single-objective problem by applying the linear scalarization method. Then the equivalent singleobjective function is optimized through a genetic algorithm (GA). The proposed optimization framework is applied to a study case and the results prove that PV and ESS could lead to a significant reduction of both the annualized cost and the pollutant emissions. Finally, a sensitivity analysis is also presented to validate the effectiveness of the proposed solution.INDEX TERMS extreme fast charging, integrated charging station, bi-objective optimization, electric vehicles, fast-charging load demand.

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Section: Load Power Demandmentioning

confidence: 99%

“…Therefore, the ESS residual value will be nil too. The final formula to compute the annual cost of each considered component is given in (17) and (18).…”

Section: ) Economic Functionmentioning

confidence: 99%

Multi-Objective Optimization of PV and Energy Storage Systems for Ultra-Fast Charging Stations

et al. 2022

View full text Add to dashboard Cite

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“…The model is used to analyze the characteristics of EV charging demand in a geographical area. An EV charging dataset that is more focused on the user behavior in residential areas is provided by Sorensen et al [15,16]. The authors analyze the charging habits and electricity load profiles of EV charging in apartment buildings in Norway.…”

Section: Literature Review 21 Electric Vehicle Charging Data Analysismentioning

confidence: 99%

Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data

Barthel

Schlund

Landes³

et al. 2021

Energies

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A successful transformation of the energy and transportation sector is one of the main targets for our society today. Battery electric vehicles can play a key role in future renewable-based energy supply systems because of their ability to store electrical power. Additionally, they provide significant charging flexibility due to the long parking durations. In this paper, we provide insights into the temporal and power-specific flexibility behavior of three different vehicle fleets. These fleets are pool vehicles of office employees, a public authority, and a logistics company. Several parameters, such as the average charging power per charging event or the average plug-in duration per charging event, are discussed. Additionally, we investigate different charging rates and their impact on the temporal flexibility of the charging events. The data analysis shows that the logistics site has the most homogeneous charging profile as well as high charging flexibility, in contrast to the office and public agency site. The results are of significant importance for future applications in the field of smart charging and ancillary services provision.

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“…In this model, they considered various random parameters that affected the charging profile, such as charging power level, initial SOC, start charging time, and charging period. In [27], the EV charging model was designed based on real EV charging data collected from a residential housing company in Norway. The authors used the plug-in duration and the energy charged information as input parameters to the proposed model to determine the EV owner's charging behavior.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Charging Model in the Urban Residential Sector

et al. 2022

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Electric vehicles (EVs) have become increasingly popular because they are highly efficient and sustainable. However, EVs have intensive electric loads. Their penetrations into the power system pose significant challenges to the operation and control of the power distribution system, such as a voltage drop or transformer overloading. Therefore, grid operators need to prepare for high-level EV penetration into the power system. This study proposes data-driven, parameterized, individual, and aggregated EV charging models to predict EV charging loads in the urban residential sector. Actual EV charging profiles in Saskatchewan, Canada, were analyzed to understand the characteristics of EV charging. A location-based algorithm was developed to identify residential EV charging from raw data. The residential EV charging data were then used to tune the EV charging model parameters, including battery capacity, charging power level, start charging time, daily EV charging energy, and the initial state of charge (SOC). These parameters were modeled by random variables using statistic methods, such as the Burr distribution, the uniform distribution, and the inverse transformation methods. The Monte Carlo method was used for EV charging aggregation. The simulation results show that the proposed models are valid, accurate, and robust. The EV charging models can predict the EV charging loads in various future scenarios, such as different EV numbers, initial SOC, charging levels, and EV types (e.g., electric trucks). The EV charging models can be embedded into load flow studies to evaluate the impact of EV penetration on the power distribution systems, e.g., sustained under voltage, line loss, and transformer overloading. Although the proposed EV charging models are based on Saskatchewan’s situation, the model parameters can be tuned using other actual data so that the proposed model can be widely applied in different cities or countries.

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Analysis of residential EV energy flexibility potential based on real-world charging reports and smart meter data

Cited by 49 publications

References 31 publications

Multi-Objective Optimization of PV and Energy Storage Systems for Ultra-Fast Charging Stations

Multi-Objective Optimization of PV and Energy Storage Systems for Ultra-Fast Charging Stations

Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data

Electric Vehicle Charging Model in the Urban Residential Sector

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