A new schedule-controlled strategy for charging large number of EVs with load shifting and voltage regulation

Mao, Tian; Lau, Wing Cheong; Shum, Chong; Chung, Henry Shu-Hung; Tsang, Kim-Fung; Tse, Norman C. F.

doi:10.1109/appeec.2015.7380880

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Table 7 shows a classification of the literature analysed based on DSO-based charging. Many of them seeks to optimise the cost of electricity [100][101][102], but there are also some strategies aiming at optimising the quality of supply [99][100][101][102][103][104][105] and reducing losses on the network [104].…”

Section: Centralisedmentioning

confidence: 99%

“…Thus, through flexible planning and simulations on a New York network, the authors achieved up to 16% energy cost reduction, 9% higher than with valley filling, for a specific proportion of Level 1 and Level 2 chargers. Voltage regulation [103,105] Real Time prices [101,102] Load Factor [99,104] Capacity prices [101,106] Active power [105] Population Homogeneous [100,103] Reactive (3) Multi-Agent-Based…”

Section: Centralisedmentioning

confidence: 99%

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Electric Vehicle into the Grid: Charging Methodologies Aimed at Providing Ancillary Services Considering Battery Degradation

et al. 2019

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The necessity of transport electrification is already undeniable due to, among other facts, global Greenhouse Gas (GHG) emissions and fossil-fuel dependency. In this context, electric vehicles (EVs) play a fundamental role. Such vehicles are usually seen by the network as simple loads whose needs have to be supplied. However, they can contribute to the correct operation of the network or a microgrid and the provision of ancillary services and delay the need to reinforce the power lines. These concepts are referred to as Vehicle-to-Grid (V2G), Vehicle-to-Building (V2B) and Vehicle-to-Home (V2H). In paper, a deep classification and analysis of published charging strategies is provided. In addition, optimal charging strategies must minimise the degradation of the batteries to increase their lifetime, since it is considered that the life of a battery ends when its capacity is reduced by 20% with respect to its nominal capacity. Therefore, an optimal integration of EVs must consider both grid and batteries impact. Finally, some guidelines are proposed for further research considering the current limitations of electric vehicle technology. Thus, these proposed guidelines are focused on V2G optimal management, enabling new business models while keeping economic viability for all parts involved.

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Section: Centralisedmentioning

confidence: 99%

Section: Centralisedmentioning

confidence: 99%

Electric Vehicle into the Grid: Charging Methodologies Aimed at Providing Ancillary Services Considering Battery Degradation

et al. 2019

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“…These different applications require the results of EVSCF for the scheduling of renewable energy or load at different time scales [33,34]. For example, real-time load forecasting has a time horizon of several seconds to 10 minutes and is used for frequency/voltage regulation, in order to eliminate the effect of volatility of renewable energies [35]; ultra-short-term load forecasting has a time scale of one hour or short-term load forecasting of several hours to tens of hours for economic dispatch and peak shaving and valley filling [36,37]. Forecasting of renewable generation is usually required for ultra-short-term 15 minutes to four hours ahead and for short-term 24-72 hours ahead [38].…”

Section: Introductionmentioning

confidence: 99%

Schedulable capacity forecasting for electric vehicles based on big data analysis

Mao

Zhang

Chang

et al. 2019

J. Mod. Power Syst. Clean Energy

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Fast and accurate forecasting of schedulable capacity of electric vehicles (EVs) plays an important role in enabling the integration of EVs into future smart grids as distributed energy storage systems. Traditional methods are insufficient to deal with large-scale actual schedulable capacity data. This paper proposes forecasting models for schedulable capacity of EVs through the parallel gradient boosting decision tree algorithm and big data analysis for multi-time scales. The time scale of these data analysis comprises the real time of one minute, ultra-short-term of one hour and one-day-ahead scale of 24 hours. The predicted results for different time scales can be used for various ancillary services. The proposed algorithm is validated using operation data of 521 EVs in the field. The results show that compared with other machine learning methods such as the parallel random forest algorithm and parallel k-nearest neighbor algorithm, the proposed algorithm requires less training time with better forecasting accuracy and analytical processing ability in big data environment.

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A Schedule-Control Aided Strategy for Charging Large Number of EVs Under Normal and Line Failure Scenarios

et al. 2017

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A new schedule-controlled strategy for charging large number of EVs with load shifting and voltage regulation

Cited by 5 publications

References 16 publications

Electric Vehicle into the Grid: Charging Methodologies Aimed at Providing Ancillary Services Considering Battery Degradation

Electric Vehicle into the Grid: Charging Methodologies Aimed at Providing Ancillary Services Considering Battery Degradation

Schedulable capacity forecasting for electric vehicles based on big data analysis

A Schedule-Control Aided Strategy for Charging Large Number of EVs Under Normal and Line Failure Scenarios

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