A Three-Stage Stochastic Framework for Smart Electric Vehicle Charging

As one of the most significant part of carbon neutralisation, the rapid growth of electric vehicle (EV) market in past few years has greatly expedited the transport electrification, which, however, has brought in new challenges to power system including isolated distribution network for commercial a...

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Bibliographic Details
Published in:IEEE access 2023, Vol.11, p.655-666
Main Authors: Yu, Yue, Nduka, Onyema S., Nazir, Firdous Ul, Pal, Bikash C.
Format: Article
Language:English
Subjects:
Ancillary services
Batteries
Battery degradation
chance constraint
Costs
DC microgrid
Distributed generation
Distribution networks
Electric power distribution
electric vehicle (EV)
Electric vehicle charging
Electric vehicles
Electrification
EV charging
Load modeling
Markov Chain Monte Carlo
Markov chains
Microgrids
multi-objective optimisation
Office buildings
optimal power flow
Optimization
power losses
Random walk
State of charge
vehicle-to-grid (V2G)
Workflow
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Summary:As one of the most significant part of carbon neutralisation, the rapid growth of electric vehicle (EV) market in past few years has greatly expedited the transport electrification, which, however, has brought in new challenges to power system including isolated distribution network for commercial and industrial set up. Stochastic and complex EV behaviours would violate network permissible operation region and increase costs for system operators. To address these problems, a chance-constrained smart EV charging strategy in a DC microgrid (DCMG) supporting large office complex is proposed to minimize system cost from distribution network and fleet battery degradation cost from EVs providing ancillary service to the DCMG. When dealing with uncertainties from EVs, a Markov Chain Monte Carlo (MCMC) model is built to couple different parameters in load profiles and characterize the time series of likelihood of charging and discharging. A state-of-charge (SOC) space random walk method is then proposed to solve the resultant massive recursive probabilistic charging requirements. Based on that, a three-stage optimization framework is established to illustrate the work flow in system level. Numerical results verifying the effectiveness of the proposed method are also presented.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3232963