Multi-objective charging scheduling utilizing electric vehicle load models

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Multi-Objective Charging Scheduling Utilizing Electric Vehicle Load Models.pdf

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2022-1

Author

Güzel, İven

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Utilization of electric vehicle (EV) load models can improve the performance of smart charging strategies, which increase the reliability of the grid by harnessing the flexibility of EV loads. This thesis presents methods for utilizing EV load models in real time stochastic charging control with single and finite system-time horizons. First, the drivers’ load models are found with kernel density estimation. A single system time horizon coordinated charging control algorithm is devised to ensure each EV is charged at least a critical amount given a feasible set of optimization constraints. The coordinated charging algorithm tackles the NP-hardness of single-deadline charging scheduling problems efficiently with a sorting algorithm utilizing the stochastic EV load models. Moreover, the single system-time horizon coordinated charging control algorithm is extended to a scheduling algorithm considering a finite system-time horizon. This approach utilizes the stochastic EV load models in a model predictive control based approach to decrease the complexity of stochastic online charging scheduling problem into a deterministic case. The scheduling algorithm makes assumptions about the future arrivals to the charging station, unlike the classical online EV charging scheduling algorithms, which optimize the load demand revealed at the current time but underestimate the load demand revealed in the future. Findings of the thesis work suggest the individual load models complement smart charging algorithms’ decision process by improving the fairness of charging time allocation and extending the degree of knowledge of future random data for the scheduling algorithm.

Subject Keywords

Plug-in electric vehicles, Load modeling, Smart charging, Charging scheduling, Electric vehicle grid integration

URI

https://hdl.handle.net/11511/98158

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

İ. Güzel, “Multi-objective charging scheduling utilizing electric vehicle load models,” M.S. - Master of Science, Middle East Technical University, 2022.