Coordinated charging method of electric vehicles to deal with uncertainty factors

Yang, Bing; Wang, Lifang; Liao, Chenglin; Ji, Li

doi:10.1109/itec-ap.2014.6940696

Cited by 2 publications

References 12 publications

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Cooperative Control of Charging Stations for an EV Park with Stochastic Dynamic Programming

Aragón¹,

Gümrükcü²,

Pandian³

et al. 2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

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An increasing penetration of EVs and their charging impose challenges to the energy grid stability. As a consequence, an optimal management of EV charging in parking lots becomes essential. This work presents an approach of a cooperative control of charging stations based on a stochastic optimization model for the energy management of a group of charging stations. Uncertainties regarding the number of charging EVs at each time step are modelled using a Markovian process, while the probability mass function was generated using a Monte Carlo simulation. Furthermore, the concept prioritizes the exploitation of local renewable resources and energy storage for EV charging to the import of electrical energy from the grid. The stochastic optimization model was integrated into our own developed Stochastic Optimization Software Framework (SOFW), which deploys the application as Model Predictive Control (MPC) in the real-time scenario using dynamic programming. The cooperative control of charging stations presented in this work was evaluated succesfully with a variety of EV driving scenarios. The approach will be validated on the field in a car park of a DSO company including renewable generation and energy storage system.

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Cooperative Control of Charging Stations for an EV Park with Stochastic Dynamic Programming

Aragón¹,

Gümrükcü²,

Pandian³

et al. 2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

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A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids

et al. 2022

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This paper reviews the current techniques used in energy management systems to optimize energy schedules into microgrids, accounting for uncertainties for various time frames (day-ahead and real-time operations). The current uncertainties affecting applications, including residential, commercial, virtual power plants, electric mobility, and multi-carrier microgrids, are the main subjects of this article. We outline the most recent modeling approaches to describe the uncertainties associated with various microgrid applications, such as prediction errors, load consumption, degradation, and state of health. The modeling approaches discussed in this article are probabilistic, possibilistic, information gap theory, and deterministic. Then, the paper presents and compares the current optimization techniques, considering the uncertainties in their problem formulations, such as stochastic, robust, fuzzy optimization, information gap theory, model predictive control, multiparametric programming, and machine learning techniques. The optimization techniques depend on the model used, the data available, the specific application, the real-time platform, and the optimization time. We hope to guide researchers to identify the best optimization technique for energy scheduling, considering the specific uncertainty and application. Finally, the most challenging issues to enhance microgrid operations, despite uncertainties by considering new trends, are discussed.

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Coordinated charging method of electric vehicles to deal with uncertainty factors

Cited by 2 publications

References 12 publications

Cooperative Control of Charging Stations for an EV Park with Stochastic Dynamic Programming

Cooperative Control of Charging Stations for an EV Park with Stochastic Dynamic Programming

A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids

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