شارژ باتری شبکه از وسایل نقلیه الکتریکی
Abstract
1- Introduction
2- Problem formulation
3- Solution approach
4- Case studies
5- Conclusion
Acknowledgement
References
Abstract
Large-scale integration of electric vehicles (EV) and wind power could have significantly negative impacts on power systems security. So, it is becoming an increasingly important issue to develop an effective network security-aware charging strategy of EVs. This paper proposes a multi-objective formulation for the optimal charging schedule of EVs while considering N − 1 security constraints. An EV aggregator representing a cluster of controllable EVs is modeled for determining the optimal charging schedule based on a trilevel hierarchy. On the top level, the grid control center determines the EV charging strategy from the proposed formulation, where bus voltage fluctuations, network power losses, and EV charging adjustments are considered as multi-objective functions. To reduce the computational burden, Lagrangian Relaxation (LR) is introduced to handle time coupled constraints and Benders Decomposition is introduced to handle contingencies. Case studies have been conducted on the New England 39-bus system, and the results verify the necessity of considering N − 1 security constraints and the effectiveness of the proposed formulation and solution approach.
Introduction
Electric vehicles (EVs) have been receiving considerable attentions worldwide as they are clean and green. However, the large-scale integration of EVs, without coordination, may bring negative impacts on power systems operation, such as lower voltage quality, larger power losses, and more harmonics [1]. Therefore, effective strategies should be developed to schedule the charging of EVs to mitigate the negative impacts and even benefit the grid [2]. In the literatures, studies about EV charging schedule are concentrated on distribution network. Up to now, only a few literatures discussed the charging issues of EVs from the transmission network viewpoint. Ref. [3] presented a bi-level model for coordinating the charging/discharging schedules of EVs. The upper-level model minimizes the system load variance to implement peak load shifting by dispatching each aggregator, and the lower one traces the dispatching scheme determined by the upper-level decision-maker by figuring out an appropriate charging/discharging schedules throughout a specific day. Ref. [4] proposed a multi-objective non-linear mixed integer optimization model for EV charging scheduling considering the uncertainties of photovoltaic and wind power in regional power grids. The fuzzy theory was used to change the multi-objective optimization model into a single-objective non-linear optimization problem.