Decentralized charging control for large populations of plug-in electric vehicles

Ma, Zhongjing; Callaway, Duncan S.; Hiskens, Ian A.

doi:10.1109/cdc.2010.5717547

Cited by 349 publications

(486 citation statements)

References 11 publications

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“…Then we define the Lagrangian for the primal problem (6) as (7) Note that the first term in the Lagrangian (7) is separable in terms of EVs, thus the dual function which minimizes the Lagrangian (7) over and can be described as follows: (8) where is a subproblem corresponding to the scheduling on the charging/discharging behavior of EV . Note that each subproblem can be tackled independently, which simplifies the whole solving process.…”

Section: B Primal-dual Approach and Subproblem Of Evmentioning

confidence: 99%

“…As EVs gradually penetrate into our daily life, they will consume a tremendous amount of electricity energy. According to [8], Manuscript if 30% of conventional vehicles in the US were substituted by EVs, the total charging load would reach around 18% of the US summer peak load. Moreover, uncoordinated charging of EVs easily leads to a load peak which coincides with the original base load peak [9], and may crash the distribution grid.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Cooperative Charging Strategy for a Smart Charging Station of Electric Vehicles

You

Yang

Chow

et al. 2016

IEEE Trans. Power Syst.

131

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This paper proposes a novel cooperative charging strategy for a smart charging station in the dynamic electricity pricing environment, which helps electric vehicles (EVs) to economically accomplish the charging task by the given deadlines. This strategy allows EVs to share their battery-stored energy with each other under the coordination of an aggregator, so that more flexibility is given to the aggregator for better scheduling. Mathematically, the scheduling problem is formulated as a constrained mixed-integer linear program (MILP) to capture the discrete nature of the battery states, i.e., charging, idle and discharging. Then, an efficient algorithm is proposed to solve the MILP by means of dual decomposition and Benders decomposition. At last, the algorithm can be implemented in a distributed fashion, which makes it scalable and thus suitable for large-scale scheduling problems. Numerical results validate our theoretical analysis.

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Section: B Primal-dual Approach and Subproblem Of Evmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Cooperative Charging Strategy for a Smart Charging Station of Electric Vehicles

You

Yang

Chow

et al. 2016

IEEE Trans. Power Syst.

131

View full text Add to dashboard Cite

show abstract

“…Let x 1 = 5, K = 1, and KP = 24. From (17) M = 6 and the feasible intervals for each charging action are obtained from (20) and (21): [1,6], [4,10], [8,14], [12,18], [16,22], and [20,24]. It is clear that [s m , e m ] ∩ s m+1 , e m+1 = ∅; therefore, it is possible that the consecutive charging actions are coupled together.…”

Section: A Coupled Chainmentioning

confidence: 99%

Optimal Charging Strategy for Plug-In Electric Taxi With Time-Varying Profits

Yang

Sun

et al. 2014

IEEE Trans. Smart Grid

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This paper studies the optimal charging problem for future plug-in electric taxi (PET) with time-varying profits, i.e., time-varying service incomes and charging costs. Aiming at maximizing the average profit of a PET in long term under the constraint of state of charge (SoC) dynamics of PET battery, this problem is formulated as a constrained binary programming problem in infinite time horizon. The main contribution of this paper consists of three parts. First, because the original infinite binary programming problem cannot be directly solved, it is divided into a series of periodic subproblems. Each of the subproblems is in finite time horizon and much easier to solve, which is also proven rigorously to be equivalent to the original one. Second, an efficient and optimal algorithm is proposed to solve the finite time constrained binary programming subproblem, which also yields the optimal initial SoC of PET. Third, a transient control strategy is proposed to transfer the any initial SoC to the optimal one, if they are not identical. The performance of the proposed method is verified by numerical results.Index Terms-Binary programming, infinite time horizon, optimization, periodicity, plug-in electric taxi (PET).

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“…Some other methods have focused on decentralized control and optimization of EV charging, which requires reduced communications infrastructure and computational burden. In [9], the coordination of EVs was performed using noncooperative games to minimize generation cost. A distributed algorithm that considers the EV battery state of charge (SOC) was proposed in [10] to level the load at night.…”

Section: Introductionmentioning

confidence: 99%

A Voltage-Based Controller for an Electric-Vehicle Charger

Al‐Awami

Sortomme

Akhtar

et al. 2016

IEEE Trans. Veh. Technol.

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Electric vehicle (EV) integration into the distribution system has been a topic of great interest lately due to the potential challenges it poses. Previous works have focused on either centralized charge control or distributed charge control to solve these issues. In this paper, an adaptive voltage feedback controller for an onboard EV charger is proposed that, unlike other proposed methods, does not require any real-time communication between the EV and the utility. This controller compares the system voltage at the point of charging with a preset reference voltage. The EV charging is reduced as the system voltage approaches this reference. The reduced charging rate takes into account the EV battery state of charge (SOC) and the owner's end-of-charge time (ECT) preference. To validate the proposed control structure, extensive simulations are carried out on a distribution system with and without other voltage control mechanisms. The simulation results show that this method can eliminate system voltage violations that would otherwise be caused by EV charging while ensuring fairness among the various EVs even with different system configurations and EV penetration levels. The proposed controller shows a good performance in the presence of other voltage control devices and distributed generation units. Also, it can integrate with Vehicleto-Grid services as a lowest level of hierarchical control.

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Decentralized charging control for large populations of plug-in electric vehicles

Cited by 349 publications

References 11 publications

Optimal Cooperative Charging Strategy for a Smart Charging Station of Electric Vehicles

Optimal Cooperative Charging Strategy for a Smart Charging Station of Electric Vehicles

Optimal Charging Strategy for Plug-In Electric Taxi With Time-Varying Profits

A Voltage-Based Controller for an Electric-Vehicle Charger

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