A scenario of vehicle-to-grid implementation and its double-layer optimal charging strategy for minimizing load variance within regional smart grids

Jian, Linni; Zhu, Xinyu; Shao, Ziyun; Niu, Shuangxia; Chan, C. C.

doi:10.1016/j.enconman.2013.11.007

Cited by 81 publications

(45 citation statements)

References 23 publications

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“…The network operator will decide on when the most suitable periods for charging/discharging occur and on the amount of power [27]. Different goals can be considered in the charge/discharge scheduling, namely the operation costs minimization, the greenhouse gas emissions minimization [28] or the load variations minimization [29].…”

Section: Impact Of Electric Vehiclesmentioning

confidence: 99%

Evaluation of the electric vehicle impact in the power demand curve in a smart grid environment

Morais

Sousa

Vale

et al. 2014

Energy Conversion and Management

125

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a b s t r a c tSmart grids with an intensive penetration of distributed energy resources will play an important role in future power system scenarios. The intermittent nature of renewable energy sources brings new challenges, requiring an efficient management of those sources. Additional storage resources can be beneficially used to address this problem; the massive use of electric vehicles, particularly of vehicle-to-grid (usually referred as gridable vehicles or V2G), becomes a very relevant issue. This paper addresses the impact of Electric Vehicles (EVs) in system operation costs and in power demand curve for a distribution network with large penetration of Distributed Generation (DG) units. An efficient management methodology for EVs charging and discharging is proposed, considering a multi-objective optimization problem. The main goals of the proposed methodology are: to minimize the system operation costs and to minimize the difference between the minimum and maximum system demand (leveling the power demand curve). The proposed methodology perform the day-ahead scheduling of distributed energy resources in a distribution network with high penetration of DG and a large number of electric vehicles. It is used a 32-bus distribution network in the case study section considering different scenarios of EVs penetration to analyze their impact in the network and in the other energy resources management.

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Section: Impact Of Electric Vehiclesmentioning

confidence: 99%

Evaluation of the electric vehicle impact in the power demand curve in a smart grid environment

Morais

Sousa

Vale

et al. 2014

Energy Conversion and Management

125

View full text Add to dashboard Cite

show abstract

“…In equation (9), L a is the averaged consumed load of system, and E a represents the energy of consumed load curve. L f and L p are load factor index and peak consumed load level respectively.…”

Section: Comparison Of First and Second Scenarios Resultsmentioning

confidence: 99%

“…In [9], the authors have proposed a model for optimal strategy for charging electric vehicles in an intelligent network with the objective of load variations minimization. The impact of electric vehicle charging on household load curve and its effectiveness on distribution transformer reliability is discussed in [10], and a solution for increasing the reliability is presented as well.…”

mentioning

confidence: 99%

Optimal Planning of Charging and Power Delivering from/to the Network for Electric Vehicles Considering Types of Trip

Samadi¹,

Afshar²

2017

IJET

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Abstract-In this paper, a new model is proposed for optimal planning of daily charging and power delivering from/to the power system considering time schedule of trips for electric vehicles. In this model, trips are categorized in job, shopping, educational, and pleasure and possible times to charge/injection are considered based on type of trip and limitations of charging stations. In the optimal planning, it is assumed the hourly electricity price is known and also the rate of charging is fast. The load curve of system and also charging costs are analyzed which are important for system operator and car owners, respectively. Simulation results show by using the proposed planning for time scheduling of charge/injection, a smoother load curve is obtained and the costs of customer are decreased.

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“…Two contradictory objectives-minimizing the charging cost of an EV parking station and maximizing EV users' satisfaction-were simultaneously achieved in [15] via LP and a modified convex relaxation scheme. Other optimization techniques such as quadratic programming (QP) [16,17], dynamic programming (DP) [18], swarm intelligence [19][20][21], and game theory [22] were also used for EV charging scheduling. Different objective functions including the minimization of EV charging cost and grid's load variance, maximization of the aggregator's profit, average state-of-charge (SOC) of EVs, and EV owners' utilization rate in the parking station, were utilized in these works.…”

Section: Review Of Existing Literaturementioning

confidence: 99%

“…The works in [7,11,12,[14][15][16][17][18][19][20][21][22] rely on the electricity generated from conventional units for EV charging. The proposed work considers PVS as a power source for EV charging, hence is designed to fully deliver the economic and environmental advantages of EVs.…”

mentioning

confidence: 99%

Optimal Charging and Discharging Scheduling for Electric Vehicles in a Parking Station with Photovoltaic System and Energy Storage System

2017

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Abstract:The economic and environmental benefits brought by electric vehicles (EVs) cannot be fully delivered unless these vehicles are fully or partially charged by renewable energy sources (RES) such as photovoltaic system (PVS). Nevertheless, the EV charging management problem of a parking station integrated with RES is challenging due to the uncertain nature of local RES generation. This paper aims to address these difficulties by deploying an energy storage system (ESS) in parking stations and exploiting the charging and discharging scheduling of EVs to achieve better utilization of intermittent PVS for EV charging. A real-time charging optimization scheme is also formulated, using mixed-integer linear programming (MILP) to coordinate the charging or discharging power of EVs along with the power dispatches of power grid and ESS based on the vehicles' charging or discharging priorities and electricity price preferences. Extensive simulations show that the proposed approach not only maximizes the satisfaction of EV owners in terms of fulfilling all charging and discharging requests, but also minimizes the overall operational cost of the parking station by prioritizing the utilization of energy from PVS, ESS, and scheduling of every EV's charging and discharging.

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A scenario of vehicle-to-grid implementation and its double-layer optimal charging strategy for minimizing load variance within regional smart grids

Cited by 81 publications

References 23 publications

Evaluation of the electric vehicle impact in the power demand curve in a smart grid environment

Evaluation of the electric vehicle impact in the power demand curve in a smart grid environment

Optimal Planning of Charging and Power Delivering from/to the Network for Electric Vehicles Considering Types of Trip

Optimal Charging and Discharging Scheduling for Electric Vehicles in a Parking Station with Photovoltaic System and Energy Storage System

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