Peak load reduction through dynamic pricing for electric vehicle charging

Limmer, Steffen; Rodemann, Tobias

doi:10.1016/j.ijepes.2019.05.031

Cited by 78 publications

(35 citation statements)

References 21 publications

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“…They propose a strategy for the setting of the prices with the target to minimize power distribution losses. In Reference [88], Limmer and Rodemann investigate the setting of prices for different charging deadlines with the objectives of maximizing the charging provider's profit and minimizing the peak load.…”

Section: Flexibility In the Charging Durationmentioning

confidence: 99%

Dynamic Pricing for Electric Vehicle Charging—A Literature Review

Limmer

2019

Energies

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Time-varying pricing is seen as an appropriate means for unlocking the potential flexibility from electric vehicle users. This in turn facilitates the future integration of electric vehicles and renewable energy resources into the power grid. The most complex form of time-varying pricing is dynamic pricing. Its application to electric vehicle charging is receiving growing attention and an increasing number of different approaches can be found in the literature. This work aims at providing an overview and a categorization of the existing work in this growing field of research. Furthermore, user studies and the modeling of user preferences via utility functions are discussed.

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Section: Flexibility In the Charging Durationmentioning

confidence: 99%

Dynamic Pricing for Electric Vehicle Charging—A Literature Review

Limmer

2019

Energies

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“…In prior work [25,26], we proposed a dynamic pricing scheme, where charging station operators offer different prices to customers depending on the customers' flexibility. The framework proposed in this paper could be used to better understand the impact of different pricing schemes on user behavior.…”

Section: Discussionmentioning

confidence: 99%

Using Agent-Based Customer Modeling for the Evaluation of EV Charging Systems

et al. 2019

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The development of efficient electric vehicle (EV) charging infrastructure requires a modeling of customer behavior at an appropriate level of detail. Since only limited information about real customers is available, most simulation approaches employ a stochastic approach by combining known or estimated customer features with random variations. A typical example is to model EV charging customers by an arrival and a targeted departure time, plus the requested amount of energy or increased state of charge (SoC), where values are drawn from normal (Gaussian) distributions with mean and variance values derived from user studies of obviously limited sample size. In this work, we compare this basic approach with a more detailed customer model employing a multi-agent simulation (MAS) framework in order to investigate how a customer behavior that responds to external factors (like weather) or historical data (like satisfaction in past charging sessions) impacts the essential key performance indicators of the charging system. Our findings show that small changes in the way customers are modeled can lead to quantitative and qualitative differences in the simulated performance of EV charging systems.

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“…The charge and discharge control strategy of EV is modeled in detail in [13]- [15], but the uncertainty of renewable energy generation is not taken into account. In order to improve the enthusiasm of EVs to participate in charge and discharge management, a series of studies on the formulation of charge and discharge price are carried out in [16]- [21]. As a variable load, EV is not only variable in time scale, but also uncertain in space scale due to the difference of driving plan.…”

Section: B Related Workmentioning

confidence: 99%

“…Therefore, this chapter models the battery capacity, driving plan of EVs and traffic network. According to the research results on the European market of EV in [16], four types of EVs are summarized as follows. Type L7e is used for carrying goods, which maximum empty mass is 400kg or 550kg.…”

Section: Characteristics Model Of Electric Vehiclementioning

confidence: 99%

Research on Flexibility Evaluation Method of Distribution System Based on Renewable Energy and Electric Vehicles

Li¹

2020

IEEE Access

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High penetration of renewable energy brings economic and environmental benefits to the grid, but also causes challenges to the distribution system flexibility due to its uncertainty. As a new variable load, electric vehicles (EVs) can increase system's flexibility through interactions with the grid and promote the consumption of renewable energy. This paper studies the flexibility evaluation of distribution system considering the interaction between EVs and the grid. Firstly, the charging and discharging control strategies of EVs have been analyzed to reduce the impact of renewable energy output fluctuations on the distribution system flexibility, such as the G2V (Grid to vehicle) and V2G (Vehicle to grid) modes. Secondly, the battery capacity, driving plan, and transportation network are modeled to study the impact of the EVs connected to the grid on the distribution system flexibility. Thirdly, the flexibility evaluation method of distribution network is proposed based on the feasibility analysis of uncertain region. Finally, the analyses of the example show that the proposed method can realize the evaluation of the time series flexibility of the distribution system, which proves the effectiveness of the method proposed in this paper. INDEX TERMS ST time span, such as 24 ｡ the Hadamard product a, b, c the operation cost coefficients of the thermal power unit F the flexibility evaluation index Pg,t the output of the thermal power unit at time t d the element value corresponding to the uncertain variable n in the time series direction matrix D at time t λpv the cost coefficient of PV output curtailment valuend the normalized deviation value Ppvc,t the planned PV output curtailment x the decision variable in the optimization process xt, yt, zt the 0-1 variable representing the status, start and stop of the thermal power unit Pi, Qi injected active power and injected reactive power at node i respectively. Pg,min, Pg,max the lower and upper limits of thermal power unit output Ui, Uj voltage amplitude of node i and j Rramp the ramp rate of thermal power unit Gij, Bij conductance and susceptance of branch ij , the predicted output of PV θij voltage phase angle difference between node i and j PV,t the charging power of the grid to the electric vehicle V at time t Il the branch l current amplitude Nev the total number of EVs the maximum value of branch l current amplitude PVmin,t, PVmax,t the minimum and maximum charging power of electric vehicle V at time t Ui he voltage amplitude of node i PL,t the total load demand except for electric vehicles Uimax, Uimin the upper and lower limits of node i voltage amplitude DN the set of distribution network nodes PREi,t, QREi,t the active power and reactive power of renewable energy output at node i and time t

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Peak load reduction through dynamic pricing for electric vehicle charging

Cited by 78 publications

References 21 publications

Dynamic Pricing for Electric Vehicle Charging—A Literature Review

Dynamic Pricing for Electric Vehicle Charging—A Literature Review

Using Agent-Based Customer Modeling for the Evaluation of EV Charging Systems

Research on Flexibility Evaluation Method of Distribution System Based on Renewable Energy and Electric Vehicles

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