Optimal EV charging and discharging control considering dynamic pricing

Bitencourt, Leonardo; Borba, Bruno Soares Moreira César; Maciel, Renan Silva; Fortes, Márcio Zamboti; Ferreira, Vitor Hugo

doi:10.1109/ptc.2017.7981231

Cited by 24 publications

(22 citation statements)

References 22 publications

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“…Therefore, the RTP rate design follows the structure developed by Geng et al Other authors also used that concept, such as Tan and Wang and Momber et al . The distribution transformer load profile is reflected in the tariff by the price, in [pu] (The RTP could also be designed based on substation charging level as proposed by Bitencourt et al who compares TOU and RTP efficiency based on the substation and the distribution transformer load curve. Here, however, the approach used is a tariff based on the distribution transformer loading level).…”

Section: Modeling Of Ev Charging and Distribution Gridmentioning

confidence: 99%

Electric Vehicles Charging Optimization to Improve the Insertion Considering the Brazilian Regulatory Framework

et al. 2019

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Electric vehicles (EV) charging can impact the electrical grid by causing voltage imbalance, power quality disturbances, and transformer overloading. Nonetheless, price signals at coordinated charging are capable of performing energy management and mitigating the negative effects of EV charging. This may, therefore, stimulate the EVs to charge during the period of lower energy consumption. In this context, this work aims to analyze the electric vehicle charging impact on distribution transformer loading, considering smart meter real data. For the analysis, this paper creates scenarios considering two seasons of the year (summer and winter seasons); the real time pricing and time of use electric charging tariffs; and the uncoordinated and coordinated vehicle charge control methods. The results of this study also support the elaboration of public policies proposals for the transport sector in order to encourage investment in the area of electric mobility.

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Section: Modeling Of Ev Charging and Distribution Gridmentioning

confidence: 99%

Electric Vehicles Charging Optimization to Improve the Insertion Considering the Brazilian Regulatory Framework

et al. 2019

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“…When CSs formulate the power plan, guiding EVs to charge orderly by setting an appropriate charging price is necessary, and the revenue of CSs should also be maximized. The electricity pricing problem of charging stations is a classical problem in power systems [2][3][4][5][6][7][8][9][10][11], which can be decomposed into the security checking problem of DSO, charging pricing problem of CSs, and charging mode optimization problem of EVs. In the security checking problem of DSO, the objective function of DSO is shown in Equation 1and the constraints are shown in Equation 2, including power demand equation constraints, bus power balance constraints, bus voltage safety constraints, and line power transmission capacity constraints.…”

Section: The Model Of Electricity Pricing Of Charging Stationsmentioning

confidence: 99%

“…According to the linear duality theory [21], the dual expression of Equation 5is shown in Equation (9). At the same time, the optimum condition is Equation (10). The optimization problem of electric vehicle users is transformed into a series of Karush-Kuhn-Tucker (KKT) optimality conditions.…”

Section: The Model Of Electricity Pricing Of Charging Stationsmentioning

confidence: 99%

“…In [7][8][9], the distribution locational marginal pricing (DLMP) method is applied to optimize the charging price of EVs to reduce the impact of charging load on distribution network. In [10][11][12], the electricity demand and travel characteristics of EV users are considered, and charging price is optimized by the 2 of 14 price demand elasticity coefficient. In [13], a Stackelberg leadership game model is applied to optimize the electricity prices for encouraging EV users to participate in the electricity market, improving their own interests, and promoting the development of smart energy systems.…”

Section: Introductionmentioning

confidence: 99%

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A Convex Optimization Algorithm for Electricity Pricing of Charging Stations

Zhang¹,

Zhan

Li³

et al. 2019

Algorithms

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The problem of electricity pricing for charging stations is a multi-objective mixed integer nonlinear programming. Existing algorithms have low efficiency in solving this problem. In this paper, a convex optimization algorithm is proposed to get the optimal solution quickly. Firstly, the model is transformed into a convex optimization problem by second-order conic relaxation and Karush–Kuhn–Tucker optimality conditions. Secondly, a polyhedral approximation method is applied to construct a mixed integer linear programming, which can be solved quickly by branch and bound method. Finally, the model is solved many times to obtain the Pareto front according to the scalarization basic theorem. Based on an IEEE 33-bus distribution network model, simulation results show that the proposed algorithm can obtain an exact global optimal solution quickly compared with the heuristic method.

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“…Transformers thermal overloading and voltage unbalancing issues between supply and demand are some of the power quality problems generated by inclusion of fast charging stations and high frequency convertors in distribution system . The impact of uncoordinated and coordinated charging control strategy with real time pricing in a distribution network is analyzed in Bitencourt et al In order to evaluate increase in load factor and peak shaving, an optimal algorithm is proposed for charging and discharging of the EV with least electricity cost in Bitencourt et al Factors influencing the development of charging infrastructure with corresponding expert solutions to increase EV adoption for electrified transportation are proposed in Zhang et al and Palomino and Parvania . Most literature have studied the influence of dumb or uncoordinated random charging, which studies the impact of random EVs plug in; tariff based real electricity price based control, which influences the user to charge at off‐peak cheap price hours; charge‐at‐park, which enables the users to charge EV after each trip.…”

Section: Introductionmentioning

confidence: 99%

A heuristically optimized comprehensive charging scheme for large‐scale EV integration

Abbas

Feng

Habib

et al. 2019

Int Trans Electr Energ Syst

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Summary The expectation of large‐scale electric vehicles (EVs) charging and discharging load could bring security and reliability challenges to the power system. As a smart load, EV requires an intelligently designed scheduling and pricing algorithm that takes into account the stochastic EVs user behavior, grid charging capacity, battery characteristics, and real‐time electricity price variations. In this paper, a multi‐objective comprehensive stand‐alone solution is proposed considering the time‐of‐use (TOU) price to intelligently regulate EVs charging/discharging activities. The proposed alternative heuristic charging strategy optimally configures solution indices and provide a tradeoff between considered evaluation parameters, thus mitigating the effect of uncontrolled charging introduced by stochastic EV charge/discharge activities. The objective is to shift the peak hours load to nonpeak hours with reduction in average‐to‐peak ratio, charging cost minimization and maximize the availability of charging capacity for pledging the traveling plan determined by EV users. Different EVs penetration are tested to validate the performance of the proposed solution under massive EV integration, with a driving pattern obtained from the Beijing National Travel Survey.

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Optimal EV charging and discharging control considering dynamic pricing

Cited by 24 publications

References 22 publications

Electric Vehicles Charging Optimization to Improve the Insertion Considering the Brazilian Regulatory Framework

Electric Vehicles Charging Optimization to Improve the Insertion Considering the Brazilian Regulatory Framework

A Convex Optimization Algorithm for Electricity Pricing of Charging Stations

A heuristically optimized comprehensive charging scheme for large‐scale EV integration

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