Optimal-Cost Scheduling of Electrical Vehicle Charging Under Uncertainty

Zhou, Yaqin; Yau, David K. Y.; You, Pengcheng; Cheng, Peng

doi:10.1109/tsg.2017.2662801

Cited by 58 publications

(31 citation statements)

References 17 publications

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“…Indeed, the station's operator aggregates its EVs to participate in joint energy and reserve market, but the uncertain behaviors of EVs are not considered. In [18], the uncertainty of EVs arrival times is considered in a supply cost minimization problem by the Lyapunov optimization technique, and the battery capacities of them are unknown. In [19], the uncertainties in arrival times, dwell times, SoC, walking ranges of drivers and demand of EVs are handled by stochastic programming.…”

Section: Related Workmentioning

confidence: 99%

“…In (17), P i,t,s is the generating power of micro-turbine i in scenario s at time t and is limited to their bounded maximum and minimum capacity, i.e., P max i and P min i . RU i and RD i are the ramping up and down rates of micro-turbine i and are limited in (18) and (19), respectively. The (20) and (21) impose the minimum up/down time constraints for micro-turbines.…”

Section: Operating Constraints For Micro-turbinesmentioning

confidence: 99%

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Optimal Day-Ahead Scheduling of a Smart Micro-Grid via a Probabilistic Model for Considering the Uncertainty of Electric Vehicles’ Load

et al. 2019

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This paper presents a new model based on the Monte Carlo simulation method for considering the uncertainty of electric vehicles’ charging station’s load in a day-ahead operation optimization of a smart micro-grid. In the proposed model, some uncertain effective factors on the electric vehicles’ charging station’s load including battery capacity, type of electric vehicles, state of charge, charging power level and response to energy price changes are considered. In addition, other uncertainties of operating parameters such as market price, photovoltaic generation and loads are also considered. Therefore, various stochastic scenarios are generated and involved in a cost minimization problem, which is formulated in the form of mixed-integer linear programming. Finally, the proposed model is simulated on a typical micro-grid with two 60 kW micro-turbines, a 60 kW photovoltaic unit and some loads. The results showed that by applying the proposed model for estimation of charging station load, the total operation cost decreased.

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Section: Related Workmentioning

confidence: 99%

Section: Operating Constraints For Micro-turbinesmentioning

confidence: 99%

Optimal Day-Ahead Scheduling of a Smart Micro-Grid via a Probabilistic Model for Considering the Uncertainty of Electric Vehicles’ Load

et al. 2019

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“…Since the charging power of the CS is a constant, it was used to check the charging time of the EV arriving at the charging station and apply it to the charging schedule. Equations (5) and (6) are constraints on dynamic prices as the dynamic pricing signal. The dynamic pricing signal should be added or subtracted to the existing TOU (Time-of-Use) price, and the reflected price should be positive.…”

Section: Charging Strategy For Charging Stationsmentioning

confidence: 99%

“…As EV infrastructure continues to grow, there are more active data on the operations of charging stations [4][5][6]. In [4], an operating method is introduced that plans to charge in a highway environment and minimize total travel time.…”

Section: Introductionmentioning

confidence: 99%

“…In [4], an operating method is introduced that plans to charge in a highway environment and minimize total travel time. In [5], an operation that minimizes costs is suggested, including the cost of a power supply to EVs and the uncertainty of renewable energy. In [6], a charging strategy between charging stations (CSs) is proposed to reduce EV charging waiting time by considering traffic information and the statuses of CSs.…”

Section: Introductionmentioning

confidence: 99%

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Economic Routing of Electric Vehicles using Dynamic Pricing in Consideration of System Voltage

2019

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There is a growing market for electric vehicles (EVs) in recent years. Due to this, many studies on electric vehicles are in progress and research on charging operations for EVs are especially active. Recent research trends on electric vehicle routes rely on the stochastic modelling of various factors such as convenience of a user’s point of view, charging station (CS), location, destination, and so on. In this paper, a charging control scheme for electric vehicles is proposed from the point of view of the system operators rather than the user. From a user’s point of view, the EV route can be set up directly, but it is difficult for the system operator to directly participate in the route of the EV. In this paper, a method is proposed to indirectly change the route of the EV by changing the charging cost through real-time dynamic pricing, in order to prevent risks in the system operation due to voltage fluctuations in the system. With dynamic pricing, the voltage of the system is kept within a stable range, and the EV user sets the route with an economic benefit. The proposed scheme is verified through Dijkstra’s algorithm and a control strategy via a simulation model using MATLAB.

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S2NA‐GEO method–based charging strategy of electric vehicles to mitigate the volatility of renewable energy sources

Ilango¹,

Rajesh

Shajin

2021

Int Trans Electr Energ Syst

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In this manuscript, an efficient hybrid strategy is proposed to mitigate the negative impact of RES output fluctuations and smart charging method of electric vehicles (EVs). The proposed hybrid system is the joint implementation of Spike Neural Network Learning Algorithm (S2NA) algorithm and Golden Eagle Optimizer (GEO) algorithm; hence, it is known as S2NA-GEO strategy. An innovative uncertainty mode of renewable energy sources (RESs) based upon S2NA-GEO strategy is proposed, which can avert the difficult parameter collection and formula derivation. Price-based mode is accepted as EVs are considered responsive loads, which also take into account the spatial-temporal uncertainty of electric vehicles. Here, two fluctuation indexes are determined as quantitative evaluation of the volatility of RESs, then the charging cost is adopted as a guideline for the satisfaction of electric vehicle charging. The main purpose of the proposed system is diminishing

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Optimal-Cost Scheduling of Electrical Vehicle Charging Under Uncertainty

Cited by 58 publications

References 17 publications

Optimal Day-Ahead Scheduling of a Smart Micro-Grid via a Probabilistic Model for Considering the Uncertainty of Electric Vehicles’ Load

Optimal Day-Ahead Scheduling of a Smart Micro-Grid via a Probabilistic Model for Considering the Uncertainty of Electric Vehicles’ Load

Economic Routing of Electric Vehicles using Dynamic Pricing in Consideration of System Voltage

S2NA‐GEO method–based charging strategy of electric vehicles to mitigate the volatility of renewable energy sources

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