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A Unit Combination Scheduling Method Considering Electric Vehicle Travel Correlation

A combination technology of electric vehicles and units, which is applied to electric vehicles, collectors, vehicle components, etc., and can solve problems such as line overruns

Active Publication Date: 2020-09-01
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

None of the above literatures takes into account the line flow constraints of the power grid. In order to avoid the problem of line overruns when charging and discharging electric vehicles, and to ensure the safety of the system, further research is needed on the grid-connected scheduling of electric vehicles.

Method used

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  • A Unit Combination Scheduling Method Considering Electric Vehicle Travel Correlation
  • A Unit Combination Scheduling Method Considering Electric Vehicle Travel Correlation
  • A Unit Combination Scheduling Method Considering Electric Vehicle Travel Correlation

Examples

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Embodiment 1

[0147] In order to demonstrate a research method for the unit combination problem that considers the correlation of electric vehicle travel proposed in this paper, the correlation between the departure time, access time and daily mileage data in the travel characteristics of electric vehicles is studied. For electric vehicles For a single electric vehicle, the initial value of its SOC is related to the daily mileage d of the electric vehicle, and the time interval of charging and discharging is related to the first departure time of the vehicle every day t out and the first daily access time t in related. Taking the driving characteristics of a single private car as an example, by extracting three types of data in NHTS, the departure time, access time and daily mileage of electric vehicles, and using the non-parametric kernel density estimation method to estimate the probability density of these three types of data, and then Derive the probability density function. Then use ...

Embodiment 2

[0153] In order to demonstrate a research method for the unit combination problem that considers the correlation of electric vehicle travel proposed in this paper, and to study the impact of optimal scheduling on the optimization scheme with the charging and discharging power of electric vehicles as a variable, a comparative analysis of the following two schemes is carried out:

[0154] Scheme 1: Using the number of charging and discharging vehicles of electric vehicles at each time as a variable, optimize the charging and discharging plan of electric vehicles.

[0155] Scheme 2: Taking the charging and discharging power of the electric vehicle as a variable, the charging and discharging plan of the electric vehicle is optimized by adjusting and shifting the charging and discharging power of the electric vehicle and the charging and discharging period.

[0156] Assuming that the number of electric vehicles connected to each node at each time period in the two schemes is the sam...

Embodiment 3

[0166] In order to demonstrate a research method for the unit combination problem proposed in this paper that considers the correlation of electric vehicle travel, and to study the influence of network power flow constraints on node and branch power flows after electric vehicles are connected to the grid, the network power flow constraints in the model are canceled and calculated. 1, 1.5, 2, 2.5, and 3 times the original load of the system are simulated to compare and observe the network flow exceeding the limit after the load of the node system is increased.

[0167] Table 6 shows the transgression of branch power flow under different system loads. When the system load is the original load, only 2 branches in the system exceed the limit; when the system load increases to 1.5 times of the original, the number of branches exceeds the limit is 3, and the overload has a certain increase; when the system load increases When the original value is doubled, the number of out-of-limit...

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Abstract

The invention relates to a unit combination dispatching method considering electric vehicle travel correlation. The unit combination dispatching method comprises the following steps of 1) obtaining the driving characteristics of each type of electric vehicle; 2) performing Latin hypercube sampling according to a probability density function of the driving characteristics of each type of electric vehicle to generate a data matrix; 3) obtaining correlation of the departure moment, the access moment and the daily traveled distance in the data matrix; 4) generating the driving characteristic dataof each type of vehicle through a Copula model, and establishing a scene matrix with correlation; 5) establishing an electric vehicle-containing random unit combination model by taking the lowest general thermal power generating unit cost as the target; 6) performing linearization on the nonlinear conditions in the random unit combination model; and 7) solving the random unit combination model byadopting a mixed integer planning method. Compared with the prior art, the unit combination dispatching method has the advantages of quickness and reliability, high reliability, wide applicable rangeand the like.

Description

technical field [0001] The invention relates to the field of electric vehicle travel scheduling, in particular to a unit combination scheduling method considering the correlation of electric vehicle travel. Background technique [0002] The proposal of electric vehicle and grid interaction technology (vehicle to grid, V2G) enables electric vehicles to be regarded as a distributed energy storage device. By guiding its charge and discharge, it can serve the grid during idle periods, bringing about peak load reduction. The benefits of filling valleys and reducing system costs. Therefore, in order to make full use of V2G technology to create economic benefits, the problem of unit combination including electric vehicles has been widely concerned. [0003] The travel characteristic parameters of electric vehicles include access time, off-grid time, daily mileage, and the number of connected vehicles in each period, etc. These travel characteristics are highly random. For the res...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H02J3/46H02J3/32B60L55/00
CPCB60L55/00H02J3/32H02J3/46H02J2203/20Y02T10/70
Inventor 葛晓琳郝广东金言
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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