WO2020259307A1

WO2020259307A1 - Method and device for determining available capacity of grid-connected parking lot, and computing apparatus

Info

Publication number: WO2020259307A1
Application number: PCT/CN2020/095647
Authority: WO
Inventors: 曾博; 龚传正; 徐富强; 刘裕; 孙博
Original assignee: 华北电力大学
Priority date: 2019-06-25
Filing date: 2020-06-11
Publication date: 2020-12-30
Also published as: CN110276135B; CN110276135A

Abstract

Embodiments of the present invention disclose a method for determining the available capacity of a grid-connected parking lot. The method comprises: comparing a first reliability index of a first intelligent power distribution system with a second reliability index of a second intelligent power distribution system; if the first reliability index is not less than the second reliability index, calculating a third reliability index of a third intelligent power distribution system; and determining, on the basis of the second reliability index and the third reliability index, the available capacity of a grid-connected parking lot. The embodiments of the invention further disclose a corresponding device for determining the available capacity of a grid-connected parking lot, a computing apparatus, and a storage medium.

Description

Method, device and computing equipment for determining available capacity of grid-connected parking lot

Technical field

The invention relates to the field of electric power systems, and in particular to a method, device and computing equipment for determining the available capacity of a grid-connected parking lot.

Background technique

With the rapid development of electric vehicles driven by electricity, the energy demand of plug-in electric vehicles (PEV) has received widespread attention.

Generally, a charging infrastructure similar to a grid-connected parking lot (GPL) can be used to meet the requirements. GPL is generally located in densely populated areas and is equipped with distributed charging points and low-power charging/discharging facilities. Through these facilities, PEV users can not only obtain the required vehicle charging services, but also provide electrical energy to the grid by discharging PEV batteries. In fact, since most domestically-produced cars spend more than 95% of their daily parking time, the total storage capacity of a GPL equipped with hundreds of integrated electric vehicles can be used as a controllable load during charging (that is, the grid-to-vehicle operation mode, G2V), or grid-to-ground alternative distributed energy (ie, vehicle-to-grid operation mode, V2G). It is precisely because of this two-way capability that the emergence of GPL provides a broad spectrum range for future energy systems, which can be effectively used during operation. Therefore, it is very important to evaluate the capacity of the GPL.

Nowadays, the research on the potential benefits of GPL in the power system has been very well laid. However, in these studies, few people are devoted to exploring the impact of GPL on system reliability. In practical applications, the GPL with a two-way charger can be used as a backup power source, and by extracting energy from the PEV battery, it can provide capacity support for the grid in an emergency. This can reduce the risk of load loss and significantly improve the power supply reliability of the Smart Power Distribution System (SDS).

In view of this, the issue of GPL reliability assessment has attracted more and more attention from researchers. The present invention proposes a new scheme for determining the available capacity (CV) of the grid-connected parking lot. Under the background of the intelligent power distribution system, the reliability benefit of the GPL can be approximated by the available capacity of the grid-connected parking lot.

Summary of the invention

To this end, the embodiments of the present invention provide a method, a device, and a computing device for determining the available capacity of a grid-connected parking lot, in an effort to solve or at least alleviate at least one of the above problems.

According to one aspect of the embodiments of the present invention, there is provided a method for determining the available capacity of a grid-connected parking lot, which is suitable for parking plug-in electric vehicles and meets the requirements for charging and discharging of the plug-in electric vehicles. As required, the method includes: comparing a first reliability index of a first intelligent power distribution system with a second reliability index of a second intelligent power distribution system, the first intelligent power distribution system does not include grid-connected parking lots, so The second smart power distribution system is obtained by adding the grid-connected parking lot to the first smart power distribution system, wherein the second reliability index is calculated according to the following steps: obtain the plug-in electric vehicle User behavior characteristics, the user behavior characteristics including the arrival time and parking time of the plug-in electric vehicle in the grid-connected parking lot, the required charging level when leaving the grid-connected parking lot, and the availability of V2G programs; Based on the user behavior characteristics, determine the charging and discharging time of the plug-in electric vehicle; determine the available power generation of the grid-connected parking lot for each moment of the simulation year; determine the total amount of the second intelligent power distribution system Available power generation and total load demand; use the power flow analysis method to determine whether the second smart power distribution system violates constraints; based on the judgment result, calculate the reliability index of the second smart power distribution system at that time; Reliability index, calculating the annual reliability index of the simulation year; calculating the second reliability index based on the annual reliability index of each simulation year; in the case that the first reliability index is not less than the second reliability index, Calculate the third reliability index of the third intelligent power distribution system, and determine the available capacity of the grid-connected parking lot based on the second reliability index and the third reliability index. It is obtained by adding a generator set to the first intelligent power distribution system.

According to another aspect of the embodiments of the present invention, there is provided a device for determining the available capacity of a grid-connected parking lot, which is suitable for parking plug-in electric vehicles and satisfies the charging of the plug-in electric vehicles. For discharge requirements, the device includes: an index comparison unit adapted to compare the first reliability index of the first smart power distribution system with the second reliability index of the second smart power distribution system, and the first smart power distribution system does not Including a grid-connected parking lot, the second smart power distribution system is obtained by adding the grid-connected parking lot to the first smart power distribution system, wherein the index comparison unit further includes an index calculation unit, and the index calculation The unit is adapted to calculate the second reliability index according to the following steps: obtain user behavior characteristics of the plug-in electric vehicle, the user behavior characteristics including the plug-in electric vehicle in the grid-connected parking lot Arrival time and parking time, the required charging level when leaving the grid-connected parking lot, and V2G program availability; based on the user behavior characteristics, determine the charging and discharging time of the plug-in electric vehicle; At each moment, determine the available power generation of the grid-connected parking lot; determine the total available power generation and total load demand of the second smart power distribution system; use the power flow analysis method to determine whether the second smart power distribution system violates constraints; According to the judgment result, calculate the reliability index of the second intelligent power distribution system at that moment; calculate the annual reliability index of the simulation year according to the reliability index of multiple moments; calculate the annual reliability index based on the simulation year The second reliability index; the capacity determining unit is adapted to calculate the third reliability index of the third intelligent power distribution system when the first reliability index is not less than the second reliability index, and based on the second reliability index And a third reliability index to determine the available capacity of the grid-connected parking lot, and the third intelligent power distribution system is obtained by adding a generator set to the first intelligent power distribution system.

According to another aspect of the embodiments of the present invention, there is provided a computing device, including: one or more processors; and a memory; one or more programs, wherein one or more programs are stored in the memory and configured to be configured by One or more processors execute, and one or more programs include instructions for executing any one of the methods for determining the available capacity of a grid-connected parking lot according to an embodiment of the present invention.

According to another aspect of the embodiments of the present invention, there is provided a computer-readable storage medium storing one or more programs. The one or more programs include instructions. When the instructions are executed by a computing device, the computing device executes the Any of the methods for determining the available capacity of the grid-connected parking lot of the embodiment.

The solution for determining the available capacity of the grid-connected parking lot according to the embodiment of the present invention can objectively estimate and compare the contribution of the grid-connected parking lot to the capacity of the intelligent power distribution system by determining the available capacity of the grid-connected parking lot. Conventional power generation resources. Among them, the randomness of user behavior of plug-in electric vehicles and its potential dependence on externalities are considered. On this basis, the sequential Monte Carlo simulation method is used to calculate the reliability index, which can completely express the characteristics of the grid-connected parking lot.

Description of the drawings

In order to achieve the above and related purposes, this article describes certain illustrative aspects in conjunction with the following description and drawings. These aspects indicate various ways in which the principles disclosed herein can be practiced, and all aspects and their equivalents are intended to be Into the scope of the claimed subject matter. By reading the following detailed description in conjunction with the accompanying drawings, the above and other objectives, features and advantages of the present disclosure will become more apparent. Throughout this disclosure, the same reference numerals generally refer to the same parts or elements.

Fig. 1 shows a schematic diagram of a computing device 100 according to an embodiment of the present invention;

FIG. 2 shows a flowchart of a method 200 for determining the available capacity of a grid-connected parking lot according to an embodiment of the present invention; and

Fig. 3 shows a structural block diagram of a device 300 for determining the available capacity of a grid-connected parking lot according to an embodiment of the present invention.

Detailed ways

Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

Fig. 1 shows a schematic diagram of a computing device 100 according to an embodiment of the present invention. As shown in FIG. 1, in a basic configuration 107, the computing device 100 typically includes a system memory 106 and one or more processors 104. The memory bus 108 may be used for communication between the processor 104 and the system memory 106.

Depending on the desired configuration, the processor 104 may be any type of processor, including but not limited to: a microprocessor (μP), a microcontroller (μC), a digital information processor (DSP), or any combination thereof. The processor 104 may include one or more levels of cache, such as the first level cache 110 and the second level cache 112, the processor core 114, and the registers 116. The exemplary processor core 114 may include an arithmetic logic unit (ALU), a floating point number unit (FPU), a digital signal processing core (DSP core), or any combination thereof. The example memory controller 118 may be used with the processor 104, or in some implementations, the memory controller 118 may be an internal part of the processor 104.

Depending on the desired configuration, the system memory 106 may be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. The system memory 106 may include an operating system 120, one or more applications 122, and program data 124. In some embodiments, the application 122 may be arranged to be executed by one or more processors 104 using program data 124 on an operating system.

The computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (eg, output device 142, peripheral interface 144, and communication device 146) to the basic configuration 102 via the bus/interface controller 130. The example output device 142 includes a graphics processing unit 148 and an audio processing unit 150. They can be configured to facilitate communication with various external devices such as displays or speakers via one or more A/V ports 152 or HDMI interfaces. The example peripheral interface 144 may include a serial interface controller 154 and a parallel interface controller 156, which may be configured to facilitate communication via one or more I/O ports 158 and input devices such as keyboards, mice, pens, etc. , Voice input devices, touch input devices, remote control input devices) or other peripherals (such as printers, scanners, etc.) to communicate. The example communication device 146 may include a network controller 160, which may be arranged to facilitate communication with one or more other computing devices 162 via a network communication link via one or more communication ports 164.

A network communication link may be an example of a communication medium. The communication medium may generally be embodied as computer readable instructions, data structures, and program modules in a modulated data signal such as a carrier wave or other transmission mechanism, and may include any information delivery medium. A "modulated data signal" can be a signal, one or more of its data set or its change can be done in a way of encoding information in the signal. As a non-limiting example, communication media may include wired media such as a wired network or a dedicated line network, and various wireless media such as sound, radio frequency (RF), microwave, infrared (IR), or other wireless media. The term computer readable media used herein may include both storage media and communication media.

The computing device 100 may be implemented as a server, such as a database server, an application server, a WEB server, etc., or may be implemented as a personal computer including a desktop computer and a notebook computer configuration. Of course, the computing device 100 can also be implemented as a small-sized portable (or mobile) electronic device.

In the embodiment according to the present invention, the computing device 100 can be implemented at least as each component in the device 300 for determining the available capacity of the grid-connected parking lot, and is configured to execute the available capacity of the grid-connected parking lot according to the embodiment of the invention. Determine method 200. Wherein, the application 122 of the computing device 100 contains multiple instructions for executing the method 200 for determining the available capacity of a grid-connected parking lot according to an embodiment of the present invention, and the program data 124 may also store the information of the device 300 for determining the available capacity of a grid-connected parking lot. Configuration information, etc.

Fig. 2 shows a flowchart of a method 200 for determining the available capacity of a grid-connected parking lot according to an embodiment of the present invention. As shown in FIG. 2, the method 200 for determining the available capacity of a grid-connected parking lot is suitable to be executed in the device 300 for determining the available capacity of a grid-connected parking lot, and starts at step S210.

Among them, the available capacity of the grid-connected parking lot refers to the available capacity of the grid-connected parking lot during the process of plug-in electric vehicles participating in V2G (Vehicle-to-Grid). Understandably, grid-connected parking lot (GPL) is suitable for parking plug-in electric vehicles (PEV) and meets the charging and discharging requirements of plug-in electric vehicles. Specifically, the grid-connected parking lot can charge plug-in electric vehicles and can also receive the discharge of plug-in electric vehicles. In other words, the grid-connected parking lot can usually realize the two-way power exchange between the plug-in electric vehicle and the grid under the coordinated control of the grid-connected parking lot operator (GPL operator, GPLO). When the grid-connected parking lot is charging the plug-in electric vehicle, the plug-in electric vehicle runs G2V (Grid-to-vehicle) mode. When the grid-connected parking lot receives the discharge of the plug-in electric vehicle, the plug-in electric vehicle runs in V2G (Vehicle-to-Grid) mode.

In step S210, the first reliability index of the first smart power distribution system is calculated, and the first smart power distribution system does not include the grid-connected parking lot.

Before calculating the reliability index of the first smart power distribution system component, the state duration time sequence of the first smart power distribution system component can be generated to obtain the state duration time sequence of the first smart power distribution system, and determine the first smart power distribution system. The available power generation of the power distribution system at each time in the state duration sequence. The first smart power distribution system component may include power generating units, transformers, dual chargers, and other components, which are not limited in the present invention.

In some embodiments, the available power generation of the power generation unit

for:

Where

It is a binary variable that represents the mechanical state of the power generating unit at time t. When the power generation unit is working under normal conditions,

otherwise,

In addition,

Represents the potential power output of the power generating unit in the rated state during the period t.

Available discharge capacity of the transformer at time t

It can be expressed as:

Where

with

Respectively represent the grid power and mechanical availability of the transformer at time t.

According to some embodiments of the present invention, a sequential Monte Carlo simulation method may be used to calculate the first reliability index of the first intelligent power distribution system.

Specifically, the sequential Monte Carlo simulation method can realize the statistical calculation of reliability indicators by simulating the random process of the operation of the intelligent power distribution system. The calculation process is described below.

First, specify the number of simulation years n, and set the simulation start year i=1. Under the condition that the time span is 1 year, that is, 8760 hours, the duration of the intelligent power distribution system component j (j = 1, 2,..., m) is randomly sampled to obtain each The "operation-repair-operation-repair" state alternate process of the component in the i-th year.

Combine the operation and repair process of each component to obtain the state duration time sequence of the intelligent power distribution system in chronological order

Duration sequence of the state of the system

Analyze and calculate the system status at each moment in the process, including power flow calculation, optimal load reduction, etc. For example, first perform a power flow calculation to determine whether the system state has a violation of operating constraints such as node voltage violations and line overloads. If the operating constraints are violated, the optimal load reduction calculation is performed.

Then, calculate the annual reliability index of the intelligent power distribution system for the i-th year according to the following formula;

D(x _ij ) represents the duration of the system state x _ij , f(x _ij ) represents the system performance measurement function with the system state as an independent variable, and I _i represents the annual reliability index of the i-th year. It should be noted that when f(x _ij ) takes different measurement functions, I _i also represent different reliability indicators.

Calculate the expected value of the annual reliability index according to the following formula, which is the reliability index of the distribution intelligent system

Calculate the coefficient of variance according to the following formula:

Indicates the reliability index of the distribution intelligent system

Standard deviation,

Indicates the reliability index of the distribution intelligent system

Expectations.

Then, let i=i+1, if i>n, or the variance coefficient β is less than the termination condition, the simulation ends, and the reliability index obtained in this simulation is used as the final value; otherwise, the sequential Monte Carlo simulation method is repeated Calculate the reliability index of the intelligent power distribution system.

It is also possible to calculate a second reliability index of the second intelligent power distribution system in step S220. Of course, before step S220, the second smart power distribution system can be obtained by adding a grid-connected parking lot to the above-mentioned first smart power distribution system.

Similarly, the sequential Monte Carlo simulation method can be used to calculate the second reliability index of the second intelligent power distribution system.

Specifically, the user behavior characteristics of the plug-in electric vehicle may be acquired first, and the user behavior characteristics may include at least one of the following characteristics: the arrival time of the plug-in electric vehicle in the grid-connected parking lot

And parking time

The charge level required when the plug-in electric vehicle leaves the grid-connected parking lot

And the availability of V2G programs for plug-in electric vehicles

Then, based on the user behavior characteristics and the operation policy of the grid-connected parking lot operator (GPLO), the charging and discharging time of the plug-in electric vehicle is determined. The operating policy of the grid-connected parking lot operator can be as follows: when the plug-in electric vehicle PEV reaches the grid-connected parking lot GPL, the plug-in electric vehicle PEV will first be charged until the SOC of its battery reaches a predetermined value, for example, 90% . Then, the PEV will be eligible for V2G operation from now on until its SOC drops to the SOC target (ie, charge level) required by its owner.

Then, similar to the first reliability index, the available power generation of the grid-connected parking lot and the total available power generation and total load demand of the second intelligent power distribution system can be determined for each moment of the simulation year. For example, the total available power generation of the second intelligent power distribution system at each moment can be determined according to the following formula

And total load demand

In the formula, P ^dg represents the available power generation of the power generation unit (DG), P ^dt represents the available power generation of the transformer, P ^agc represents the available power generation of the grid-connected parking lot, Ω _D represents the system bus set, and P ^ch represents the plug-in type The charging level of electric vehicles,

To indicate whether the plug-in electric vehicle k'is involved in the binary variable of the grid-connected parking lot at time t,

As a binary variable indicating the availability of the bilateral charger k at time t, Ω _CP represents the set of bilateral chargers, and Ω _EV represents the set of plug-in electric vehicles.

Represents the conventional load demand of system bus i at time t,

It is a binary variable used to indicate whether the plug-in electric vehicle k'is in a charging state at time t.

The result of can be determined by the following formula:

among them,

It is a binary variable used to specify the availability of the V2G program of the plug-in electric vehicle k'at time t, P ^ch and η ^bc respectively represent the rated charging power and working efficiency of the bilateral charger,

The arrival time of plug-in electric vehicles k'at the grid-connected parking lot can be determined from

Random sampling in.

Indicates the time when the SOC of plug-in electric vehicle k'reaches the threshold, which can be calculated according to the following formula:

P ^ch and η ^bc respectively represent the rated charging power and working efficiency of the bilateral charger,

It is the threshold SoC value used by GPLO for V2G implementation, and is a given (known prior) parameter.

According to the initial state of charge when the vehicle arrives and the expected driving distance in the subsequent journey, the plug-in electric vehicle k'is determined to be the charging demand of the grid-connected parking lot operator

Where

Is the size of the plug-in electric vehicle k'battery (unit: kWh),

Represent the initial SoC of the plug-in electric vehicle k'upon arrival and the SoC (charge level) required when leaving the grid-connected parking lot. among them

Then, the power flow analysis method is used to determine whether the second intelligent power distribution system violates the constraints. And based on the judgment result, calculate the reliability index of the second intelligent power distribution system at this moment. For example, taking the expected value of insufficient power (ENNS) as an example, if the operating constraint is not violated, it indicates that the system is operating normally, and the unsupplied power (ENN) at this moment is 0. If the operating constraint is violated, it indicates that the system is in an emergency state and the optimal load reduction calculation is required. The unsupplied power (ENN) at this moment is

among them

Indicates that the load demand of bus i at time t is not met.

Then, calculate the annual reliability index for the simulation year based on the reliability index at multiple times. Then calculate the second reliability index and variance coefficient based on the annual reliability index of each simulation year. Determine whether the variance coefficient meets the termination condition. If not, repeat the steps of obtaining user behavior characteristics, determining the charging and discharging time of plug-in electric vehicles, calculating the annual reliability index of the simulation year, and calculating the second reliability index and the variance coefficient, until the variance coefficient is satisfied Termination condition. Finally, the second reliability index when the termination condition is met is used as the final second reliability index.

In some embodiments, the termination condition may be β≦0.05.

After the first reliability index and the second reliability index are obtained, the first reliability index and the second reliability index can be compared. If the first reliability index is less than the second reliability index, the available capacity of the grid-connected parking lot can be considered as zero.

If the first reliability index is not less than the second reliability index, the equivalent fixed capacity, or equivalent conventional capacity, or effective carrying capacity can be selected as the available capacity of the grid-connected parking lot.

According to an embodiment of the present invention, when the first reliability index is not less than the second reliability index, if the equivalent fixed capacity or equivalent conventional capacity is selected to measure the available capacity of the grid-connected parking lot, then In step S230, the third reliability index of the third intelligent power distribution system is calculated, and the available capacity of the grid-connected parking lot is determined based on the second reliability index and the third reliability index. Of course, before step S230, the third smart power distribution system can be obtained by adding a generator set to the first smart power distribution system.

The process of calculating the third reliability index is similar to the process of calculating the first reliability index, and will not be repeated here.

Specifically, after the third reliability index is obtained, the convergence coefficient may be calculated based on the second reliability and the third reliability index. For example, calculate the convergence coefficient α _V-GPL according to the following formula:

α _V-GPL =|I ^V -I ^GPL |/I ^GPL ,

I ^V is the third reliability index, and I ^GPL is the second reliability index.

Determine whether the convergence coefficient α _V-GPL satisfies the convergence condition. In some embodiments, the convergence condition may be α _V-GPL <ζ.

When the convergence coefficient α _V-GPL does not meet the convergence conditions, repeat the steps of adjusting the capacity of the generator set based on the second reliability and the third reliability index, calculating the third reliability index, and calculating the convergence coefficient until the convergence coefficient Meet the convergence condition.

Wherein, the capacity of the generator set is determined based on the first parameter and the second parameter. For example, determine the capacity C ^{bm of the} generator set according to the following formula:

C ^bm =(C _max +C _min )/2, C _max represents the first parameter, and C _min represents the second parameter. In the initial situation, let C _max =C ^rat , C _min =0, and C ^rat is an arbitrarily selected positive value.

If the convergence coefficient α _V-GPL does not meet the convergence condition, the process of adjusting the capacity of the generator set based on the second reliability and the third reliability index can be as follows:

Compare the second reliability index and the third reliability index. Then, based on the comparison result of the second reliability index and the third reliability index, the first parameter or the second parameter is adjusted according to the current capacity of the generator set, so as to adjust the capacity of the generator set accordingly.

For example, if the third reliability index is greater than the second reliability index, the second parameter C _min is adjusted according to the following formula to adjust the capacity C ^{bm of the} generator set:

C _min =C ^bm .

If the third reliability index is not greater than the second reliability index, adjust the first parameter C _max according to the following formula to adjust the capacity C ^{bm of the} generator set:

C _max =C ^bm .

Finally, the available capacity of the grid-connected parking lot is determined according to the capacity of the generator set when the convergence coefficient α _V-GPL meets the convergence condition. That is, according to the capacity of the generator set, the equivalent fixed capacity or equivalent conventional capacity of the generator set is determined as the available capacity of the grid-connected parking lot. For example, determine the equivalent fixed capacity EFC or equivalent conventional capacity ECC of a generator set according to the following formula:

EFC/ECC=C ^bm .

According to another embodiment of the present invention, in the case where the first reliability index is not less than the second reliability index, if the effective carrying capacity is selected to measure the available capacity of the grid-connected parking lot, then in step S240, the second reliability index can be calculated. 4. The fourth reliability index of the intelligent power distribution system, and the available capacity of the grid-connected parking lot is determined based on the first reliability index and the fourth reliability index. Of course, before step S240, the fourth smart power distribution system can be obtained by adding a virtual load to the second smart power distribution system.

The process of calculating the fourth reliability index is similar to the process of calculating the first reliability index, and will not be repeated here.

Specifically, after the fourth reliability index is obtained, the convergence coefficient may be calculated based on the first reliability and the fourth reliability index. For example, calculate the convergence coefficient according to the following formula

Is the fourth reliability index, and I ^base is the first reliability index.

Judging the convergence coefficient

Whether the convergence condition is met. In some embodiments, the convergence condition can be

In convergence coefficient

If the convergence condition is not met, the steps of adjusting the capacity of the generator set based on the first reliability index and the fourth reliability index, calculating the fourth reliability index, and calculating the convergence coefficient are repeated until the convergence coefficient meets the convergence conditions.

Among them, the capacity of the virtual load is determined based on the third parameter and the fourth parameter. For example, determine the virtual load capacity D ^vl according to the following formula:

D ^vl = (D _max + D _min )/2, D _max represents the third parameter, and D _min represents the fourth parameter. In the initial situation, let D _max =D ^rat , D _min =0, and D ^rat is an arbitrarily selected positive value.

If the convergence coefficient

If the convergence condition is not met, the process of adjusting the capacity of the virtual load based on the first reliability index and the fourth reliability index may be as follows:

Compare the first reliability index and the fourth reliability index. Then, based on the comparison result of the first reliability index and the fourth reliability index, the third parameter or the fourth parameter is adjusted according to the current capacity of the virtual load, so as to adjust the capacity of the virtual load accordingly.

For example, if the fourth reliability index is smaller than the first reliability index, the fourth parameter D _min is adjusted according to the following formula to adjust the virtual load capacity D ^vl :

D _min =D ^vl .

If the fourth reliability index is not less than the first reliability index, the third parameter D _max is adjusted according to the following formula, thereby adjusting the virtual load capacity D ^vl :

D _max =D ^vl .

Finally, according to the convergence coefficient

The capacity of the virtual load when the convergence condition is met, and the available capacity of the grid-connected parking lot is determined. That is, according to the capacity of the virtual load, the effective carrying capacity of the virtual load is determined as the available capacity of the grid-connected parking lot. For example, determine the effective carrying capacity ELCC of the virtual load according to the following formula:

ELCC=D ^vl .

According to various embodiments of the present invention, the aforementioned reliability indicators (first, second, third, and fourth reliability indicators) may include at least one of the following indicators: load shedding probability PLC, load shedding frequency EFLC, load shedding Duration EDLC, average load shedding duration ADLC, load shedding expected value ELC, system power outage index BPII, system power reduction index BPECI, severity index SI, and insufficient battery expected value EENS. Preferably, the expected value of insufficient power EENS can be used to measure the reliability of the system.

Fig. 3 shows a structural block diagram of a device 300 for determining the available capacity of a grid-connected parking lot according to an embodiment of the present invention. As shown in FIG. 3, the device 300 for determining the available capacity of a grid-connected parking lot includes an index comparison unit 310 and a capacity determination unit 320.

The index comparison unit 310 is adapted to compare the first reliability index of the first smart power distribution system with the second reliability index of the second smart power distribution system. The first smart power distribution system does not include grid-connected parking lots and the second smart power distribution system The electrical system is obtained by adding a grid-connected parking lot to the first intelligent power distribution system.

The index comparison unit 310 also includes an index calculation unit 311, which is adapted to calculate the second reliability index according to the following steps: obtain the user behavior characteristics of the plug-in electric vehicle, and the user behavior characteristics include the presence of the plug-in electric vehicle. Describe the arrival time and parking time of the grid-connected parking lot, the charging level required when leaving the grid-connected parking lot, and the availability of V2G programs. Then, based on the user's behavior characteristics, the charging and discharging time (ie, charging and discharging time) of the plug-in electric vehicle is determined. Then for each moment of the simulation year, determine the available power generation of the grid-connected parking lot; determine the total available power generation and total load demand of the second intelligent power distribution system; use the power flow analysis method to determine whether the second intelligent power distribution system violates Constraint: Based on the judgment result, calculate the reliability index of the second intelligent power distribution system at this moment. Then, calculate the annual reliability index for the simulation year based on the reliability index at multiple times. Finally, the second reliability index is calculated based on the annual reliability index of each simulation year.

The capacity determining unit 320 is adapted to calculate the third reliability index of the third intelligent power distribution system based on the second reliability index and the third reliability index when the first reliability index is not less than the second reliability index To determine the available capacity of the grid-connected parking lot, the third smart power distribution system is obtained by adding a generator set to the first smart power distribution system.

The capacity determination unit 320 is further adapted to calculate a fourth reliability index of the fourth intelligent power distribution system based on the first reliability index and the fourth reliability index when the first reliability index is not less than the second reliability index. Indicators to determine the available capacity of the grid-connected parking lot, the fourth intelligent power distribution system is obtained by adding a virtual load to the second intelligent power distribution system.

For the detailed processing logic and implementation process of each unit in the device 300 for determining the available capacity of the grid-connected parking lot, please refer to the previous description of the method 200 for determining the available capacity of the grid-connected parking lot in conjunction with Figures 1 and 2, which will not be repeated here. .

In summary, the available capacity determination scheme of the grid-connected parking lot according to the embodiment of the present invention can objectively estimate and compare the contribution of the grid-connected parking lot to the capacity of the intelligent power distribution system by determining the available capacity of the grid-connected parking lot. It serves as a conventional power generation resource in the market. Among them, the randomness of user behavior of plug-in electric vehicles and its potential dependence on externalities are considered. On this basis, the sequential Monte Carlo simulation method is used to calculate the reliability index, which can completely express the characteristics of the grid-connected parking lot.

It should be understood that, in order to simplify the present disclosure and help understand one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or In its description. However, the disclosed method should not be interpreted as reflecting the intention that the claimed invention requires more features than those explicitly stated in each claim. More precisely, as reflected in the following claims, the inventive aspect lies in less than all the features of a single embodiment disclosed previously. Therefore, the claims following the specific embodiment are thus explicitly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present invention.

Those skilled in the art should understand that the modules or units or components of the device in the example disclosed herein can be arranged in the device as described in this embodiment, or alternatively can be positioned differently from the device in this example In one or more devices. The modules in the foregoing examples can be combined into one module or further divided into multiple sub-modules.

Those skilled in the art can understand that it is possible to adaptively change the modules in the device in the embodiment and set them in one or more devices different from the embodiment. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and/or processes or units are mutually exclusive, any combination can be used to compare all features disclosed in this specification (including the accompanying claims, abstract and drawings) and any method or methods disclosed in this manner or All the processes or units of the equipment are combined. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

In addition, those skilled in the art can understand that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments means that they are within the scope of the present invention. Within and form different embodiments. For example, in the following claims, any one of the claimed embodiments can be used in any combination.

In addition, some of the embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other devices that perform the described functions. Therefore, a processor with the necessary instructions for implementing the method or method element forms a device for implementing the method or method element. In addition, the elements described herein of the device embodiments are examples of devices for implementing functions performed by the elements for the purpose of implementing the invention.

As used herein, unless otherwise specified, the use of ordinal numbers "first", "second", "third", etc. to describe ordinary objects merely refers to different instances of similar objects, and is not intended to imply such The described objects must have a given order in terms of time, space, order, or in any other way.

Although the present invention has been described in terms of a limited number of embodiments, benefiting from the above description, those skilled in the art understand that other embodiments can be envisaged within the scope of the invention thus described. In addition, it should be noted that the language used in this specification is mainly selected for the purpose of readability and teaching, not for explaining or limiting the subject of the present invention. Therefore, without departing from the scope and spirit of the appended claims, many modifications and alterations are obvious to those of ordinary skill in the art. For the scope of the present invention, the disclosure of the present invention is illustrative rather than restrictive, and the scope of the present invention is defined by the appended claims.

Claims

A method for determining the available capacity of a grid-connected parking lot, which is suitable for parking plug-in electric vehicles and meets the charging and discharging requirements of the plug-in electric vehicles, the method comprising:

Compare the first reliability index of the first smart power distribution system with the second reliability index of the second smart power distribution system. The first smart power distribution system does not include grid-connected parking lots, and the second smart power distribution system Obtained by adding the grid-connected parking lot to the first intelligent power distribution system, where

The second reliability index is calculated according to the following steps:

Obtain the user behavior characteristics of the plug-in electric vehicle, and the user behavior characteristics include the arrival time and parking time of the plug-in electric vehicle in the grid-connected parking lot, and the required time when leaving the grid-connected parking lot. Charging level and V2G program availability;

Determining the charging and discharging time of the plug-in electric vehicle based on the user behavior characteristics;

For each moment of the simulation year, determine the available power generation of the grid-connected parking lot; determine the total available power generation and total load demand of the second intelligent power distribution system; determine whether the second intelligent power distribution system is Violation of constraints; based on the judgment result, calculating the reliability index of the second intelligent power distribution system at this moment;

Calculate the annual reliability index of the simulation year according to the reliability index at multiple times;

Calculating the second reliability index based on the annual reliability index of each simulation year;

In the case that the first reliability index is not less than the second reliability index, the third reliability index of the third intelligent power distribution system is calculated, and the grid connection is determined based on the second reliability index and the third reliability index The available capacity of the parking lot is obtained by the third smart power distribution system by adding a generator set to the first smart power distribution system.
The method of claim 1, further comprising:

In the case that the first reliability index is not less than the second reliability index, the fourth reliability index of the fourth intelligent power distribution system is calculated, and the grid connection is determined based on the first reliability index and the fourth reliability index The available capacity of the parking lot is obtained by the fourth smart power distribution system by adding a virtual load to the second smart power distribution system.
The method of claim 1, wherein the step of determining the available capacity of the grid-connected parking lot based on the second reliability index and the third reliability index comprises:

Calculate the convergence coefficient based on the second reliability and the third reliability index;

Judge whether the convergence coefficient meets the convergence condition;

In the case that the convergence coefficient does not meet the convergence condition, repeat the steps of adjusting the capacity of the generator set based on the second reliability index and the third reliability index, calculating the third reliability index, and calculating the convergence coefficient until the convergence coefficient meets Convergence condition

Determine the available capacity of the grid-connected parking lot according to the capacity of the generator set when the convergence condition is met.
The method according to claim 3, wherein the convergence coefficient α V-GPL is calculated according to the following formula based on the second reliability index and the third reliability index:

α V-GPL =|I V -I GPL |/I GPL , where I V is the third reliability index, and I GPL is the second reliability index.
The method according to claim 2, wherein the step of determining the available capacity of the grid-connected parking lot based on the first reliability index and the fourth reliability index comprises:

Calculate the convergence coefficient based on the first reliability and the fourth reliability index;

Judge whether the convergence coefficient meets the convergence condition;

In the case that the convergence coefficient does not meet the convergence condition, repeat the steps of adjusting the capacity of the virtual load based on the first reliability index and the fourth reliability index, calculating the fourth reliability index, and calculating the convergence coefficient until the convergence coefficient meets Convergence condition

Determine the available capacity of the grid-connected parking lot according to the capacity of the virtual load when the convergence condition is met.
The method of claim 5, wherein the convergence coefficient is calculated according to the following formula based on the first reliability index and the fourth reliability index

among them
Is the fourth reliability index, and I base is the first reliability index.
The method of claim 3, wherein the capacity of the generator set is determined based on the first parameter and the second parameter, and the step of adjusting the capacity of the generator set based on the second reliability index and the third reliability index comprises:

Compare the second reliability index and the third reliability index;

Based on the comparison result, the first parameter or the second parameter is adjusted according to the current capacity of the generator set, so as to adjust the capacity of the generator set accordingly.
The method according to claim 5, wherein the capacity of the generator set is determined based on the third parameter and the fourth parameter, and the step of adjusting the capacity of the virtual load based on the first reliability index and the fourth reliability index comprises:

Compare the first reliability index and the fourth reliability index;

Based on the comparison result, the third parameter or the fourth parameter is modified based on the current capacity of the virtual load, so as to adjust the capacity of the virtual load accordingly.
The method of claim 3, wherein the step of determining the available capacity of the grid-connected parking lot according to the capacity of the generator set when the convergence condition is met comprises:

According to the capacity of the generator set, the equivalent fixed capacity or equivalent conventional capacity of the generator set is determined as the available capacity of the grid-connected parking lot.
The method according to claim 5, wherein the step of determining the available capacity of the grid-connected parking lot according to the capacity of the virtual load when the convergence condition is met comprises:

According to the capacity of the virtual load, the effective carrying capacity of the virtual load is determined as the available capacity of the grid-connected parking lot.
The method of claim 1, wherein the step of calculating the second reliability index based on the annual reliability index of each simulation year further comprises:

Calculate the variance coefficient based on the annual reliability index of each simulation year;

Determine whether the variance coefficient meets the termination condition;

If not, repeat the steps of obtaining user behavior characteristics, determining the charging and discharging time of the plug-in electric vehicle, calculating the annual reliability index of the simulation year, and calculating the second reliability index and the coefficient of variance, until the coefficient of variance Meet the termination conditions;

The second reliability index when the termination condition is satisfied is used as the final second reliability index.
The method of claim 1, further comprising:

Generating a state duration sequence of the first intelligent power distribution system component;

The available power generation of the first intelligent power distribution system at each time in the state duration time sequence is determined.
The method according to any one of claims 1-12, wherein a sequential sampling Monte Carlo simulation method is used to calculate the first reliability index, the second reliability index, the third reliability index, and the fourth reliability index. Sexual indicators.
The method according to any one of claims 1-12, wherein the reliability index comprises at least one of the following: load shedding probability PLC, load shedding frequency EFLC, load shedding duration EDLC, average load shedding duration ADLC, load shedding expected value ELC, system power outage indicator BPII, system power reduction indicator BPECI, severity indicator SI, and insufficient battery expected value EENS.
A device for determining the available capacity of a grid-connected parking lot, which is suitable for parking plug-in electric vehicles and meets the charging and discharging requirements of the plug-in electric vehicles, the device comprising:

An index comparison unit, adapted to compare the first reliability index of the first smart power distribution system with the second reliability index of the second smart power distribution system, the first smart power distribution system does not include a grid-connected parking lot, the The second smart power distribution system is obtained by adding the grid-connected parking lot to the first smart power distribution system, wherein the index comparison unit further includes an index calculation unit,

The index calculation unit is adapted to calculate the second reliability index according to the following steps:

Obtain user behavior characteristics of the plug-in electric vehicle, the user behavior characteristics including the plug-in electric vehicle's arrival time and parking time in the grid-connected parking lot, and the required time when leaving the grid-connected parking lot Charging level and V2G program availability;

Determining the charging and discharging time of the plug-in electric vehicle based on the user behavior characteristics;

For each moment of the simulation year, determine the available power generation of the grid-connected parking lot; determine the total available power generation and total load demand of the second smart power distribution system; use the power flow analysis method to determine whether the second smart power distribution system is Violation of constraints; based on the judgment result, calculating the reliability index of the second intelligent power distribution system at this moment;

Calculate the annual reliability index of the simulation year according to the reliability index at multiple times;

Calculating the second reliability index based on the annual reliability index of each simulation year;

The capacity determining unit is adapted to calculate the third reliability index of the third intelligent power distribution system when the first reliability index is not less than the second reliability index, and based on the second reliability index and the third reliability index To determine the available capacity of the grid-connected parking lot, the third smart power distribution system is obtained by adding a generator set to the first smart power distribution system.
A computing device including:

One or more processors; and

Memory

One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include a program for executing according to claim 1 -14 Instructions for any of the methods described.
A computer-readable storage medium storing one or more programs, the one or more programs including instructions, which when executed by a computing device, cause the computing device to perform the method according to claims 1-14 Any method.