CN110276135B - Available capacity determination method and device for grid-connected parking lot and computing equipment - Google Patents
Available capacity determination method and device for grid-connected parking lot and computing equipment Download PDFInfo
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Abstract
The embodiment of the invention discloses a method for determining available capacity of a grid-connected parking lot, which comprises the following steps: and comparing the first reliability index of the first intelligent power distribution system with the second reliability index of the second intelligent power distribution system, calculating a third reliability index of a third intelligent power distribution system under the condition that the first reliability index is not smaller than the second reliability index, and determining the available capacity of the grid-connected parking lot based on the second reliability index and the third reliability index. The embodiment of the invention also discloses a corresponding available capacity determining device of the grid-connected parking lot, a computing device and a storage medium.
Description
Technical Field
The invention relates to the field of power systems, in particular to a method and a device for determining available capacity of a grid-connected parking lot and computing equipment.
Background
With the rapid development of electric vehicles driven by electricity, the problem of energy demand of plug-in electric vehicles (PEVs) is receiving much attention.
Typically, charging infrastructure like grid-tied parking lots (GPL) may be employed to suffice. GPLs are typically located in densely populated areas and are equipped with distributed charging points and low power consumption 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 the PEV battery. In fact, since most domestic cars stop more than 95% of the day, the total storage capacity of a GPL with hundreds of integrated electric cars can be used as a controllable load during charging (i.e., grid-to-vehicle mode of operation, G2V) or as an alternative distributed energy source to grid-to-ground (i.e., vehicle-to-grid mode of operation, V2G). Due to such bi-directional capabilities, the emergence of GPL provides a wide spectrum range for future energy systems, which can be effectively utilized during operation. It is therefore important to evaluate the capacity of the GPL.
There has been very substantial bedding work today with respect to potential benefit studies of GPLs in power systems. However, in these studies, little effort has been devoted to exploring the effect of GPL on system reliability. In practical applications, a GPL with a bidirectional charger can be used as a backup power source and provide capacity support for the grid in emergency situations by drawing energy from the PEV battery. This may reduce the risk of load loss and significantly improve the power reliability of an intelligent power distribution system (SDS).
In view of this, the GPL reliability assessment problem is receiving increasing attention from researchers. The invention provides a novel scheme for determining available Capacity (CV) of a grid-connected parking lot, and under the background of an intelligent power distribution system, the reliability benefit of GPL is approximately estimated through the available capacity of the grid-connected parking lot.
Disclosure of Invention
To this end, embodiments of the present invention provide a method, an apparatus, and a computing device for determining an 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 an aspect of an embodiment of the present invention, there is provided a method for determining an available capacity of a grid-connected parking lot, the grid-connected parking lot being suitable for parking a plug-in electric vehicle and satisfying a charge and discharge demand of the plug-in electric vehicle, the method including: comparing a first reliability index of a first intelligent power distribution system with a second reliability index of a second intelligent power distribution system, wherein the first intelligent power distribution system does not comprise a grid-connected parking lot, the second intelligent power distribution system is obtained by adding the grid-connected parking lot into the first intelligent power distribution system, and the second reliability index is obtained by calculation according to the following steps: acquiring user behavior characteristics of the plug-in electric vehicle, wherein the user behavior characteristics comprise arrival time and parking time of the plug-in electric vehicle at the grid-connected parking lot, required charging level when the plug-in electric vehicle leaves the grid-connected parking lot, and V2G program availability; determining charging and discharging time of the plug-in electric vehicle based on the user behavior characteristics; determining the available power generation amount of the grid-connected parking lot at each moment of the simulation year; determining a total available power generation capacity and a total load demand of the second intelligent power distribution system; judging whether the second intelligent power distribution system violates the constraint or not by adopting a power flow analysis method; calculating the reliability index of the second intelligent power distribution system at the moment based on the judgment result; calculating the annual reliability index of the simulation year according to the reliability indexes at a plurality of moments; calculating the second reliability index based on the annual reliability index of each simulation year; and under the condition that the first reliability index is not smaller than the second reliability index, calculating a third reliability index of a third intelligent power distribution system, and determining the available capacity of the grid-connected parking lot based on the second reliability index and the third reliability index, wherein the third intelligent power distribution system is obtained by adding a generator set in the first intelligent power distribution system.
According to another aspect of the embodiments of the present invention, there is provided an available capacity determination apparatus for a grid-connected parking lot adapted to park a plug-in electric vehicle and satisfy a charge and discharge demand of the plug-in electric vehicle, the apparatus including: the index comparison unit is suitable for 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 comprise a grid-connected parking lot, the second intelligent power distribution system is obtained by adding the grid-connected parking lot into the first intelligent power distribution system, the index comparison unit further comprises an index calculation unit, and the index calculation unit is suitable for calculating the second reliability index according to the following steps: acquiring user behavior characteristics of the plug-in electric vehicle, wherein the user behavior characteristics comprise arrival time and parking time of the plug-in electric vehicle at the grid-connected parking lot, required charging level when the plug-in electric vehicle leaves the grid-connected parking lot, and V2G program availability; determining charging and discharging time of the plug-in electric vehicle based on the user behavior characteristics; determining the available power generation amount of the grid-connected parking lot at each moment of the simulation year; determining a total available power generation capacity and a total load demand of the second intelligent power distribution system; judging whether the second intelligent power distribution system violates the constraint or not by adopting a power flow analysis method; calculating the reliability index of the second intelligent power distribution system at the moment based on the judgment result; calculating the annual reliability index of the simulation year according to the reliability indexes at a plurality of moments; calculating the second reliability index based on the annual reliability index of each simulation year; and the capacity determining unit is suitable for calculating a third reliability index of a third intelligent power distribution system under the condition that the first reliability index is not smaller than the second reliability index, and determining the available capacity of the grid-connected parking lot based on the second reliability index and the third reliability index, wherein the third intelligent power distribution system is obtained by adding a generator set in the first intelligent power distribution system.
According to another aspect of embodiments of the present invention, there is provided a computing device including: one or more processors; and a 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, the one or more programs including instructions for performing any one of the methods for determining available capacity of a grid-connected parking lot according to embodiments of the present invention.
According to still another aspect of embodiments of the present invention, there is provided 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 any one of the available capacity determination methods of a grid-connected parking lot according to embodiments of the present invention.
According to the available capacity determination scheme of the grid-connected parking lot, the available capacity of the grid-connected parking lot is determined, so that the contribution of the grid-connected parking lot to the capacity of the intelligent power distribution system can be objectively estimated and compared, and the grid-connected parking lot can be used as a conventional power generation resource in the market. In which the randomness of the user behavior of the plug-in electric vehicle and its potential dependency on the externality are taken into account. On the basis, the reliability index is calculated by adopting a sequential Monte Carlo simulation method, and the characteristics of the grid-connected parking lot can be completely expressed.
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To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings, which are indicative of various ways in which the principles disclosed herein may be practiced, and all aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description read in conjunction with the accompanying drawings. Throughout this disclosure, like reference numerals generally refer to like parts or elements.
FIG. 1 shows a schematic diagram of a computing device 100, according to one embodiment of the invention;
fig. 2 shows a flowchart of a method 200 for determining available capacity of a grid-connected parking lot according to an embodiment of the present invention; and
fig. 3 is a block diagram illustrating a configuration of an available capacity determination apparatus 300 for a grid-connected parking lot according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
FIG. 1 shows a schematic diagram of a computing device 100, according to one embodiment of the invention. As shown in FIG. 1, in a basic configuration 107, computing device 100 typically includes system memory 106 and one or more processors 104. A 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 (μ P), a microcontroller (μ C), a Digital Signal Processor (DSP), or any combination thereof. The processor 104 may include one or more levels of cache, such as a level one cache 110 and a level two cache 112, a processor core 114, and registers 116. The example processor core 114 may include an Arithmetic Logic Unit (ALU), a Floating Point 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, 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. System memory 106 may include an operating system 120, one or more applications 122, and program data 124. In some implementations, the application 122 can be arranged to execute instructions on an operating system with program data 124 by one or more processors 104.
Computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (e.g., output devices 142, peripheral interfaces 144, and communication devices 146) to the basic configuration 107 via the bus/interface controller 130. The example output device 142 includes a graphics processing unit 148 and an audio processing unit 150. They may be configured to facilitate communication with various external devices, such as a display or speakers, via one or more a/V ports 152 or HDMI interfaces. Example peripheral interfaces 144 may include a serial interface controller 154 and a parallel interface controller 156, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device, remote input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 158. An example communication device 146 may include a network controller 160, which may be arranged to facilitate communications with one or more other computing devices 162 over a network communication link via one or more communication ports 164.
A network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media, such as carrier waves or other transport mechanisms, in a modulated data signal. A "modulated data signal" may be a signal that has one or more of its data set or its changes made in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or private-wired network, and various wireless media such as acoustic, Radio Frequency (RF), microwave, Infrared (IR), or other wireless media. The term computer readable media as used herein may include both storage media and communication media.
Computing device 100 may be implemented as a server, such as a database server, an application server, a WEB server, and the like, or as a personal computer including both desktop and notebook computer configurations. Of course, computing device 100 may also be implemented as a small-sized portable (or mobile) electronic device.
In an embodiment according to the present invention, the computing device 100 may be implemented at least as components in an available capacity determination apparatus 300 of a grid-connected parking lot and configured to execute the available capacity determination method 200 of a grid-connected parking lot according to an embodiment of the present invention. The application 122 of the computing device 100 includes a plurality of instructions for executing the available capacity determination method 200 for the grid-connected parking lot according to the embodiment of the present invention, and the program data 124 may further store configuration information of the available capacity determination device 300 for the grid-connected parking lot, and the like.
Fig. 2 shows a flowchart of an available capacity determination method 200 of a grid-connected parking lot according to an embodiment of the present invention. As shown in fig. 2, the available capacity determination method 200 for the grid-connected parking lot is adapted to be executed in the available capacity determination device 300 for the grid-connected parking lot and starts at step S210.
The available capacity of the Grid-connected parking lot refers to the available capacity of the plug-in electric Vehicle in the process of participating in V2G (Vehicle-to-Grid). It can be understood that a Grid-connected parking lot (GPL) is suitable for parking a Plug-in electric vehicle (PEV) and meets the charging and discharging requirements of the Plug-in electric vehicle. Specifically, the grid-connected parking lot may charge the plug-in electric vehicle and may receive discharge of the plug-in electric vehicle. That is, the grid-connected parking lot can generally realize bidirectional power exchange between the plug-in electric vehicle and the power grid under the coordinated control of a grid-connected parking lot operator (GPLO). When the Grid-connected parking lot charges the plug-in electric vehicle, the plug-in electric vehicle operates in a 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 operates in a V2G (Vehicle-to-Grid) mode.
In step S210, a first reliability index of a first intelligent power distribution system is calculated, where the first intelligent power distribution system does not include a grid-connected parking lot.
Before calculating the reliability index of the first intelligent power distribution system component, a state duration sequence of the first intelligent power distribution system component may be generated to obtain the state duration sequence of the first intelligent power distribution system, and an available power generation amount of the first intelligent power distribution system at each time in the state duration sequence may be determined. The first intelligent power distribution system component may include a power generation unit, a transformer, a dual sided charger, etc., as the present invention is not limited in this respect.
In some embodiments, the available power generation of the power generation unit
Comprises the following steps:
in the formula (I), the compound is shown in the specification,
is a binary variable representing the mechanical state of the power generating unit at time t. When the power generating unit is operating under normal conditions,
if not, then,
in addition, the first and second substrates are,
representing the potential power output of the power generating unit at nominal condition during the period t.
Available discharge of the transformer at time t
Can be expressed as:
in the formula (I), the compound is shown in the specification,
and
respectively representing the grid power and the mechanical availability of the transformer at the time t.
According to some embodiments of the invention, a sequential monte carlo simulation method may be employed to calculate a first reliability indicator for a first smart power distribution system.
Specifically, the sequential monte carlo simulation method can realize statistical calculation of the reliability index by simulating a random process of the operation of the intelligent power distribution system. This calculation process is described below.
First, the number of simulation years n is given, and the simulation starting year i is 1. Randomly sampling the duration of the intelligent power distribution system component j (j is 1, 2.. said., m) in the running and repairing states under the condition of the time span of 1 year, namely 8760 hours, so as to obtain the state alternation process of 'running-repairing-running-repairing' in the ith year of each component.
Combining the operation and repair processes of each component to obtain a state duration time sequence of the intelligent power distribution system with time sequence
To the systemSequence of state durations
The system state at each moment in time is analyzed and calculated, and the method comprises load flow calculation, optimal load reduction and the like. For example, load flow calculation is performed to determine whether the system state is violated by operation constraints such as node voltage violation, line overload, etc., and if the operation constraints are violated, optimal load reduction calculation is performed.
Then, calculating the annual reliability index of the intelligent power distribution system in the ith year according to the following formula;
D(xij) Represents the system state xijDuration of (d), f (x)ij) Representing a function of a measure of the performance of the system with the state of the system as an argument, IiThe annual reliability index of the i-th year is shown. It should be noted that when f (x)ij) Taking functions of different measures, IiAnd thus represent different reliability indicators.
Calculating the expected value of the annual reliability index according to the following formula, namely the reliability index of the power distribution intelligent system
The variance coefficient is calculated according to the following formula:
Then, if i is greater than n or the variance coefficient beta is smaller than a termination condition, ending the simulation, and taking the reliability index obtained by the current simulation as a final value; otherwise, the sequential Monte Carlo simulation method is repeatedly adopted to calculate the reliability index of the intelligent power distribution system.
A second reliability index of the second intelligent power distribution system may also be calculated in step S220. Of course, before step S220, the second intelligent power distribution system may be obtained by adding a grid-connected parking lot to the first intelligent power distribution system.
Likewise, a sequential monte carlo simulation method may be employed to calculate a second reliability index for a second smart power distribution system.
Specifically, the user behavior characteristics of the plug-in electric vehicle may be obtained first, and the user behavior characteristics may include at least one of the following characteristics: arrival time of plug-in electric vehicle in grid-connected parking lot
And parking time
Charging level required when plug-in electric vehicle leaves grid-connected parking lot
And V2G program availability for plug-in electric vehicles
Next, charge and discharge time of the plug-in electric vehicle is determined based on the user behavior characteristics and an operation policy of a grid-connected parking lot operator (GPLO). The operation policy of the grid-connected parking lot operator may be as follows: when the plug-in electric vehicle PEV reaches the grid-connected parking lot GPL, the plug-in electric vehicle PEV will be charged first until the SOC of its battery reaches a predetermined value, for example 90%. The PEV will then be eligible for V2G operation from then on until its SOC falls to the SOC target (i.e., charge level) required by its owner.
Then, similarly to the first reliability index, the available power generation amount of the grid-connected parking lot, and the total available power generation amount and the total load demand of the second intelligent power distribution system may be determined for each time of the simulation year. For example, the total available power generation of the second intelligent power distribution system at each time may be determined according to the following formula
And total load demand
In the formula, PdgRepresents the available power generation of the power generation unit (DG), PdtRepresenting the available power production of the transformer, PagcRepresents the available power generation amount, omega, of the grid-connected parking lotDRepresenting a set of system buses, PchIndicating the charge level of the plug-in electric vehicle,
to indicate whether the plug-in electric vehicle k' is involved in a binary variable of the grid-connected parking lot at time t,
to indicate the availability of the dual-sided charger k at time t, a binary variable, ΩCPRepresents a bilateral charger set, ΩEVRepresenting a collection of plug-in electric vehicles.
Representing the normal load demand of the system bus i at time t,
is a binary variable for indicating whether the plug-in electric vehicle k' is in a charging state at time t.
wherein the content of the first and second substances,
is a binary variable, P, for specifying the availability of the program V2G during the time t for the plug-in electric vehicle kchAnd ηbcRespectively representing the rated charging power and the working efficiency of the double-side charger,
the arrival time of the plug-in electric vehicle k' at the grid-connected parking lot can be determined from
And (4) obtaining the target by random sampling.
The time for the SOC of the plug-in electric vehicle k' to reach the threshold value can be calculated according to the following formula:
Pchand ηbcRespectively representing the rated charging power and the working efficiency of the double-side charger,
is the threshold SoC value that GPLO uses for V2G implementation, given (known a priori) parameters.
Determining the charging demand of the plug-in electric vehicle k' on the grid-connected parking lot operator according to the initial charge state of the vehicle when the vehicle arrives and the expected driving distance of the vehicle in the subsequent journey
In the formula (I), the compound is shown in the specification,
for the size of a plug-in electric vehicle k' battery (unit: kWh),
the initial SoC of the plug-in electric vehicle k' at arrival and the SoC (charge level) required at leaving the grid-connected parking lot are respectively represented. Wherein
And then, judging whether the second intelligent power distribution system violates the constraint or not by adopting a power flow analysis method. And based on the judgment result, calculating the reliability index of the second intelligent power distribution system at the moment. For example, taking the expected power shortage (ENNS) as an example, if the operation constraint is not violated, the system is indicated to operate normally, and the non-supplied power (ENN) at this time is 0. If the running constraint is violated, the system is in an emergency state, the optimal load reduction calculation is needed, and the non-supplied electricity quantity (ENN) at the moment is
Wherein
Indicating that the bus i is not meeting the load demand at time t.
And then, according to the reliability indexes at a plurality of moments, calculating the annual reliability index of the simulation year. And calculating a second reliability index and a variance coefficient based on the annual reliability index of each simulation year. And judging whether the variance coefficient meets a termination condition. If the variance coefficient does not meet the termination condition, repeating the steps of obtaining the 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 variance coefficient until the variance coefficient meets the termination condition. And finally, taking the second reliability index meeting the termination condition as a final second reliability index.
In some embodiments, the termination condition may be β ≦ 0.05.
After obtaining the first and second reliability indicators, the first and second reliability indicators may be compared. If the first reliability index is smaller than the second reliability index, the available capacity of the grid-connected parking lot can be considered to be 0.
If the first reliability index is not smaller than the second reliability index, the equivalent fixed capacity, the equivalent conventional capacity or the effective bearing capacity can be selected as the available capacity of the grid-connected parking lot.
According to an embodiment of the present invention, in case that the first reliability index is not less than the second reliability index, if the equivalent fixed capacity or the equivalent regular capacity is selected to measure the available capacity of the grid-connected parking lot, a third reliability index of the third intelligent power distribution system may be calculated in step S230, and the available capacity of the grid-connected parking lot may be determined based on the second reliability index and the third reliability index. Of course, before step S230, a third intelligent power distribution system may be obtained by adding a generator set in the first intelligent power distribution system.
The process of calculating the third reliability index is similar to the process of calculating the first reliability index, and is not repeated here.
Specifically, after the third reliability index is obtained, a convergence coefficient may be calculated based on the second reliability and the third reliability index. The convergence coefficient α is calculated, for example, according to the following formulaV-GPL：
αV-GPL＝|IV-IGPL|/IGPL，
IVAs a third reliability index, IGPLIs the second reliability indicator.
Judging the convergence coefficient alphaV-GPLWhether a convergence condition is satisfied. In some embodiments, the convergence condition may be αV-GPL＜ζ。
At the convergence coefficient alphaV-GPLAnd under the condition that the convergence condition is not met, repeating 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 meets the convergence condition.
Wherein the capacity of the genset is determined based on the first parameter and the second parameter. For example, capacity C of the genset is determined according to the following equationbm：
Cbm＝(Cmax+Cmin)/2，CmaxDenotes a first parameter, CminRepresenting the second parameter. In the initial case, let Cmax＝Crat，Cmin＝0，CratIs an arbitrarily chosen positive value.
If the convergence coefficient α isV-GPLThe process of adjusting the capacity of the genset based on the second reliability and the third reliability indicator may be as follows, without satisfying the convergence condition:
the second reliability index and the third reliability index are compared. And then adjusting the first parameter or the second parameter according to the current capacity of the generator set based on the comparison result of the second reliability index and the third reliability index so as to correspondingly adjust the capacity of the generator set.
For example, if the third reliability index is greater than the second reliability index, the second parameter C is adjusted according to the following equationminThereby adjusting the capacity C of the generator setbm：
Cmin＝Cbm。
If the third reliability index is not greater than the second reliability index, adjusting the first parameter C according to the following formulamaxThereby adjusting the capacity C of the generator setbm：
Cmax＝Cbm。
Finally, according to the convergence coefficient alphaV-GPLAnd when the convergence condition is met, determining the available capacity of the grid-connected parking lot by the capacity of the generator set. That is, according to the capacity of the generator set, an equivalent fixed capacity or an equivalent regular capacity of the generator set is determined as an available capacity of the grid-connected parking lot. For example, the equivalent fixed capacity, EFC, or equivalent regular capacity, ECC, of the genset is determined according to the following formula:
EFC/ECC＝Cbm。
according to another embodiment of the present invention, in case that 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, a fourth reliability index of a fourth intelligent power distribution system may be calculated in step S240, and the available capacity of the grid-connected parking lot may be determined based on the first reliability index and the fourth reliability index. Of course, before step S240, a fourth intelligent power distribution system may be obtained by adding a dummy load in the second intelligent power distribution system.
The process of calculating the fourth reliability index is similar to the process of calculating the first reliability index, and is not repeated here.
Specifically, after the fourth reliability index is obtained,a convergence coefficient may be calculated based on the first reliability and the fourth reliability index. The convergence factor is calculated, for example, according to the following formula
Determining a convergence factor
Whether a convergence condition is satisfied. In some embodiments, the convergence condition may be
At the convergence coefficient
And under the condition that the convergence condition is not met, repeating 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 until the convergence coefficient meets the convergence condition.
Wherein the capacity of the virtual load is determined based on the third parameter and the fourth parameter. For example, the capacity D of the dummy load is determined according to the following formulavl：
Dvl＝(Dmax+Dmin)/2，DmaxDenotes a third parameter, DminRepresenting a fourth parameter. In the initial case, let Dmax＝Drat，Dmin＝0，DratIs an arbitrarily chosen positive value.
If the convergence coefficient is
The process of adjusting the capacity of the virtual load based on the first reliability indicator and the fourth reliability indicator without satisfying the convergence condition may be as follows:
the first reliability index and the fourth reliability index are compared. And then adjusting the third parameter or the fourth parameter according to the current capacity of the virtual load based on the comparison result of the first reliability index and the fourth reliability index so as to correspondingly adjust the capacity of the virtual load.
For example, if the fourth reliability index is less than the first reliability index, the fourth parameter D is adjusted according to the following formulaminThereby adjusting the capacity D of the virtual loadvl：
Dmin＝Dvl。
If the fourth reliability index is not less than the first reliability index, the third parameter D is adjusted according to the following formulamaxThereby adjusting the capacity D of the virtual loadvl：
Dmax＝Dvl。
Finally, according to the convergence coefficient
And determining the available capacity of the grid-connected parking lot by the capacity of the virtual load when the convergence condition is met. 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, the effective load carrying capacity ELCC of the virtual load is determined according to the following formula:
ELCC＝Dvl。
according to various embodiments of the present invention, the above 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, expected load shedding value ELC, system power failure index BPII, system reduced electric quantity index BPECI, severity index SI and expected electric quantity shortage value EENS. Preferably, the low battery expected value EENS can be used to measure the reliability of the system.
Fig. 3 is a block diagram illustrating a configuration of an available capacity determination apparatus 300 for a grid-connected parking lot according to an embodiment of the present invention. As shown in fig. 3, the available capacity determination device 300 for 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 a first reliability index of a first intelligent power distribution system, which does not include a grid-connected parking lot, with a second reliability index of a second intelligent power distribution system, which is obtained by adding a grid-connected parking lot to the first intelligent power distribution system.
The index comparing unit 310 further comprises an index calculating unit 311, the index calculating unit 311 is adapted to calculate a second reliability index according to the following steps: acquiring user behavior characteristics of the plug-in electric vehicle, wherein the user behavior characteristics comprise arrival time and parking time of the plug-in electric vehicle at the grid-connected parking lot, required charging level when the plug-in electric vehicle leaves the grid-connected parking lot, and V2G program availability. Then, based on the user behavior characteristics, the charging and discharging time (i.e., the charging and discharging time) of the plug-in electric vehicle is determined. Next, determining the available power generation amount of the grid-connected parking lot at each moment of the simulation year; determining the total available power generation and the total load demand of the second intelligent power distribution system; judging whether the second intelligent power distribution system violates the constraint or not by adopting a power flow analysis method; and calculating the reliability index of the second intelligent power distribution system at the moment based on the judgment result. And then, according to the reliability indexes at a plurality of moments, calculating the annual reliability index of the simulation year. And finally, calculating a second reliability index based on the annual reliability index of each simulation year.
The capacity determination unit 320 is adapted to calculate a third reliability index of a third intelligent power distribution system, which is obtained by adding a generator set to the first 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, when the first reliability index is not smaller than the second reliability index.
The capacity determination unit 320 is further adapted to calculate a fourth reliability index of a fourth intelligent power distribution system, which is obtained by adding a dummy load to the second intelligent power distribution system, and determine the available capacity of the grid-connected parking lot based on the first reliability index and the fourth reliability index, if the first reliability index is not less than the second reliability index.
For detailed processing logic and implementation procedures of each unit in the available capacity determination apparatus 300 for a grid-connected parking lot, reference may be made to the foregoing description of the available capacity determination method 200 for a grid-connected parking lot in conjunction with fig. 1-2, and details are not repeated here.
In summary, according to the available capacity determination scheme of the grid-connected parking lot in the embodiment of the invention, by determining the available capacity of the grid-connected parking lot, the contribution of the grid-connected parking lot to the capacity of the intelligent power distribution system can be objectively estimated and compared, and the contribution can be used as a conventional power generation resource in the market. In which the randomness of the user behavior of the plug-in electric vehicle and its potential dependency on the externality are taken into account. On the basis, the reliability index is calculated by adopting a sequential Monte Carlo simulation method, and the characteristics of the grid-connected parking lot can be completely expressed.
It should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
The present invention may further comprise: a8, the method of A5, wherein the capacity of the genset is determined based on a third parameter and a fourth parameter, and the step of adjusting the capacity of the virtual load based on the first reliability indicator and the fourth reliability indicator comprises: comparing the first reliability index and the fourth reliability index; based on the comparison, modifying the third parameter or the fourth parameter based on the current capacity of the virtual load to adjust the capacity of the virtual load accordingly. A9, the method as in A3, 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 satisfied comprises: and determining the equivalent fixed capacity or the equivalent conventional capacity of the generator set according to the capacity of the generator set to serve as the available capacity of the grid-connected parking lot. A10, the method as claimed in a5, wherein the step of determining the available capacity of the grid-connected parking lot from the capacity of the dummy load when the convergence condition is satisfied comprises: and determining the effective bearing capacity of the virtual load according to the capacity of the virtual load to serve as the available capacity of the grid-connected parking lot. A11, the method of A1, wherein the step of calculating the second reliability indicator based on the annual reliability indicators for each simulated year further comprises: calculating a variance coefficient based on the annual reliability index of each simulation year; judging whether the variance coefficient meets a termination condition; if not, repeating the steps of obtaining the 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 a second reliability index and a variance coefficient until the variance coefficient meets the termination condition; and taking the second reliability index when the termination condition is met as a final second reliability index. A12, the method as in A1, further comprising: generating a state duration sequence for the first intelligent power distribution system component; determining an available power generation amount of the first intelligent power distribution system at each time in the state duration sequence. A13, the method of any one of A1-12, wherein the first, second, third, and fourth reliability indicators are calculated using a sequential sampling Monte Carlo simulation method. A14, the method of any one of A1-12, wherein the reliability indicator includes at least one of: load shedding probability PLC, load shedding frequency EFLC, load shedding duration EDLC, average load shedding duration ADLC, expected load shedding value ELC, system power failure index BPII, system reduced electric quantity index BPECI, severity index SI and expected electric quantity shortage value EENS.
Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in a device as described in this embodiment or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into multiple sub-modules.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
Furthermore, some of the described embodiments are described herein as a method or combination of method elements that can be performed by a processor of a computer system or by other means of performing the described functions. A processor having the necessary instructions for carrying out the method or method elements thus forms a means for carrying out the method or method elements. Further, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is used to implement the functions performed by the elements for the purpose of carrying out the invention.
As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present invention has been disclosed in an illustrative rather than a restrictive sense, and the scope of the present invention is defined by the appended claims.
Claims (15)
1. A method for determining available capacity of a grid-connected parking lot, wherein the grid-connected parking lot is suitable for parking a plug-in electric vehicle and meets the charging and discharging requirements of the plug-in electric vehicle, and the method comprises the following steps:
comparing a first reliability index of a first intelligent power distribution system with a second reliability index of a second intelligent power distribution system, wherein the first intelligent power distribution system does not include a grid-connected parking lot, and the second intelligent power distribution system is obtained by adding the grid-connected parking lot to the first intelligent power distribution system, wherein
The second reliability index is calculated according to the following steps:
acquiring user behavior characteristics of the plug-in electric vehicle, wherein the user behavior characteristics comprise arrival time and parking time of the plug-in electric vehicle at the grid-connected parking lot, required charging level when the plug-in electric vehicle leaves the grid-connected parking lot, and V2G program availability;
determining charging and discharging time of the plug-in electric vehicle based on the user behavior characteristics;
determining the available power generation amount of the grid-connected parking lot at each moment of the simulation year; determining a total available power generation capacity and a total load demand of the second intelligent power distribution system; judging whether the second intelligent power distribution system violates the constraint or not by adopting a power flow analysis method; calculating the reliability index of the second intelligent power distribution system at the moment based on the judgment result;
calculating the annual reliability index of the simulation year according to the reliability indexes at a plurality of moments;
calculating the second reliability index based on the annual reliability index of each simulation year;
and under the condition that the first reliability index is not smaller than the second reliability index, calculating a third reliability index of a third intelligent power distribution system by adopting a sequential Monte Carlo simulation method, and determining the available capacity of the grid-connected parking lot based on the second reliability index and the third reliability index, wherein the third intelligent power distribution system is obtained by adding a generator set in the first intelligent power distribution system.
2. The method of claim 1, further comprising:
under the condition that the first reliability index is not smaller than the second reliability index, a fourth reliability index of a fourth intelligent power distribution system is calculated by adopting a sequential Monte Carlo simulation method, the available capacity of the grid-connected parking lot is determined based on the first reliability index and the fourth reliability index, and the fourth intelligent power distribution system is obtained by adding a virtual load in the second intelligent power distribution system.
3. The method of claim 1, wherein determining the available capacity of the grid-connected parking lot based on the second reliability index and the third reliability index comprises:
calculating a convergence coefficient alpha based on the second reliability and the third reliability index according to the following formulaV-GPL：
αV-GPL＝|IV-IGPL|/IGPLIn which IVAs a third reliability index, IGPLIs a second reliability index;
judging whether the convergence coefficient meets a convergence condition;
under the condition that the convergence coefficient does not meet the convergence condition, repeating 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 the convergence condition;
and determining the available capacity of the grid-connected parking lot according to the capacity of the generator set when the convergence condition is met.
4. The method of claim 2, wherein determining the available capacity of the grid-connected parking lot based on the first reliability index and the fourth reliability index comprises:
calculating a convergence coefficient based on the first reliability and the fourth reliability index according to the following formula
judging whether the convergence coefficient meets a convergence condition;
under the condition that the convergence coefficient does not meet the convergence condition, repeating 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 the convergence condition;
and determining the available capacity of the grid-connected parking lot according to the capacity of the virtual load when the convergence condition is met.
5. The method of claim 3, wherein the capacity C of the generator setbmDetermining, based on the first parameter and the second parameter:
Cbm＝(Cmax+Cmin)/2，Cmaxis a first parameter, CminIs a second parameter;
the step of adjusting the capacity of the generator set based on the second reliability indicator and the third reliability indicator includes:
comparing the second reliability index with the third reliability index;
based on the comparison, adjusting the first parameter or the second parameter according to the current capacity of the generator set to adjust the capacity of the generator set accordingly.
6. The method of claim 4, wherein the dummy load DvlIs determined based on the third parameter and the fourth parameter:
Dvl＝(Dmax+Dmin)/2，Dmaxis a third parameter, DminIs a fourth parameter;
the step of adjusting the capacity of the virtual load based on the first and fourth reliability indicators comprises:
comparing the first reliability index and the fourth reliability index;
based on the comparison, modifying the third parameter or the fourth parameter based on the current capacity of the virtual load to adjust the capacity of the virtual load accordingly.
7. The method of claim 3, wherein determining the available capacity of the grid-connected parking lot based on the capacity of the generator set when a convergence condition is satisfied comprises:
and determining the equivalent fixed capacity or the equivalent conventional capacity of the generator set according to the capacity of the generator set to serve as the available capacity of the grid-connected parking lot.
8. The method of claim 4, 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 satisfied comprises:
and determining the effective bearing capacity of the virtual load according to the capacity of the virtual load to serve as the available capacity of the grid-connected parking lot.
9. The method of claim 1, wherein calculating the second reliability indicator based on the annual reliability indicators for each simulated year further comprises:
calculating a variance coefficient based on the annual reliability index of each simulation year;
judging whether the variance coefficient meets a termination condition;
if not, repeating the steps of obtaining the 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 a second reliability index and a variance coefficient until the variance coefficient meets the termination condition;
and taking the second reliability index when the termination condition is met as a final second reliability index.
10. The method of claim 1, further comprising:
generating a state duration sequence for the first intelligent power distribution system component;
determining an available power generation amount of the first intelligent power distribution system at each time in the state duration sequence.
11. The method of any one of claims 1-10, wherein the first, second, third, and fourth reliability indicators are calculated using a sequential sampling monte carlo simulation method.
12. The method of any one of claims 1-10, wherein the reliability indicator comprises at least one of: load shedding probability PLC, load shedding frequency EFLC, load shedding duration EDLC, average load shedding duration ADLC, expected load shedding value ELC, system power failure index BPII, system reduced electric quantity index BPECI, severity index SI and expected electric quantity shortage value EENS.
13. An available capacity determination apparatus of a grid-connected parking lot adapted to park a plug-in electric vehicle and satisfy a charge and discharge demand of the plug-in electric vehicle, the apparatus comprising:
an index comparison unit adapted to compare a first reliability index of a first intelligent power distribution system not including a grid-connected parking lot with a second reliability index of a second intelligent power distribution system obtained by adding the grid-connected parking lot to the first intelligent 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:
acquiring user behavior characteristics of the plug-in electric vehicle, wherein the user behavior characteristics comprise arrival time and parking time of the plug-in electric vehicle at the grid-connected parking lot, required charging level when the plug-in electric vehicle leaves the grid-connected parking lot, and V2G program availability;
determining charging and discharging time of the plug-in electric vehicle based on the user behavior characteristics;
determining the available power generation amount of the grid-connected parking lot at each moment of the simulation year; determining a total available power generation capacity and a total load demand of the second intelligent power distribution system; judging whether the second intelligent power distribution system violates the constraint or not by adopting a power flow analysis method; calculating the reliability index of the second intelligent power distribution system at the moment based on the judgment result;
calculating the annual reliability index of the simulation year according to the reliability indexes at a plurality of moments;
calculating the second reliability index based on the annual reliability index of each simulation year;
and the capacity determining unit is suitable for calculating a third reliability index of a third intelligent power distribution system by adopting a sequential Monte Carlo simulation method under the condition that the first reliability index is not smaller than the second reliability index, and determining the available capacity of the grid-connected parking lot based on the second reliability index and the third reliability index, wherein the third intelligent power distribution system is obtained by adding a generator set in the first intelligent power distribution system.
14. A computing device, comprising:
one or more processors; and
a 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, the one or more programs comprising instructions for performing any of the methods of claims 1-12.
15. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-12.
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| FI128774B (en) * | 2019-11-04 | 2020-11-30 | Liikennevirta Oy / Virta Ltd | Electric vehicle charging station reliability assessment method and device |
| CN112329215B (en) * | 2020-10-20 | 2024-02-27 | 华北电力大学 | Reliability evaluation method and computing equipment for power distribution network comprising electric automobile power exchange station |
| CN115221472B (en) * | 2022-07-21 | 2024-01-30 | 国网江苏省电力有限公司电力科学研究院 | Self-adaptive setting method and device for user voltage limit value, memory and computing equipment |
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| EP3077246B8 (en) * | 2013-12-06 | 2021-06-23 | Schneider Electric USA, Inc. | Branch energy management for multiple evses |
| CN104951614A (en) * | 2015-06-30 | 2015-09-30 | 国家电网公司 | EV-charging-controllability considered unit combination model and modeling method |
| CN105322559B (en) * | 2015-11-11 | 2017-07-28 | 重庆大学 | A kind of electric automobile distribution dispatch control method based on V2G technologies |
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| CN108197765B (en) * | 2018-03-23 | 2020-06-26 | 华北电力大学 | Parking lot charging scheduling method and computing device for battery loss equal-amount distribution |
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