CN106227986A - A kind of distributed power source combines dispositions method and device with intelligent parking lot - Google Patents

Abstract

The invention discloses a kind of distributed power source and intelligent parking lot combines dispositions method, including: set up first stage model, its control variable include characterizing in distributed power source candidate site and at its candidate site the primary vector of the distributed power source quantity of configuration, and characterize in intelligent parking lot candidate site and at its candidate site the secondary vector of the two-way charging pile quantity of configuration；Setting up second stage model, its control variable is to characterize meritorious in intelligent parking lot and the 3rd vector of reactive power；Use pre-defined algorithm that first stage, second stage model are solved, obtain the optimal solution of primary vector, secondary vector, using this optimal solution as the associating deployment scheme of distributed power source and intelligent parking lot, wherein optimal solution draws by iterating the solving result of first stage model and second stage model.What the present invention disclosed a kind of distributed power source and intelligent parking lot the most in the lump combines deployment device.

Description

Joint deployment method and device for distributed power supply and intelligent parking lot

Technical Field

The invention relates to the field of deployment design of intelligent parking lots, in particular to a joint deployment scheme of a distributed power supply and an intelligent parking lot.

Background

With the advance of industrialization, the increasing of greenhouse gas emission is one of the important environmental problems in large and medium-sized cities, and electric vehicles are widely concerned by society due to the advantages of flexible energy supply, simplicity, portability, low energy consumption, light pollution, effective reduction of the dependence on fossil fuels, and the like. The remarkable advantages of electric vehicles in terms of energy utilization and the growing social demand make the development of electric vehicles necessary and urgent.

When an Electric Vehicle (EV) enters a network, for example, in a system including an intelligent parking lot and a distributed power supply active power distribution network, because both the distributed power supply and the intelligent parking lot transmit Electric energy to a power grid, the original trend distribution in the power grid is changed, and thus the original voltage distribution and network loss are changed. Therefore, the mutual influence of the electric vehicle and the distributed power supply is comprehensively considered when the power grid is deployed, the electric vehicle grid connection is accepted as much as possible, the consumption of renewable energy sources is fully promoted, and the adverse influence of the electric vehicle and the distributed power supply on the power distribution network is made up as much as possible. At present, scholars at home and abroad research the optimal deployment of electric vehicles and distributed power supplies, however, the uncertainty of the output of the electric vehicles is rarely considered in the existing research, which causes great irrationality, especially for uncontrollable intermittent power supplies such as wind power, photovoltaic and the like.

In addition, the R/X of the power distribution network is large, and both active power and reactive power have obvious influence on network loss and voltage quality. In a traditional power distribution network, reactive power of a line is changed mainly through reactive power optimization deployment (capacitor configuration), namely, by switching capacitors, so that the network loss is reduced, the voltage quality is improved, and the power supply quality and the economic operation of the power distribution network are further guaranteed. In fact, the electric vehicle can also be used as a reactive power supply to provide reactive support to the power grid to different degrees.

Therefore, a distributed power supply and intelligent parking lot combined deployment scheme considering the reactive power of the electric vehicle is needed, the reactive power of the power battery can be fully exerted, the operation quality of a power grid is improved, the consumption of renewable energy sources is promoted to the maximum extent, and the economic, safe and stable operation of a power distribution network is realized.

Disclosure of Invention

To this end, the present invention provides a method and an apparatus for jointly deploying a distributed power source and an intelligent parking lot, in an attempt to solve or at least alleviate at least one of the above problems.

According to one aspect of the invention, a joint deployment method of a distributed power supply and an intelligent parking lot is provided, which comprises the following steps: set up the firstThe control variables of the stage model comprise a first vector for representing the candidate sites in the distributed power supply and the quantity of the distributed power supply configured at the candidate sites and a second vector for representing the candidate sites in the intelligent parking lot and the quantity of the bidirectional charging piles configured at the candidate sites, and the objective function of the stage model is minOF₁＝minC₁Wherein, C₁Representing the annual construction investment cost of the intelligent parking lot and the distributed power supply; establishing a second-stage model, wherein the control variable is a third vector for representing active power and reactive power in the intelligent parking lot, and the objective function is minOF₂＝min(C₂+C₃+C₄) Wherein, C₂Represents the annual loss cost of the bidirectional converter of the charging pile, C₃Represents the annual cost of carbon emissions, C₄Representing the annual cost of the network loss; and solving the first-stage model and the second-stage model by adopting a predetermined algorithm to obtain an optimal solution OF the first vector and the second vector, and taking the optimal solution as a joint deployment scheme OF the distributed power supply and the intelligent parking lot, wherein the optimal solution is obtained by repeatedly iterating the solution results OF the first-stage model and the second-stage model, and the total objective function is minOF min (OF)₁+OF₂)。

Optionally, in the method according to the present invention, further comprising the step of: and constructing an expected scene set for describing the operation states of the distributed power supply and the intelligent parking lot, wherein the expected scene set comprises a plurality of expected scenes and the occurrence probability corresponding to each expected scene.

Optionally, in the method according to the present invention, the step of constructing the set of envisioned scenes comprises: establishing a probability model of the distributed power supply according to the resource data corresponding to the distributed power supply; establishing a traditional load model according to user load data; according to travel data of the traditional fuel vehicle, establishing an electric vehicle charging load model by using a calculation method based on travel requirements; and constructing an expected scene set for describing the operation states of the distributed power supply and the intelligent parking lot by combining the probability model of the distributed power supply, the traditional load model and the electric vehicle charging load model.

Optionally, in the method according to the present invention, the distributed power source includes wind power generation, photovoltaic power generation, and biomass power generation, and the corresponding resource data includes historical statistical data of wind resources, solar light resources, and biomass resources.

Optionally, in the method according to the invention, the predetermined algorithm is a genetic algorithm. The step of solving the first-stage model and the second-stage model by adopting a predetermined algorithm to obtain the optimal solution of the first vector and the second vector comprises the following steps: randomly generating a population sample of the first-stage model as an initial population; calculating objective function values of all individuals of the initial population in the first-stage model, and calculating objective function values of all individuals of the initial population in the second-stage model to obtain total objective function values of all individuals of the initial population; calculating the fitness of each individual of the initial population according to the total objective function value, and performing selection-cross-variation-reinsertion genetic operation according to the fitness to generate a new population; and repeating the steps of calculating the total objective function value for the new population until convergence, wherein the corresponding first vector and second vector are the optimal solution.

According to another aspect of the present invention, there is provided a joint deployment apparatus for a distributed power supply and an intelligent parking lot, comprising: the first model establishing unit is suitable for establishing a first-stage model, the control variables of the first-stage model comprise a first vector for representing the candidate sites in the distributed power supply and the quantity of the distributed power supply configured at the candidate sites and a second vector for representing the candidate sites in the intelligent parking lot and the quantity of the bidirectional charging piles configured at the candidate sites, and the target function of the first-stage model establishing unit is minOF₁＝minC₁Wherein, C₁Representing the annual construction investment cost of the intelligent parking lot and the distributed power supply; a second model establishing unit suitable for establishing a second stage model, wherein the control variable comprises a third vector for representing active and reactive power in the intelligent parking lot, and the objective function is minOF₂＝min(C₂+C₃+C₄) Wherein, C₂Represents the annual loss cost of the bidirectional converter of the charging pile, C₃Represents the annual cost of carbon emissions, C₄Representing the annual cost of the network loss; and a computing unit adapted to adoptSolving the first-stage model and the second-stage model by using a predetermined algorithm to obtain an optimal solution OF a first vector and a second vector, and using the optimal solution as a joint deployment scheme OF the distributed power supply and the intelligent parking lot, wherein the optimal solution is obtained by repeatedly iterating the solution results OF the first-stage model and the second-stage model, and the total objective function is minOF min (OF)₁+OF₂)。

Optionally, in the apparatus according to the present invention, further comprising: and the scene construction unit is suitable for constructing an expected scene set for describing the operation states of the distributed power supply and the intelligent parking lot, wherein the expected scene set comprises a plurality of expected scenes and the occurrence probability corresponding to each expected scene.

Optionally, in an apparatus according to the present invention, the scene constructing unit includes: the first modeling subunit is suitable for establishing a probability model of the distributed power supply according to the resource data corresponding to the distributed power supply; the second modeling subunit is suitable for establishing a traditional load model according to the user load data; the third modeling subunit is suitable for establishing an electric automobile charging load model by utilizing a calculation method based on travel demands according to travel data of the traditional fuel oil vehicle; and the building subunit is suitable for building an expected scene set for describing the operation states of the distributed power supply and the intelligent parking lot by combining the probability model of the distributed power supply, the traditional load model and the electric vehicle charging load model.

Optionally, in the apparatus according to the present invention, the distributed power source includes wind power generation, photovoltaic power generation, and biomass power generation, and the corresponding resource data includes historical statistical data of wind resources, solar light resources, and biomass resources.

Optionally, in the device according to the invention, the predetermined algorithm is a genetic algorithm.

Optionally, in an apparatus according to the present invention, the calculation unit comprises: the population subunit is suitable for randomly generating a population sample of the first-stage model as an initial population; the calculation subunit is suitable for calculating the objective function value of each individual of the initial population in the first-stage model, and calculating the objective function value of each individual of the initial population in the second-stage model to obtain the total objective function value of each individual of the initial population; the population subunit is also suitable for calculating the fitness of each individual of the initial population according to the total objective function value, and performing selection-crossing-variation-reinsertion genetic operation according to the fitness to generate a new population; the calculation subunit is further adapted to calculate a total objective function value for the new population; and the population subunit and the calculation subunit are suitable for repeatedly executing the steps until convergence, and the first vector and the second vector which are correspondingly solved are the optimal solution.

According to the joint deployment scheme of the distributed power supply and the intelligent parking lot, the capacity of the power battery serving as an active power supply and a reactive power supply at the same time is fully exerted, the consumption of renewable energy sources is promoted on the basis of ensuring the voltage quality and the operation economy of a power grid, and the most benefits of the environment, the power grid company and EV users are realized.

Meanwhile, the scheme considers the cost caused by the service life loss of the power battery in the deployment model, namely the benefit and loss of the EV users participating in power grid scheduling are considered, although the economic benefit and the system voltage quality of the second stage deployment are slightly poor, the scheme is more in line with the actual operation scheduling of the power grid and is more reasonable.

Drawings

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 basic deployment framework diagram containing a smart car park, distributed power active distribution grid, according to one embodiment of the present invention;

fig. 2 shows a flow diagram of a method 200 for co-deployment of distributed power sources with intelligent parking lots, according to an embodiment of the present invention;

FIG. 3 illustrates a flow diagram of a method of constructing a set of envisioning scenarios describing the operational status of a distributed power source and an intelligent parking lot, in accordance with one embodiment of the present invention;

FIG. 4 illustrates a two-stage V2G (Vehicle-to-Grid) bidirectional converter architecture typically employed in a charge-discharge machine;

FIG. 5A shows a 33-node power distribution system wiring diagram, and FIG. 5B shows a deployment result wiring diagram for a 33-node power distribution system;

FIG. 6 is a graph showing voltage characteristics of a power grid in three cases of an original state when a distributed power supply and a parking lot are not installed, and whether reactive power of the intelligent parking lot is considered;

fig. 7 shows a block diagram of a co-deployment apparatus 700 of a distributed power supply and an intelligent parking lot according to an embodiment of the invention; and

fig. 8 shows a block diagram of a co-deployment apparatus 700 of a distributed power supply and an intelligent parking lot according to another 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.

A basic deployment framework involving smart car parks, distributed power active distribution grids is exemplarily shown in fig. 1. It should be noted that the Distributed power Generation (DG) in fig. 1 is only exemplary of wind power, but is equally applicable to systems including photovoltaic power Generation, gas turbines, and biomass power Generation.

In the active power distribution network, electric energy can be obtained from a superior power grid, and can also be obtained from an internal DG and an intelligent parking lot. The DG and the intelligent parking lot transmit electric energy to the power grid, and original tide distribution in the power grid can be changed, so that original voltage distribution and grid loss are changed, and the economical efficiency and safety stability of power grid operation are influenced. The construction of the intelligent parking lot plays a role in overall dispatching coordination of electric vehicles in the deployment area, promotes the power battery of the EV to participate in energy dispatching of the power grid, and plays roles in energy storage and reactive power supply.

Therefore, the embodiment of the invention provides a scheme for joint deployment of a distributed power supply and an intelligent parking lot so as to fully exert the capacity of a power battery as an active power supply and a reactive power supply at the same time.

Referring to fig. 2, a flow chart of a method 200 for co-deployment of distributed power sources and intelligent parking lots according to an embodiment of the present invention is shown. Referring to FIG. 2, the method 200 begins in step S210, where in step S210 a first stage model is built. First stage deployment targeting annual construction costs OF₁At a minimum, determining the control variables of the first stage model comprises: the intelligent parking lot comprises a first vector for representing the number of candidate stations in the distributed power supply and the distributed power supply configured at the candidate stations, and a second vector for representing the number of the candidate stations in the intelligent parking lot and the bidirectional charging piles configured at the candidate stations.

Then, the objective function of the first stage is constructed as: annual construction investment costs (C) for intelligent parking lots and distributed power farms₁) At a minimum, the expression is as follows:

minOF₁＝minC₁(1)

wherein,

wherein d is the discount rate, and λ denotes the λ year in the deployment term, Y_plFor the total number of years of deployment,respectively establishing the quantity of the bidirectional charging piles and the distributed power supplies at the position i, j of the candidate station site in the lambda-th year, A_csi、A_dwgjInvestment cost omega of newly building a bidirectional charging pile and a distributed power supply at the position i, j of the candidate station site respectively_csIs a set of candidate sites, Ω, for a parking lot_dwgIs a set of candidate sites for the distributed power supply.

The constraints of the first stage model are:

$s.t.\left\{\begin{array}{c}{S}_{i\_cs}\≤S_{i\_cs.max}\\ S_{j\_dwg}\≤S_{j\_dwg.max}\end{array}\right.---\left(2\right)$

in the formula, S_{i_cs}、S_{i_cs.max}Respectively representing the construction area and the maximum available land area of the intelligent parking lot at the candidate station site i, S_{j_dwg}、S_{j_dwg.max}Respectively represents the construction area and the maximum available land area of the distributed power supply field at the candidate station site j, and i ∈ omega_cs，j∈Ω_dwg。

According to an embodiment of the present invention, the method 200 further comprises the step of constructing a set of forecast scenarios describing the operational status of the distributed power supply and the intelligent parking lot. The expected scene set includes a plurality of expected scenes and occurrence probabilities corresponding to the expected scenes. The process of building the forecast scenario set is shown in fig. 3, and the specific steps are described below with reference to fig. 3.

In step S301, a probabilistic model of the distributed power source is established according to the resource data corresponding to the distributed power source.

In the embodiment of the invention, the distributed power source may include wind power generation, photovoltaic power generation, biomass power generation and the like, and the corresponding resource data includes historical statistical data of wind resources, solar illumination resources and biomass resources. For simplicity of description, the distributed power supply referred to hereinafter considers wind power generation only, and assumes that the distributed power supply delivers active power only to the grid. The process of establishing a probabilistic model of a distributed power source is as follows:

the output power of wind power mainly depends on the wind speed at a site, and for the characteristic of wind resource intermittence, Weibull distribution is a common random behavior for describing the wind speed, and describes the probability that the wind speed is equal to or less than a certain specific value through the shape and index parameters. The Weibull distribution concept is simple and is suitable for long-term deployment; however, the probabilistic method does not represent the temporal behavior of the wind speed behavior.

In the embodiment, a time series stochastic model is established to establish a probabilistic model of the distributed power supply, historical wind speed data of last five years in meteorological data of a certain area in north China are collected, the data of the whole year is processed according to four seasons, and wind speed data of one representative day of each season is obtained through calculation. Specifically, wind speed data of each day is divided by taking hours as a time period, an expected value of the wind speed of each hour is taken as the wind speed data of the time period, and then 24 data exist each day; according to 30 days per month, each representative day has 450 data per hour (5 years × 30 days/month × 3 months/season), and the average value of 450 data per hour is calculated to be used as the wind speed data per hour of the season, so that the wind speed data of 24 hours of the season representative day can be obtained, one representative day is called as a wind speed scene, and the probabilities of occurrence of four season representative days, namely four wind speed scenes are the same and are all 0.25.

It should be noted that, in the present embodiment, the data of each day is divided by taking the step size of the hour as a step size, but in practical application, the step size needs to be determined by balancing the calculation accuracy with the calculation workload.

On the basis, the average value of the wind speed is used, and the output power is calculated according to the output characteristic of the fan shown in the formula (3):

$P_{gw}\left(v_{gw}\right)=\left\{\begin{array}{cc}0,& 0\≤v_{gw}\≤v_{ci}\\ P_{rated}\frac{v_{gw}-v_{ci}}{v_r-v_{ci}},& v_{ci}\≤v_{gw}\≤v_r\\ P_{rated},& v_r\≤v_{gw}\≤v_{co}\\ 0,& v_{gw}\≥v_{co}\end{array}\right.---\left(3\right)$

in the formula, v_ci，v_r，v_coRespectively cut-in wind speed, rated wind speed and cut-out speed, P_gwIs the output power of the fan, v_gwIs the average wind speed.

In step S302, a conventional load model is built according to the user load data.

The load behavior of the traditional users also has the characteristic of randomness, and similar to wind speed modeling, the fluctuation of the traditional load demand can also be represented by establishing the time series model. And still utilizing the user load data of the region in nearly five years to calculate, dividing the load of one day into 24 time periods by taking the hour as a step length, and carrying out dimensionless standardization processing, namely calculating the ratio of the average load of the user per hour to the annual peak load of the user. On the basis, the average value of the load of each time period in each season representative day is calculated, and then a load curve of 4 seasons representative days is generated to represent the fluctuation situation of the load in one year.

In step S303, an electric vehicle charging load model is established according to travel data of the conventional fuel vehicle by using a calculation method based on travel demand.

1) Analyzing the travel behavior of the user:

the charging load of the electric automobile has the characteristics of strong mobility and uncertainty, due to the fact that the historical data related to the family use EV is short, and the fact that the user trip characteristics cannot be influenced by the alternative use of the EV on the conventional vehicle is assumed, the trip data of the conventional fuel vehicle in the region in the last five years are adopted in the embodiment, and the charging load of the electric automobile is modeled by a calculation method based on trip requirements.

The private car owner is used for the car owner to go to work and leave work, the parking place is a resident parking lot, the charging time is concentrated, and the parking time is generally longer from the time of coming home to the time of going to work next morning, so that the private car owner can carry out conventional or slow charging on the private car owner.

In order to calculate the total charging load in the deployment area, firstly, the randomness of the travel starting time, the travel ending time and the daily driving mileage of the EV is analyzed. Assuming that the charging behavior of the electric vehicle user is not controlled by the power grid, and considering that the working hours and the working hours of different units are different, according to the traffic data, the trip starting time and the trip ending time of a single vehicle are obtained by fitting and obey respectively [9, 0.5 ]²]、[19.5，1.5²]The daily mileage satisfies the following normal distribution:

$f_D\left(x\right)=\frac{1}{a_1{\mathrm{\σ}}_{D1}\sqrt{2\mathrm{\π}}}\exp \[-\frac{{(x-{\mathrm{\μ}}_{D1})}^2}{2{\mathrm{\σ}}_{D1}^2}\]+\frac{1}{a_2{\mathrm{\σ}}_{D2}\sqrt{2\mathrm{\π}}}\exp \[-\frac{{(x-{\mathrm{\μ}}_{D2})}^2}{2{\mathrm{\σ}}_{D2}^2}\]---\left(4\right)$

wherein, a₁、a₂、σ_D1、σ_D2Respectively the coefficient and standard deviation of normal distribution function of daily mileage of the electric automobile_D1＝17.79，μ_D2＝38.35。

2) Calculating a charging load:

on the basis of analyzing the travel behavior of the EV, the charging load of the EV in the deployment area is analyzed as follows. The method includes the steps that the EVs with the same driving behaviors are divided into a cluster, and the EVs in the cluster have the same travel law. In particular, the EVs in the same cluster,the starting time and the ending time of the trip are basically the same as the daily driving mileage, and the fluctuation is in a relatively small range. For example, the EVs with the travel starting time of 7:30-7:35, the travel ending time of 19:00-19:05 and the daily mileage of 80-85km are divided into a cluster, and the travel starting time and the travel ending time of all the EVs in the cluster can be considered to be 7:30 and 19:00 respectively, and the daily mileage of 83 km. In practical application, the number of clusters needs to be determined in a trade-off manner according to the development scale of the EV in the deployment area, the travel rule, the calculation accuracy requirement and the calculation complexity. Assuming p clusters, the model output is p three-dimensional vectors { v }^ev}_3×pIt is called p scenes, and each vector (scene) v^ev _s’The probability of occurrence of (s' ═ 1,2, … p) is:

$P\left(v_{s^{\′}}^{ev}\right)=N_{s^{\′}}^{ev}/N_{to}^{ev}---\left(5\right)$

in the formula,representing the number of EVs in cluster s' and the number of EVs in the entire deployment area, respectively.

In step S304, an expected scene set describing the operation states of the distributed power source and the intelligent parking lot is constructed by combining the probabilistic model of the distributed power source, the traditional load model and the electric vehicle charging load model obtained in the above steps S301, S302 and S303.

Wind speed, conventional load level, and electric vehicle charging load are all uncertainty factors for the problem studied in this example. As mentioned above, taking wind power generation as an example, the wind speed and the traditional load are divided into 4 operation scenes according to seasons, and the probability of each scene occurrence is denoted as P_seaHaving a value of P_seaIf the charging load of the electric vehicle has p operating scenarios, there are 4 × p possible operating scenarios in the deployment phase.

Assuming that the charging load of the electric vehicle is not related to seasonal factors, the probability PR of occurrence of each scenario s_sAs shown in formula (6):

${\mathrm{PR}}_s=P_{sea}\·P\left(v_{s^{\′}}^{ev}\right)---\left(6\right)$

thus, an expected scene set for describing the operation states of the distributed power supply and the intelligent parking lot is formed.

Subsequently, in step S220, a second stage model is established. Second stage deployment targeted annual operational cost OF₂Minimum, including fill electric pile two-way conversion of current device annual loss expense, carbon emission annual expense and network loss annual expense, its expression is:

min OF₂＝min(C₂+C₃+C₄) (7)

in the formula, C₂Represents the annual loss cost of the bidirectional converter of the charging pile, C₃Represents the annual cost of carbon emissions, C₄Representing the annual cost of the network loss. Specifically, the method comprises the following steps:

1) annual loss cost of converter

When the electric automobile exchanges power with a power grid through the bidirectional AC-DC converter, loss is generated on power electronic devices in the converter, and the loss mainly comprises on-off loss of a diode and a switching tube (IGBT), loss of a filter, loss of a control system and the like.

Considering three situations of the electric automobile as an active load, an active power supply and a reactive power supply respectively, C₂Comprises the following steps:

$C_2=\underset{\mathrm{\λ}=1}{\overset{Y_{pl}}{\Σ}}\left[\begin{array}{c}{\mathrm{\π}}_{e,\mathrm{\λ}}^{Net}\frac{1}{{(1+d)}^{\mathrm{\λ}}}\·\\ \underset{s=1}{\overset{m_s}{\Σ}}{\mathrm{PR}}_s\underset{j\∈{\mathrm{\Ω}}_{cs}}{\Σ}\left[\begin{array}{c}{\mathrm{\μ}}_1{T}_{\mathrm{\λ},s}^{1\max }(1-{\mathrm{\η}}_1)P_s^{cs}\\ +{\mathrm{\μ}}_2{T}_{\mathrm{\λ},s}^{2\max }(1-{\mathrm{\η}}_2)P_s^{cs}\\ +{\mathrm{\μ}}_3{T}_{\mathrm{\λ},s}^{3\max }{\mathrm{\ϵ}}_{re}{P}_s^{cs}\end{array}\right]\end{array}\right]---\left(8\right)$

in the formula, m_sFor total number of operational scenarios, PR_sAs the probability of scene s occurring (obtained by method 300 above),price of electricity, mu, charged for vehicle owner in the lambda year₁、μ₂、μ₃Respectively represents the operation coefficients of the charging station as a load, an active power supply and a reactive power supply,the number of annual maximum utilization hours of the charging station as a load, an active power supply and a reactive power supply in a scene s in the lambda-th year is represented by η₁、η₂For the operation efficiency of the converter system when the charging station is used as a load and an active power supply,_rethe loss rate of the converter system (which is generally small) for a charging station as a reactive power source,representing the rated power of the charging station in scene s.

2) Annual cost of carbon emissions

$C_3=\underset{\mathrm{\λ}=1}{\overset{Y_{pl}}{\Σ}}\[\frac{1}{{(1+d)}^{\mathrm{\λ}}}\underset{s=1}{\overset{m_s}{\Σ}}{\mathrm{PR}}_s\·{\mathrm{\π}}_{emi,\mathrm{\λ}}{\mathrm{\χ}}_{\mathrm{\λ}}^{gsp}{P}_s^{gsp}\]---\left(9\right)$

In the formula, pi_emi,λIs year lambda CO₂The discharge tax of (a) is not required,for the carbon emission coefficient of the lambda year superior grid during deployment,the active power obtained from the upper-level power transmission network under the scene s.

3) Annual cost of network loss

The electric automobile is connected into a power grid for charging, and the original trend distribution of the power distribution network is influenced. According to the resistance R of each branch_brAnd reactance X_brAnd the voltage and the injection current of each node can be obtained through load flow calculation. The loss cost can be calculated according to equation (10):

$C_4=\underset{\mathrm{\λ}=1}{\overset{Y_{pl}}{\Σ}}\[\frac{1}{{(1+d)}^{\mathrm{\λ}}}{\mathrm{\π}}_{e,\mathrm{\λ}}^{Net}(\underset{s=1}{\overset{m_s}{\Σ}}{\mathrm{PR}}_s\·{\mathrm{\μ}}_1{T}_{\mathrm{\λ},s}^{1max}{\mathrm{\ΔP}}_{\mathrm{\λ},s}^{loss})\]---\left(10\right)$

in the formula,is the sum of the network loss of each line in the system under the scene s in the lambda-th year.

And the control variable of the second-stage model is a third vector representing active power and reactive power in the intelligent parking lot.

The constraints of the second stage model are: the method comprises the following steps of power flow constraint, node voltage amplitude constraint, feeder line maximum current constraint, transformer capacity constraint, distributed power supply utilization rate constraint, user benefit cost ratio constraint and electric vehicle charging and discharging power factor constraint. Each constraint is described below.

a) Flow restraint

The injection current of each node depends on the type and capacity of the DG connected; the current is the sum of the electric vehicle load and the normal load power. Assuming that the EV operates at unity power factor during charging, the power flow constraint equation of the grid is:

$P_{i,s}^{gsp}+P_{i,s}^{wdg}+P_{i,s}^{pv}-D_{i,s}^{pv}-D_{i,s}=\underset{j\∈\mathrm{\Ω},j\≠i}{\Σ}{U}_{i,s}{U}_{j,s}{Y}_{ij}\×\cos ({\mathrm{\θ}}_{ij}+{\mathrm{\δ}}_{j,s}-{\mathrm{\δ}}_{i,s}),\∀i\∈\mathrm{\Ω}---\left(11\right)$

$Q_{i,s}^{gsp}+Q_{i,s}^{pv}-D_{i,s}^q=-\underset{j\∈\mathrm{\Ω},j\≠i}{\Σ}{U}_{i,s}{U}_{j,s}{Y}_{ij}\×sin({\mathrm{\θ}}_{ij}+{\mathrm{\δ}}_{j,s}-{\mathrm{\δ}}_{i,s}),\∀i\∈\mathrm{\Ω}---\left(12\right)$

b) node voltage amplitude constraints

U_mini≤U_i≤U_maxi(13)

In the formula of U_iFor each load node voltage value, U_miniAnd U_maxiThe lower limit and the upper limit of the voltage of each node are respectively.

c) Maximum current constraint of feeder

I_ij≤I_ijmax(14)

In the formula I_ijFor the feeder current between node I and node j, I_ijmaxThe maximum operating current of the feeder line.

d) Transformer capacity constraint

The power injected from the upper grid must be less than the rated capacity of the substation transformer and no power return to the upper grid is allowed, as shown in equation (15):

$0\≤P_s^{gsp}\≤{\mathrm{Cap}}_{max}---\left(15\right)$

in the formula,for power injected from the upper grid, Cap_maxThe rated capacity of the transformer substation.

e) Distributed power (wind power) utilization constraint

In a distribution network, when a certain line is lightly loaded, especially when the line is interrupted by the injection of distributed power, the terminal voltage may be over-limited. In order to avoid the operation of a large amount of DGs by cutting machines and reduce the economy of a deployed system, the utilization coefficient, namely the ratio of the actual output power to the installed capacity, is specified to be not lower than a certain value, as shown in the following formula:

$1-\underset{g\∈{\mathrm{\Ω}}_{dwg}}{\Σ}(P_{g,s}^{cur}/P_{g,s})\≥{\mathrm{\α}}_c---\left(16\right)$

in the formula,the power of the g-th fan set is reduced under the scene s; p_g,sThe installed capacity of the g-th fan under the scene s.

f) User benefit to cost ratio (BCR) constraints

Defining the BCR of the user as the ratio of the electricity selling income under the unit discharge capacity to the service life loss of the battery, as shown in a formula (17), in order to ensure the benefit of the user and ensure that the user voluntarily uses the parking lot to charge the electric automobile, the value of the BCR is required to be more than 1.

$BCR=\frac{{\mathrm{\μ}}_2\[{\mathrm{\η}}_2\underset{i=1}{\overset{m_{cs}}{\Σ}}{\mathrm{\π}}_e^{{\mathrm{Net}}^{\′}}{P}_{ch\arg e\_csi}-\underset{j=1}{\overset{m_{br}}{\Σ}}{\mathrm{\π}}_e^{Net}{R}_j{i}_j^2\]}{\mathrm{\ζ}(T^{1\max },T^{2\max },T^{3\max })\frac{C_{bat}}{L\left(D_{DOD}\right)E_s{D}_{DOD}}}\≥1---\left(17\right)$

In the formula:indicating the electricity selling price of the owner i_jRepresenting the current, P, of each branch in the distribution network in the discharge state_{charge_csi}Indicating the discharge power of the ith parking lot; ζ (T)^1max,T^2max,T^3max) Reduction factor of the effect on the service life of a parking lot operating as a reactive power source, and the operating state of the parking lot, i.e. T^1max、T^2max、T^3maxIs related to the value of C_batPurchase cost of battery E_sAs the capacity of the battery, D_DODIs the depth of discharge of the battery and is defined as:

D_DOD＝S_SOC1-S_SOC2(18)

in the formula, S_SOC1And S_SOC2The SOC values of the battery before and after discharge, respectively.

L(D_DOD) At a certain D_DODThe battery cycle life is closely related to the working mode, and the larger the discharge depth is, the smaller the cycle life is, as shown in formula (19):

$\begin{array}{cc}L\left(D_{DOD}\right)=2151D_{DOD}^{-2.301}& D_{DOD}\∈\[\mathrm{0.0.9}\]\end{array}---\left(19\right)$

g) charge-discharge power factor constraint of electric automobile

The bidirectional charging and discharging machine is classified according to the power conversion stage number in the electric vehicle charging and discharging system, and can be divided into a single-stage type and a multi-stage type. Although the single-stage type has a simple structure, the control is complex, the isolation is difficult, the capacitance of the connection part of the single-stage type and the battery is large, and the current protection cannot be carried out. On the contrary, the two-stage structure can compensate the above-mentioned disadvantage of the single-stage structure although the number of devices is large. In addition, the two-stage charge and discharge machine can control the charge and discharge speed of the battery and meet the instantaneous charge requirement of the battery. Therefore, the two-stage structure is the mainstream topology at present. At present, a charging and discharging machine generally adopts a two-stage V2G (Vehicle-to-Grid) bidirectional converter structure shown in fig. 4, a front stage of the converter topology is a single-phase PWM rectifier, and a rear stage of the converter topology is a Double Active Bridge (DAB) high-frequency isolation DC/DC two-stage structure, so that the safety is improved, and the volume of the equipment is reduced. The topological DC/DC part adopts a phase-shifting control mode, the leading and lagging relations (namely the polarity of a phase shifting angle) among 2 groups of full bridges determine the flowing direction of energy, the size of the phase shifting angle determines the size of output power, and the topological DC/DC part can be used for various charging modes.

In fig. 4, when considering the converter exchanging current I with the grid_c(taking a discharging process as an example in the figure), under a certain condition, the active power, the reactive power and the voltage at the grid-connected point satisfy the relations shown in the expressions (20) to (21):

$P_s^2+Q_s^2={\left(V_g{I}_c\right)}^2---\left(20\right)$

$P_s^2+{(Q_s+I_c^2{X}_c)}^2={\left(V_c{I}_c\right)}^2---\left(21\right)$

in the formula, Xc is the total impedance value of each stage of transformer and filter from the power battery outlet to the grid-connected point, Vc is the voltage value of the inverter port, and formula (21) can be rewritten as follows:

$P_s^2+{(Q_s+\frac{{(V_g-V_c)}^2}{X_c})}^2={\left(\frac{(V_g-V_c)V_g}{X_c}\right)}^2---\left(22\right)$

approximately represents:

comprises the following steps: $P_s^2+{(Q_s+\frac{V_g^2}{X_c})}^2={\left(\frac{V_c{V}_g}{X_c}\right)}^2---\left(23\right)$

by combining the formulas (20) and (23), when the active power takes a certain value, the reactive power of the power battery and the power grid is exchanged with the port current I_cAnd port voltage V_gIn this regard, the port current may be expressed as:

$I_c=\frac{\sqrt{P_s^2+Q_s^2}}{V_g}---\left(24\right)$

when the output power is rated and the voltage of the grid-connected point port is minimum, I_cThe maximum value is reached:

$I_{c,max}=\frac{\sqrt{P_{sR}^2+Q_{sR}^2}}{V_{g,min}}=\frac{\sqrt{P_{sR}^2+{\left(P_{sR}{\mathrm{tan\θ}}_R\right)}^2}}{V_{g,min}}---\left(25\right)$

in the formula I_c,maxFor maximum output current of port, V_g,minTo the minimum voltage value of the grid-connected point, P_sRAnd Q_sRThe active power rating and the reactive power rating of the power battery are respectively, and tan theta R is a rated power factor.

From formula (23):

${\left(\frac{V_c{V}_g}{X_c}\right)}^2=P_s^2+{(P_{s}tan\mathrm{\θ}+\frac{V_g^2}{X_c})}^2---\left(26\right)$

thus, the inverter port voltage V_cAlso expressed as:

$V_c=\frac{X_c{P}_s}{V_g}\sqrt{1+{(tan\mathrm{\θ}+\frac{V_g^2}{P_s{X}_c})}^2}---\left(27\right)$

due to X_cIs positively correlated with the system frequency, so when both the port voltage and the system frequency take a maximum value, V_cReaches a maximum value of V_c,maxCan be expressed as:

$V_{c,max}=\frac{X_{c,\max }{P}_s}{V_{g,max}}\sqrt{1+{(tan\mathrm{\θ}+\frac{V_{g,max}^2}{P_s{X}_{c,max}})}^2}---\left(28\right)$

V_g,maxis the maximum voltage value at the grid-connected point, f_maxFor the maximum frequency value of the system, Q can be further obtained_sThe maximum regulating capacity of (a) is:

$Q_c=\±\sqrt{{\left(V_g{I}_{c,max}\right)}^2-P_s^2}---\left(29\right)$

$Q_v=\±\sqrt{{\left(\frac{V_{c,max}{V}_g}{X_{c,max}}\right)}^2-P_s^2}-\frac{V_g^2}{X_{c,max}}---\left(30\right)$

Q_s＝min{Q_c,Q_v} (31)

therefore, Q is the point of integration of the parking lot_sThe maximum adjustment capacity of (2) is shown in formulas (29) to (31).

Then, in step S230, a predetermined algorithm is used to solve the first-stage model and the second-stage model to obtain an optimal solution of the first vector and the second vector, and the optimal solution is used as a joint deployment scheme of the distributed power supply and the intelligent parking lot, wherein the optimal solution is obtained by repeatedly iterating the solution results of the first-stage model and the second-stage model, and a total objective function is as shown in formula (32):

$\begin{array}{c}minOF=min({\mathrm{OF}}_1+{\mathrm{OF}}_2)\\ s.t.\left\{\begin{array}{c}G(\·)\\ g(\·)\\ b(\·)\end{array}\right.\end{array}---\left(32\right)$

wherein OF is the overall objective function, OF₁、OF₂The target functions are respectively deployed for the first phase and the second phase, G (-) and G (-) respectively represent inequality constraint sets of the first phase model and the second phase model, and b (-) is an equality constraint set of the second phase model.

The formula (32) is a nonlinear planning model, a final joint deployment scheme is obtained by solving the formula (32), generally, the formula (32) can adopt algorithms such as an interior point method, a particle swarm algorithm, a genetic algorithm and the like, and the embodiment of the invention does not limit specific algorithms, and a person skilled in the art can reasonably select the algorithms according to needs.

Optionally, the predetermined algorithm is a genetic algorithm. The genetic algorithm is a highly parallel, random and self-adaptive optimization algorithm, simulates the evolution process of organisms, expresses the problem as the survival process of a suitable person of 'chromosome', and finally converges to an individual of 'most suitable environment' through the generation evolution of 'chromosome' groups, replication, intersection, variation and other operations, thereby solving the optimal solution or approximate optimal solution of the problem. The genetic algorithm can simultaneously use the search information of a plurality of search points, has good parallelism and universality, and can find the global optimal solution of the problem to be solved as a random search technology.

The process of solving the first vector and the second vector by using the genetic algorithm is described as follows.

A population sample of the first stage model is randomly generated as an initial population. The initial population is the number M of chromosome genes deployed in the first stage determined according to the number of distributed power supplies to be installed (taking wind power as an example) and candidate sites of the intelligent parking lot of the electric automobile, and M is equal to M_DWG+M_CSWherein M is_DWG、M_CSThe numbers of the distributed power supplies to be installed and the candidate parking lot sites are respectively, and the selectable basic characters of the chromosome gene correspond to the selectable installation capacity values of the distributed power supplies and the parking lots at the sites one by one.

And calculating the objective function value of each individual of the initial population in the first-stage model, and calculating the objective function value of each individual of the initial population in the second-stage model to obtain the total objective function value of each individual of the initial population.

As described above, the control variables of the first-stage model include the first vector representing the candidate site in the distributed power supply and the number of distributed power supplies arranged at the candidate site thereof, and the second vector representing the candidate site in the intelligent parking lot and the number of bidirectional charging piles arranged at the candidate site thereof.

And the control variable of the second-stage model is a third vector representing active power and reactive power at the intelligent parking lot. Dividing chromosome codes into a control variable initial value, a grid-connected value of active power and reactive power in each period and a control variable operation period 3 part, wherein the specific format is as follows:

in formula (33), X_nIs a control variable for the node n,the initial value of the control variable for node n,controlling a variable for node n over a period of timeThe grid-connected value of (a) is,the period of operation during which the k-th change occurs is controlled for node n.

And after the total objective function value of the initial population is calculated according to the control variable, the fitness of each individual of the initial population is calculated according to the total objective function value, and selection-cross-variation-reinsertion genetic operation is performed according to the fitness to generate a new population.

And then, repeating the step of calculating the total objective function value for the new population until convergence, wherein the corresponding first vector and second vector are the optimal solution.

The method 200 for jointly deploying the distributed power supply and the intelligent parking lot according to the embodiment of the invention is ended.

To demonstrate the effectiveness of the method 200, an example analysis is performed using the IEEE 33 node distribution network system as an example, as shown in fig. 5A. The system contains residential, commercial and industrial loads, accounting for 23%, 67%, 10% of the total load of the system, respectively. The total peak load of the system was 6.7 MVA. The simulation area is provided with 400 electric automobiles, and according to the national standard of power batteries, the rated voltage of a lithium ion battery used by a private automobile is assumed to be 320V, the rated capacity is 100Ah, and the endurance mileage is 160 km; considering the development of the future charging technology, the charging current is 0.2C, the battery needs 5h from zero charge full, and therefore the conventional charging power is 5 kW. Since excessive charging and discharging can shorten the service life of the power battery, the lower capacity limit of the power battery is set to 0.2, and the upper capacity limit is set to 0.8. The grid loss electricity price of the system is 0.4 yuan/(kWh), and the exhaust emission cost is 0.14 yuan/kg.

The two-stage deployment model of this embodiment is adopted to perform comprehensive optimized deployment on the distributed power sources and the intelligent parking lots in the distribution network system, and the obtained optimal deployment scheme is shown in fig. 5B and table 1.

TABLE 1 optimal deployment scenario

Table 2 gives detailed calculations of the selected optimal deployment scenario, both with and without EV reactive capability.

TABLE 2 calculation results of optimal deployment scenarios under different deployment modes

It can be seen that, in the light-load distribution network system researched by this embodiment, when the EV reactive power is considered, the utilization rate of the distributed power (wind farm) and the expected annual emission reduction yield of the system are better than those of the deployment without considering the EV reactive power, the expected annual loss reduction yield of the system is relatively small, and the annual loss cost of the system converter device is improved to a small extent compared with the case without considering the EV reactive power. This is because when the line is lightly loaded, after a large amount of distributed power supplies are connected to the system, the voltages of the distributed power supply access points and the line end nodes are increased to some extent and exceed 1.0 p.u.. When the EV parking lot transmits inductive reactive power to the power grid, the situation that the voltage at the tail end of the line is higher than the upper limit can be improved, so that the utilization rate of the distributed power supply can be improved, and the carbon emission of the system can be reduced. Considering the reactive capability of the EV, the requirement of considering the user BCR constraint may result in a reduction in the annual maximum utilization hours of the intelligent parking lot as a power source, which makes the increase in the annual loss expected revenue less obvious. In addition, since the discharge process is slightly less efficient than the charge process converter system, this results in a substantially unchanged annual loss of the system converter equipment with only a relatively small improvement. In summary, when the EV reactive power is considered, the total objective function value OF is 2173830; when the EV reactive capacity is taken into account, the total objective function value OF is 1992310.

It should be noted that, previous research on the reactive capability of the electric vehicle does not relate to the influence of life attenuation of the power battery in the power distribution network deployment stage, and actually, the power battery is used as a reactive power source, and in the process of delivering reactive power to the power grid, because a certain loss exists in a converter device and the like, the power battery is actually required to provide extremely small active power, and the power battery can be charged and discharged shallowly, so that a certain loss is generated on the life of the battery. During the deployment phase of the smart car park, the cost of battery life loss is borne by the EV user rather than the grid. In practical application, in order to ensure that the intelligent parking lot can attract electric vehicles in a deployment area to enter a station to receive service, certain guarantee must exist for benefits of EV users, namely when the EV owned by a scheduling user participates in power grid energy scheduling, the power selling benefit of the scheduling user needs to be guaranteed to be larger than the loss caused by battery life loss. To ensure the rationality described above, in the model of the present embodiment, the cost of battery life loss is not included in the objective function of the second stage, but is reflected in the user BCR constraints.

When the user BCR constraint is not considered, the total objective function value OF is 1883710, and the values in table 2 are 29.77, 339910, 714910 and 8012 elements in sequence. It can be seen that the values are reduced without considering the user's interests. This is because in this case, the smart car park can send the required active and reactive power according to the grid demand within the limit of its capacity in the case of meeting the regional charging load demand, which inevitably makes the operation of the grid better, but this does not meet the rationality of the actual operation of the grid.

In addition, under the condition that the EV transmits reactive power to the power grid, the voltage quality of each node in the system is greatly improved, and fig. 6 shows the voltage characteristic curves of the power grid under the three conditions of the original state when the distributed power supply and the parking lot are not installed, and whether the reactive power capability of the intelligent parking lot is considered. Since each line of the system researched by the embodiment is a light-load line, the voltage does not exceed the upper limit and the lower limit before the distributed power supply and the parking lot are deployed, but after the wind power plant and the parking lot with only V2G capability are installed, the voltage of a nearby node can rise, and in order to meet the upper limit constraint of the node voltage, the wind power capacity is limited during deployment. After the parking lot is controlled to transmit inductive reactive power to the power grid, the node voltage is reduced to some extent, and the utilization rate of wind power is increased to some extent. The model method provided by the embodiment can also be used for joint deployment of DG and EV charging facilities in a heavy-load system, and in the heavy-load system, the lower limit of the voltage of the end node may be exceeded, and at this time, the trigger pulse of the converter device can be adjusted, so that the power battery can deliver capacitive reactive power to the power grid, the voltage of the end node can be increased to the constraint range, and the quality of the power supplied by the power battery can be ensured.

Through the calculation results and analysis, the joint deployment scheme of the distributed power supply and the intelligent parking lot fully exerts the capacity of the power battery as an active power supply and a reactive power supply at the same time, promotes the consumption of renewable energy sources on the basis of ensuring the voltage quality and the operation economy of a power grid, and realizes the most benefits of the environment, the power grid company and EV users.

Meanwhile, the scheme considers the cost caused by the service life loss of the power battery in the deployment model, namely the benefit and loss of the EV users participating in power grid scheduling are considered, although the economic benefit and the system voltage quality of the second stage deployment are slightly poor, the scheme is more in line with the actual operation scheduling of the power grid and is more reasonable.

Corresponding to the method 200, fig. 7 shows a block diagram of a co-deployment apparatus 700 of a distributed power supply and an intelligent parking lot according to an embodiment of the invention. The apparatus 700 comprises: a first model building unit 710, a second model building unit 720 and a calculating unit 730.

The first model building unit 710 is adapted to build a first stage model, the control variables of which include a first vector characterizing the candidate sites in the distributed power supply and the number of distributed power supplies configured at the candidate sites, and a second vector characterizing the candidate sites in the intelligent parking lot and the number of bidirectional charging piles configured at the candidate sites, and the objective function of which is minOF₁＝minC₁Wherein, C₁And the annual construction investment cost of the intelligent parking lot and the distributed power supply is represented. For C₁The specific description and the constraint condition refer to the description based on fig. 2 above, and are not repeated herein.

The second model building unit 720 is adapted to build a second stage model with control variables comprising a third vector characterizing active and reactive power in the intelligent parking lot and an objective function of miniF₂＝min(C₂+C₃+C₄) Wherein, C₂Represents the annual loss cost of the bidirectional converter of the charging pile, C₃Represents the annual cost of carbon emissions, C₄Representing the annual cost of the network loss. For the specific description and constraint conditions of each parameter in the objective function, the above description based on fig. 2 is also referred to, and details are not repeated here.

According to one implementation, the apparatus 700 further includes a scene construction unit 740, as shown in fig. 8. The scenario construction unit 740 is adapted to construct an expected scenario set describing the operation states of the distributed power source and the intelligent parking lot, wherein the expected scenario set comprises a plurality of expected scenarios and an occurrence probability corresponding to each expected scenario. The scene building unit 740 includes: a first modeling subunit 742, a second modeling subunit 744, a third modeling subunit 746, and a build subunit 748.

The first modeling subunit 742 is adapted to establish a probability model of the distributed power supply according to the resource data corresponding to the distributed power supply; the second modeling subunit 744 is adapted to build a traditional load model based on the user load data; the third modeling subunit 746 is adapted to establish a charging load model of the electric vehicle by using a calculation method based on travel demand according to travel data of the conventional fuel vehicle; the building subunit 748 is adapted to build an expected scene set describing the operation states of the distributed power sources and the intelligent parking lot by combining the probability model of the distributed power sources, the traditional load model and the electric vehicle charging load model.

Optionally, the distributed power source includes wind power generation, photovoltaic power generation, and biomass power generation, and the corresponding resource data includes historical statistical data of wind resources, solar illumination resources, and biomass resources.

The calculating unit 730 is adapted to solve the first-stage model and the second-stage model by using a predetermined algorithm (e.g., a genetic algorithm), obtain an optimal solution OF the first vector and the second vector, and use the optimal solution as a joint deployment scheme OF the distributed power source and the intelligent parking lot, where the optimal solution is obtained by repeatedly iterating solution results OF the first-stage model and the second-stage model, and an overall objective function is minOF min (OF)₁+OF₂)。

According to an embodiment of the present invention, the calculating unit 730 is divided into: a population subunit 732 and a calculation subunit 734, as shown in fig. 8.

The population subunit 732 is adapted to randomly generate a population sample of the first stage model as an initial population. The calculation subunit 734 is adapted to calculate an objective function value of each individual of the initial population in the first stage model, and calculate an objective function value of each individual of the initial population in the second stage model, to obtain a total objective function value of each individual of the initial population. The population subunit 732 is further adapted to calculate fitness of each individual of the initial population according to the total objective function value, and perform selection-cross-variation-reinsertion genetic operations according to the fitness to generate a new population. The calculation subunit is then further adapted to calculate an overall objective function value for the new population. The population subunit 732 and the calculation subunit 734 repeatedly perform the above steps until convergence, and the first vector and the second vector corresponding to the solution are the optimal solution.

For the specific solving process of the calculating unit 730, reference may be made to the description based on fig. 2, and details are not repeated here.

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.

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.

The invention also discloses:

a6, the method as in A5, wherein the constraints of the first stage model are:

$s.t.\left\{\begin{array}{c}{S}_{i\_cs}\≤S_{i\_cs.max}\\ S_{j\_dwg}\≤S_{j\_dwg.max}\end{array}\right.$

wherein S is_{i_cs}、S_{i_cs.max}Respectively representing the construction area and the maximum available land area of the intelligent parking lot at the candidate station site i, S_{j_dwg}、S_{j_dwg.max}Respectively represents the construction area and the maximum available land area of the distributed power supply field at the candidate station site j, and i ∈ omega_cs，_j∈Ω_dwg。

A7, the method according to A5, wherein, in the objective function of the second-stage model,

$C_2=\underset{\mathrm{\λ}=1}{\overset{Y_{pl}}{\Σ}}\left[\begin{array}{c}{\mathrm{\π}}_{e,\mathrm{\λ}}^{Net}\frac{1}{{(1+d)}^{\mathrm{\λ}}}\·\\ \underset{s=1}{\overset{m_s}{\Σ}}{\mathrm{PR}}_s\underset{j\∈{\mathrm{\Ω}}_{cs}}{\Σ}\left[\begin{array}{c}{\mathrm{\μ}}_1{T}_{\mathrm{\λ},s}^{1\max }(1-{\mathrm{\η}}_1)P_s^{cs}\\ +{\mathrm{\μ}}_2{T}_{\mathrm{\λ},s}^{2\max }(1-{\mathrm{\η}}_2)P_s^{cs}\\ +{\mathrm{\μ}}_3{T}_{\mathrm{\λ},s}^{3\max }{\mathrm{\ϵ}}_{re}{P}_s^{cs}\end{array}\right]\end{array}\right],$

in the formula, m_sFor total number of operational scenarios, PR_sIs the probability of the occurrence of the scene s,price of electricity, mu, charged for vehicle owner in the lambda year₁、μ₂、μ₃Respectively represents the operation coefficients of the charging station as a load, an active power supply and a reactive power supply,the number of annual maximum utilization hours of the charging station as a load, an active power supply and a reactive power supply in a scene s in the lambda-th year is represented by η₁、η₂For the operation efficiency of the converter system when the charging station is used as a load and an active power supply,_refor the loss rate of the converter system when the charging station is used as a reactive power source,representing the rated power of the charging station in scene s;

$C_3=\underset{\mathrm{\λ}=1}{\overset{Y_{pl}}{\Σ}}\[\frac{1}{{(1+d)}^{\mathrm{\λ}}}\underset{s=1}{\overset{m_s}{\Σ}}{\mathrm{PR}}_s\·{\mathrm{\π}}_{emi,\mathrm{\λ}}{\mathrm{\χ}}_{\mathrm{\λ}}^{gsp}{P}_s^{gsp}\],$

in the formula, pi_emi,λIs year lambda CO₂The discharge tax of (a) is not required,for the carbon emission coefficient of the lambda year superior grid during deployment,obtaining active power from a superior power transmission network in a scene s; and

$C_4=\underset{\mathrm{\λ}=1}{\overset{Y_{pl}}{\Σ}}\[\frac{1}{{(1+d)}^{\mathrm{\λ}}}{\mathrm{\π}}_{e,\mathrm{\λ}}^{Net}(\underset{s=1}{\overset{m_s}{\Σ}}{\mathrm{PR}}_s\·{\mathrm{\μ}}_1{T}_{\mathrm{\λ},s}^{1max}{\mathrm{\ΔP}}_{\mathrm{\λ},s}^{loss})\],$

in the formula,is the sum of the network loss of each line in the system under the scene s in the lambda-th year.

A8, the method as in A7, wherein the constraints of the second stage model include one or more of: the method comprises the following steps of power flow constraint, node voltage amplitude constraint, feeder line maximum current constraint, transformer capacity constraint, distributed power supply utilization rate constraint, user benefit cost ratio constraint and electric vehicle charging and discharging power factor constraint.

A9, the method of any one of A1-8, wherein the predetermined algorithm is a genetic algorithm.

A10, the method as in a9, wherein the step of solving the first-stage model and the second-stage model by using a predetermined algorithm to obtain the optimal solution of the first vector and the second vector comprises:

randomly generating a population sample of the first-stage model as an initial population; calculating objective function values of all individuals of the initial population in the first-stage model, and calculating objective function values of all individuals of the initial population in the second-stage model to obtain total objective function values of all individuals of the initial population; calculating the fitness of each individual of the initial population according to the total objective function value, and performing selection-cross-variation-reinsertion genetic operation according to the fitness to generate a new population; and repeating the step of calculating the total objective function value for the new population until convergence, wherein the corresponding first vector and second vector are the optimal solution.

B16, the apparatus as described in B15, wherein the constraints of the first stage model are:

$s.t.\left\{\begin{array}{c}{S}_{i\_cs}\≤S_{i\_cs.max}\\ S_{j\_dwg}\≤S_{j\_dwg.max}\end{array}\right.$

wherein S is_{i_cs}、S_{i_cs.max}Respectively representing the construction area and the maximum available land of the intelligent parking lot at the candidate station site iArea, S_{j_dwg}、S_{j_dwg.max}Respectively represents the construction area and the maximum available land area of the distributed power supply field at the candidate station site j, and i ∈ omega_cs，j∈Ω_dwg。

B17, the apparatus according to B15, wherein, in the objective function of the second stage model,

$C_2=\underset{\mathrm{\λ}=1}{\overset{Y_{pl}}{\Σ}}\left[\begin{array}{c}{\mathrm{\π}}_{e,\mathrm{\λ}}^{Net}\frac{1}{{(1+d)}^{\mathrm{\λ}}}\·\\ \underset{s=1}{\overset{m_s}{\Σ}}{\mathrm{PR}}_s\underset{j\∈{\mathrm{\Ω}}_{cs}}{\Σ}\left[\begin{array}{c}{\mathrm{\μ}}_1{T}_{\mathrm{\λ},s}^{1\max }(1-{\mathrm{\η}}_1)P_s^{cs}\\ +{\mathrm{\μ}}_2{T}_{\mathrm{\λ},s}^{2\max }(1-{\mathrm{\η}}_2)P_s^{cs}\\ +{\mathrm{\μ}}_3{T}_{\mathrm{\λ},s}^{3\max }{\mathrm{\ϵ}}_{re}{P}_s^{cs}\end{array}\right]\end{array}\right],$

in the formula, m_sFor total number of operational scenarios, PR_sIs the probability of the occurrence of the scene s,price of electricity, mu, charged for vehicle owner in the lambda year₁、μ₂、μ₃Respectively represents the operation coefficients of the charging station as a load, an active power supply and a reactive power supply,the number of annual maximum utilization hours of the charging station as a load, an active power supply and a reactive power supply in a scene s in the lambda-th year is represented by η₁、η₂For the operation efficiency of the converter system when the charging station is used as a load and an active power supply,_refor the loss rate of the converter system when the charging station is used as a reactive power source,representing the rated power of the charging station in scene s;

$C_3=\underset{\mathrm{\λ}=1}{\overset{Y_{pl}}{\Σ}}\[\frac{1}{{(1+d)}^{\mathrm{\λ}}}\underset{s=1}{\overset{m_s}{\Σ}}{\mathrm{PR}}_s\·{\mathrm{\π}}_{emi,\mathrm{\λ}}{\mathrm{\χ}}_{\mathrm{\λ}}^{gsp}{P}_s^{gsp}\],$

in the formula, pi_emi,λIs year lambda CO₂The discharge tax of (a) is not required,for the carbon emission coefficient of the lambda year superior grid during deployment,obtaining active power from a superior power transmission network in a scene s; and

$C_4=\underset{\mathrm{\λ}=1}{\overset{Y_{pl}}{\Σ}}\[\frac{1}{{(1+d)}^{\mathrm{\λ}}}{\mathrm{\π}}_{e,\mathrm{\λ}}^{Net}(\underset{s=1}{\overset{m_s}{\Σ}}{\mathrm{PR}}_s\·{\mathrm{\μ}}_1{T}_{\mathrm{\λ},s}^{1max}{\mathrm{\ΔP}}_{\mathrm{\λ},s}^{loss})\],$

in the formula,is the sum of the network loss of each line in the system under the scene s in the lambda-th year.

B18, the apparatus as in B17, wherein the constraints of the second stage model include one or more of: the method comprises the following steps of power flow constraint, node voltage amplitude constraint, feeder line maximum current constraint, transformer capacity constraint, distributed power supply utilization rate constraint, user benefit cost ratio constraint and electric vehicle charging and discharging power factor constraint.

B19, the apparatus according to any of B11-18, wherein the predetermined algorithm is a genetic algorithm.

B20, the apparatus as described in B19, wherein the computing unit includes:

the population subunit is suitable for randomly generating a population sample of the first-stage model as an initial population; the calculation subunit is suitable for calculating the objective function value of each individual of the initial population in the first-stage model, and calculating the objective function value of each individual of the initial population in the second-stage model to obtain the total objective function value of each individual of the initial population; the population subunit is also suitable for calculating the fitness of each individual of the initial population according to the total objective function value, and performing selection-crossing-variation-reinsertion genetic operation according to the fitness to generate a new population; the calculation subunit is further adapted to calculate a total objective function value for the new population; and the population subunit and the calculation subunit are suitable for repeatedly executing the steps until convergence, and the first vector and the second vector which are correspondingly solved are the optimal solution.

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 (10)

1. A joint deployment method of a distributed power supply and an intelligent parking lot comprises the following steps:

establishing a first-stage model, wherein control variables of the first-stage model comprise a first vector for representing candidate sites in the distributed power supply and the quantity of the distributed power supply configured at the candidate sites and a second vector for representing the candidate sites in the intelligent parking lot and the quantity of the bidirectional charging piles configured at the candidate sites, and an objective function of the first-stage model is minOF₁＝minC₁Wherein, C₁Representing the annual construction investment cost of the intelligent parking lot and the distributed power supply;

establishing a second-stage model, wherein the control variable is a third vector for representing active power and reactive power in the intelligent parking lot, and the objective function is minOF₂＝min(C₂+C₃+C₄) Wherein, C₂Represents the annual loss cost of the bidirectional converter of the charging pile, C₃Represents the annual cost of carbon emissions, C₄Representing the annual cost of the network loss; and

solving the first-stage model and the second-stage model by adopting a predetermined algorithm to obtain an optimal solution OF the first vector and the second vector, and using the optimal solution as a joint deployment scheme OF the distributed power supply and the intelligent parking lot, wherein the optimal solution is obtained by repeatedly iterating the solution results OF the first-stage model and the second-stage model, and the total objective function is minOF min (OF)₁+OF₂)。

2. The method of claim 1, further comprising the steps of:

and constructing an expected scene set describing the operation states of the distributed power supply and the intelligent parking lot, wherein the expected scene set comprises a plurality of expected scenes and the occurrence probability corresponding to each expected scene.

3. The method of claim 2, wherein constructing the set of envisioning scenarios comprises:

establishing a probability model of the distributed power supply according to the resource data corresponding to the distributed power supply;

establishing a traditional load model according to user load data;

according to travel data of the traditional fuel vehicle, establishing an electric vehicle charging load model by using a calculation method based on travel requirements; and

and constructing an expected scene set for describing the operation states of the distributed power supply and the intelligent parking lot by combining a probability model of the distributed power supply, a traditional load model and an electric vehicle charging load model.

4. The method of claim 3, wherein,

the distributed power supply comprises wind power generation, photovoltaic power generation and biomass power generation, and the corresponding resource data comprises historical statistical data of wind resources, solar illumination resources and biomass resources.

5. The method of any one of claims 1-4, wherein, in the objective function of the first stage model,

$C_1=\underset{\mathrm{\λ}=1}{\overset{Y_{pl}}{\Σ}}\frac{1}{{(1+d)}^{\mathrm{\λ}}}\[\underset{i\∈{\mathrm{\Ω}}_{cs}}{\Σ}{A}_{cs}{n}_{i,\mathrm{\λ}}^{cs}+\underset{j\∈{\mathrm{\Ω}}_{dwg}}{\Σ}{A}_{dwg}{n}_{j,\mathrm{\λ}}^{dwg}\],$

wherein d is the discount rate, and the lambda is the lambda year in the deployment term, Y_plFor the total number of years of deployment,respectively establishing the quantity of the bidirectional charging piles and the distributed power supplies at the position i, j of the candidate station site in the lambda-th year, A_csi、A_dwgjInvestment cost omega of newly building a bidirectional charging pile and a distributed power supply at the position i, j of the candidate station site respectively_csIs a set of candidate sites, Ω, for a parking lot_dwgIs a set of candidate sites for the distributed power supply.

6. A joint deployment apparatus of a distributed power source and an intelligent parking lot, the apparatus comprising:

the first model establishing unit is suitable for establishing a first-stage model, the control variables of the first-stage model comprise a first vector for representing the candidate sites in the distributed power supply and the quantity of the distributed power supply configured at the candidate sites and a second vector for representing the candidate sites in the intelligent parking lot and the quantity of the bidirectional charging piles configured at the candidate sites, and the target function of the first-stage model establishing unit is minOF₁＝minC₁Wherein, C₁Representing the annual construction investment cost of the intelligent parking lot and the distributed power supply;

a second model establishing unit suitable for establishing a second stage model, wherein the control variable comprises a third vector for representing active and reactive power in the intelligent parking lot, and the objective function is minOF₂＝min(C₂+C₃+C₄) Wherein, C₂Represents the annual loss cost of the bidirectional converter of the charging pile, C₃Represents the annual cost of carbon emissions, C₄Representing the annual cost of the network loss; and

a calculating unit, adapted to solve the first-stage model and the second-stage model by using a predetermined algorithm to obtain an optimal solution OF the first vector and the second vector, and use the optimal solution as a joint deployment scheme OF the distributed power supply and the intelligent parking lot, wherein the optimal solution is obtained by repeatedly iterating solution results OF the first-stage model and the second-stage model, and a total objective function is minOF min (OF OF)₁+OF₂)。

7. The apparatus of claim 6, further comprising:

the scene construction unit is suitable for constructing an expected scene set for describing the operation states of the distributed power supply and the intelligent parking lot, wherein the expected scene set comprises a plurality of expected scenes and occurrence probability corresponding to each expected scene.

8. The apparatus of claim 7, wherein the scene construction unit comprises:

the first modeling subunit is suitable for establishing a probability model of the distributed power supply according to the resource data corresponding to the distributed power supply;

the second modeling subunit is suitable for establishing a traditional load model according to the user load data;

the third modeling subunit is suitable for establishing an electric automobile charging load model by utilizing a calculation method based on travel demands according to travel data of the traditional fuel oil vehicle; and

and the building subunit is suitable for building an expected scene set for describing the operation states of the distributed power supply and the intelligent parking lot by combining the probability model of the distributed power supply, the traditional load model and the electric vehicle charging load model.

9. The apparatus of claim 8, wherein the distributed power source comprises wind power generation, photovoltaic power generation, biomass power generation, and the corresponding resource data comprises historical statistics of wind resources, solar light resources, biomass resources.

10. The apparatus according to any one of claims 6-9, wherein, in the objective function of the first-stage model,

$C_1=\underset{\mathrm{\λ}=1}{\overset{Y_{pl}}{\Σ}}\frac{1}{{(1+d)}^{\mathrm{\λ}}}\[\underset{i\∈{\mathrm{\Ω}}_{cs}}{\Σ}{A}_{cs}{n}_{i,\mathrm{\λ}}^{cs}+\underset{i\∈{\mathrm{\Ω}}_{dwg}}{\Σ}{A}_{wdg}{n}_{j,\mathrm{\λ}}^{dwg}\],$

wherein d is the discount rate, and the lambda is the lambda year in the deployment term, Y_plFor the total number of years of deployment,respectively establishing the quantity of the bidirectional charging piles and the distributed power supplies at the position i, j of the candidate station site in the lambda-th year, A_csi、A_dwgjInvestment cost omega of newly building a bidirectional charging pile and a distributed power supply at the position i, j of the candidate station site respectively_csIs a set of candidate sites, Ω, for a parking lot_dwgIs a set of candidate sites for the distributed power supply.