CN114448044A - Bidirectional quick-charging ordered charging and discharging method and system for power changing station - Google Patents

Bidirectional quick-charging ordered charging and discharging method and system for power changing station Download PDF

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CN114448044A
CN114448044A CN202210108953.4A CN202210108953A CN114448044A CN 114448044 A CN114448044 A CN 114448044A CN 202210108953 A CN202210108953 A CN 202210108953A CN 114448044 A CN114448044 A CN 114448044A
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charging
station
power
discharging
battery
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刘志珍
丁冉
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Shandong University
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    • H—ELECTRICITY
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    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0013—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries acting upon several batteries simultaneously or sequentially
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    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
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    • B—PERFORMING OPERATIONS; TRANSPORTING
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Abstract

The invention provides a method and a system for bidirectional quick charge and discharge in order of a charging station, wherein a decision variable of a charge/discharge state of a charge motor in the charging station is established; constructing a multi-objective function and constraint conditions to form a multi-objective optimization model, wherein the objective function of the optimization model comprises the steps of maximizing the benefit of a power conversion station, minimizing the root-mean-square of the load of a power grid and minimizing the peak-valley difference of the power grid; and solving the multi-target optimization model by using the decision variable as a particle and utilizing a discrete particle swarm algorithm of self-adaptive inertial weight, and determining the charging/discharging power of each charger in the battery replacement station in different time periods under the condition of meeting the battery replacement requirement and each constraint condition. According to the method and the system provided by the invention, the charger can select quick charging, normal charging, slow charging and discharging according to the load condition of a power grid and the battery replacement requirement of a user at a certain period of time, and a control strategy is determined under the condition of known multi-objective function multi-constraint.

Description

Bidirectional quick-charging ordered charging and discharging method and system for power change station
Technical Field
The invention belongs to the technical field of sequential charge and discharge control of a power change station, and particularly relates to a bidirectional rapid charge and discharge sequential method and system for the power change station.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Electric vehicles are attracting more and more attention as clean new energy transportation tools. A battery replacement station (BSS) provides a replacement method for charging an Electric Vehicle (EV), under a replacement mode, unified standardized batteries are adopted to supplement electric energy for the EV, wherein the batteries can be recycled, a complete battery cycle comprises a battery replacement process and a battery charging process, the principle is shown in figure 1, in the battery charging process, a large number of batteries to be charged enter a charger in the replacement station for charging, along with popularization of a replacement technology and continuous expansion of replacement scale, the charging load in the replacement station is increased day by day, if ordered charging scheduling is not arranged, the phenomenon of peak-to-peak addition can be formed by superposition of the charging load and power grid basic load, and the increase of the peak-valley difference can cause the increase of the operation loss of a power system and the impact on the power grid load.
Disclosure of Invention
According to the method and the system provided by the invention, a charger can select quick charging, normal charging, slow charging and discharging according to the load condition of a power grid and the battery replacement requirement of a user at a certain period of time, and a control strategy is determined under the condition of known multi-objective function multi-constraint.
According to some embodiments, the invention adopts the following technical scheme:
a bidirectional quick-charging ordered charging and discharging method for a power station comprises the following steps:
establishing a decision variable of a charging/discharging state of a charging motor in a battery replacement station;
constructing a multi-objective function and constraint conditions to form a multi-objective optimization model, wherein the objective function of the optimization model comprises the steps of maximizing the benefit of a power conversion station, minimizing the root-mean-square of the load of a power grid and minimizing the peak-valley difference of the power grid;
and solving the multi-target optimization model by using the decision variable as a particle and utilizing a discrete particle swarm algorithm of self-adaptive inertial weight, and determining the charging/discharging power of each charger in the battery replacement station in different time periods under the condition of meeting the battery replacement requirement and each constraint condition.
As an alternative embodiment, the specific process of establishing the decision variable of the charging/discharging state of the charging motor in the charging station includes: different state variables are used for representing the states of slow charging, normal charging, fast charging and discharging of each charger in different time periods, the chargers can only be in one state in different time periods, and if the state variable is 1, the chargers are in the state in the time period, and if the state variable is 0, the chargers are not in the state in the time period.
As an alternative embodiment, the power station benefit includes the sum of the compensation for the peak exchange auxiliary service and the charge paid by the electric vehicle user, minus the total electricity purchase cost.
As an alternative embodiment, the minimum power grid load root mean square is a root mean square minimum of a difference between a sum of the net charging power of all chargers and the base load of each period of the regional power grid and an average power load obtained after the charging motors in the charging station are superposed with the power grid load.
As an alternative embodiment, the minimum grid peak-valley difference is the maximum value of the sum of the base load of each time interval of the minimum regional power grid and the net charging power of all chargers in the corresponding time interval battery replacement station.
As an optional implementation mode, the multi-objective optimization model performs normalization processing on each objective function and corresponding weight coefficients.
As an alternative embodiment, the constraint conditions include a charger power constraint, an intra-station battery power constraint and a battery replacement requirement constraint;
the charger power is restricted to maintain the voltage and the current within a normal threshold range;
the station battery power is restricted to keep the residual power in the battery within a reasonable range;
the battery swapping requirement constraint is that the sum of the battery pack storage amount and the supplement amount at each moment is not less than the required amount at the next moment, a certain margin is reserved, and the sum of the rated battery pack storage amount and the supplement amount of the charging and swapping station at each stage cannot exceed the storage limit of the charging and swapping station.
As an alternative embodiment, the specific process of solving the multi-objective optimization model by using the discrete particle swarm algorithm with adaptive inertial weight includes:
(1) configuring various parameters of the power conversion station, including configuration parameters of the power conversion station, time-of-use electricity price of a power grid and probability distribution of the number of the electric vehicles arriving at the station per hour;
(2) initializing various parameters of particles and an algorithm, including iteration times, learning factors, inertia weight, search space dimension, number of initialized group individuals and convergence precision;
(3) judging whether an iteration termination condition is met, if so, outputting a solution vector, and otherwise, turning to the step (5);
(4) outputting each optimized objective function value including load mean square error, load peak-valley difference and exchange station income;
(5) calculating the self-adaptive weight of the particles according to the current iteration times;
(6) updating the speed of the particles, and mapping the speed of the particles to be between 0 and 1 by using a sigmoid function;
(7) updating the positions of the particles and shaping the positions of the particles;
(8) calculating the fitness value of the particle, and updating the local optimal solution and the global optimal solution;
(9) randomly generating N particle groups, and forming M + N particle groups together with the original M particles;
(10) and (4) selecting N proper particles through a concentration selection mechanism, immunizing M improper particles, and turning to the step (3).
As a further limited implementation, in the solving process, the discrete problem space is mapped to the continuous particle motion space, the value of the particle in the state space is limited to two values, i.e. 0 or 1, and each bit of the velocity represents the possibility that the bit corresponding to the particle position takes a value of 0 or 1.
The utility model provides a trade two-way quick charge ordered charge-discharge system of power station, includes:
the module is used for establishing a decision variable of a charging/discharging state of a charging motor in the battery replacement station;
the system comprises a module, a module and a module, wherein the module is used for constructing a multi-objective function and constraint conditions to form a multi-objective optimization model, and the objective function of the optimization model comprises a module for maximizing the benefit of a power conversion station, minimizing the root mean square of the load of a power grid and minimizing the peak-valley difference of the power grid;
and the module is used for solving the multi-target optimization model by using the decision variable as a particle and utilizing a discrete particle swarm algorithm of self-adaptive inertia weight, and determining the charging/discharging power of each charger in the battery replacement station in different time periods under the condition of meeting the battery replacement requirement and each constraint condition.
As an alternative embodiment, a plurality of chargers are arranged in the battery replacement station, and each charger can charge a battery independently, or a plurality of chargers can be combined to rapidly charge a battery.
Compared with the prior art, the invention has the beneficial effects that:
the method establishes 0-1 type decision variables of the charging state of the charger, perfects a model of the dispatching operation of the battery changing station, provides a control strategy under the condition of multi-objective function multi-constraint, optimizes a model solving algorithm, and improves the global search capability and convergence accuracy of the algorithm.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a schematic diagram of a battery cycle process of an electric vehicle in a battery swapping station;
fig. 2 is a schematic view of a quick charging device according to the present embodiment;
FIG. 3 is a diagram of a typical trimf function of this embodiment;
FIG. 4 is a schematic diagram illustrating a particle position updating method according to the present embodiment;
fig. 5 is a flowchart for solving the fast charging model of the power transformation cabinet in this embodiment.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The charging station comprises a plurality of charging and discharging modules (or chargers). The present embodiment takes three charging modules as an example for explanation.
As shown in fig. 2, when the main power portion works, three charging modules may respectively charge corresponding batteries, two charging modules may correspond to a group of batteries, or three charging modules may correspond to a group of batteries, so as to achieve fast charging, a rated charging power of each charging and discharging module is 20Kw, and fig. 2 is a schematic diagram of the main power portion. The function of the switch logic control module is realized by six 2P direct current contactors of Break1, Break2, Break3, Break4, Break5 and Break6, wherein the Break1, the Break2 and the Break3 need to bear 60kW of charging current, and the Break4, the Break5 and the Break6 need to bear 20kW of charging current.
When Break1 is closed and the other switches are opened, the charge and discharge module 1 charges the battery 1. The cells 2, 3 are derived similarly.
And secondly, when the Break1 and the Break4 are closed and other switches are opened, the charging and discharging modules 1 and 2 charge the battery 1 at the same time. The cells 2, 3 are derived similarly.
And thirdly, when the Break1, Break4 and Break5 are closed and other switches are opened, the charging and discharging modules 1, 2 and 3 charge the battery 1 at the same time. The cells 2, 3 are derived similarly.
Because the power swapping station should firstly ensure that the full-charged battery number in each time period should meet the power swapping requirement in the time period, and a certain margin is left so as to prevent the battery supply in the next time period from being so tight, the number state of the batteries in the station will be an important reference factor of the scheduling control strategy. Firstly, setting the upper limit of the storage capacity of a full-charge battery in a power conversion station as NmaxLower limit of NminThen the number of fully charged batteries of the battery replacement station at any time in the day can determine the possibility of which number state the fully charged batteries are in through a specific membership function. We chose to use a typical trimf function embedded in MATLAB, as shown in figure 3.
Figure BDA0003494406700000071
Consider that we only choose NmaxAnd NminTwo values are used as function parameters, and the upper function is divided into two half-triangular membership functions.
Figure BDA0003494406700000081
Figure BDA0003494406700000082
the lower half-triangular trimfL function of the battery number valley characteristic of each time period is defined by taking the lower storage limit of the battery as a reference of the trimfL, and it can be seen that when the number N of the batteries in the jth time period is greaterjThe closer to the lower limit, the closer to 1 the trimfL is, and otherwise, the closer to 0; defining a high-off type half-triangular trimfH function of battery number peak characteristics in each time period by taking the battery storage upper limit as a reference, and determining the number N of batteries in the jth time periodjThe closer to the upper limit, the closer to 1 the trimfH, and vice versa, the closer to 0.
Correspondingly, a battery number state division function is set:
Figure BDA0003494406700000083
when σ is 0.9, S is 0, the number of batteries is insufficient, when S is 1, the number of batteries is in a normal range, and when S is 2, the number of batteries is close to the upper limit value.
The method comprises the steps that a power conversion station runs in a grid-connected mode, the output of a charger and an energy storage system of a battery is optimized and scheduled, the economical efficiency of the operation of the power conversion station is considered, if the number of the battery is within a normal range or in an insufficient state during the load valley of a power grid, the power conversion station needs to carry out rapid charging as much as possible after optimized and scheduled, the requirement of the power conversion station in the time period is met preferentially, the 'valley filling effect' of the load is achieved, the total load is the superposed value of the conventional basic load and the ordered charging load, and if the number of the battery is in a higher level, redundant electric energy is stored in the energy storage system or sold with the power grid according to the time-sharing electricity price of the power grid. The high electricity price period corresponds to the time of a power grid load peak, the number of the charged motors in the power changing station after scheduling is extremely small, the energy storage system can discharge to the power grid to bear part of power supply functions under the state that the number of batteries is large and the power changing requirement can be met, the effect of reducing the power grid load peak value is achieved, and the load is mainly the conventional basic load minus the power supply power of the power changing station. As to whether the charging mode or the discharging mode should be adopted and the charging mode of which power should be adopted in different states, table 1 gives the ordered charging and discharging control strategy under various combinations of load states and battery number states.
TABLE 1 ordered Charge and discharge control strategy
Figure BDA0003494406700000091
Performing mathematical modeling on a power conversion station with a quick charge characteristic, firstly determining all charge states, wherein the rated power of each charge-discharge module in fig. 2 is 20Kw, and when only Break1 is closed, the power is in a slow charge state and is 20 Kw; represents a normal state of charge when Break1, Break4 are closed; when Break1, Break4 and Break5 are closed, the device is in a quick charging state, and the power is 60 kW; in the discharge state, the EV battery supplies power to the charge and discharge module, then the EV battery finally flows to a power grid, the power is 20Kw, and therefore a 0-1 type decision variable is defined:
C1ijthe value of 1 indicates that the ith charger is slowly charged in the jth time interval, I is 1, 2, … … I, J is 1, 2, … … J
C2ijA value of 1 indicates that the ith charger is normally charged in the jth period, I is 1, 2, … … I, J is 1, 2, … … J
C3ijThe value of 1 indicates that the ith charger is fast charged in the jth time interval, I is 1, 2, … … I, J is 1, 2, … … J
DijA value of 1 indicates that the ith charger is discharging in the jth period, I is 1, 2, … … I, J is 1, 2, … … J
Secondly, the condition that the same charger can only be in one of the discharging, charging or standing states in a certain period must be met. The above four decision variables satisfy the intrinsic constraints:
c1ij+c2ij+c3ij+dij≥0
c1ij+c2ij+c3ij+dij≤1
and 1, taking the maximum benefit of the power swapping station as an objective function, wherein the benefit of the power swapping station comprises compensation of the peak exchange auxiliary service, charging paid by an electric automobile user and reduction of total electricity purchasing cost before and after optimization. After the batteries of the electric automobile are charged and managed in order, the batteries of the electric automobile are used as independent main body resources of a third party to participate in the peak clipping auxiliary service to obtain auxiliary service compensation, and the compensation is shared by the battery swapping station and the users of the electric automobile.
Figure BDA0003494406700000101
Figure BDA0003494406700000102
QCij＝(p1·c1ij+p2·c2ij+p3·c3ij)·ΔT
QDij＝pddij·ΔT
Delta represents the charge for the battery replacement service, 10 yuan/n timesjRepresenting the number of the electric automobiles served in the time period j, wherein x is the exchange retail price of the electric automobile user of 1.5 yuan/kwh, QrateRated for the battery, QjkFor the remaining electric quantity of the electric automobile arriving at the kth vehicle in the period j, COST represents the total charge quantity COST of all chargers in the power changing station in one day.
QCijRepresents the charging capacity, eta of the ith charger in the jth time periodchargeFor charging efficiency, beta1QD for power selling price of electric networkijRepresents the discharge electric quantity of the ith charger in the jth time period, etadischargeRepresents the discharge efficiency, beta2For the on-line electricity price of the energy storage system to the power grid, delta T represents the length of the divided time period, p1,p2,p3,pdRespectively representing the slow charging power, the normal charging power, the fast charging power and the discharging power of the charger.
The objective function 2: with the root mean square of the restraining load as an objective function: the load root mean square represents the degree of spread of the grid load. The large load change of the power grid indicates that the power grid load fluctuation is large and the power grid loss is large. And conversely, the load fluctuation and the power loss of the power grid are smaller. From the viewpoint of safe operation of the power grid, it is necessary to properly smooth the fluctuation of the load of the power grid and reduce the energy loss of the power grid. In order to avoid the complex topological structure which needs to be considered when the aim of minimizing the energy loss of the power grid is taken as the target, modeling is carried out by taking the root mean square of the load of the power grid as the target.
Figure BDA0003494406700000111
Figure BDA0003494406700000112
Figure BDA0003494406700000113
The net charging power of all chargers in the jth time slot charging station is PSjAnd J represents the number of time segments in a day, PLjBase load, P, for the regional grid j periodavTo adjust the daily average load, PavAnd the average power load after the power grid load is superposed on the charging motor in the power change station in one day.
The objective function 3: taking the peak value of the restraining load curve as an objective function:
F13＝min{max(PLj+PSj)},j＝1,2,3…J
and (3) joint optimization scheduling of the multi-objective function: in order to carry out uniform measurement and uniform units of the multi-objective function, normalization processing is carried out on the three objective functions, and corresponding weight coefficients are given.
F＝min{λ1·F1rated/F11+λ2F12/F2rated+λ3F13/F3rated}
Wherein F1rated、F2rated、F3ratedRated for three objective functions respectivelyThe value is obtained.
Constraint 1: the purpose of the power constraint of the charger is to maintain the voltage and the current within a normal threshold range, so that the charging equipment is prevented from being damaged by overhigh power or the charging and discharging rate is prevented from being influenced by overlow power.
Pcmin≤p1,p2,p3≤Pcmax
Pcmax＝min(Pkemax,Pkbmax,Pklmax)
Pdmin≤Pd≤Pdmax
Pcmin、PcmaxRepresenting the battery minimum and maximum charge load power. PkemaxRepresenting the maximum charging power that the charging motor can provide to the battery to be charged during the period j. PkbmaxThe maximum charging power which can be borne by the battery to be charged in the battery bank of the power station is obtained. PklmaxThe power transmission capacity of the power supply line in the power change station is obtained. Pdmin、PdmaxRespectively, the minimum discharge load power and the maximum discharge load power of the battery.
Constraint 2: and the battery power in the station is restricted, and the restriction ensures that the residual power in the battery is kept in a reasonable range, so that the service life of the battery is shortened due to overcharging, and the residual power is too low to influence the trip of an electric vehicle user.
Qmin≤Qjk≤Qmax,j＝1,2,…J,k＝1,2,…K
Qjk＝Qj-1k+{(p1·c1ij+p2·c2ij+p3·c3ij)·ηcharge-pddij/ηdischarge}·ΔT
QjkFor the electric quantity of the battery changed by the kth block in the power changing station in the period of j, QminThe lower limit of the charging electric quantity, Q, representing that the battery to be charged in the period of j meets the battery replacement requirementmaxAnd representing the upper limit of the charging electric quantity of the battery to be charged in the j time period to meet the battery replacement requirement.
Constraint 3: the battery replacement demand constraint is that the sum of the battery pack storage amount and the supplement amount of the battery replacement station at each stage must be capable of meeting the battery replacement demand of a user, that is, the sum at each moment is not less than the demand at the next moment, and a certain margin is reserved. Meanwhile, the sum of the rated battery pack storage amount and the supplement amount of the charging and replacing station at each stage cannot exceed the storage limit M of the charging and replacing station, and the initial value of the storage amount of the fully charged battery at each day is defaulted to be M.
(1+μ)·Mj≤Sj≤M,j＝1,2,…,J
Figure BDA0003494406700000131
SjFor reserve number, M, in battery banks of a j-slot converting stationjAnd representing the power change requirement of the power change station in the period of j. And M represents the total amount of the standby batteries of the power station at the initial time in one day, and the default is the maximum storable battery number M of the power station. FjIndicates the number of batteries charged during period j, and μ represents the margin. The constraint is expressed on the SOC of the battery and represents that the spare capacity in the battery bank of the power station is larger than the power demand of the inbound user in the time period.
In general, the optimal scheduling of the battery replacement station is to set how much charging (discharging) power is set by each charger of the battery replacement station in 96 time periods in a day, so that the comprehensive benefit of an objective function is maximized, namely the minimization of the mean square error and the peak-valley difference of the load of a power grid and the maximization of the benefit of the battery replacement station, and meanwhile, the battery replacement requirements of electric vehicle users and the various constraint conditions mentioned above can be met. The method belongs to a multi-target nonlinear constrained optimization problem, the solution efficiency is high by adopting a classical intelligent algorithm, the convergence rate is high, and the global optimal solution is easier to find.
The particle swarm optimization algorithm belongs to one of intelligent evolutionary algorithms, starts from random solutions, searches for optimal solutions through iteration, evaluates the quality of the solutions through fitness and searches for global optimization by following the optimal values searched currently. Each particle in the population has its own position vector and velocity vector, and most importantly an fitness value, which is determined by the objective function. Here, since our decision variables are type 0-1 variables, discrete particle swarm optimization is used for solving. The updating mode of the position and the speed of the particle (decision variable) in the iteration process is as follows:
vi,j(k+1)＝ωvi,j(k)+c1r1[pi,j(k)-xi,j(k)]+c2r2[pg,j(k)-xi,j(k)]
xi,j(k+1)＝xi,j(k)+avi,j(k+1),j＝1,2…,n
Figure BDA0003494406700000141
the above formulas are all solving formulas of the particle swarm algorithm under continuous variables, k represents iteration times, wherein omega is a non-negative number and is called an inertia factor, the search range of a solution space is adjusted, and c1And c2As a learning factor, c1Is a constant judged from the individual's own experience, c2According to the experience of the population, r1And r2Is [ 01 ]]Random numbers transformed within a range. p is a radical ofi,j(k) For individual historical optimal solution of particles, pg,j(k) For an optimal solution of the population of particles, a is a constraint factor, the objective being to control the weight of the velocity, the value of which is affected by the learning factor. In the discrete particle swarm optimization, a discrete problem space can be mapped to a continuous particle motion space, appropriate modification is made, a speed-position updating strategy of the continuous variable particle swarm optimization is still reserved, the values of the particles in a state space are limited to 0 and 1, and each bit of the speed represents the possibility that the bit value corresponding to the position of the particle is 0/1. Therefore, in the discrete particle swarm algorithm, the updating formula of the particle velocity still remains unchanged, but the value of each bit of the individual optimal position and the global optimal position can only be 0 or 1. The particle position is updated as follows:
Figure BDA0003494406700000151
Figure BDA0003494406700000152
the sigmoid function is used here to map the velocity of the particle between 0-1. Because the performance of the traditional particle swarm algorithm is greatly influenced by the parameter of the inertia weight omega, in order to overcome the defect of the fixed parameter, a self-adaptive inertia weight factor is adopted:
ω＝ωmax-(ωmax-ωmin)·k/MaxDT
at the beginning of the iteration, the value of k is small, and ω is close to the maximum value ωmaxThe method is beneficial to jumping out of a local optimal solution, when iteration is terminated quickly, the value of k is close to the iteration upper limit MaxDT, omega is close to the minimum value, and the convergence rate of the algorithm is increased.
The discrete particle swarm algorithm comprises the following solving steps:
inputting various parameters of the power change station, including configuration parameters of the power change station, time-of-use electricity price of a power grid and probability distribution of the number of electric vehicles arriving at the station per hour;
initializing various parameters of the particles and the algorithm, including iteration times, learning factors, inertia weight, search space dimension, number of initialized group individuals, convergence accuracy and the like;
judging whether the iteration termination condition is met, if so, outputting a solution vector, otherwise, turning to the fifth step;
fourthly, outputting each optimized objective function value including load mean square error, load peak-valley difference and BSS income;
calculating the self-adaptive weight of the particles according to the current iteration times;
updating the speed of the particle, and mapping the speed of the particle to be between 0 and 1 by using a sigmoid function;
seventhly, updating the positions of the particles and shaping the positions of the particles;
calculating the fitness value of the particles, and updating local and global optimal solutions;
ninthly, randomly generating N particle groups, and forming M + N particle groups together with the original M particles;
selecting N proper particles by concentration selection mechanism, immunizing M improper particles, and turning to the third step.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (10)

1. A bidirectional quick-charging ordered charging and discharging method for a power station is characterized by comprising the following steps:
establishing a decision variable of a charging/discharging state of a charging motor in a charging station;
constructing a multi-objective function and constraint conditions to form a multi-objective optimization model, wherein the objective function of the optimization model comprises the steps of maximizing the benefit of a power conversion station, minimizing the root-mean-square of the load of a power grid and minimizing the peak-valley difference of the power grid;
and solving the multi-target optimization model by using the decision variable as a particle and utilizing a discrete particle swarm algorithm of self-adaptive inertial weight, and determining the charging/discharging power of each charger in the battery replacement station in different time periods under the condition of meeting the battery replacement requirement and each constraint condition.
2. The method for charging and discharging the charging station in order in a bidirectional and fast manner as claimed in claim 1, wherein the specific process for establishing the decision variable of the charging/discharging state of the charging motor in the charging station comprises the following steps: different state variables are used for representing the states of slow charging, normal charging, fast charging and discharging of each charger in different time periods, the chargers can only be in one state in different time periods, and if the state variable is 1, the chargers are in the state in the time period, and if the state variable is 0, the chargers are not in the state in the time period.
3. The method as claimed in claim 1, wherein the charging station benefit comprises a sum of compensation for peak exchange auxiliary services and charges paid by electric vehicle users, and a total electricity purchase cost is subtracted.
4. The bidirectional rapid charging and discharging ordered charging and discharging method of the power change station as claimed in claim 1, wherein the minimum power grid load root mean square is a root mean square minimum of a difference between a sum of net charging power of all chargers and a base load of each period of a regional power grid and an average power load after the charging motors in the power change station are superposed with the power grid load.
5. The method as claimed in claim 1, wherein the minimum grid peak-to-valley difference is a maximum value of a sum of a base load of each time interval of a minimum regional power grid and net charging power of all chargers in a corresponding time interval charging station.
6. The bidirectional rapid charging and discharging method for the power changing station as claimed in claim 1, wherein the multi-objective optimization model performs normalization processing on each objective function and corresponding weight coefficients.
7. The bidirectional fast charging and discharging ordered charging and discharging method for the battery replacement station as claimed in claim 1, wherein the constraint conditions include a charger power constraint, a battery power constraint in the station and a battery replacement requirement constraint;
the charger power is restricted to maintain the voltage and the current within a normal threshold range;
the station battery power is restricted to keep the residual power in the battery within a reasonable range;
the battery swapping requirement constraint is that the sum of the battery pack storage amount and the supplement amount at each moment is not less than the required amount at the next moment, a certain margin is reserved, and the sum of the rated battery pack storage amount and the supplement amount of the charging and swapping station at each stage cannot exceed the storage limit of the charging and swapping station.
8. The method for charging and discharging the battery replacement station in the bidirectional and fast charging order as claimed in claim 1, wherein the specific process for solving the multi-objective optimization model by using the discrete particle swarm algorithm of the adaptive inertial weight comprises the following steps:
(1) configuring various parameters of the power conversion station, including configuration parameters of the power conversion station, time-of-use electricity price of a power grid and probability distribution of the number of the electric vehicles arriving at the station per hour;
(2) initializing various parameters of particles and an algorithm, including iteration times, learning factors, inertia weight, search space dimension, number of initialized group individuals and convergence precision;
(3) judging whether iteration termination conditions are met, if so, outputting solution vectors, and otherwise, turning to the step (5);
(4) outputting each optimized objective function value including load mean square error, load peak-valley difference and exchange station income;
(5) calculating the self-adaptive weight of the particles according to the current iteration times;
(6) updating the speed of the particles, and mapping the speed of the particles to be between 0 and 1 by using a sigmoid function;
(7) updating the positions of the particles and shaping the positions of the particles;
(8) calculating the fitness value of the particle, and updating the local optimal solution and the global optimal solution;
(9) randomly generating N particle groups, and forming M + N particle groups together with the original M particles;
(10) and (4) selecting N proper particles through a concentration selection mechanism, immunizing M improper particles, and turning to the step (3).
9. The bidirectional fast charging and discharging method for the battery swapping station as claimed in claim 1 or 8, wherein in the solving process, discrete problem space is mapped to the continuous particle motion space, the value of the particle in the state space is limited to 0 or 1, and each bit of the velocity represents the possibility that the bit corresponding to the position of the particle takes a value of 0 or 1.
10. The utility model provides a trade two-way quick charge in order charge-discharge system of power station, characterized by includes:
the module is used for establishing a decision variable of a charging/discharging state of a charging motor in the battery replacement station;
the system comprises a module, a module and a module, wherein the module is used for constructing a multi-objective function and constraint conditions to form a multi-objective optimization model, and the objective function of the optimization model comprises a module for maximizing the benefit of a power conversion station, minimizing the root mean square of the load of a power grid and minimizing the peak-valley difference of the power grid;
and the module is used for solving the multi-target optimization model by using the decision variable as a particle and utilizing a discrete particle swarm algorithm of self-adaptive inertia weight, and determining the charging/discharging power of each charger in the battery replacement station in different time periods under the condition of meeting the battery replacement requirement and each constraint condition.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114987262A (en) * 2022-08-03 2022-09-02 深圳大学 Multi-type battery-based dynamic charging scheduling method and system for battery replacement station
CN115310726A (en) * 2022-10-10 2022-11-08 宁波小遛共享信息科技有限公司 Calculation method of battery replacement threshold of shared electric bicycle, server and storage medium
CN116862192A (en) * 2023-07-26 2023-10-10 中国铁塔股份有限公司 Policy information generation method and device and related equipment
CN117081059A (en) * 2023-08-24 2023-11-17 国网北京市电力公司 Optimal control method, device, equipment and medium for charging and replacing power station cluster

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114987262A (en) * 2022-08-03 2022-09-02 深圳大学 Multi-type battery-based dynamic charging scheduling method and system for battery replacement station
CN114987262B (en) * 2022-08-03 2022-10-28 深圳大学 Multi-type battery-based dynamic charging scheduling method and system for battery replacement station
CN115310726A (en) * 2022-10-10 2022-11-08 宁波小遛共享信息科技有限公司 Calculation method of battery replacement threshold of shared electric bicycle, server and storage medium
CN116862192A (en) * 2023-07-26 2023-10-10 中国铁塔股份有限公司 Policy information generation method and device and related equipment
CN116862192B (en) * 2023-07-26 2024-06-07 中国铁塔股份有限公司 Policy information generation method and device and related equipment
CN117081059A (en) * 2023-08-24 2023-11-17 国网北京市电力公司 Optimal control method, device, equipment and medium for charging and replacing power station cluster
CN117081059B (en) * 2023-08-24 2024-06-11 国网北京市电力公司 Optimal control method, device, equipment and medium for charging and replacing power station cluster

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