CN116681468B - Light storage straight-flexible system cost optimization method and device based on improved whale algorithm - Google Patents

Light storage straight-flexible system cost optimization method and device based on improved whale algorithm Download PDF

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CN116681468B
CN116681468B CN202310932184.4A CN202310932184A CN116681468B CN 116681468 B CN116681468 B CN 116681468B CN 202310932184 A CN202310932184 A CN 202310932184A CN 116681468 B CN116681468 B CN 116681468B
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王朝亮
裘华东
陈宋宋
沈百强
肖涛
张洪志
李磊
陆春光
陈珂
芦鹏飞
刘炜
叶菁
金旭洁
章江铭
宋磊
李亦龙
王佳颖
周瑶
薛友
徐耀辉
朱欢
刘主光
吴亮
金挺超
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China Electric Power Research Institute Co Ltd CEPRI
Marketing Service Center of State Grid Zhejiang Electric Power Co Ltd
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Marketing Service Center of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention relates to a method and a device for optimizing the cost of an optical storage direct-soft system based on an improved whale algorithm, which belong to the field of direct-current micro-grids, and aim at the problems of inaccurate model and non-ideal optimization effect of the existing algorithm, the method adopts the following technical scheme: the cost optimization method of the light storage straight-flexible system based on the improved whale algorithm comprises the steps of setting the number of electric vehicles, and predicting the charging load of the electric vehicles in one day by adopting a Monte Carlo algorithm to obtain the total charging load of the electric vehicles in one day in the set number as the system load; setting limiting conditions for the operation of each part of the light storage straight-flexible system, and establishing an optimization objective function for the operation cost of the light storage straight-flexible system; and (3) adopting an improved whale algorithm for multiple iterations, and calculating the optimal output of each energy storage and power grid port so as to minimize the calculation result of the established objective function. The algorithm considers the charging load of the electric vehicle, and an operation scheme corresponding to lower total cost can be quickly obtained by adopting the improved whale algorithm, so that the accuracy and the speed of the model algorithm are both improved.

Description

Light storage straight-flexible system cost optimization method and device based on improved whale algorithm
Technical Field
The invention belongs to the field of direct current micro-grids, and particularly relates to a cost optimization method and device for an optical storage direct-soft system based on an improved whale algorithm.
Background
The light Chu Zhirou is a direct current system which utilizes photovoltaic power generation and energy storage equipment to participate in load flexible regulation, wherein the most representative photovoltaic power generation in new energy power generation is widely applied due to the characteristics of cleanness, high efficiency and zero carbon emission. However, the photovoltaic output is greatly influenced by factors such as illumination, environment and the like, and direct grid connection can cause fluctuation to a power grid so as to influence the stable operation of the power grid. In order to ensure the safety and stability of the power system, the photovoltaic power generation, the energy storage system and part of load can be integrated to form a micro-grid, photovoltaic output is consumed through the coordinated operation in the micro-grid, and energy support is provided for an alternating current network, so that the effects of peak clipping, valley filling and flexible adjustment are achieved on the continuously-changing load including the electric automobile. Meanwhile, the photovoltaic and energy storage are usually exchanged with the outside in a direct current mode, so that the photovoltaic and energy storage ports can be flexibly and efficiently regulated and controlled by adopting the common direct current bus structure for connection.
Aiming at a plurality of optical storage direct-flexible systems of ports, how to effectively coordinate the input-output relation of each port, and further effectively reduce the overall cost of the system, the method has great significance for realizing larger-scale application of renewable energy sources under the background of power market construction and micro-grid commercialization. The existing research mainly adopts an optimization algorithm to calculate the economic operation of the system from the perspective of a mathematical model of the system, and mainly comprises the steps of generating an objective function by considering each factor of the economic operation of the micro-grid after establishing each port mathematical model, and adopting an intelligent algorithm under constraint conditions to realize the overall economic operation. However, the traditional algorithm adopted in the current research has the problems of low convergence speed, low precision and the like, and meanwhile, the load with obvious time characteristics such as an electric automobile is not considered, the checking model is inaccurate, and the optimization effect is not ideal.
Disclosure of Invention
Aiming at the problems of inaccurate existing algorithm models and non-ideal optimization effects, the application provides a cost optimization method and device for an optical storage direct-soft system based on an improved whale algorithm.
The application adopts the following technical scheme: a cost optimization method of a light storage straight-flexible system based on an improved whale algorithm comprises the following steps:
s1, setting the number N of electric vehicles, and predicting the charging load of the electric vehicles in one day by adopting a Monte Carlo algorithm to obtain the total charging load of the electric vehicles in one day with the set number as a system load;
s2, setting limiting conditions of operation of each part of the light storage straight-flexible system, and establishing an operation cost optimization objective function of the light storage straight-flexible system, wherein the light storage straight-flexible system comprises a photovoltaic module, an energy storage module, a power grid port and the system load obtained in the step S1;
and S3, adopting an improved whale algorithm to iterate for a plurality of times, and calculating the optimal output of each energy storage and power grid port, so that the objective function calculation result established in the step S2 is minimum.
In the application, the energy emitted by the photovoltaic is absorbed by the system, the condition that the output power of the photovoltaic is not utilized is avoided, the energy storage regulation function enables the power distribution of the system to be more flexible, and the cost can be effectively reduced, so that compared with the existing method, the energy utilization rate of the application is higher; calculating the charging load of the electric automobile through a prediction algorithm, taking the fluctuation of the load into consideration, and adaptively replacing; the improved whale algorithm is improved in convergence factor, probability judgment factor and population diversity of the traditional whale algorithm, and compared with the traditional algorithm, the improved whale algorithm has the advantages of higher convergence speed and higher precision, and can more rapidly find an operation scheme corresponding to lower total cost.
Further, in step S1, the system load is obtained according to the monte carlo algorithm as follows:
s1.1, establishing a model of daily driving mileage of the electric automobile:
（1）；
in the method, in the process of the application,f（L) Is a probability density distribution function of daily driving mileage of the electric automobile,Lthe daily driving mileage of the electric automobile is obtained,μ L andσ L the expected value and standard deviation of the daily driving mileage are respectively;
s1.2, establishing the charge state of the battery of the electric vehicle during chargingSOC EV The formula:
（2）；
in the method, in the process of the application,L m the maximum daily driving mileage of the electric automobile is obtained;
S1.3, establishing the required charging time length of the electric automobileTIs a model of (a):
（3）；
in the method, in the process of the invention,Cfor the capacity of the battery,Pin order for the charging power to be high,k c is the charging efficiency;
s1.4, establishing a charging start time model:
（4）；
in the method, in the process of the invention,f（t s ) The probability density distribution function of the charging starting time of the electric automobile is set;t s the charging time is started for the electric automobile;μ t andσ t respectively the starting charging timetIs a standard deviation and the expected value of (2); wherein, the electric automobile starts charging timet s The method comprises the steps of connecting charging time after finishing all driving tasks of an electric automobile in one day;
s1.5, calculating charging load curves of the electric vehicles one by one according to the formulas (1) to (4), and then superposing the charging load curves to obtain the total charging load curves of the electric vehicles with set quantity in one day.
Further, in S2, a system overall optimization objective function is establishedfThe following are provided:
（5）；
in the method, in the process of the invention,C CRF for the overall annual cost of the system,C M for the overall maintenance cost of the system,C cons the interaction cost of the micro-grid system and the alternating current power grid is; lambda (lambda) 1 、λ 2 、λ 3 The weight coefficients of the overall annual cost of the system, the overall maintenance cost of the system and the interaction cost of the micro-grid system and the alternating-current grid are respectively calculated;
the limiting conditions are as follows:
the optical storage straight-flexible system meets the power dynamic balance among ports, and is specifically as follows:
（6）；
In the method, in the process of the invention,P PV (t) is the output power of the photovoltaic,P grid （t) For the grid-side power,P ESS （t) In order to store the output power of the energy,P EV （t) Charging the electric automobile with a load;tis the runtime.
Charging and discharging power of energy storage moduleP ESS （t) Current state of charge of an energy storage moduleSOC（t) All need to be within the limit value, specifically as follows:
（7）；
（8）；
in the method, in the process of the invention,maximum charging power for energy storage->For the purpose of storing the maximum discharge power,SOC min as a lower SOC limit for the energy storage module,SOC max an upper limit of state of charge, SOC, for the energy storage module;
current state of charge of energy storage moduleSOC（t) The calculation method comprises the following steps:
（9）；
in the method, in the process of the invention,E max in order to store the energy in the maximum capacity,ηis the energy storage charge-discharge coefficient;SOC（t-1) is the state of charge of the energy storage module at the previous moment;
grid port converter power, i.e. grid side powerP grid （t) The flow needs to be within limits, specifically as follows:
（10）；
in the method, in the process of the invention,maximum charging power for the grid, < >>And the maximum discharge power of the power grid.
Further, the overall annual cost of the systemC CRF The method meets the following conditions:
（11）；
in the method, in the process of the invention,for the photovoltaic cost conversion factor, +.>For photovoltaic installation costs, < >>For energy storage cost conversion coefficient, < >>The energy storage installation cost is set;
cost of overall maintenance of the systemC M The method meets the following conditions:
（12）；
in the method, in the process of the invention,maintaining a cost factor for the grid-side converter, < > >A cost factor is maintained for the energy storage port,a cost factor is maintained for the photovoltaic port,t G for the service time of the power grid end converter,t ESS for energy-storage portsThe time of use of the device is set,t PV the photovoltaic port service time is given;
micro-grid system and alternating current grid interaction costC cons The following are provided:
（13）；
in the method, in the process of the invention,for grid output power, < >>The power is fed back to the power grid,C Grid is the electricity charge of the power grid,C feed the power price is the internet.
Further, the specific process of step S3 is as follows:
s3.1, initializing: reading the photovoltaic output power and the charging load power of the electric automobile, and randomly initializing the power of the energy storage module;
s3.2, randomly searching positions, and meeting the following formula:
（19）；
（20）；
（21）；
（22）；
in the method, in the process of the invention,for the next time position +.>For the current time position +.>For randomly generated starting positions +.>For the distance between the current individual and the random individual, < ->And->Coefficient vector, < >>Is a convergence factor;a random number between 0 and 1;
for convergence factorsThe global search effect is better when it is larger, the local search effect is better when it is smaller, in order to find the position closer to the prey in the whole as soon as possible in the early stage of the search and accurately lock the position of the prey in the vicinity of the position, the requirement +.>Larger at the beginning of the cycle, followed by a faster decrease;
Will converge the factorThe method comprises the following steps:
（14）；
in the method, in the process of the invention,t r for the number of cycles to be counted,t max in order to have a maximum number of cycles,λis a cyclic coefficient;
s3.3, reducing the search rangeSurrounding: after a plurality of cycles, the position of the prey is locked by the optimal individual, other whales gradually get close to the optimal position in a surrounding manner, and the position is at the next momentCan be expressed as:
（23）；
（24）；
in the method, in the process of the invention,for the distance between the current individual and the optimal individual, < ->The optimal position found currently is determined;
s3.4, optimizing the search position: near the optimal location, whales prey on prey in a spiral form, at the next moment in timeExpressed as:
（25）；
in the method, in the process of the invention,bfor the log-helix to be well-done,la random number between-1 and 1;
s3.5, updating the position:
（26）；
in the method, in the process of the invention,pis a probability judgment factor;
pthe values of (2) satisfy the following:
（15）；
in the method, in the process of the invention,rsn（nlocation, scale, shape) is a bias distribution random generation function;nrepresented as a dimension of the biased distribution variable,locationrepresented as the position of the occurrence of the peak of the off-set distribution,scalerepresented as a range of the skewed distribution,shapeexpressed as a biased distribution shape parameter;
in the early stage of circulation, whales are far away from the prey, and mainly should carry out surrounding strategies; in the middle of the cycle, some individuals who are close to the prey are hunting, and the other part is further close to the prey, so that the probability of two behaviors is close; in the later period of circulation, most individuals already reach the hunting sites and mainly should perform hunting actions;
S3.6, setting a normal disturbance factor to avoid the result to be trapped into local optimum:
（16）；
（17）；
（18）；
wherein,；
s3.7, calculating the current objective function value in the formula (5) after the circulation starts, comparing the current objective function value with the minimum value of the function value obtained by the current iteration, and randomly generating a convergence factor according to the formula (14) and the formula (15)And probability judgment factorpAnd according to the convergence factor->And probability judgment factorpAnd (3) obtaining the position of the next cycle according to formulas (16) to (18), judging whether the position meets the constraint condition in the step S2, if so, entering the next cycle, if not, limiting the position within the constraint condition, and then entering the next cycle until the preset maximum iteration times are reached, wherein the power of the photovoltaic module, the energy storage module, the power grid interface and the system load corresponding to the minimum value obtained by the objective function is the respective optimal output.
An apparatus for cost optimization of a light storage straight-flexible system based on an improved whale algorithm, comprising:
the prediction module is used for predicting the charging load of the electric vehicles in one day by adopting a Monte Carlo algorithm according to the set number of the electric vehicles to obtain the total charging load of the electric vehicles in one day in the set number as a system load;
The setting module is used for setting the limiting conditions of the operation of each part of the light storage straight-flexible system and establishing the operation cost optimization objective function of the light storage straight-flexible system; the light storage direct-soft system comprises a photovoltaic module, an energy storage module, a power grid port and a system load obtained by a prediction module;
and the calculation module is used for adopting an improved whale algorithm to iterate for a plurality of times, calculating the optimal output force of each energy storage and power grid port, and enabling the calculation result of the objective function established by the setting module to be minimum.
The model established by the prediction module comprises a daily driving mileage model of the electric vehicle, a charging time period model required by the electric vehicle, a charging starting time model and a charging load model, and the total charging load of the electric vehicle in a day with a set number is output by inputting the daily driving mileage model calculation result of the electric vehicle, the charging time period model calculation result required by the electric vehicle and the charging time period model calculation result required by the electric vehicle into the charging load model;
the daily driving mileage model of the electric automobile is as follows:
（1）；
in the method, in the process of the invention,f（L) Is a probability density distribution function of daily driving mileage of the electric automobile, LThe daily driving mileage of the electric automobile is obtained,μ L andσ L the expected value and standard deviation of the daily driving mileage are respectively;
electric vehicle battery state of charge during chargingSOC EV The model is as follows:
（2）；
in the method, in the process of the invention,L m the maximum daily driving mileage of the electric automobile is obtained;
the required charging duration model of the electric automobile is as follows:
（3）；
in the method, in the process of the invention,Cfor the capacity of the battery,Pin order for the charging power to be high,k c is the charging efficiency;
the charge start time model is:
（4）；
in the method, in the process of the invention,f（t s ) The probability density distribution function of the charging starting time of the electric automobile is set;t s the charging time is started for the electric automobile;μ t andσ t respectively the starting charging timetIs a standard deviation and the expected value of (2); wherein, the electric automobile starts charging timet s The charging time is accessed after the electric automobile finishes all driving tasks in one day.
Further, the setting module comprises an optimizing module and a limiting module, and is communicated with the optimizing module to input the overall annual cost of the system, the overall maintenance cost of the system and the interaction cost of the micro-grid system and the alternating-current grid, so as to obtain an overall optimizing objective function model of the system; the limiting condition model of the integral optimization objective function value of the system is obtained by inputting the power dynamic balance relation between ports of the optical storage direct-soft system, the charge and discharge power of the energy storage module, the charge state of the energy storage module and the power flow limit value of the power grid port converter to the limiting module;
The overall optimization objective function model of the system is obtained by the following formula:
（5）；
in the method, in the process of the invention,C CRF for the overall annual cost of the system,C M for the overall maintenance cost of the system,C cons the interaction cost of the micro-grid system and the alternating current power grid is; lambda (lambda) 1 、λ 2 、λ 3 The weight coefficients of the overall annual cost of the system, the overall maintenance cost of the system and the interaction cost of the micro-grid system and the alternating-current grid are respectively calculated;
the power dynamic balance among all ports of the optical storage straight-flexible system is satisfied:
（6）；
in the method, in the process of the invention,P PV (t) is the output power of the photovoltaic,P grid （t) For the grid-side power,P ESS （t) In order to store the output power of the energy,P EV （t) Charging the electric automobile with a load;tis the running time;
charging and discharging power of energy storage moduleP ESS （t) State of charge of an energy storage moduleSOC（t) The limit value of (2) satisfies:
（7）；
（8）；
in the method, in the process of the invention,maximum charging power for energy storage->For the purpose of storing the maximum discharge power,SOC min as a lower SOC limit for the energy storage module,SOC max an upper limit of state of charge, SOC, for the energy storage module;
current state of charge of energy storage moduleSOC（t) The calculation method comprises the following steps:
（9）；
in the method, in the process of the invention,E max in order to store the energy in the maximum capacity,ηis the energy storage charge-discharge coefficient;SOC（t-1) is the state of charge of the energy storage module at the previous moment;
grid side powerP grid （t) The flow needs to be within limits, specifically as follows:
（10）；
in the method, in the process of the invention,maximum charging power for the grid, < > >And the maximum discharge power of the power grid.
Further, the overall annual cost of the systemC CRF The method meets the following conditions:
（11）；
in the method, in the process of the invention,for the photovoltaic cost conversion factor, +.>For photovoltaic installation costs, < >>For energy storage cost conversion coefficient, < >>The energy storage installation cost is set;
cost of overall maintenance of the systemC M The method meets the following conditions:
（12）；
in the method, in the process of the invention,maintaining a cost factor for the grid-side converter, < >>A cost factor is maintained for the energy storage port,a cost factor is maintained for the photovoltaic port,t G for the service time of the power grid end converter,t ESS for the time of use of the energy storage port,t PV the photovoltaic port service time is given;
micro-grid system and alternating current grid interaction costC cons The following are provided:
（13）；
in the method, in the process of the invention,for grid output power, < >>The power is fed back to the power grid,C Grid is the electricity charge of the power grid,C feed the power price is the internet.
Further, the calculation module comprises an initialization model, a position searching model and an algorithm model, and the randomized power of the energy storage module is obtained by inputting a convergence factor, photovoltaic output power and charging load power of the electric automobile to the initialization model; the current circulation position and the position of the next circulation are obtained by inputting a convergence factor, a probability judgment factor and a normal disturbance factor into the position searching model; the method comprises the steps of inputting a current objective function value and a minimum value of a function value obtained by current iteration into an algorithm model to obtain the minimum value of the objective function and power of a photovoltaic module, an energy storage module, a power grid interface and a system load corresponding to the minimum value obtained by the objective function;
The position search model uses the following formula:
the random search position satisfies the following formula:
（19）；
（20）；
（21）；
（22）；
in the method, in the process of the invention,for the next time position +.>For the current time position +.>For randomly generated starting positions +.>For the distance between the current individual and the random individual, < ->And->Coefficient vector, < >>Is a convergence factor;a random number between 0 and 1;
will converge the factorThe method comprises the following steps:
（14）；
in the method, in the process of the invention,t r for the number of cycles to be counted,t max in order to have a maximum number of cycles,λis a cyclic coefficient;
the searching range is reduced: after a plurality of cycles, the position of the prey is locked by the optimal individual, other whales gradually get close to the optimal position in a surrounding manner, and the position is at the next momentCan be expressed as:
（23）；
（24）；
in the method, in the process of the invention,for the distance between the current individual and the optimal individual, < ->The optimal position found currently is determined;
optimizing the search position: near the optimal location, whales prey on prey in a spiral form, at the next moment in timeExpressed as:
（25）；
in the method, in the process of the invention,bfor the log-helix to be well-done,la random number between-1 and 1;
updating the position:
（26）；
in the method, in the process of the invention,pis a probability judgment factor;
pthe values of (2) satisfy the following:
（15）；
in the method, in the process of the invention,rsn（nlocation, scale, shape) is a bias distribution random generation function;nrepresented as a dimension of the biased distribution variable, locationRepresented as the position of the occurrence of the peak of the off-set distribution,scalerepresented as a range of the skewed distribution,shapeexpressed as a biased distribution shape parameter;
setting a normal disturbance factor to avoid the result from falling into local optimum:
（16）；
（17）；
（18）；
wherein,；
and calculating the minimum value obtained by the objective function according to the result of the position searching model by the algorithm model, and obtaining the power of the photovoltaic module, the energy storage module, the power grid interface and the system load corresponding to the minimum value as the respective optimal output.
A computer device comprising a memory and one or more processors, the memory storing executable code, the one or more processors when executing the executable code, for implementing the improved whale algorithm based method of cost optimization of a light storage direct flexible system described above.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of cost optimization for a light storage direct flex system based on an improved whale algorithm as described above.
The application has the beneficial effects that: the application relates to a cost optimization method and a device for an optical storage direct-soft system based on an improved whale algorithm, wherein the optical storage direct-soft system is connected with a photovoltaic module, a charging module, an energy storage module and a power grid port through a direct-current bus, so that the energy utilization rate is improved; the charging load of the electric automobile is calculated through a prediction algorithm, and the fluctuation of the load is taken into consideration, so that the method can be better suitable for large-scale application of new energy automobiles in the future; the improved whale algorithm is improved in convergence factor, probability judgment factor and population diversity of the traditional whale algorithm, so that compared with the traditional algorithm, the improved whale algorithm has faster convergence speed and higher precision, and can more quickly find an operation scheme corresponding to lower total cost. Therefore, the application is reliable and easy to operate and is suitable for the actual system operation.
Drawings
Fig. 1 is a block diagram of the whole optical storage straight-flexible system of the invention.
Fig. 2 is a flowchart of an electric vehicle charging load prediction algorithm according to the present invention.
Fig. 3 is a flowchart of the improved whale algorithm of the present invention.
Figure 4 is a graph of the port profiles of the improved whale algorithm of the present invention after optimization.
Fig. 5 is a state of charge SOC diagram of the resulting energy storage module after optimization of the improved whale algorithm of the present invention.
Fig. 6 is a graph of the improved whale algorithm of the present invention versus the number of iterations of the other algorithm.
Detailed Description
The technical solutions of the embodiments of the present invention will be explained and illustrated below with reference to the drawings of the present invention, but the following embodiments are only preferred embodiments of the present invention, and not all the embodiments. Based on the examples in the implementation manner, other examples obtained by a person skilled in the art without making creative efforts fall within the protection scope of the present invention.
Example 1
The cost optimization method of the light storage straight-flexible system based on the improved whale algorithm of the embodiment comprises the following steps:
s1, setting the number N of electric vehicles, and predicting the charging load of the electric vehicles in one day by adopting a Monte Carlo algorithm to obtain the total charging load of the electric vehicles in one day with the set number as a system load; as shown in fig. 2, the specific manner is as follows:
S1.1, establishing a model of daily driving mileage of the electric automobile:
（1）；
in the method, in the process of the invention,f（L) Is a probability density distribution function of daily driving mileage of the electric automobile,Lthe daily driving mileage of the electric automobile is obtained,μ L andσ L the expected value and standard deviation of the daily driving mileage are respectively;
s1.2, establishing the charge state of the battery of the electric vehicle during chargingSOC EV The formula:
（2）；
in the method, in the process of the invention,L m the maximum daily driving mileage of the electric automobile is obtained;
s1.3, establishing a model of the required charging time length T of the electric automobile:
（3）；
in the method, in the process of the invention,Cfor the capacity of the battery,Pin order for the charging power to be high,k c is the charging efficiency;
s1.4, establishing a charging start time model:
（4）；
in the method, in the process of the invention,f（t s ) The probability density distribution function of the charging starting time of the electric automobile is set;t s the charging time is started for the electric automobile;μ t andσ t respectively the starting charging timetIs a standard deviation and the expected value of (2); wherein, the electric automobile starts charging timet s The method comprises the steps of connecting charging time after finishing all driving tasks of an electric automobile in one day;
s1.5, calculating charging load curves of the electric vehicles one by one according to the formulas (1) to (4), and then superposing the charging load curves to obtain the total charging load curves of the electric vehicles with set quantity in one day.
S2, setting limiting conditions of operation of each part of the light storage straight-flexible system, and establishing an operation cost optimization objective function of the light storage straight-flexible system, wherein the light storage straight-flexible system comprises a photovoltaic module, an energy storage module, a power grid port and the system load obtained in the step S1; as shown in fig. 1, the whole optical storage direct-soft system is in a structural block diagram, and a photovoltaic module and an energy storage module are respectively connected into a direct-current bus through a DC/DC converter, wherein the DC/DC converter of the energy storage module can realize bidirectional flow of energy; the power grid is accessed through a DC/AC converter; the electric automobile load is predicted by using a Monte Carlo algorithm;
Establishing a system integral optimization objective functionfThe following are provided:
（5）；
in the method, in the process of the invention,C CRF for the overall annual cost of the system,C M for the overall maintenance cost of the system,C cons the interaction cost of the micro-grid system and the alternating current power grid is; lambda (lambda) 1 、λ 2 、λ 3 The weight coefficients of the overall annual cost of the system, the overall maintenance cost of the system and the interaction cost of the micro-grid system and the alternating current grid are respectively calculated.
The limiting conditions are as follows:
the optical storage straight-flexible system meets the power dynamic balance among ports, and is specifically as follows:
（6）；
in the method, in the process of the invention,P PV (t) is the output power of the photovoltaic,P grid (t) is the output power of the photovoltaic,P ESS (t) is the energy storage output power,P EV (t) charging load for an electric vehicle;tis the running time;
charging and discharging power of energy storage moduleP ESS （t) Current state of charge of an energy storage moduleSOC（t) All need to be within the limit value, specifically as follows:
（7）；
（8）；
in the method, in the process of the invention,maximum charge power for energy storageRate of->For the purpose of storing the maximum discharge power,SOC min as a lower SOC limit for the energy storage module,SOC max an upper limit of state of charge, SOC, for the energy storage module;
current state of charge of energy storage moduleSOC（t) The calculation method comprises the following steps:
（9）；
in the method, in the process of the invention,E max in order to store the energy in the maximum capacity,ηis the energy storage charge-discharge coefficient;SOC（t-1) is the state of charge of the energy storage module at the previous moment; the results are shown in FIG. 5.
Grid port converter power, i.e. grid side power P grid （t) The flow needs to be within limits, specifically as follows:
（10）；
in the method, in the process of the invention,maximum charging power for the grid, < >>And the maximum discharge power of the power grid.
Annual cost of the system as a wholeC CRF The method meets the following conditions:
（11）；/>
in the method, in the process of the invention,for the photovoltaic cost conversion factor, +.>For photovoltaic installation costs, < >>For energy storage cost conversion coefficient, < >>The energy storage installation cost is set;
cost of overall maintenance of the systemC M The method meets the following conditions:
（12）；
in the method, in the process of the invention,maintaining a cost factor for the grid-side converter, < >>A cost factor is maintained for the energy storage port,a cost factor is maintained for the photovoltaic port,t G for the service time of the power grid end converter,t ESS for the time of use of the energy storage port,t PV the photovoltaic port service time is given;
micro-grid system and alternating current grid interaction costC cons The following are provided:
（13）；
in the method, in the process of the invention,for grid output power, < >>The power is fed back to the power grid,C Grid is the electricity charge of the power grid,C feed the power price is the internet.
S3, adopting an improved whale algorithm to iterate for a plurality of times, and calculating the optimal output force of each energy storage and power grid port so as to minimize the objective function calculation result established in the step S2, wherein the specific process is as shown in FIG. 3:
s3.1, initializing: reading the photovoltaic output power and the charging load power of the electric automobile, and randomly initializing the power of the energy storage module;
s3.2, randomly searching positions, and meeting the following formula:
（19）；
（20）；
（21）；
（22）；
In the method, in the process of the invention,for the next time position +.>For the current time position +.>For randomly generated starting positions +.>For the distance between the current individual and the random individual, < ->And->Coefficient vector, < >>Is a convergence factor;a random number between 0 and 1;
will converge the factorThe method comprises the following steps:
（14）；
in the method, in the process of the invention,t r for the number of cycles to be counted,t max in order to have a maximum number of cycles,λis a cyclic coefficient;
s3.3, narrowing the search range: after a plurality of cycles, the position of the prey is locked by the optimal individual, other whales gradually get close to the optimal position in a surrounding manner, and the position is at the next momentCan be expressed as:
（23）；
（24）；
in the method, in the process of the invention,for the distance between the current individual and the optimal individual, < ->The optimal position found currently is determined;
s3.4, optimizing the search position: near the optimal location, whales prey on prey in a spiral form, at the next moment in timeExpressed as:
（25）；
in the method, in the process of the invention,bfor the log-helix to be well-done,la random number between-1 and 1;
s3.5, updating the position:
（26）；
in the method, in the process of the invention,pis a probability judgment factor;
pthe values of (2) satisfy the following:
（15）；
in the method, in the process of the invention,rsn（nlocation, scale, shape) is a bias distribution random generation function;nrepresented as a dimension of the biased distribution variable,locationrepresented as the position of the occurrence of the peak of the off-set distribution, scaleRepresented as a range of the skewed distribution,shapeexpressed as a biased distribution shape parameter;
s3.6, setting a normal disturbance factor to avoid the result to be trapped into local optimum:
（16）；
（17）；
（18）；
wherein,；
s3.7, calculating the formula after the start of the cycle(5) The current objective function value is compared with the minimum value of the function value obtained by the current iteration, and then a convergence factor and a probability judgment factor are randomly generated according to a formula (14) and a formula (15)pAnd judges the factor according to the convergence factor and the probabilitypAnd (3) obtaining the position of the next cycle according to formulas (16) to (18), judging whether the position meets the constraint condition in the step S2, if so, entering the next cycle, if not, limiting the position within the constraint condition, and then entering the next cycle until the preset maximum iteration times are reached, wherein the power of the photovoltaic module, the energy storage module, the power grid interface and the system load corresponding to the minimum value obtained by the objective function is the respective optimal output.
An example was used to verify the feasibility of the proposed improved whale algorithm in terms of optimizing costs, and the system-related parameters are shown in the following table.
The output of each port of the light storage straight-flexible system obtained after optimization of the improved whale algorithm is shown in figure 4. The photovoltaic output is calculated by a typical sunny condition of July in certain city, the load part is obtained by predicting the charging load of the electric automobile by adopting a Monte Carlo algorithm, the output of the energy storage part and the power of the power grid meet the dynamic balance condition of the whole energy of the system, the state of charge SOC (t) of the energy storage module is shown in a graph as shown in fig. 5, and the SOC (t) obtained from the graph fluctuates between the limit value of 0.2 and 0.8, so that the constraint condition is met.
Under the condition that the photovoltaic output and the load are the same, a Genetic Algorithm (GA), a particle swarm algorithm (PSO) and a traditional whale algorithm (WOA) are adopted to solve the same objective function, and the relation between the iteration times and the cost is shown in figure 6. The genetic algorithm requires more iterations than the modified whale algorithm (IWOA), the cost of the traditional whale algorithm and particle swarm algorithm is relatively high, and the modified whale algorithm can find the optimal cost in a smaller number of iterations, which has the best optimization effect. The final optimization cost obtained by adopting the improved WOA is 41.29 yuan, and compared with the traditional whale algorithm, the cost is reduced by 2.52%.
In the embodiment, based on a micro-grid of a common direct current bus, the optimal output of each port is calculated through improved whale optimization calculation, so that the overall cost of the optical storage direct-flexible system is optimal. The light storage direct-soft system mainly comprises a photovoltaic module, an energy storage module, an electric vehicle charging module and a power grid port, wherein the load of the system is the electric vehicle charging load. According to the known photovoltaic power generation power and the predicted electric vehicle charging load, an improved whale algorithm is adopted for multiple iterations, and the optimal output of each energy storage and power grid port is calculated so that the value of an optimal objective function considering the cost of each aspect of the optical storage direct-soft system is minimum. The calculated output of each port can be used as reference data when the optical storage straight-flexible system operates, and data support is provided for the optimal cost operation of the system.
In the application, the energy emitted by the photovoltaic is absorbed by the system, the condition that the output power of the photovoltaic is not utilized is avoided, the energy storage regulation function enables the power distribution of the system to be more flexible, and the cost can be effectively reduced, so that compared with the existing method, the energy utilization rate of the application is higher; calculating the charging load of the electric automobile through a prediction algorithm, taking the fluctuation of the load into consideration, and adaptively replacing; the improved whale algorithm is improved in convergence factor, probability judgment factor and population diversity of the traditional whale algorithm, and compared with the traditional algorithm, the improved whale algorithm has the advantages of higher convergence speed and higher precision, and can more rapidly find an operation scheme corresponding to lower total cost.
Example 2
An apparatus for cost optimization of a light storage straight-flexible system based on an improved whale algorithm, comprising:
the prediction module is used for predicting the charging load of the electric vehicles in one day by adopting a Monte Carlo algorithm according to the set number of the electric vehicles to obtain the total charging load of the electric vehicles in one day in the set number as a system load;
the setting module is used for setting the limiting conditions of the operation of each part of the light storage straight-flexible system and establishing the operation cost optimization objective function of the light storage straight-flexible system; the light storage direct-soft system comprises a photovoltaic module, an energy storage module, a power grid port and a system load obtained by a prediction module;
And the calculation module is used for adopting an improved whale algorithm to iterate for a plurality of times, calculating the optimal output force of each energy storage and power grid port, and enabling the calculation result of the objective function established by the setting module to be minimum.
The model established by the prediction module comprises a daily driving mileage model of the electric automobile, a charging state model of a battery of the electric automobile during charging, a charging duration model required by the electric automobile, a charging starting time model and a charging load model, and the daily driving mileage of the electric automobile is input into the daily driving mileage model of the electric automobile to obtain a probability density distribution function value of the daily driving mileage of the electric automobile; the daily driving mileage of the electric vehicle is input into the charge state model of the battery of the electric vehicle during charging to obtain the charge state of the battery of the electric vehicle during chargingSOC EV The method comprises the steps of carrying out a first treatment on the surface of the Inputting the charge state of the battery of the electric vehicle during charging obtained by the charge state model of the battery of the electric vehicle during charging through the charge time model required by the electric vehicleSOC EV Obtaining the required charging time of the electric automobile; inputting the charging starting time of the electric automobile into the charging starting time model to obtain a probability density distribution function of the charging starting time of the electric automobile; by inputting probability density distribution function of daily driving mileage of electric vehicle into charging load model, charging state of battery of electric vehicle SOC EV And obtaining the total charging load of the electric vehicles in a day in a set number by the charging time value required by the electric vehicles and the charging time required by the electric vehicles.
The daily driving mileage model of the electric automobile is as follows:
（1）；
in the method, in the process of the invention,f（L) Is a probability density distribution function of daily driving mileage of the electric automobile,Lthe daily driving mileage of the electric automobile is obtained,μ L andσ L the expected value and standard deviation of the daily driving mileage are respectively;
electric vehicle battery state of charge during chargingSOC EV The model is as follows:
（2）；
in the method, in the process of the invention,L m the maximum daily driving mileage of the electric automobile is obtained;
the required charging duration model of the electric automobile is as follows:
（3）；
in the method, in the process of the invention,Cfor the capacity of the battery,Pin order for the charging power to be high,k c is the charging efficiency;
the charge start time model is:
（4）；
in the method, in the process of the invention,f（t s ) The probability density distribution function of the charging starting time of the electric automobile is set;t s the charging time is started for the electric automobile;μ t andσ t respectively the starting charging timetIs a standard deviation and the expected value of (2); wherein, the electric automobile starts charging timet s The charging time is accessed after the electric automobile finishes all driving tasks in one day.
The setting module comprises an optimizing module and a limiting module, and is communicated with the optimizing module to input the overall annual cost of the system, the overall maintenance cost of the system and the interaction cost of the micro-grid system and the alternating-current grid, so as to obtain an overall optimizing objective function model of the system; the limiting condition model of the integral optimization objective function value of the system is obtained by inputting the power dynamic balance relation between ports of the optical storage direct-soft system, the charge and discharge power of the energy storage module, the charge state of the energy storage module and the power flow limit value of the power grid port converter to the limiting module;
And the calculation module is used for adopting an improved whale algorithm to iterate for a plurality of times, calculating the optimal output force of each energy storage and power grid port, and enabling the calculation result of the objective function established by the setting module to be minimum.
System-wide optimization objective functionfObtained by the following formula:
（5）；
in the method, in the process of the invention,C CRF for the overall annual cost of the system,C M for the overall maintenance cost of the system,C cons the interaction cost of the micro-grid system and the alternating current power grid is; lambda (lambda) 1 、λ 2 、λ 3 The weight coefficients of the overall annual cost of the system, the overall maintenance cost of the system and the interaction cost of the micro-grid system and the alternating-current grid are respectively calculated;
the power dynamic balance among all ports of the optical storage straight-flexible system is satisfied:
（6）；
in the method, in the process of the invention,P PV (t) is the output power of the photovoltaic,P grid （t) For the grid-side power,P ESS （t) In order to store the output power of the energy,P EV （t) Charging the electric automobile with a load;tis the running time;
charging and discharging power of energy storage moduleP ESS （t) State of charge of an energy storage moduleSOC（t) The limit value of (2) satisfies:
（7）；
（8）；
in the method, in the process of the invention,maximum charging power for energy storage->For the purpose of storing the maximum discharge power,SOC min as a lower SOC limit for the energy storage module,SOC max an upper limit of state of charge, SOC, for the energy storage module;
current state of charge of energy storage moduleSOC（t) The calculation method comprises the following steps:
（9）；
in the method, in the process of the invention,E max in order to store the energy in the maximum capacity, ηIs the energy storage charge-discharge coefficient;SOC（t-1) is the state of charge of the energy storage module at the previous moment;
grid side powerP grid （t) The flow needs to be within limits, specifically as follows:
（10）；
in the method, in the process of the invention,maximum charging power for the grid, < >>And the maximum discharge power of the power grid.
Annual cost of the system as a wholeC CRF The method meets the following conditions:
（11）；
in the method, in the process of the invention,for the photovoltaic cost conversion factor, +.>For photovoltaic installation costs, < >>To store energyCost reduction coefficient->The energy storage installation cost is set;
cost of overall maintenance of the systemC M The method meets the following conditions:
（12）；
in the method, in the process of the invention,maintaining a cost factor for the grid-side converter, < >>A cost factor is maintained for the energy storage port,a cost factor is maintained for the photovoltaic port,t G for the service time of the power grid end converter,t ESS for the time of use of the energy storage port,t PV the photovoltaic port service time is given;
micro-grid system and alternating current grid interaction costC cons The following are provided:
（13）；
in the method, in the process of the invention,for grid output power, < >>The power is fed back to the power grid,C Grid is the electricity charge of the power grid,C feed the power price is the internet.
The calculation module comprises an initialization model, a position search model and an algorithm model, and the randomized power of the energy storage module is obtained by inputting a convergence factor, photovoltaic output power and charging load power of the electric automobile into the initialization model; the current circulation position and the position of the next circulation are obtained by inputting a convergence factor, a probability judgment factor and a normal disturbance factor into the position searching model; and inputting the current objective function value and the minimum value of the function value obtained by the current iteration into the algorithm model to obtain the minimum value of the objective function and the power of the photovoltaic module, the energy storage module, the power grid interface and the system load corresponding to the minimum value obtained by the objective function.
The position search model uses the following formula:
the random search position satisfies the following formula:
（19）；
（20）；
（21）；
（22）；
in the method, in the process of the invention,for the next time position +.>For the current time position +.>For randomly generated starting positions +.>For the distance between the current individual and the random individual, < ->And->Coefficient vector, < >>Is a convergence factor; />A random number between 0 and 1;
will converge the factorThe method comprises the following steps:
（14）；
in the method, in the process of the invention,t r for the number of cycles to be counted,t max in order to have a maximum number of cycles,λis a cyclic coefficient;
the searching range is reduced: after a plurality of cycles, the position of the prey is locked by the optimal individual, other whales gradually get close to the optimal position in a surrounding manner, and the position is at the next momentCan be expressed as:
（23）；/>
（24）；
in the method, in the process of the invention,for the distance between the current individual and the optimal individual, < ->The optimal position found currently is determined;
optimizing the search position: near the optimal position, whales are hunting in spiral formPredation of the object takes place at the next momentExpressed as:
（25）；
in the method, in the process of the invention,bfor the log-helix to be well-done,la random number between-1 and 1;
updating the position:
（26）；
in the method, in the process of the invention,pis a probability judgment factor;
pthe values of (2) satisfy the following:
（15）；
in the method, in the process of the invention,rsn（nlocation, scale, shape) is a bias distribution random generation function;nrepresented as a dimension of the biased distribution variable, locationRepresented as the position of the occurrence of the peak of the off-set distribution,scalerepresented as a range of the skewed distribution,shapeexpressed as a biased distribution shape parameter;
setting normal disturbance factorAvoiding the result from sinking into local optimum:
（16）；
（17）；
（18）；
wherein,；
and calculating the minimum value obtained by the objective function according to the result of the position searching model by the algorithm model, and obtaining the power of the photovoltaic module, the energy storage module, the power grid interface and the system load corresponding to the minimum value as the respective optimal output. Calculating the current objective function value in the formula (5) after the circulation starts, comparing the current objective function value with the minimum value of the function value obtained by the current iteration, and randomly generating a convergence factor and a probability judgment factor according to the formula (14) and the formula (15)pAnd judges the factor according to the convergence factor and the probabilitypAnd (3) obtaining the position of the next cycle according to formulas (16) to (18), judging whether the position meets the constraint condition, entering the next cycle if the position meets the constraint condition, limiting the position within the constraint condition if the position does not meet the constraint condition, and then entering the next cycle until the preset maximum iteration times are reached, wherein the power of the photovoltaic module, the energy storage module, the power grid interface and the system load corresponding to the minimum value obtained by the objective function is the respective optimal output.
Example 3
A computer device comprising a memory and one or more processors, the memory storing executable code, the one or more processors configured to implement the improved whale algorithm based method of optimizing cost of a light storage direct flex system of embodiment 1 when the executable code is executed.
Example 4
A computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of optimizing the cost of a light storage direct flex system based on an improved whale algorithm as described in embodiment 1.
While the invention has been described in terms of specific embodiments, it will be appreciated by those skilled in the art that the invention is not limited thereto but includes, but is not limited to, those shown in the drawings and described in the foregoing detailed description. Any modifications which do not depart from the functional and structural principles of the present invention are intended to be included within the scope of the appended claims.

Claims (8)

1. The cost optimization method of the light storage straight-flexible system based on the improved whale algorithm is characterized by comprising the following steps of:
s1, setting the number N of electric vehicles, and predicting the charging load of the electric vehicles in one day by adopting a Monte Carlo algorithm to obtain the total charging load of the electric vehicles in one day with the set number as a system load;
S2, setting limiting conditions of operation of each part of the light storage straight-flexible system, and establishing an operation cost optimization objective function of the light storage straight-flexible system, wherein the light storage straight-flexible system comprises a photovoltaic module, an energy storage module, a power grid port and the system load obtained in the step S1;
establishing a system integral optimization objective functionfThe following are provided:
（5）；
in the method, in the process of the invention,C CRF for the overall annual cost of the system,C M for the overall maintenance cost of the system,C cons the interaction cost of the micro-grid system and the alternating current power grid is;λ 1 、λ 2 、λ 3 the weight coefficients of the overall annual cost of the system, the overall maintenance cost of the system and the interaction cost of the micro-grid system and the alternating-current grid are respectively calculated;
the limiting conditions are as follows:
the optical storage straight-flexible system meets the power dynamic balance among ports, and is specifically as follows:
（6）；
in the method, in the process of the invention,P PV (t) is the output power of the photovoltaic,P grid （t) For the grid-side power,P ESS （t) In order to store the output power of the energy,P EV （t) Charging the electric automobile with a load;tfor transportingRow time;
charging and discharging power of energy storage moduleP ESS （t) Current state of charge of an energy storage moduleSOC（t) All need to be within the limit value, specifically as follows:
（7）；
（8）；
in the method, in the process of the invention,maximum charging power for energy storage->For the purpose of storing the maximum discharge power,SOC min as a lower SOC limit for the energy storage module,SOC max an upper limit of state of charge, SOC, for the energy storage module;
Current state of charge of energy storage moduleSOC（t) The calculation method comprises the following steps:
（9）；
in the method, in the process of the invention,E max in order to store the energy in the maximum capacity,ηis the energy storage charge-discharge coefficient;SOC（t-1) is the state of charge of the energy storage module at the previous moment;
grid side powerP grid （t) The flow needs to be within limits, specifically as follows:
（10）；
in the method, in the process of the invention,maximum charging power for the grid, < >>Maximum discharge power for the grid;
s3, adopting an improved whale algorithm to iterate for a plurality of times, and calculating the optimal output force of each energy storage and power grid port so as to minimize the objective function calculation result established in the step S2; the specific process is as follows:
s3.1, initializing: reading the photovoltaic output power and the charging load power of the electric automobile, and randomly initializing the power of the energy storage module;
s3.2, randomly searching positions:
will converge the factorThe method comprises the following steps:
（14）；
in the method, in the process of the invention,t r for the number of cycles to be counted,t max in order to have a maximum number of cycles,λis a cyclic coefficient;
s3.3, narrowing the search range;
s3.4, optimizing the searching position;
s3.5, updating the position:
probability judgment factorpThe values of (2) satisfy the following:
（15）；
in the method, in the process of the invention,rsn（nlocation, scale, shape) is a bias distribution random generation function,nrepresented as a dimension of the biased distribution variable,locationrepresented as the position of the occurrence of the peak of the off-set distribution,scalerepresented as a range of the skewed distribution, shapeExpressed as a biased distribution shape parameter;
s3.6, setting a normal disturbance factor to avoid the result to be trapped into local optimum:
（16）；
（17）；
（18）；
wherein,；
s3.7, calculating the current objective function value in the formula (5) after the circulation starts, comparing the current objective function value with the minimum value of the function value obtained by the current iteration, and randomly generating a convergence factor according to the formula (14) and the formula (15)And probability judgment factorpAnd according to the convergence factor->And probability judgment factorpAnd (3) obtaining the position of the next cycle according to formulas (16) to (18), judging whether the position meets the constraint condition in the step S2, if so, entering the next cycle, if not, limiting the position within the constraint condition, and then entering the next cycle until the preset maximum iteration times are reached, wherein the power of the photovoltaic module, the energy storage module, the power grid interface and the system load corresponding to the minimum value obtained by the objective function is the respective optimal output.
2. The method for optimizing the cost of a light-storing straight-flexible system based on an improved whale algorithm according to claim 1, wherein in step S1, the system load is obtained according to the monte carlo algorithm as follows:
s1.1, establishing a model of daily driving mileage of the electric automobile:
（1）；
In the method, in the process of the invention,f（L) Is a probability density distribution function of daily driving mileage of the electric automobile,Lthe daily driving mileage of the electric automobile is obtained,μ L andσ L the expected value and standard deviation of the daily driving mileage are respectively;
s1.2, establishing the charge state of the battery of the electric vehicle during chargingSOC EV The formula:
（2）；
in the method, in the process of the invention,L m the maximum daily driving mileage of the electric automobile is obtained;
s1.3, establishing the required charging time length of the electric automobileTIs a model of (a):
（3）；
in the method, in the process of the invention,Cfor the capacity of the battery,Pin order for the charging power to be high,k c is the charging efficiency;
s1.4, establishing a charging start time model:
（4）；
in the method, in the process of the invention,f（t s ) The probability density distribution function of the charging starting time of the electric automobile is set;t s the charging time is started for the electric automobile;μ t andσ t respectively the starting charging timetIs a standard deviation and the expected value of (2); wherein, the electric automobile starts charging timet s The method comprises the steps of connecting charging time after finishing all driving tasks of an electric automobile in one day;
s1.5, calculating charging load curves of the electric vehicles one by one according to the formulas (1) to (4), and then superposing the charging load curves to obtain the total charging load curves of the electric vehicles with set quantity in one day.
3. The method for optimizing the cost of a light-storage straight-flexible system based on an improved whale algorithm according to claim 1, wherein the overall annual cost of the system C CRF The method meets the following conditions:
（11）；
in the method, in the process of the invention,for the photovoltaic cost conversion factor, +.>For photovoltaic installation costs, < >>For energy storage cost conversion coefficient, < >>The energy storage installation cost is set;
cost of overall maintenance of the systemC M The method meets the following conditions:
（12）；
in the method, in the process of the invention,maintaining a cost factor for the grid-side converter, < >>Maintaining cost coefficients for energy storage ports,/->A cost factor is maintained for the photovoltaic port,t G for the service time of the power grid end converter,t ESS for the time of use of the energy storage port,t PV the photovoltaic port service time is given;
micro-grid system and alternating current grid interaction costC cons The following are provided:
（13）；
in the method, in the process of the invention,for grid output power, < >>The power is fed back to the power grid,C Grid is the electricity charge of the power grid,C feed the power price is the internet.
4. The method for optimizing the cost of the light storage straight-flexible system based on the improved whale algorithm according to claim 1, wherein the step 3.2 is as follows:
（19）；
（20）；
（21）；
（22）；
in the method, in the process of the invention,for the next time position +.>For the current time position +.>In order to generate the starting position at random,for the distance between the current individual and the random individual, < ->And->Coefficient vector, < >>Is a convergence factor; />Is a random number between 0 and 1.
5. The method for optimizing the cost of the light storage straight-flexible system based on the improved whale algorithm according to claim 4, wherein the specific process of the step 3.3 is as follows: after a plurality of cycles, the position of the prey is locked by the optimal individual, other whales gradually get close to the optimal position in a surrounding manner, and the position is at the next moment Can be expressed as:
（23）；
（24）；
in the method, in the process of the invention,for the distance between the current individual and the optimal individual, < ->And the best position found for the current time.
6. The method for optimizing the cost of the light storage straight-flexible system based on the improved whale algorithm according to claim 5, wherein the specific process of the step 3.4 is as follows: near the optimal location, whales prey on prey in a spiral form, at the next moment in timeExpressed as:
（25）；
in the method, in the process of the invention,bfor the log-helix to be well-done,lis a random number between-1 and 1.
7. The method for optimizing the cost of the light storage straight-flexible system based on the improved whale algorithm according to claim 6, wherein the specific process of the step 3.5 is as follows:
（26）；
in the method, in the process of the invention,pis a probability judgment factor.
8. An apparatus for cost optimization of a light storage straight-flexible system based on an improved whale algorithm, comprising:
the prediction module is used for setting the number of the electric vehicles, constructing a prediction model for the charging load of the electric vehicles in one day by adopting a Monte Carlo algorithm according to the set number of the electric vehicles, and obtaining the total charging load of the electric vehicles in one day with the set number as a system load prediction model;
the setting module is used for setting the limiting conditions of the operation of each part of the light storage straight-flexible system and establishing the operation cost optimization objective function of the light storage straight-flexible system; the light storage direct-soft system comprises a photovoltaic module, an energy storage module, a power grid port and a system load obtained by a prediction module;
And the calculation module is used for adopting an improved whale algorithm to iterate for a plurality of times, calculating the optimal output force of each energy storage and power grid port, and enabling the calculation result of the objective function established by the setting module to be minimum.
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