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CN112418605A - Optimal operation method for energy storage system of optical storage type charging station - Google Patents

Optimal operation method for energy storage system of optical storage type charging station Download PDF

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CN112418605A
CN112418605A CN202011118101.0A CN202011118101A CN112418605A CN 112418605 A CN112418605 A CN 112418605A CN 202011118101 A CN202011118101 A CN 202011118101A CN 112418605 A CN112418605 A CN 112418605A
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power
energy storage
storage system
electric
charging station
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时珊珊
张宇
王育飞
方陈
薛花
王皓靖
高小飞
付张杰
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Shanghai University of Electric Power
State Grid Shanghai Electric Power Co Ltd
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State Grid Shanghai Electric Power Co Ltd
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Abstract

The invention relates to an optimized operation method of an energy storage system of an optical storage type charging station, which is used for analyzing the system structure, the operation strategy and the load characteristics of the optical storage type charging station; establishing an energy storage system multi-objective optimization operation model by taking the minimum load variance at the side of a power grid, the minimum operation and maintenance cost of an energy storage system and the minimum electricity purchasing cost to the power grid as objective functions and taking the power, the state of charge and the power supply at the side of the power grid as constraint conditions; solving the extracted model by adopting an NSGA-III algorithm by combining typical sunlight photovoltaic power generation power, the Monte Carlo method-based sampled electric vehicle charging power and the time-of-use electricity price basic data of the region where the energy storage system is located to obtain a Pareto optimal solution set; and screening the Pareto optimal solution set by using a fuzzy clustering method to obtain an optimal compromise operation scheme of the energy storage system. By adopting an optimization algorithm combining NSGA-III and fuzzy clustering, the problem that the solving process is trapped in local optimum is effectively avoided, and the load fluctuation level of the power grid side is effectively improved.

Description

Optimal operation method for energy storage system of optical storage type charging station
Technical Field
The invention relates to the technical field of photovoltaic energy storage, in particular to an optimized operation method of an energy storage system of a light storage type charging station.
Background
The rapid spread of electric vehicles has led to increased attention being paid to public charging facilities. The optical storage charging station is widely accepted as a brand new charging facility, and can realize the on-site integration of renewable energy and electric vehicles. The energy storage system is one of the most important components of the optical storage type charging station, and the capacity of the energy storage system participating in operation in the dispatching cycle is directly related to the comprehensive performance of the charging station. The operation and maintenance cost of the energy storage system can be increased due to the fact that the capacity of the energy storage system participating in operation is too high, the power supply power and the peak clipping and valley filling capacity of the system can be weakened due to the fact that the capacity of the energy storage system participating in operation is too low, and meanwhile the photovoltaic energy utilization efficiency can be reduced to a large extent, so that the optimization research on the operation process of the energy storage system is of great significance.
Currently, corresponding achievements are obtained for the optimization research of the energy storage system participating in operation. The particle swarm algorithm is mostly adopted to solve the operation problem of the energy storage system. The algorithm has certain subjectivity on the setting of the optimized target weight, so that the optimization process is easy to fall into a local optimal solution, the comprehensive operation cost of the charging station is overhigh, and the load fluctuation level of the power grid side is higher.
Disclosure of Invention
The invention provides an optimal operation method of an energy storage system of a light storage type charging station, aiming at the problem of optimal operation of photovoltaic energy storage.
The technical scheme of the invention is as follows: an optimal operation method of an energy storage system of an optical storage type charging station is characterized in that the system structure, the operation strategy and the load characteristic of the optical storage type charging station are analyzed; establishing an energy storage system multi-objective optimization operation model by taking the minimum load variance at the side of a power grid, the minimum operation and maintenance cost of an energy storage system and the minimum electricity purchasing cost to the power grid as objective functions and taking the power, the state of charge and the power supply at the side of the power grid as constraint conditions; solving the extracted model by adopting an NSGA-III algorithm by combining typical sunlight photovoltaic power generation power, the Monte Carlo method-based sampled electric vehicle charging power and the time-of-use electricity price basic data of the region where the energy storage system is located to obtain a Pareto optimal solution set; and finally, screening the Pareto optimal solution set by using a fuzzy clustering method to obtain an optimal compromise operation scheme of the energy storage system.
The analysis of the operation strategy and the load characteristic of the optical storage type charging station specifically refers to the following steps: in the light storage type light storage charging station, the principle that photovoltaic electric energy preferentially supplies power to loads is followed, so that the requirement of the charging station on the power of a power grid is reduced; when the photovoltaic power generation power is larger than the load charging power, charging the energy storage battery pack with the electric quantity being less than the full by using the residual photovoltaic electric energy; when the photovoltaic power generation power is smaller than the load charging power and the time-of-use electricity price is the valley electricity price, the public power grid charges the energy storage battery pack with less than full electricity and supplies power to the differential load; when the photovoltaic power generation power is smaller than the load charging power and the time-of-use electricity price is higher than the valley electricity price, the energy storage battery pack meeting the discharging condition is coordinated with the public power grid to supply power to the difference load;
according to the travel rule of the electric automobile, the initial charging time of the electric private automobile obeys normal distribution, and the probability density function is as follows:
Figure BDA0002731024490000021
wherein σSAnd muSRespectively the expected value and standard deviation, t, of the initial charging time of the electric private car1Starting charging time for the electric private car;
assuming that the initial charging time of the electric taxis follows a uniform distribution, i.e.
fS(t2)＝randperm(24,1)
Wherein randderm (24,1) is in the interval [1,24 ]]Generated random integer, t2Starting charging time for the electric taxi;
the daily driving distances of the electric private car and the electric taxi are respectively subjected to lognormal distribution and normal distribution, and the probability density function is as follows:
Figure BDA0002731024490000022
Figure BDA0002731024490000023
wherein,
Figure BDA0002731024490000024
and
Figure BDA0002731024490000025
and
Figure BDA0002731024490000026
the expected daily driving distance and standard deviation s of the electric private car and the electric taxi respectively1And s2The daily driving distances of the electric private car and the electric taxi are respectively;
according to the probability density function of the travel rule of the electric vehicles, the initial charging time and the daily driving distance of various electric vehicles are sampled randomly by adopting a Monte Carlo algorithm, the charging quantity of the electric vehicles and the initial charge state of the power battery in the charging station at different time intervals can be analyzed, and the daily load requirement of the charging station can be further calculated.
The specific steps of solving the extracted model by adopting the NSGA-III algorithm to obtain the Pareto optimal solution set are as follows:
1) inputting initial parameters of NSGA-III, and simultaneously initializing reference points with the quantity of H on a generated unit hyperplane;
2) setting the upper and lower limits of a control variable by taking the charge-discharge power of an energy storage system in one day as the control variable, namely the individual in the population, and then randomly generating an initial population P with the size of NtThe individual is Pt i，i＝1,2,...,N；
3) Calculating the value of the multi-target function and comparing P according to the valuetPerforming fast non-dominated sorting;
4) after sorting is completed, screening PtCarrying out cross variation on the first N/2 dominant individuals to obtain filial generation QtWhich is individually
Figure BDA0002731024490000031
j 1,2, N/2, followed by PtAnd QtAre combined to obtain RtWhich is individually
Figure BDA0002731024490000032
k＝1,2,...,3N/2；
5) To RtPerforming fast non-dominant sorting, and putting the individuals in the non-dominant layer into a newly defined population StIn (1) to (S)tIs greater than N;
6) to eachEstablishing a space coordinate system for the unit vector of the objective function as the x, y and z axes, and searching StFurther calculating to obtain extra points of each coordinate axis at ideal points of each objective function in a coordinate system, connecting the extra points of each coordinate axis to construct a hyperplane to which the objective function belongs, and normalizing the objective function according to the intercept between the hyperplane and the coordinate axis;
7) calculating StThe shortest distance from each individual to the reference point and the number of individuals associated with each reference point are recorded;
8) according to the value of each target function and the number of the individuals related to each reference pointtScreening the first N individuals as a parent population Pt+1；
9) And judging whether iteration times are finished, if the iteration is not finished, continuing to perform the steps 2) -8), and if the iteration is finished, outputting a Pareto optimal solution set.
In the optimal operation method of the energy storage system of the optical storage charging station, the reference points are initialized in the step 1) as shown in the following formula, H reference points generated by the formula are uniformly distributed on a unit hyperplane, the diversity of subsequent excellent individual screening is ensured,
Figure BDA0002731024490000041
wherein: the unit hyperplane is a plane constructed by taking (0,0,1), (0,1,0) and (0,0,1) as vertexes; h is the total number of reference points; m is the number of optimization targets; and p is the number of segments of each optimization target, and the distribution positions of the reference points with the number H on the unit hyperplane are determined.
The invention has the beneficial effects that: according to the optimization operation method of the energy storage system of the optical storage type charging station, an optimization algorithm combining NSGA-III and fuzzy clustering is adopted, the problem that the solving process is in local optimization is effectively avoided, the load fluctuation level of the power grid side can be effectively improved, and the comprehensive cost of the operation of the charging station is reduced.
Drawings
Fig. 1 is a flowchart of an optimized operation method of an energy storage system of a light storage charging station according to the present invention;
fig. 2 is a schematic structural diagram of an optical storage charging station system;
FIG. 3 is a graph of charging load versus photovoltaic power generation in an example;
FIG. 4 is a schematic diagram of the Pareto optimal solution set in the embodiment;
FIG. 5 is a graph comparing the power supplied by the grid side according to the two algorithms in the embodiment;
FIG. 6 is a comparison graph of the charging and discharging power of the energy storage system under two algorithms in the embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
The optimal operation method of the energy storage system of the optical storage charging station shown in fig. 1 comprises the following steps:
s1, analyzing the system structure, the operation strategy and the load characteristics of the optical storage type charging station;
s2, considering both the economic index of the charging station and the technical index of the power grid side operation, and establishing a multi-objective optimization operation model of the energy storage system by taking the minimum load variance of the power grid side, the minimum operation and maintenance cost of the energy storage system and the minimum electricity purchasing cost to the power grid as objective functions and taking the power, the state of charge and the power supply power of the power grid side as constraint conditions;
and S3, solving the extracted model by adopting an NSGA-III algorithm according to the typical sunlight photovoltaic power generation power, the Monte Carlo method-based sampled electric vehicle charging power and the time-of-use electricity price basic data of the region where the energy storage system is located, and obtaining a Pareto optimal solution set.
And S4, screening the Pareto optimal solution set by using a fuzzy clustering method to obtain an optimal compromise operation scheme of the energy storage system.
The analyzing the operation strategy and the load characteristic of the optical storage charging station in the step S1 specifically includes:
in the light storage type light storage charging station, the principle that photovoltaic electric energy preferentially supplies power to loads is followed, so that the requirement of the charging station on the power of a power grid is reduced; when the photovoltaic power generation power is larger than the load charging power, charging the energy storage battery pack with the electric quantity being less than the full by using the residual photovoltaic electric energy; when the photovoltaic power generation power is smaller than the load charging power and the time-of-use electricity price is the valley electricity price, the public power grid charges the energy storage battery pack with less than full electricity and supplies power to the differential load; when the photovoltaic power generation power is smaller than the load charging power and the time-of-use electricity price is higher than the valley electricity price, the energy storage battery pack meeting the discharging condition is coordinated with the public power grid to supply power to the difference load;
according to the travel rule of the electric automobile, the initial charging time of the electric private automobile obeys normal distribution, and the probability density function is as follows:
Figure BDA0002731024490000051
wherein σSAnd muSRespectively the expected value and standard deviation, t, of the initial charging time of the electric private car1Starting charging time for the electric private car;
assuming that the initial charging time of the electric taxis follows a uniform distribution, i.e.
fS(t2)＝randperm(24,1)
Wherein randderm (24,1) is in the interval [1,24 ]]Generated random integer, t2Starting charging time for the electric taxi;
the daily driving distances of the electric private car and the electric taxi are respectively subjected to lognormal distribution and normal distribution, and the probability density function is as follows:
Figure BDA0002731024490000052
Figure BDA0002731024490000061
wherein,
Figure BDA0002731024490000062
and
Figure BDA0002731024490000063
and
Figure BDA0002731024490000064
the expected daily driving distance and standard deviation s of the electric private car and the electric taxi respectively1And s2The daily driving distances of the electric private car and the electric taxi are respectively;
according to the probability density function of the travel rule of the electric vehicles, the initial charging time and the daily driving distance of various electric vehicles are sampled randomly by adopting a Monte Carlo algorithm, the charging quantity of the electric vehicles and the initial charge state of the power battery in the charging station at different time intervals can be analyzed, and the daily load requirement of the charging station can be further calculated.
In this embodiment, a typical optical storage charging station system structure is taken as an example for analysis, as shown in fig. 2. In fig. 2, the charge station adopts a direct current fast charge mode, the number of the charge piles in the charge station is 30, and the charge power of a single charge pile is 60 kW. The rated power of the lithium battery energy storage system is 2MW, the rated electric quantity is 10MW & h, and the rated capacity of the distribution transformer is 2 MVA. The rated power of the AC/DC conversion module is 1500 kW. Assuming that the number of electric vehicles in the service range of the charging station is 500, wherein the number ratio of the electric private cars to the electric taxis is 7:3, the daily charging load demand of the charging station is obtained through Monte Carlo algorithm simulation, meanwhile, photovoltaic prediction data of a certain typical day is selected as basic data, the photovoltaic output ratio is set to be 18%, and a power curve is shown in FIG. 3.
The NSGA-III algorithm parameter settings are as follows: the initial population number N is 200, and the maximum iteration number GenmaxThe cross probability is 1000, the cross probability is 0.9, the variation probability is 1/24, and the energy storage operation scheme is optimized by combining the photovoltaic output characteristics, the load data and the charging station operation strategy.
The method of the present invention is applied to the embodiment, and the specific process is as follows:
1. inputting initial parameters of NSGA-III, and simultaneously initializing reference points with the quantity of H on a generated unit hyperplane;
2. inputting typical solar photovoltaic power data, analyzing the trip characteristics of charging loads in a station, counting the charging quantity of the electric vehicles in different time periods by adopting a Monte Carlo method, and calculating to obtain a daily load power curve;
3. setting the upper and lower limits of a control variable by taking the charge-discharge power of an energy storage system in one day as the control variable (namely, an individual in a population), and then randomly generating an initial population P with the size of NtThe individual is Pt i，i＝1,2,...,N；
4. Calculating the value of the multi-target function and comparing P according to the valuetPerforming fast non-dominated sorting;
5. after sorting is completed, screening PtCarrying out cross variation on the first N/2 dominant individuals to obtain offspring Qt
Figure BDA0002731024490000071
j 1,2, N/2, followed by PtAnd Qt to get RtWhich is individually
Figure BDA0002731024490000072
k＝1,2,...,3N/2。
6. To RtPerforming a fast non-dominant ranking, placing the individuals in the non-dominant layer (F1, F2 …) into a newly defined population StIn (1) to (S)tIs greater than N.
7. Establishing a space coordinate system by taking the unit vector of each objective function as an x axis, a y axis and a z axis, and searching StAnd further calculating the ideal point of each objective function in the coordinate system to obtain the extra points of each coordinate axis, connecting the extra points of each coordinate axis to construct the associated hyperplane, and normalizing the objective function according to the intercept between the hyperplane and the coordinate axis.
8. Calculating StThe shortest distance of each individual to the reference point, and the number of individuals associated with each reference point is recorded.
9. According to the value of each target function and the number of the individuals related to each reference pointtScreening the first N individuals as a parent population Pt+1。
10. And judging whether iteration times are finished, if the iteration is not finished, continuing to perform the steps 3-9, and if the iteration is finished, outputting a Pareto optimal solution set.
11. And screening the Pareto optimal solution set by adopting a fuzzy clustering method to obtain the optimal compromise operation scheme of the energy storage system.
In step 1, the NSGA-III algorithm parameters are set as follows: the initial population number N is 200, and the maximum iteration number GenmaxThe crossover probability was 1000, the crossover probability was 0.9, and the mutation probability was 1/24. The reference points are initialized as shown in the following formula, and H reference points generated by the formula are uniformly distributed on a unit hyperplane, so that the diversity of subsequent excellent individual screening is ensured.
Figure BDA0002731024490000073
Wherein: the unit hyperplane is a plane constructed by taking (0,0,1), (0,1,0) and (0,0,1) as vertexes; h is the total number of reference points, M is the number of optimization targets, p is the number of segments of each optimization target, the distribution position of the reference points with the number of H on the unit hyperplane is determined, and the value of p in the text is 4.
In step 3, the population PtThe ith individual P in (1)t iRepresenting the charge and discharge plan of the energy storage system during the optimization cycle, can be expressed as:
Figure BDA0002731024490000081
in step 4, the objective function of minimizing the load variance on the side of the power grid, minimizing the operation and maintenance cost of the energy storage system and minimizing the electricity purchasing cost to the power grid is taken as
Grid side load variance RVarThe calculation formula is as follows:
Figure BDA0002731024490000082
wherein T is the number of time segments; pgrid(t) supplying power to the power grid in a time period t;Pgrid_avrthe average value of the power supplied to the power grid during the operation period can be expressed as:
Figure BDA0002731024490000083
operating and maintaining cost C of energy storage systemBessThe calculation formula is as follows:
CBess＝CpPBess_run+CeEBess_run
wherein, CBessThe cost of operating and maintaining the energy storage system in the operating period; pBess_runAnd EBess_runRespectively obtaining the maximum power value and the total electric quantity of the energy storage system participating in operation; cpAnd CeThe maintenance costs of the energy storage system per unit power and per unit electric quantity are respectively.
PBess_runAnd EBess_runCan be calculated according to the stored energy charging and discharging power in each time interval in one day, namely PBess_run＝max[PBess,ch(1),...,PBess,disch(n),...,PBess,ch(24)]
Figure BDA0002731024490000084
Wherein, PBess,ch(t) is the charging power of the energy storage system in a time period t; pBess,disch(t) is the discharge power of the energy storage system in a time period t; the time period is 1h, i.e., Δ t is 1 h.
Purchase of electricity to the grid charge CgridThe calculation formula is as follows:
Figure BDA0002731024490000085
wherein, CgridThe electricity purchasing cost to the power grid in the operation period is calculated; priceAnd (t) is the power grid electricity price of the time period t.
The constraint conditions are specifically as follows:
power supply power constraint of a power grid:
Pgrid≤min(PTr,PAD)
wherein, PTrAnd PADRated capacities of the charging station transformer and the AC/DC converter module, respectively.
And (3) energy storage system charge state constraint:
when the energy storage system is charged, the following requirements are met:
PPV(t)+Pgrid(t)＝Pload(t)+PBess,ch(t)
when the energy storage system is charged, the following requirements are met:
Pload(t)＝PPV(t)+PBess,disch(t)+Pgrid(t)
wherein, PPV(t) photovoltaic power generation power for a time period t, Pload(t) load charging power for time period t.
And (3) energy storage system charge state constraint:
Figure BDA0002731024490000091
wherein E (t) is the electric quantity of the energy storage system in a time period t, EBess_runFor the total quantity of electricity, SOC, in which the energy storage system is involved in operationmaxAnd SOCminRespectively an upper limit value and a lower limit value of the charge state of the energy storage system.
In step 7, the population StThe ideal point in (1) is defined as
Figure BDA0002731024490000092
Figure BDA0002731024490000093
For the minimum value of the ith optimization objective, the calculation formula for normalization of the additional points and the objective function is specifically as follows:
Figure BDA0002731024490000094
wherein, ASF is the extra point corresponding to each coordinate axis, x is the population StOf (a) of (b), fi(x) For the (i) th conversion target,
Figure BDA0002731024490000095
is the minimum value of the ith optimization objective, and w is the conversion weight.
Figure BDA0002731024490000096
Wherein f isi n(x) Normalized value for individual xth target, aiIs the intercept on the ith coordinate axis.
Step 9, according to the objective function value and the number of the individual associated reference points from StScreening N individuals, wherein the specific screening steps are as follows: 1) firstly, according to the value pair S of each objective functiontThe individuals in (1) are subjected to non-dominant sorting to obtain non-dominant layers (F1, F2 …); 2) the individuals in each non-dominant layer (F1, F2 …) are accumulated and summed until the total number of individuals is more than N (marking that the non-dominant layer is Fm at the moment), and the individuals in the F1 to Fm-1 layers are put into a new parent population Pt+1Meanwhile, if the total number of the individuals is N1, only N-N1 individuals need to be screened in Fm; 3) by calculating the number of individuals associated with each reference point in Fm, i.e. preferentially selecting individuals near the reference point with the least number of associated individuals in Fm to enter Pt+1Up to Pt+1The number of (2) reaches the population size N.
In step 11, the fuzzy membership function is calculated as:
Figure BDA0002731024490000101
wherein,
Figure BDA0002731024490000102
for the value after the individual single object fuzzification,
Figure BDA0002731024490000103
is as follows
Figure BDA0002731024490000104
The mth leading edge solution of each object,
Figure BDA0002731024490000105
and
Figure BDA0002731024490000106
is respectively the first in Pareto solution set
Figure BDA0002731024490000107
The maximum and minimum values of the objective function.
Further, the single target clustering summation calculation formula is as follows:
Figure BDA0002731024490000108
wherein, γmThe summed values of the clusters are weighted for the individual targets,
Figure BDA0002731024490000109
is as follows
Figure BDA00027310244900001010
And the weight of each target, wherein M is the total number of the optimization targets, and W is the number of Pareto leading edge solutions.
The Pareto optimal solution set obtained by optimization based on the algorithm mentioned above is shown in fig. 4. As can be seen from fig. 4, the optimal solution for the optimal operation of the energy storage system is uniformly distributed on the Pareto front-end curved surface, so that the diversity and convergence of the solution set are reflected, and various schemes can be provided for the optimal operation of the energy storage system.
In order to verify the effectiveness of the optimization algorithm provided by the invention in solving the optimization operation problem of the energy storage system, from the overall view of the charging station, the weight coefficient of each optimization target is obtained by using an entropy weight method, the optimization algorithm and the particle swarm optimization are respectively adopted for solving, and the capacity of the energy storage system participating in operation is shown in table 1.
As can be seen from the energy storage operation capacity solving results of the two algorithms in the table 1, compared with the particle swarm algorithm, the optimization algorithm combining NSGA-III and fuzzy clustering reduces the power and the electric quantity of the energy storage system participating in operation by 71kW and 95kWh respectively.
TABLE 1
Figure BDA0002731024490000111
And solving to obtain a compromise optimal solution of the system operation optimization indexes based on the two optimization algorithms, wherein the result of solving the optimization indexes of the two algorithms is shown in table 2. As can be seen from table 2, for the optimized operation of the energy storage system of the optical storage charging station, compared with the particle swarm algorithm, the optimization algorithm combining NSGA-III and fuzzy clustering reduces the operation maintenance cost of the energy storage system and the electricity purchasing cost to the power grid by 1.56% and 0.93%, respectively, thereby further reducing the unnecessary comprehensive operation cost of the charging station; the power grid side load variance is reduced by 6619kW2, and meanwhile, the optimization algorithm combining NSGA-III and fuzzy clustering can be obtained by combining the graph 5, so that the power grid side load fluctuation level is improved to a large extent, the power grid operation stability is improved, and the effectiveness of the algorithm is proved.
TABLE 2
Figure BDA0002731024490000112
The energy storage charging and discharging power curves of the two optimization algorithms in the operation period are shown in fig. 6 according to the optimization operation results of the energy storage systems in table 1 and table 2.
As can be seen from fig. 6, the load power level is low in the valley electricity price period, the energy storage system is in a charging state, the load is supplied with power from the power grid, and the energy storage system and the power grid coordinate to supply power to the load in the rest periods. The enlarged partial view in fig. 6 again demonstrates that the optimization algorithm provided by the present invention can further reduce the maximum power value of the energy storage system participating in operation, thereby reducing the operation and maintenance cost of the energy storage system.
The case simulation analysis can obtain that: compared with a particle swarm algorithm, the optimization algorithm provided by the invention has the advantages that the operation maintenance cost of the energy storage system, the electricity purchasing cost to the power grid and the power grid side load variance are all reduced, and the economic index and the power grid operation technical index of the optical storage type charging station are further improved.

Claims (4)

1. An optimal operation method of an energy storage system of an optical storage type charging station is characterized in that the system structure, the operation strategy and the load characteristics of the optical storage type charging station are analyzed; establishing an energy storage system multi-objective optimization operation model by taking the minimum load variance at the side of a power grid, the minimum operation and maintenance cost of an energy storage system and the minimum electricity purchasing cost to the power grid as objective functions and taking the power, the state of charge and the power supply at the side of the power grid as constraint conditions; solving the extracted model by adopting an NSGA-III algorithm by combining typical sunlight photovoltaic power generation power, the Monte Carlo method-based sampled electric vehicle charging power and the time-of-use electricity price basic data of the region where the energy storage system is located to obtain a Pareto optimal solution set; and finally, screening the Pareto optimal solution set by using a fuzzy clustering method to obtain an optimal compromise operation scheme of the energy storage system.
2. The optimal operation method of the optical storage charging station energy storage system according to claim 1, wherein the analyzing the optical storage charging station operation strategy and the load characteristic specifically includes:
in the light storage type light storage charging station, the principle that photovoltaic electric energy preferentially supplies power to loads is followed, so that the requirement of the charging station on the power of a power grid is reduced; when the photovoltaic power generation power is larger than the load charging power, charging the energy storage battery pack with the electric quantity being less than the full by using the residual photovoltaic electric energy; when the photovoltaic power generation power is smaller than the load charging power and the time-of-use electricity price is the valley electricity price, the public power grid charges the energy storage battery pack with less than full electricity and supplies power to the differential load; when the photovoltaic power generation power is smaller than the load charging power and the time-of-use electricity price is higher than the valley electricity price, the energy storage battery pack meeting the discharging condition is coordinated with the public power grid to supply power to the difference load;
according to the travel rule of the electric automobile, the initial charging time of the electric private automobile obeys normal distribution, and the probability density function is as follows:
Figure FDA0002731024480000011
wherein σSAnd muSRespectively the expected value and standard deviation, t, of the initial charging time of the electric private car1Starting charging time for the electric private car;
assuming that the initial charging time of the electric taxis follows a uniform distribution, i.e.
fS(t2)＝randperm(24,1)
Wherein randderm (24,1) is in the interval [1,24 ]]Generated random integer, t2Starting charging time for the electric taxi;
the daily driving distances of the electric private car and the electric taxi are respectively subjected to lognormal distribution and normal distribution, and the probability density function is as follows:
Figure FDA0002731024480000021
Figure FDA0002731024480000022
wherein,
Figure FDA0002731024480000023
and
Figure FDA0002731024480000024
Figure FDA0002731024480000025
and
Figure FDA0002731024480000026
the expected daily driving distance and standard deviation s of the electric private car and the electric taxi respectively1And s2The daily driving distances of the electric private car and the electric taxi are respectively;
according to the probability density function of the travel rule of the electric vehicles, the initial charging time and the daily driving distance of various electric vehicles are sampled randomly by adopting a Monte Carlo algorithm, the charging quantity of the electric vehicles and the initial charge state of the power battery in the charging station at different time intervals can be analyzed, and the daily load requirement of the charging station can be further calculated.
3. The optimal operation method of the energy storage system of the optical storage charging station according to claim 1, wherein the specific steps of solving the extracted model by using the NSGA-III algorithm to obtain the Pareto optimal solution set are as follows:
1) inputting initial parameters of NSGA-III, and simultaneously initializing reference points with the quantity of H on a generated unit hyperplane;
2) setting the upper and lower limits of a control variable by taking the charge-discharge power of an energy storage system in one day as the control variable, namely the individual in the population, and then randomly generating an initial population P with the size of NtWhich is individually
Figure FDA0002731024480000029
Figure FDA00027310244800000210
3) Calculating the value of the multi-target function and comparing P according to the valuetPerforming fast non-dominated sorting;
4) after sorting is completed, screening PtCarrying out cross variation on the first N/2 dominant individuals to obtain filial generation QtWhich is individually
Figure FDA0002731024480000027
Then P is addedtAnd QtAre combined to obtain RtWhich is individually
Figure FDA0002731024480000028
5) To RtPerforming fast non-dominant sorting, and putting the individuals in the non-dominant layer into a newly defined population StIn (1) to (S)tIs large in number of individualsAt N;
6) establishing a space coordinate system by taking the unit vector of each objective function as an x axis, a y axis and a z axis, and searching StFurther calculating to obtain extra points of each coordinate axis at ideal points of each objective function in a coordinate system, connecting the extra points of each coordinate axis to construct a hyperplane to which the objective function belongs, and normalizing the objective function according to the intercept between the hyperplane and the coordinate axis;
7) calculating StThe shortest distance from each individual to the reference point and the number of individuals associated with each reference point are recorded;
8) according to the value of each target function and the number of the individuals related to each reference pointtScreening the first N individuals as a parent population Pt+1；
9) And judging whether iteration times are finished, if the iteration is not finished, continuing to perform the steps 2) -8), and if the iteration is finished, outputting a Pareto optimal solution set.
4. The optimal operation method of the energy storage system of the optical storage charging station according to claim 3, wherein the reference points are initialized in step 1) as shown in the following formula, and the H reference points generated by the formula are uniformly distributed on the unit hyperplane, so that the diversity of subsequent excellent individual screening is ensured,
Figure FDA0002731024480000031
wherein: the unit hyperplane is a plane constructed by taking (0,0,1), (0,1,0) and (0,0,1) as vertexes; h is the total number of reference points; m is the number of optimization targets; and p is the number of segments of each optimization target, and the distribution positions of the reference points with the number H on the unit hyperplane are determined.
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