CN113733963A - Day-ahead scheduling method, system and device for light storage and charging integrated station and storage medium - Google Patents

Day-ahead scheduling method, system and device for light storage and charging integrated station and storage medium Download PDF

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CN113733963A
CN113733963A CN202111014791.XA CN202111014791A CN113733963A CN 113733963 A CN113733963 A CN 113733963A CN 202111014791 A CN202111014791 A CN 202111014791A CN 113733963 A CN113733963 A CN 113733963A
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charging
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ahead
photovoltaic
photovoltaic output
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CN113733963B (en
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薛贵挺
汪柳君
杨震
单博雅
刘哲
刘长江
杨卫杰
刘云瀚
张加霂
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State Grid Corp of China SGCC
State Grid Beijing Electric Power Co Ltd
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State Grid Beijing Electric Power Co Ltd
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Abstract

The invention discloses a method, a system, a device and a storage medium for day-ahead scheduling of a light storage and charging integrated station, wherein the method comprises the following steps: clustering photovoltaic output historical data to obtain a photovoltaic historical output set, describing the probability distribution of photovoltaic output, and randomly sampling the probability distribution to generate a photovoltaic output curve; training charging load historical data through a variational self-encoder, learning a probability distribution model of EV charging, and generating a charging load demand curve; and carrying out day-ahead optimization on the charging station based on the photovoltaic output curve and the charging load demand curve. Scene generation of photovoltaic output and charging requirements is carried out through a data-model hybrid driving method, and day-ahead optimized operation strategies of the charging station under different typical scenes are obtained through simulation analysis of each scene under the condition that carbon emission cost is considered.

Description

Day-ahead scheduling method, system and device for light storage and charging integrated station and storage medium
Technical Field
The invention belongs to the technical field of power system scheduling, and particularly relates to a day-ahead scheduling method, system, device and storage medium for a light storage and charging integrated station.
Background
Under the dual pressure of energy and environmental protection, the electric automobile becomes the main development direction of future automobiles, a large amount of EV charging can bring impact to a power grid, and a 'light storage and charging integrated station' is generated at the right moment in order to promote new energy consumption and stabilize the impact. How to optimize the operation of the light storage and charging integrated station can improve the utilization rate of new energy and reduce the carbon emission, so that the improvement of the economy and the cleanness of the system becomes one of research hotspots. For the operation of the light storage and charging integrated station, a day-ahead scheduling method including energy storage system configuration and power exchange between a charging station and a power grid is formulated according to the charging behavior and photovoltaic output of a vehicle. In the light storage and charging integrated station, electric energy generated by a photovoltaic power generation system firstly meets the requirements of a charging station, when power supply in the system does not meet the load requirements, an energy storage system discharges, and if the power supply in the system cannot meet the load requirements, electricity is purchased from a large power grid; when the photovoltaic output is excessive, the residual electric energy can be used for charging the stored energy, and the electricity can be sold to a large power grid, so that certain economic benefit can be obtained.
The photovoltaic output depends on the intensity of illumination, different weather can make the photovoltaic output curve have very big difference for the photovoltaic output produces the uncertainty, and the charging load demand also has the uncertainty, easily produces the influence to the operation income of storing up and filling integrative station.
Disclosure of Invention
The invention aims to provide a method, a system, a device and a storage medium for day-ahead scheduling of an optical storage and charging integrated station, so as to solve the problem that the operation uncertainty of the optical storage and charging integrated station is large in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect of the present invention, a method for scheduling a light storage and charging integrated station in the day ahead includes the following steps:
acquiring photovoltaic output historical data, and clustering the photovoltaic output historical data to obtain a photovoltaic historical output set capable of reflecting weather conditions;
describing the probability distribution of photovoltaic output based on the photovoltaic historical output set, and randomly sampling the probability distribution to generate a photovoltaic output curve;
training charging load historical data through a variational self-encoder, learning a probability distribution model of EV charging, and generating a charging load demand curve of a charging station working day and a non-working day;
and forming eight different scenes based on the photovoltaic output curve and the charging load demand curve, and respectively carrying out day-ahead optimization on the charging station to obtain a corresponding day-ahead optimized scheduling method.
Preferably, after the Gaussian kernel function is selected to map the photovoltaic output historical data to a feature space with higher dimensionality, clustering is carried out on the photovoltaic output historical data by using a kernel k-means clustering method, and the specific method is as follows:
confirming the clustering number k of the photovoltaic output data according to an elbow method, and generating k initial clustering centers: c. C1、c2、……、ck；
Respectively calculating the distance from each point to the k clustering centers, taking the cluster where the clustering center with the minimum distance is located as the cluster to which the point belongs, and then recalculating a new clustering center for each cluster;
when the sum of the distances of all data points to the center of the cluster is minimal, the result is taken as the result of the clustering.
Preferably, a Beta distribution is used to describe the probability distribution of the photovoltaic contribution.
Preferably, the probability distribution is randomly sampled by using a Monte Carlo direct sampling method, and photovoltaic output curves under k types of weather are generated.
Preferably, the specific manner of generating the charging load demand curve is as follows:
inferring a network training sample resulting distribution qΦ(z | x); generating a network as pθ(z)pθ(z | x) generating a scene; the encoder trains a load data set X for charging the electric automobile to obtain probability distribution of each implicit attribute, and obtains a corresponding mean value and a corresponding variance of the probability distribution, which are expressed by vectors mu and delta; (ii) a Randomly sampling an epsilon from a Gaussian distribution N (0,1) probability distribution, and generating a vector Z as an input of a decoder by calculating Z ═ mu + epsilon sigma; and decoding by a decoder to obtain the EV load demand curve.
Preferably, day-ahead optimization of the charging station is performed in the following specific manner:
inputting the photovoltaic output curve and the load demand curve into a preset day-ahead optimization strategy model, and respectively solving to obtain corresponding optimization strategies; the objective function of the day-ahead optimization strategy model comprises electricity selling income, electricity purchasing cost, carbon emission cost, capacity electricity fee and photovoltaic and energy storage operation cost of the electric automobile and the power grid.
Preferably, the constraint conditions of the day-ahead optimization strategy model include: energy storage constraints, power balance constraints, and power exchange constraints of the charging station with the grid.
In a second aspect of the present invention, a system for the light storage and charging integrated station day-ahead scheduling method includes:
the photovoltaic output historical data clustering module is used for clustering the photovoltaic output historical data to obtain a photovoltaic historical output set capable of reflecting weather conditions;
the second module is used for describing the probability distribution of photovoltaic output based on the photovoltaic historical output set, and randomly sampling the probability distribution to generate a photovoltaic output curve;
the third module is used for training charging load historical data through a variational self-encoder, learning a probability distribution model of EV charging and generating a charging load demand curve of a charging station working day and a non-working day;
and the fourth module is used for forming eight different scenes based on the photovoltaic output curve and the charging load demand curve, respectively carrying out day-ahead optimization on the charging station, and simultaneously meeting system constraint conditions in the optimization process to obtain a corresponding day-ahead optimization scheduling method.
In a third aspect of the present invention, an apparatus for a day-ahead scheduling method of a light storage and charging integrated station includes: a memory and a processor; the memory for storing a computer program; the processor is used for realizing the day-ahead scheduling method of the optical storage and charging integrated station when executing the computer program.
In a third aspect of the present invention, a computer-readable storage medium has stored thereon a computer program, which, when executed by a processor, implements the optical storage and charging integrated station day-ahead scheduling method.
The invention has the following beneficial effects:
(1) the light storage and charging integrated station day-ahead scheduling method provided by the embodiment is based on data and model hybrid driving, and the obtained strategy comprises an exchange power curve of a charging station and a power grid and a charging and discharging curve of stored energy. Through the income of the charging station when using this strategy of comparison application under with when not using this strategy, can make corresponding adjustment according to timesharing price and different scenes better, be favorable to assisting the electric wire netting to peak clipping and fill a valley, alleviate the impact that a large amount of electric automobile charges and bring the electric wire netting to bring higher income.
(2) The method for scheduling the light storage and charging integrated station in the day ahead is based on data and model hybrid drive, the difference of operation of the light storage and charging integrated station in different scenes is considered, personalized day ahead operation strategies are formulated according to the different scenes, and the comparison with the conventional fixed energy storage charging and discharging strategies shows that the day ahead operation strategy provided by the invention is more suitable for the actual situation, and the light storage and charging integrated station can obtain higher daily gain by utilizing a time-of-use electricity price mechanism.
(3) According to the day-ahead scheduling method of the light storage and charging integrated station, the improved kernel k-means algorithm is adopted for clustering, and after the data samples are mapped to a higher-dimensional space through a kernel function, the data samples are more obviously distinguished, so that the situation that the characteristics of different weathers cannot be reflected due to the fact that the data samples are clustered to the same class with similar overall output levels can be avoided.
(4) The traditional photovoltaic scene is generated by a statistical method generally, the statistical result is subjective and unreliable, and the data-driven method is used for processing the result more objectively by the day-ahead scheduling method of the light storage and charging integrated station provided by the embodiment.
(5) According to the day-ahead scheduling method of the light storage and charging integrated station, photovoltaic output scene generation is described according to different probability distributions obeyed by photovoltaic output values at different moments, the output values at all time points are generated by sampling by adopting a method of respectively training Beta probability distributions at all the moments, and finally the output values generated at all the moments are connected into a complete curve, so that a new scene is obtained, and the uncertain characteristics of photovoltaic output are better met.
(6) According to the light storage and charging integrated station day-ahead scheduling method provided by the embodiment, the carbon emission cost is increased in the objective function of the daily income of the charging station, and the optimization of the economy and the cleanness of the system after the energy storage is introduced can be more clearly considered.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a day-ahead scheduling method of a light storage and charging integrated station according to an embodiment of the present invention.
Fig. 2 is a flowchart of VAE scene generation in the embodiment of the present invention.
Fig. 3 is a graph of photovoltaic output curves for different weather conditions in an embodiment of the present invention.
FIG. 4 is a graph illustrating the charging load requirements of an embodiment of the present invention.
Fig. 5 is a carbon dioxide emission factor graph.
Fig. 6 is a graph of charge and discharge power for stored energy.
Fig. 7 is a diagram of the exchange power between the charging station and the grid.
Fig. 8 is a graph comparing the maximum load.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
According to the first aspect of the embodiment of the invention, the carbon emission is considered, and the day-ahead scheduling method of the light storage and charging integrated station based on data-model hybrid driving is provided. Firstly, forecasting photovoltaic output and charging load in a hybrid driving mode according to historical charging data and photovoltaic output data of a charging station, and generating eight different scenes in different weather and working days/non-working days. On the basis, a mathematical model with optimal economy as a target is established, and related constraints including energy storage charge-discharge constraint, charge state constraint, power exchange between a charging station and a power grid, system power balance and the like are constructed. And finally, obtaining an optimal day-ahead scheduling method according to the specific parameters of the optical storage and charging integrated station.
The light storage and charging integrated station day-ahead scheduling method provided by the embodiment is based on data and model hybrid driving, and the obtained strategy comprises an exchange power curve of a charging station and a power grid and a charging and discharging curve of stored energy. Through the income of the charging station when using this strategy of comparison application under with when not using this strategy, can make corresponding adjustment according to timesharing price and different scenes better, be favorable to assisting the electric wire netting to peak clipping and fill a valley, alleviate the impact that a large amount of electric automobile charges and bring the electric wire netting to bring higher income.
As shown in fig. 1, the invention relates to a method for scheduling the day-ahead operation of a light storage and charging integrated station based on data-model hybrid driving and taking carbon emission into consideration, which comprises the following steps:
s1, collecting daily photovoltaic output historical data of a certain charging station, clustering by using an improved kernel k-means algorithm, and clustering the photovoltaic output data without the relevant information such as weather to obtain a photovoltaic historical output set capable of reflecting weather conditions. The clustering method of the kernel k-means comprises the following steps:
selecting a kernel function, mapping sample data to a higher-dimensional feature space, selecting various kernel functions for comparison, and finding that the effect of the Gaussian kernel function is better:
Figure BDA0003239463900000061
in the formula, xi，xjFor sample data, σ represents the bandwidth, controlling the local range of action of the gaussian kernel. When x isiAnd xjWhen the Euclidean distance of (a) is within a certain interval range, if x is fixedjThen κ (x)i,xj) With xiThe variation is quite significant.
Confirming photovoltaic output data according to' elbow ruleK, generating k initial cluster centers: c. C1、c2、……、ck. Respectively calculating the distance from each point to the k clustering centers, taking the cluster where the clustering center with the minimum distance is located as the cluster to which the point belongs, and then recalculating a new clustering center for each cluster, wherein the calculation formula is as follows:
Figure BDA0003239463900000062
in the formula: ciRepresents the ith cluster and x is the point within the cluster.
When the sum of the distances of all data points to the center of the cluster is minimal, the result is taken as the result of the clustering.
And S2, describing the probability distribution of the photovoltaic output by adopting Beta distribution, and randomly sampling by utilizing a Monte Carlo direct sampling method to generate a general photovoltaic output curve under k types of weather. The Beta distribution is a continuous distribution distributed over the [0,1] interval, and the probability density function is:
Figure BDA0003239463900000071
in the formula:
Figure BDA0003239463900000072
pt.max and Pt.min respectively represent the maximum value and the minimum value of the photovoltaic output power at the moment, and P represents the photovoltaic output value at the moment; alpha and Beta are respectively the shape parameters of Beta distribution, and gamma is a gamma function.
The parameters α, β can be calculated from the expected value μ and the variance δ of the power over a given time interval:
Figure BDA0003239463900000073
Figure BDA0003239463900000074
let the maximum value be pt.max and the minimum value be pt.min, where [ P1, P2, P3, P4, … … ] is set as the history data Y at a certain time in a period. First, an expected value and a variance at that time are calculated, and two parameters α and β of the Beta distribution at that time, that is, a probability density curve of the Beta distribution, are obtained from equations (4) and (5). And secondly, performing cumulative integration on the probability density curve of the Beta distribution to obtain a probability distribution function of each time point.
Figure BDA0003239463900000075
In the formula: x represents a random variable, f (x) represents a probability density function, and f (x) represents a probability distribution function.
And then, random sampling is carried out on the probability distribution function by utilizing a Monte Carlo sampling method, and a random probability value Y is obtained. And (3) obtaining a value r of x corresponding to the value Y by utilizing the inversion of the formula (6), and then obtaining the power P of the photovoltaic output at the moment through r calculation.
And after the n times of acquisition, carrying out inverse operation of the cumulative distribution function on the probability value obtained by the n times of acquisition to respectively obtain n groups of different output powers P. And taking the average value of the power of the photovoltaic output corresponding to the n times as the output value at the moment. And finally, connecting the output values at 24 hours a day to generate a scene photovoltaic output curve with generality.
S3, training charging load historical data through a variational self-encoder (VAE), learning a probability distribution model of EV charging, and finally generating a charging load demand curve of a charging station working day and a non-working day:
historical data is trained by a variational auto-encoder (VAE) to learn a probability distribution model of EV charging. Whether the working day has certain influence on the use condition of the EV or not is judged, so that the probability model is learned for the load requirements of the working day and the non-working day respectively, and finally two scenes are obtained.
The process of VAE scene generation is shown in FIG. 2, the VAE is divided into encoder and decodingThe device is divided into an inference network and a generation network, and the inference network training sample obtains a distribution qΦ(z | x) generating a network of pθ(z)pθ(z | x) is used to generate the scene. The encoder is used for training a load data set X for charging the electric automobile, obtaining probability distribution of each implicit attribute, and obtaining a corresponding mean value and variance of the probability distribution, wherein the mean value and the variance are expressed by vectors mu and delta. Then, an epsilon is randomly sampled from the probability distribution of the gaussian distribution N (0,1), and a vector Z is generated as an input to the decoder by calculating Z ═ μ + epsilon σ. Finally, after decoding by a decoder, the EV load demand curve can be obtained.
And S4, forming eight different scenes based on the newly generated photovoltaic output curve and the charging load demand curve, and respectively carrying out day-ahead optimization on the charging stations to obtain corresponding day-ahead optimized scheduling methods. The specific mode is as follows:
and (4) taking the photovoltaic output curves and the load demand curves obtained from S2 and S3 as known inputs of a day-ahead optimization strategy model, and respectively solving corresponding optimization strategies.
Considering that electricity purchased by a charging station to the grid is emitted by a coal-fired thermal power plant, the corresponding carbon emission costs are borne by the charging station. In addition, the electricity purchasing cost adopts two electricity-making prices. Thus, the objective functions of the optimization model include electricity sales revenue to the electric vehicle and grid, electricity purchase cost, carbon emission cost, capacity electricity fee, and operating cost of photovoltaic and energy storage:
Figure BDA0003239463900000081
in the formula, C is the daily income of the charging station; w is a scene sequence number; alpha is alphawIs the probability of occurrence of scene w;
Figure BDA0003239463900000082
revenue for charging the scene w to the EV;
Figure BDA0003239463900000083
selling the income of the power to the power grid for the scene w;
Figure BDA0003239463900000084
the electricity purchasing cost of the charging station under the scene w is obtained;
Figure BDA0003239463900000085
the carbon dioxide emission cost for scene w; cRThe capacity electricity charge; cPV、CBSRespectively the operation and maintenance costs of photovoltaic and energy storage.
The electricity selling income comprises income for selling electricity to the EV and income for selling electricity to the power grid according to the time-of-use electricity price:
Figure BDA0003239463900000091
Figure BDA0003239463900000092
in the formula, thetaEVA unit profit for charging the EV;
Figure BDA0003239463900000093
the unit income of selling electricity to the power grid for the time period t;
Figure BDA0003239463900000094
is the charging power at time t under scene w;
Figure BDA0003239463900000095
the power delivered to the power grid by the charging station is charged for a time period t in a scene w.
When photovoltaic power output and energy storage discharge can not meet the requirement of the charging station, the charging station needs to purchase power to the power grid:
Figure BDA0003239463900000096
in the formula, ctThe unit electricity price is the electricity purchase unit electricity price in the time period t;
Figure BDA0003239463900000097
is the power taken from the grid during time t under scene w.
The electricity purchased by the charging station to the power grid is sent out by a coal-fired thermal power plant, and the corresponding carbon emission cost is borne by the charging station:
Figure BDA0003239463900000098
in the formula, λtA carbon emission factor for a period t;
Figure BDA0003239463900000099
for per kilogram carbon dioxide emissions.
The charging station uses two electricity prices, so the daily capacity cost is as follows:
Figure BDA00032394639000000910
in the formula, cRThe unit capacity electricity charge;
Figure BDA00032394639000000911
the load maximum in 1 month.
The operation and maintenance cost comprises photovoltaic and energy storage operation and maintenance cost, the energy storage operation and maintenance cost comprises a fixed part and a variable part, and the variable part is determined by the charging and discharging electric quantity of the energy storage system.
Figure BDA00032394639000000912
Figure BDA00032394639000000913
In the formula, thetaPV，θE，θBSRespectively representing the photovoltaic unit operation and maintenance cost, the energy storage unit capacity operation and maintenance cost and the unit electric quantity dynamic cost;
Figure BDA0003239463900000101
the photovoltaic maximum power;
Figure BDA0003239463900000102
rated capacity for energy storage; r ispAnd r' is the rate of conversion of photovoltaic and energy storage equipment; y and y' respectively represent the service lives of the photovoltaic device and the energy storage device;
Figure BDA0003239463900000103
and respectively storing the energy charging and discharging power for the t time period of each scene.
The constraints include energy storage constraints, power balance constraints, and power exchange constraints of the charging station with the grid.
The energy storage charge and discharge power is restricted by the rated capacity of the bidirectional power converter, the SOC starting and ending states need to be kept consistent, and the current SOC and other restrictions are as follows:
Figure BDA0003239463900000104
Figure BDA0003239463900000105
Figure BDA0003239463900000106
Figure BDA0003239463900000107
SOCfin＝SOC0
SOCmin≤SOCt≤SOCmax
in the formula (I), the compound is shown in the specification,
Figure BDA0003239463900000108
the maximum charge and discharge power is stored;
Figure BDA0003239463900000109
is a variable from 0 to 1 and represents the charge-discharge state of energy storage; etac、ηdCharge-discharge efficiency for energy storage; SOC0、SOCfinRespectively storing the charge states of the beginning and the end of a day; SOCtThe charge state at the end of the energy storage period t; SOCmax、SOCminAt a maximum minimum state of charge; SOCt-1By
Figure BDA00032394639000001010
And (5) iterative calculation.
Power balance constraint
Figure BDA00032394639000001011
Figure BDA0003239463900000111
In the formula (I), the compound is shown in the specification,
Figure BDA0003239463900000112
and (5) storing the equivalent output of the scene w.
Charging station and grid power exchange constraints
The transmission power between the charging station and the large power grid is affected by the distribution and transformation capacity and cannot exceed the maximum value of the transmission capacity.
Figure BDA0003239463900000113
Figure BDA0003239463900000114
Figure BDA0003239463900000115
In the formula (I), the compound is shown in the specification,
Figure BDA0003239463900000116
the variable is 0-1, and represents the direction of power exchange between the charging station and the power grid;
Figure BDA0003239463900000117
the maximum exchange power between the charging station and the power grid.
In this embodiment, verification is performed on a certain light and energy storage integrated station, specifically, the electric quantity of an energy storage system allocated to the certain light and energy storage integrated station is 300kWh, the maximum charge and discharge power is 54kW, and the maximum depth of discharge is 90%. The maximum photovoltaic power generation power is 146 kW. The photovoltaic output curve and the charging load curve of working day/non-working day are shown in fig. 3 and 4, and the occurrence probability of each scene is shown in table 1. The cost factors are shown in table 2, and the electric carbon dioxide emission factor for each period is shown in fig. 5.
TABLE 1 scene combinations
Figure BDA0003239463900000118
TABLE 2 Equipment parameters Table
Figure BDA0003239463900000121
The electricity price at the time of purchasing electricity at the charging station is below 10kV for the large industry in the urban area of beijing, and the capacity electricity fee is 48 yuan/(kW · month) as shown in table 3. The income of the spontaneous self-use of the distributed photovoltaic is higher than the income of the surplus electricity on line, and the price of electricity purchased by the power grid to the charging station in each period is assumed to be 80% of the price of electricity sold by the power grid.
TABLE 3 time-of-use electricity price for electricity purchase of charging station
Figure BDA0003239463900000122
The data are used as input, the optimization model is solved by calling CPLEX through MATLAB, the charging and discharging power of the stored energy in each scene is shown in figure 6, and the exchange power of the charging station and the power grid is shown in figure 7.
As shown in fig. 6, the light storage and charging integrated station can reasonably adjust the output of the energy storage system according to the time-of-use electricity price, and the energy storage is hardly charged and discharged at 8-10 points and 22-24 points in each scene; the stored energy is in a charging state at 0-8 points and 15-18 points, the electricity price is in a non-peak period, and the stored energy as much as possible can be used for discharging at the peak. The 10-15 points and the 18-21 points are peak-time electricity prices, and the energy storage system is in a discharging state, so that the electricity purchasing quantity of the power grid is reduced, and the electricity purchasing cost of the charging station is reduced. The scheduling method can carry out peak clipping and valley filling according to the time-of-use electricity price and different scenes, and achieves daily income maximization of the charging station.
In fig. 7, comparing scenario 1 with scenario 5, the photovoltaic output is higher, the power purchasing power of the charging station is low, and the remaining photovoltaic can be sold to the power grid at the peak electricity price to obtain profit; in contrast, scenes 4 and 8 are curves of working days and non-working days in the fourth type of photovoltaic scene, and the photovoltaic output is low, so that the electricity purchasing power of the charging station to the power grid is high. And the electricity purchasing power is slightly higher in the working day than in the non-working day, and the trend of less electricity purchasing at the peak of the electricity price and more electricity purchasing at the valley is shown as a whole.
The emission amount of carbon dioxide introduced into charging stations before and after energy storage and photovoltaic charging is analyzed, and the operation result shows that when no energy storage and photovoltaic charging is carried out, the system emits 1339kg of carbon dioxide every day, after photovoltaic charging is carried out, the emission amount of carbon dioxide every day is reduced to 942.62kg, and the emission amount of carbon dioxide is 937.87kg at the lowest in both cases. Therefore, compared with the strategy without energy storage and photovoltaic, the strategy in the paper reduces the emission amount of carbon dioxide by 14.6 tons every year, and saves the emission cost by 10950 yuan. Therefore, the energy storage system can realize carbon emission reduction, so that environmental benefits are brought, and the energy storage system can also be seen to have a promoting effect on the emission reduction of the power system by combining with photovoltaic power generation.
The energy storage low charging and high discharging can reduce the electric quantity and the electricity fee, and can also reduce the maximum load and the capacity and the electricity fee. A comparison graph of the maximum load before and after considering the capacity electricity fee is shown in fig. 8. As shown in fig. 8, when the capacity charge is not considered, the maximum load of the charging station is 213kW, and when the capacity charge is considered, the peak clipping effect is realized by the stored energy, and the maximum load is reduced to 185kW, thereby saving 16352 yuan of capacity charge per year.
The above results verify that the scheduling method can enable the stored energy to be flexibly adjusted according to photovoltaic output and charging requirements better, and reduce the running cost of the photovoltaic storage and charging integrated station by utilizing peak-valley electricity prices.
In a second aspect of the present invention, a system for the light storage and charging integrated station day-ahead scheduling method includes:
the photovoltaic output historical data clustering module is used for clustering the photovoltaic output historical data to obtain a photovoltaic historical output set capable of reflecting weather conditions;
the second module is used for describing the probability distribution of photovoltaic output based on the photovoltaic historical output set, and randomly sampling the probability distribution to generate a photovoltaic output curve;
the third module is used for training charging load historical data through a variational self-encoder, learning a probability distribution model of EV charging and generating a charging load demand curve of a charging station working day and a non-working day;
and the fourth module is used for forming eight different scenes based on the photovoltaic output curve and the charging load demand curve, respectively carrying out day-ahead optimization on the charging station, meeting system constraint conditions in the optimization process, and finally obtaining a corresponding day-ahead optimization scheduling method.
In a third aspect of the present invention, an apparatus for a day-ahead scheduling method of a light storage and charging integrated station includes: a memory and a processor; the memory for storing a computer program; the processor is used for realizing the day-ahead scheduling method of the optical storage and charging integrated station when executing the computer program.
In a third aspect of the present invention, a computer-readable storage medium has stored thereon a computer program, which, when executed by a processor, implements the optical storage and charging integrated station day-ahead scheduling method.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus, and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be appreciated by those skilled in the art that the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The embodiments disclosed above are therefore to be considered in all respects as illustrative and not restrictive. All changes which come within the scope of or equivalence to the invention are intended to be embraced therein.

Claims (10)

1. A day-ahead scheduling method for a light storage and charging integrated station is characterized by comprising the following steps:
acquiring photovoltaic output historical data, and clustering the photovoltaic output historical data to obtain a photovoltaic historical output set capable of reflecting weather conditions;
describing the probability distribution of photovoltaic output based on the photovoltaic historical output set, and randomly sampling the probability distribution to generate a photovoltaic output curve;
training charging load historical data through a variational self-encoder, learning a probability distribution model of EV charging, and generating a charging load demand curve of a charging station working day and a non-working day;
and forming different scenes based on the photovoltaic output curve and the charging load demand curve, and respectively carrying out day-ahead optimization on the charging station to obtain a corresponding day-ahead optimized scheduling method.
2. The method for scheduling the photovoltaic output historical data in the day before the light storage and charging integrated station according to claim 1, wherein a kernel k-means clustering method is used for clustering the photovoltaic output historical data, and the specific method is as follows:
selecting a Gaussian kernel function to map the photovoltaic output historical data to a higher-dimensionality feature space;
confirming the clustering number k of the photovoltaic output data, and generating k initial clustering centers: c. C1、c2、……、ck；
Respectively calculating the distance from each point to the k clustering centers, taking the cluster where the clustering center with the minimum distance is located as the cluster to which the point belongs, and then recalculating a new clustering center for each cluster;
when the sum of the distances of all data points to the center of the cluster is minimal, the result is taken as the result of the clustering.
3. The light-storage-and-charging integrated station day-ahead scheduling method of claim 1, wherein a Beta distribution is used to describe a probability distribution of photovoltaic output.
4. The method for scheduling the light storage and charging integrated station in the day-ahead mode according to claim 1, wherein the probability distribution is randomly sampled by a Monte Carlo sampling method, and a scene photovoltaic output curve with generality is generated.
5. The day-ahead scheduling method of the light storage and charging integrated station according to claim 1, wherein a specific manner of generating the charging load demand curve is as follows:
inferring a network training sample resulting distribution qΦ(z | x); generating a network as pθ(z)pθ(z | x) generating a scene; the encoder trains a load data set X for charging the electric automobile to obtain probability distribution of each implicit attribute, and obtains a corresponding mean value and a corresponding variance of the probability distribution, which are expressed by vectors mu and delta; (ii) a Randomly sampling an epsilon from a Gaussian distribution N (0,1) probability distribution, and generating a vector Z as an input of a decoder by calculating Z ═ mu + epsilon sigma; and decoding by a decoder to obtain the EV load demand curve.
6. The light-storage-charging integrated station day-ahead scheduling method according to claim 1, wherein the charging station day-ahead optimization is performed in the following manner:
inputting the photovoltaic output curve and the load demand curve into a preset day-ahead operation optimization strategy model, and respectively solving to obtain corresponding optimization strategies; the objective function of the day-ahead optimization strategy model comprises electricity selling income, electricity purchasing cost, carbon emission cost, capacity electricity charge and operation and maintenance cost of photovoltaic and energy storage for the electric automobile and the power grid.
7. The light storage and charging integrated station day-ahead scheduling method of claim 6, wherein the constraints of the day-ahead optimization strategy model include: energy storage constraints, power balance constraints, and power exchange constraints of the charging station with the grid.
8. A system for the light storage and charging integrated station day-ahead scheduling method of any one of claims 1 to 7, comprising:
the photovoltaic output historical data clustering module is used for clustering the photovoltaic output historical data to obtain a photovoltaic historical output set capable of reflecting weather conditions;
the second module is used for describing the probability distribution of photovoltaic output based on the photovoltaic historical output set, and randomly sampling the probability distribution to generate a photovoltaic output curve;
the third module is used for training charging load historical data through a variational self-encoder, learning a probability distribution model of EV charging and generating a charging load demand curve of a charging station working day and a non-working day;
and the fourth module is used for forming eight different scenes based on the photovoltaic output curve and the charging load demand curve, respectively carrying out day-ahead optimization on the charging station, and simultaneously meeting system constraint conditions in the optimization process to obtain a corresponding day-ahead optimization scheduling method.
9. An apparatus for the light storage and charging integrated station day-ahead scheduling method according to any one of claims 1 to 7, comprising: a memory and a processor; the memory for storing a computer program; the processor is used for realizing the light storage and charging integrated station day-ahead scheduling method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, implements the light storing and charging integrated station day-ahead scheduling method of any one of claims 1 to 7.
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