CN113733963B - Day-ahead scheduling method, system and device for optical storage and charging integrated station and storage medium - Google Patents

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

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CN113733963B
CN113733963B CN202111014791.XA CN202111014791A CN113733963B CN 113733963 B CN113733963 B CN 113733963B CN 202111014791 A CN202111014791 A CN 202111014791A CN 113733963 B CN113733963 B CN 113733963B
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charging
photovoltaic output
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CN113733963A (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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Abstract

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

Description

Day-ahead scheduling method, system and device for optical storage and charging integrated station and storage medium
Technical Field
The invention belongs to the technical field of power system dispatching, and particularly relates to a day-ahead dispatching method, system and device for an optical storage and charging integrated station and a storage medium.
Background
Under the dual pressures of energy and environmental protection, the electric automobile becomes the main development direction of the future automobile, a large amount of EV charging can bring impact to a power grid, and an optical storage and charging integrated station is generated for promoting new energy consumption and stabilizing the impact. How to optimize the operation of the optical storage and filling integrated station, so that the optical storage and filling integrated station can improve the utilization rate of new energy and reduce the carbon emission, thereby improving the economy and the cleanliness of the system has become one of the research hot spots. For the operation of the optical storage and charging integrated station, a day-ahead dispatching method comprising energy storage system configuration and power exchange between a charging station and a power grid is formulated according to the charging behavior and the photovoltaic output of a vehicle. In the optical storage and charging integrated station, the electric energy generated by the photovoltaic power generation system firstly meets the requirement of a charging station, when the power supply in the system does not meet the load requirement, the energy storage system discharges, and if the power supply in the system still cannot meet the load requirement, the power is purchased from a large power grid; when the photovoltaic output is excessive, the residual electric energy can be used for storing energy and charging, and electricity can be sold to a large power grid, so that certain economic benefit is obtained.
The magnitude of the photovoltaic output depends on the intensity of illumination, different weather can lead the photovoltaic output curve to be greatly different, the photovoltaic output is caused to be uncertain, the charging load requirement is also uncertain, and the operation income of the optical storage and charging integrated station is easily influenced.
Disclosure of Invention
The invention aims to provide a day-ahead scheduling method, a day-ahead scheduling system, a day-ahead scheduling device and a day-ahead scheduling storage medium for an optical storage and filling integrated station, so as to solve the problem of high operation uncertainty of the optical storage and filling integrated station in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
according to a first aspect of the invention, a day-ahead scheduling method of an optical storage and filling integrated station comprises 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 probability distribution of photovoltaic output based on the historical photovoltaic output set, randomly sampling the probability distribution, and generating a photovoltaic output curve;
training charging load historical data through a variation self-encoder, and learning a probability distribution model of EV charging to generate a charging load demand curve of a charging station on working days and non-working days;
based on the photovoltaic output curve and the charging load demand curve, eight different scenes are formed, and daily optimization of the charging station is respectively carried out, so that a corresponding daily optimization scheduling method is obtained.
Preferably, after the Gaussian kernel function is selected to map the photovoltaic output history data to the feature space with higher dimension, the clustering method of kernel k-means is used for clustering the photovoltaic output history data, 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 1 、c 2 、……、c k ；
Respectively calculating the distance between each point and the k cluster centers, taking the cluster with the smallest distance from the cluster center as the cluster to which the point belongs, and then recalculating a new cluster center for each cluster;
when the sum of the distances of all data points to the cluster center is minimal, the result is the result of the clustering.
Preferably, a Beta distribution is used to describe the probability distribution of the photovoltaic output.
Preferably, the probability distribution is randomly sampled by using a Monte Carlo direct sampling method, and the photovoltaic output curves under k weather are generated.
Preferably, the specific way of generating the charging load demand curve is as follows:
deducing a network training sample to obtain distribution q Φ (z|x); generating a network as p θ (z)p θ (z|x) generating a scene; the encoder trains a load data set X of the electric vehicle charging to obtain probability distribution of each hidden attribute, and obtains corresponding mean and variance of the probability distribution, and the probability distribution is represented by vectors mu and delta; the method comprises the steps of carrying out a first treatment on the surface of the 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, the charging station day-ahead optimization is performed in the following specific manner:
inputting a photovoltaic output curve and a load demand curve into a preset day-ahead optimization strategy model, and respectively solving to obtain a corresponding optimization strategy; the objective function of the day-ahead optimization strategy model comprises electricity selling benefits, electricity purchasing costs, carbon emission costs, capacity electricity fees and photovoltaic and energy storage running costs of the electric automobile and the electric network.
Preferably, the constraint condition of the day-ahead optimization strategy model includes: energy storage constraints, power balance constraints, and power exchange constraints of the charging station and the grid.
In a second aspect of the present invention, a system for a day-ahead scheduling method of the optical storage and filling integrated station includes:
the first module is used for acquiring photovoltaic output historical data, and 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 probability distribution of photovoltaic output based on the photovoltaic historical output set, randomly sampling the probability distribution and generating a photovoltaic output curve;
the third module is used for training the charging load historical data through the variation self-encoder, learning a probability distribution model of EV charging and generating a charging load demand curve of the charging station on working days and non-working days;
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 daily optimization of the charging station, and meeting the constraint condition of the system in the optimization process to obtain a corresponding daily optimization scheduling method.
In a third aspect of the present invention, an apparatus for a method for scheduling a photo-storage and charging integrated station before date, includes: a memory and a processor; the memory is used for storing a computer program; and the processor is used for realizing the day-ahead scheduling method of the optical storage and filling integrated station when executing the computer program.
In a third aspect of the present invention, a computer readable storage medium has a computer program stored thereon, which when executed by a processor, implements the optical storage and inflation all-in-one station day-ahead scheduling method.
The beneficial effects of the invention are as follows:
(1) According to the day-ahead scheduling method for the optical storage and charging integrated station, based on data and model hybrid driving, the obtained strategy comprises an exchange power curve of a charging station and a power grid and an energy storage charging and discharging curve. By comparing the benefits under the strategy with the benefits of the charging station without the strategy, corresponding adjustment can be better made according to the time-of-use electricity price and different scenes, the peak clipping and valley filling of the power grid are facilitated, the impact of a large number of electric automobile charging on the power grid is relieved, and higher benefits are brought.
(2) According to the day-ahead scheduling method for the optical storage and charging integrated station, based on data and model hybrid driving, the difference of operation of the optical storage and charging integrated station in different scenes is considered, personalized day-ahead operation strategies are formulated according to different scenes, and compared with the fixed energy storage and charging strategies in the past, the day-ahead operation strategies are found to be more suitable for the actual situation, and the optical storage and charging integrated station can obtain higher day benefits by utilizing a time-of-use electricity price mechanism.
(3) According to the day-ahead scheduling method for the optical storage and filling integrated station, the improved kernel k-means algorithm is adopted for clustering, after the data samples are mapped to a space with a higher dimension through a kernel function, the difference between the data samples is more obvious, and therefore the situation that the characteristics of different weather cannot be reflected due to the fact that the integral output levels are similar and are gathered to the same class can be avoided.
(4) The traditional photovoltaic scene is usually generated by using a statistical method, the statistical result is subjective and unreliable, and the daily scheduling method of the optical storage and filling integrated station provided by the embodiment has more objective processing result by using a data driving method.
(5) According to the day-ahead scheduling method for the optical storage and filling integrated station, photovoltaic output scene generation is described according to different probability distribution obeyed by photovoltaic output values at different moments, the output values at all moments are generated by sampling by adopting a method for training Beta probability distribution at all moments respectively, and finally the output values generated at all moments are connected into a complete curve, so that a new scene is obtained, and the uncertain characteristics of the photovoltaic output are more met.
(6) According to the day-ahead scheduling method for the optical storage and charging integrated station, which is provided by the embodiment, the carbon emission cost is increased in the objective function of the daily gain of the charging station, so that the optimization of the economy and the cleanliness of the system after energy storage can be introduced into the system in a more definite way.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
fig. 1 is a flowchart of a day-ahead scheduling method of an optical storage and filling integrated station according to an embodiment of the invention.
Fig. 2 is a flow chart of VAE scene generation in an embodiment of the present invention.
FIG. 3 is a graph of photovoltaic output for different days in an embodiment of the present invention.
Fig. 4 is a graph illustrating a charge load demand in an embodiment of the present invention.
Fig. 5 is a graph of carbon dioxide emission factors.
Fig. 6 is a charge-discharge power diagram of stored energy.
Fig. 7 is a diagram of the exchange power of a charging station with a power grid.
Fig. 8 is a maximum load comparison graph.
Detailed Description
The invention will be described in detail below with reference to the drawings in connection with embodiments. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
The following detailed description is exemplary and is intended to provide further details of the invention. Unless defined otherwise, 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 example embodiments in accordance with the invention.
According to the first aspect of the embodiment of the invention, a day-ahead dispatching method of an optical storage and filling integrated station based on data-model hybrid driving is provided, different typical scenes are generated according to historical data, and an adaptive day-ahead dispatching optimization method is formulated, so that the economical efficiency is improved. Firstly, according to historical charging data and photovoltaic output data of a charging station, a hybrid driving mode is adopted to predict photovoltaic output and charging load, and eight different scenes under different weather and working days/non-working days are generated. On the basis, a mathematical model with optimal economical efficiency as a target is established, and relevant constraints including energy storage charge and 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, according to specific parameters of the optical storage and filling integrated station, obtaining an optimal day-ahead scheduling method.
According to the day-ahead scheduling method for the optical storage and charging integrated station, based on data and model hybrid driving, the obtained strategy comprises an exchange power curve of a charging station and a power grid and an energy storage charging and discharging curve. By comparing the benefits under the strategy with the benefits of the charging station without the strategy, corresponding adjustment can be better made according to the time-of-use electricity price and different scenes, the peak clipping and valley filling of the power grid are facilitated, the impact of a large number of electric automobile charging on the power grid is relieved, and higher benefits are brought.
As shown in fig. 1, the method for scheduling the daily operation of the optical storage and filling integrated station based on data-model hybrid driving by considering carbon emission comprises the following steps:
s1, collecting daily photovoltaic output historical data of a certain charging station, clustering the photovoltaic output data without related information such as weather by using an improved kernel k-means algorithm, and obtaining a photovoltaic historical output set capable of reflecting weather conditions. The clustering method of kernel k-means is as follows:
selecting kernel functions, mapping sample data to a feature space with higher dimension, selecting a plurality of kernel functions for comparison, and finding that the Gaussian kernel functions have better effect:
Figure BDA0003239463900000061
wherein x is i ，x j For sample data, σ represents the bandwidth, controlling the local range of action of the gaussian kernel. When x is i And x j If x is fixed when the Euclidean distance of (2) is within a certain interval j Then kappa (x) i ,x j ) With x i The variation is quite significant.
Confirming the clustering number k of the photovoltaic output data according to an elbow rule, and generating k initial clustering centers: c 1 、c 2 、……、c k . The distance between each point and the k cluster centers is calculated respectively, the cluster where the cluster center with the smallest distance is located is used as the cluster to which the point belongs, and then a new cluster center is calculated for each cluster, wherein the calculation formula is as follows:
Figure BDA0003239463900000062
wherein: c (C) i Representing the ith cluster, x is the point within the cluster.
When the sum of the distances of all data points to the cluster center is minimal, the result is the result of the clustering.
S2, describing probability distribution of photovoltaic output by adopting Beta distribution, and randomly sampling by utilizing a Monte Carlo direct sampling method to generate a photovoltaic output curve with general k kinds of weather. The Beta distribution is a continuous distribution over the [0,1] interval with a probability density function of:
Figure BDA0003239463900000071
wherein:
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 the shape parameters of Beta distribution, respectively, and gamma is represented by Γ.
The parameters α, β can be calculated from the expected value μ and variance δ of the power over a given time interval:
Figure BDA0003239463900000073
Figure BDA0003239463900000074
the historical data y= [ P1, P2, P3, P4, … … ] at a certain time within a period is set to have a maximum value of pt.max and a minimum value of pt.min. First, the expected value and variance at the time are calculated, and two parameters α, β of the Beta distribution corresponding to the time, that is, probability density curves of the Beta distribution, are obtained according to the formula (4) and the formula (5). And secondly, carrying out accumulated integration on the probability density curve of the Beta distribution to obtain a probability distribution function of each moment.
Figure BDA0003239463900000075
Wherein: x represents a random variable, F (x) represents a probability density function, and F (x) represents a probability distribution function.
Furthermore, a random probability value Y is obtained by randomly sampling on the probability distribution function by using a Monte Carlo sampling method. The value r of x corresponding to the value Y can be obtained by inverting the equation (6), and the power P of the photovoltaic output at the moment can be obtained by calculating r.
After n times of collection, carrying out inverse operation of a cumulative distribution function on probability values obtained by n times of collection, and respectively obtaining n groups of different output powers P. And taking the average value of the power of the photovoltaic output corresponding to 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 variation self-encoder (VAE), learning a probability distribution model of EV charging, and finally generating a charging load demand curve of a charging station on working days and non-working days:
the historical data is trained by a variational self-encoder (variational autoencoder, VAE) to learn a probability distribution model of EV charging. Because whether the working day has a certain influence on the use condition of the EV or not, the probability model is learned for the load demands of the working day and the non-working day respectively, and finally two scenes are obtained.
The process of generating the VAE scene is shown in figure 2, the VAE is divided into an encoder and a decoder, the VAE is divided into an inference network and a generation network, and the inference network trains samples to obtain distribution q Φ (z|x), generating a network as p θ (z)p θ (z|x) is used to generate a scene. The encoder is used for training a load data set X of the electric automobile charging to obtain probability distribution of each hidden attribute, and the corresponding mean value and variance are obtained and represented by vectors mu and delta. Then, an epsilon is randomly sampled from the gaussian distribution N (0, 1) probability distribution, and a vector Z is generated as input to the decoder by calculating z=μ+epsilon sigma. Finally, after decoding by a decoder, an 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 performing daily optimization of the charging station to obtain a corresponding daily optimization scheduling method. The specific mode is as follows:
and (3) taking the photovoltaic output curve and the load demand curve obtained in the step (S2) and the step (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 a power grid is emitted by a thermal power plant burning coal, the corresponding carbon emission cost is borne by the charging station. In addition, the electricity purchase cost adopts two electricity price. Thus, the objective functions of the optimization model include sales revenue to electric vehicles and grids, purchase costs, carbon emission costs, volumetric electricity costs, and operating costs of photovoltaic and energy storage:
Figure BDA0003239463900000081
wherein, C is daily gain of the charging station; w is scene sequence number; alpha w The probability of occurrence of the scene w;
Figure BDA0003239463900000082
the benefits of charging the EV for scenario w; />
Figure BDA0003239463900000083
The income of selling electricity to the power grid for the scene w; />
Figure BDA0003239463900000084
The electricity purchasing cost of the charging station in the scene w; />
Figure BDA0003239463900000085
Carbon dioxide emission cost for scenario w; c (C) R The electricity charge is the capacity electricity charge; c (C) PV 、C BS The operation and maintenance costs of the photovoltaic and the energy storage are respectively.
The electricity selling benefits include benefits of selling electricity to the EV and selling electricity to the grid according to the time-of-use electricity price:
Figure BDA0003239463900000091
Figure BDA0003239463900000092
in the middle of，θ EV Unit benefit for charging EVs;
Figure BDA0003239463900000093
the unit income of selling electricity to the power grid in the period t; />
Figure BDA0003239463900000094
Charging power for a period t under the scene w; />
Figure BDA0003239463900000095
Power delivered to the grid for a t period charging station at the scene w.
When the photovoltaic output and the energy storage discharge cannot meet the requirements of the charging station, the charging station needs to purchase electricity to the power grid:
Figure BDA0003239463900000096
wherein, c t The electricity purchasing unit electricity price is t time period;
Figure BDA0003239463900000097
for the power taken from the grid for a period t at the scene w.
The electricity purchased by the charging station to the power grid is sent out by a thermal power plant burning coal, and the corresponding carbon emission cost is borne by the charging station:
Figure BDA0003239463900000098
wherein lambda is t Carbon emission factor for period t;
Figure BDA0003239463900000099
for an exhaust cost per kilogram of carbon dioxide.
The charging station adopts two electricity prices, so the daily capacity cost is as follows:
Figure BDA00032394639000000910
wherein, c R The electricity charge is the unit capacity;
Figure BDA00032394639000000911
is the maximum value of the medium load in 1 month.
The operation and maintenance cost comprises photovoltaic and energy storage operation and maintenance cost, and the energy storage operation and maintenance cost comprises a fixed part and a variable part, wherein the variable part is determined by the charge and discharge electric quantity of the energy storage system.
Figure BDA00032394639000000912
Figure BDA00032394639000000913
In θ PV ，θ E ，θ BS The photovoltaic unit operation and maintenance cost, the energy storage unit capacity operation and maintenance cost and the unit electric quantity dynamic cost are respectively;
Figure BDA0003239463900000101
is photovoltaic maximum power; />
Figure BDA0003239463900000102
Is the rated capacity of energy storage; r is (r) p R' is the discount rate of the photovoltaic and energy storage equipment; y and y' respectively represent the service lives of the photovoltaic and energy storage equipment; />
Figure BDA0003239463900000103
And respectively storing the charge and discharge power of energy in each scene t period.
The constraints include energy storage constraints, power balance constraints, and power exchange constraints of the charging station and the grid.
The charge and discharge power of the stored energy is constrained by the rated capacity of the bidirectional power converter, the start and end states of the SOC are required to be consistent, and the constraints of the current state of charge and the like are as follows:
Figure BDA0003239463900000104
Figure BDA0003239463900000105
Figure BDA0003239463900000106
Figure BDA0003239463900000107
SOC fin ＝SOC 0
SOC min ≤SOC t ≤SOC max
in the method, in the process of the invention,
Figure BDA0003239463900000108
the maximum charge and discharge power of the stored energy; />
Figure BDA0003239463900000109
A variable of 0-1, which represents the charge and discharge states of the stored energy; η (eta) c 、η d The charge and discharge efficiency of energy storage; SOC (State of Charge) 0 、SOC fin Respectively storing the charge states of the beginning and the end of one day; SOC (State of Charge) t The charge state at the end of the energy storage t period; SOC (State of Charge) max 、SOC min Is the maximum and minimum state of charge; SOC (State of Charge) t-1 From the following components
Figure BDA00032394639000001010
And (5) carrying out iterative calculation.
Power balance constraint
Figure BDA00032394639000001011
Figure BDA0003239463900000111
In the method, in the process of the invention,
Figure BDA0003239463900000112
equivalent force for energy storage of scene w.
Charging station and grid power exchange constraints
The transmission power of the charging station and the large power grid is influenced by the distribution transformer capacity and cannot exceed the maximum value of the transmission capacity.
Figure BDA0003239463900000113
Figure BDA0003239463900000114
Figure BDA0003239463900000115
/>
In the method, in the process of the invention,
Figure BDA0003239463900000116
a 0-1 variable, representing the direction of the power exchange between the charging station and the power grid; />
Figure BDA0003239463900000117
The maximum power exchange between the charging station and the power grid is achieved.
In this embodiment, verification is performed on a certain light storage integrated station, specifically, the electric quantity of an energy storage system configured by the following light storage and charging integrated station is 300kWh, the maximum charging and discharging power is 54kW, and the maximum discharging depth is 90%. The maximum power generation of the photovoltaic is 146kW. The photovoltaic output curve and the charging load curve on the 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 coefficients are shown in table 2, and the electrical carbon dioxide emission factors for each period are shown in fig. 5.
Table 1 scene composition
Figure BDA0003239463900000118
Table 2 device parameter table
Figure BDA0003239463900000121
The electricity price of the charging station was equal to or lower than 10kV in the urban area of Beijing city, and the electricity charge was 48 yuan/(kW.month) as shown in Table 3. The self-generating and self-using benefits of the distributed photovoltaic are higher than those of the residual electricity on the internet, and the price of electricity purchased by the power grid to the charging station in each period is assumed to be 80% of the electricity selling price of the power grid.
Table 3 charging station time of purchase electricity price
Figure BDA0003239463900000122
The data are used as input, the optimization model is called CPLEX through MATLAB to solve, and the charging and discharging power of energy storage under each scene is obtained and shown in FIG. 6, and the exchange power of the charging station and the power grid is shown in FIG. 7.
As shown in fig. 6, the optical 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 or discharged at 8-10 points and 22-24 points in each scene; the energy storage at points 0-8 and 15-18 are in a charging state, and the electricity price is in an off-peak period, and as much stored electric energy as possible can be used for discharging at peak time. And the electricity prices at the 10-15 points and the 18-21 points are peak time, and the energy storage system is in a discharging state, so that the electricity purchasing quantity from the power grid is reduced, and the electricity purchasing cost of the charging station is reduced. The scheduling method can cut peaks and fill valleys according to the time-of-use electricity price and different scenes, and achieves daily gain maximization of the charging station.
In fig. 7, comparing scenario 1 with scenario 5, the photovoltaic output is higher, the purchase power of the charging station is low, and the rest of the photovoltaic can be sold to the grid at peak electricity prices to obtain profits; in contrast, scenes 4 and 8 are curves of working days and non-working days in the fourth type of photovoltaic scene, and the charging station has high electricity purchasing power to the power grid due to lower photovoltaic output. And the power is slightly higher in working days than in non-working days, and the total power has the tendency of less power purchase and more power purchase in low valleys when the power price peaks.
The carbon dioxide emissions from charging stations before and after the introduction of energy storage and photovoltaic were analyzed, and as a result of the operation, when no energy storage and light Fu Jia was present, the system emitted 1339kg of carbon dioxide per day, and after photovoltaic addition, the daily carbon dioxide emissions decreased to 942.62kg, both of which sometimes had emissions of a minimum of 937.87kg. Compared with the strategy without energy storage and photovoltaics, the strategy reduces the carbon dioxide emission by 14.6 tons per year, and saves the emission cost by 10950 yuan. Therefore, the energy storage system can realize carbon emission reduction, thereby bringing environmental benefits, and in addition, the combination of photovoltaic power generation has a promotion effect on the emission reduction of the power system.
The energy storage low-charge high-discharge device not only can reduce the electric quantity and the electric charge, but also can reduce the maximum load and the capacity electric charge. The maximum load comparison chart before and after the consideration of the capacity electric charge is shown in fig. 8. As shown in fig. 8, when the capacity electricity is not considered, the maximum load of the charging station is 213kW, and when the capacity electricity is considered, the peak clipping effect is achieved by the stored energy, and the maximum load is reduced to 185kW, whereby the capacity electricity fee 16352 yuan can be saved each year.
The result verifies that the scheduling method can enable the energy storage to be flexibly adjusted according to the photovoltaic output and the charging requirement better, and the peak-valley electricity price is utilized to reduce the operation cost of the optical storage and charging integrated station.
In a second aspect of the present invention, a system for a day-ahead scheduling method of the optical storage and filling integrated station includes:
the first module is used for acquiring photovoltaic output historical data, and 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 probability distribution of photovoltaic output based on the photovoltaic historical output set, randomly sampling the probability distribution and generating a photovoltaic output curve;
the third module is used for training the charging load historical data through the variation self-encoder, learning a probability distribution model of EV charging and generating a charging load demand curve of the charging station on working days and non-working days;
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 daily optimization of the charging station, meeting the constraint condition of the system in the optimization process, and finally obtaining a corresponding daily optimization scheduling method.
In a third aspect of the present invention, an apparatus for a method for scheduling a photo-storage and charging integrated station before date, includes: a memory and a processor; the memory is used for storing a computer program; and the processor is used for realizing the day-ahead scheduling method of the optical storage and filling integrated station when executing the computer program.
In a third aspect of the present invention, a computer readable storage medium has a computer program stored thereon, which when executed by a processor, implements the optical storage and inflation all-in-one station day-ahead scheduling method.
It will be apparent to those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 present invention can be carried out in other embodiments without departing from the spirit or essential characteristics thereof. Accordingly, the above disclosed embodiments are illustrative in all respects, and not exclusive. All changes that come within the scope of the invention or equivalents thereto are intended to be embraced therein.

Claims (8)

1. The day-ahead scheduling method for the optical storage and filling integrated station is characterized by comprising the following steps of:
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 probability distribution of photovoltaic output based on the historical photovoltaic output set, randomly sampling the probability distribution, and generating a photovoltaic output curve;
training charging load historical data through a variation self-encoder, and learning a probability distribution model of EV charging to generate a charging load demand curve of a charging station on working days and non-working days;
based on the photovoltaic output curve and the charging load demand curve, different scenes are formed, and charging station day-ahead optimization is respectively carried out to obtain a corresponding day-ahead optimization scheduling method; wherein the scenes are associated with different weather and weekdays/non-weekdays;
clustering the photovoltaic output historical data by using a kernel k-means clustering method, wherein the clustering method comprises the following steps of:
selecting a Gaussian kernel function to map the photovoltaic output history data to a feature space with higher dimensionality;
confirming the clustering number k of the photovoltaic output data, and generating k initial clustering centers:c 1 、c 2 、……、c k ；
respectively calculating the distance between each point and the k cluster centers, taking the cluster with the smallest distance from the cluster center as the cluster to which the point belongs, and then recalculating a new cluster center for each cluster;
when the sum of the distances of all data points to the cluster center is minimal, the result is the result of the clustering.
2. The method for day-ahead scheduling of an optical storage and filling station according to claim 1, wherein a Beta distribution is used to describe probability distribution of photovoltaic output.
3. The method for scheduling the solar energy storage and filling integrated station according to claim 1, wherein the probability distribution is randomly sampled by using a Monte Carlo sampling method, and a general scene photovoltaic output curve is generated.
4. The method for day-ahead dispatching of an optical storage and charging integrated station according to claim 1, wherein the day-ahead optimization of a charging station is performed in the following specific manner:
inputting a photovoltaic output curve and a load demand curve into a preset day-ahead operation optimization strategy model, and respectively solving to obtain a corresponding optimization strategy; the objective function of the day-ahead running optimization strategy model comprises electricity selling benefits, electricity purchasing cost, carbon emission cost, capacity electricity fee and operation and maintenance cost of photovoltaic and energy storage to the electric automobile and the power grid.
5. The method for day-ahead scheduling of an optical storage and filling integrated station according to claim 4, wherein the constraint conditions of the day-ahead operation optimization strategy model include: energy storage constraints, power balance constraints, and power exchange constraints of the charging station and the grid.
6. A system for the day-ahead scheduling method of an optical storage and filling integrated station according to any one of claims 1 to 5, comprising:
the first module is used for acquiring photovoltaic output historical data, and 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 probability distribution of photovoltaic output based on the photovoltaic historical output set, randomly sampling the probability distribution and generating a photovoltaic output curve;
the third module is used for training the charging load historical data through the variation self-encoder, learning a probability distribution model of EV charging and generating a charging load demand curve of the charging station on working days and non-working days;
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 daily optimization of the charging station, and meeting the constraint condition of the system in the optimization process to obtain a corresponding daily optimization scheduling method; wherein the scenes are associated with different weather and weekdays/non-weekdays;
in the first module, the photovoltaic output historical data is clustered by using a kernel k-means clustering method, and the specific method is as follows:
selecting a Gaussian kernel function to map the photovoltaic output history data to a feature space with higher dimensionality;
confirming the clustering number k of the photovoltaic output data, and generating k initial clustering centers:c 1 、c 2 、……、c k ；
respectively calculating the distance between each point and the k cluster centers, taking the cluster with the smallest distance from the cluster center as the cluster to which the point belongs, and then recalculating a new cluster center for each cluster;
when the sum of the distances of all data points to the cluster center is minimal, the result is the result of the clustering.
7. An apparatus for use in the day-ahead scheduling method of an optical storage and filling integrated station according to any one of claims 1 to 5, comprising: a memory and a processor; the memory is used for storing a computer program; the processor is configured to implement the day-ahead scheduling method of the optical storage and inflation integrated station according to any one of claims 1 to 5 when executing the computer program.
8. A computer readable storage medium, wherein a computer program is stored on the storage medium, and when the computer program is executed by a processor, the method for scheduling the optical storage and inflation integrated station before date according to any one of claims 1 to 5 is implemented.
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