CN113991650B - Light storage polymerization peak regulation method based on K-means + + algorithm - Google Patents

Light storage polymerization peak regulation method based on K-means + + algorithm Download PDF

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CN113991650B
CN113991650B CN202111241218.2A CN202111241218A CN113991650B CN 113991650 B CN113991650 B CN 113991650B CN 202111241218 A CN202111241218 A CN 202111241218A CN 113991650 B CN113991650 B CN 113991650B
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cost
storage
aggregation
optical storage
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CN113991650A (en
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廖思阳
武艺
姚良忠
李烨
王新迎
王剑锋
陈培育
马世乾
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Wuhan University WHU
China Electric Power Research Institute Co Ltd CEPRI
State Grid Tianjin Electric Power Co Ltd
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China Electric Power Research Institute Co Ltd CEPRI
State Grid Tianjin Electric Power Co Ltd
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Abstract

The invention belongs to a distributed light storage polymerization peak regulation technology, and particularly relates to a light storage polymerization peak regulation method based on a K-means + + algorithm. The peak shaving cost and the fluctuation cost of the power system are comprehensively considered, and meanwhile, a peak shaving model of the power system is established by utilizing the light storage and aggregation group. And considering both the scheduling economy and the optical storage volatility, and issuing the scheduling result of the optical storage cluster to the individual distributed optical storage system. According to the method, a distributed light storage aggregation peak regulation model is built, a large number of distributed light storages are aggregated through similar peak regulation characteristic quantities, the variable dimension and the solving difficulty are reduced, and the distributed light storages can participate in peak regulation optimization of a power system more easily.

Description

Light storage polymerization peak regulation method based on K-means + + algorithm
Technical Field
The invention belongs to the technical field of distributed light storage polymerization peak regulation, and particularly relates to a light storage polymerization peak regulation method based on a K-means + + algorithm.
Background
In recent years, the photovoltaic power generation proportion in an electric power system is rapidly improved, the increasingly prominent environmental and resource problems are effectively relieved, meanwhile, the fluctuation of photovoltaic output and the anti-peak regulation characteristic also bring severe challenges to peak regulation of the electric power system, and the peak regulation demand of the electric power system is rapidly increased. There is a conflict between increasing peak shaving requirements and limited peak shaving resources of the power system, and the photovoltaic output needs to be adjusted on the basis of reducing the light rejection to the maximum extent so as to relieve the peak shaving pressure of the power system.
With the continuous development of modern power systems and power markets, the importance of safety, stability and economic operation of the power systems is increasingly highlighted, and the establishment of a reasonable aggregate peak shaving model plays an important role in high-proportion distributed optical storage grid-connected peak shaving. However, due to the fact that the number of the distributed optical storage is large and the capacity is small, if the distributed optical storage directly participates in the power system peak regulation, the problems of high solving difficulty, decision variable dimension explosion and the like are caused, and therefore an aggregation peak regulation model of the distributed optical storage needs to be established.
Disclosure of Invention
Aiming at the problems in the background art, the invention provides a light storage aggregation peak regulation method based on a K-means + + algorithm.
In order to solve the technical problem, the invention adopts the following technical scheme: a light storage polymerization peak regulation method based on a K-means + + algorithm comprises the following steps:
step 1, extracting and analyzing aggregation characteristic quantities influencing distributed light storage peak shaving, and dividing an aggregation process into two steps of partitioning and layering according to priorities of different characteristic quantities;
step 2, obtaining a light storage polymerization group with similar characteristic quantity and a corresponding polymerization model thereof by using a K-means + + algorithm;
step 3, establishing a peak shaving model of the power system by using the light storage aggregation group to realize the aggregation peak shaving process of the distributed light storage and obtain an optimized scheduling result;
and 4, considering both the scheduling economy and the optical storage volatility, and issuing the scheduling result of the optical storage cluster to the distributed optical storage system individuals.
In the above optical storage polymerization peak shaving method based on the K-means + + algorithm, the implementation of step 1 includes:
step 1.1, performing preliminary aggregation according to the spatial geographic position, and partitioning a large number of distributed optical storage based on the position of a bus connected with the distributed optical storage;
step 1.2, performing secondary polymerization according to the light storage peak regulation characteristics, including distributed light storage peak regulation capacity, distributed light storage peak regulation cost and response time, and realizing the layered processing of the distributed light storage group;
step 1.2.1, a distributed light storage peak regulation capacity formula:
Figure SMS_1
/>
wherein, the first and the second end of the pipe are connected with each other,
Figure SMS_2
is an adjustable up-regulation capacity of the light store, including the discharge capacity of the light store>
Figure SMS_3
Is an adjustable turndown capacity of the light store, including a charging capacity of the light store->
Figure SMS_4
And a maximum permissible quantity of light discarded>
Figure SMS_5
Step 1.2.2, distributed light storage peak regulation cost; the total cost comprises initial investment cost and operation period cost, and the calculation formula is as follows:
Figure SMS_6
wherein, I pv-es Dividing the initial investment cost of the light storage construction into the initial investment cost I of the photovoltaic system pv Energy storage construction investment cost I bess And construction cost I b (ii) a The light storage operation cost comprises photovoltaic cell cleaning management cost O t And the energy storage system maintains the operation cost V t ；
Step 1.2.3, distributed optical storage peak shaving response time; setting different light storage clusters to adjust response time t, taking the light storage clusters with the response time t larger than the reliable value Tc as reliable peak regulation clusters to preferentially participate in peak regulation, and if the peak regulation capacity does not meet the peak regulation requirement, using the standby clusters to participate in peak regulation.
In the above optical storage polymerization peak shaving method based on the K-means + + algorithm, the implementation of step 2 includes:
step 2.1, K-means + + polymerization algorithm;
step 2.1.1, index quantification; firstly, normalizing index data; dividing the index into a positive index and a negative index according to different properties of the indexes, wherein the larger the positive index is, the better the positive index is, and the smaller the negative index is, the better the negative index is;
Figure SMS_7
Figure SMS_8
in the formula (I), the compound is shown in the specification,
Figure SMS_9
respectively representing the positive index and the inverse index after quantization; x ij Before representing quantizationThe index data of (2); min(s) i X ij 、max i X ij Minimum and maximum values representing index data;
step 2.1.2, determining a clustering number K; and (3) determining a clustering number K by adopting an elbow method, wherein the core indexes are error square sums:
Figure SMS_10
where p denotes all points in each cluster, m i Represents the polymerization center of each class;
with the increase of K, the aggregation degree of each aggregation cluster is gradually improved, the sum of squared errors is gradually reduced, when K reaches the real aggregation number, the return of the aggregation degree is sharply reduced, the maximum value of the decrease of one corresponding SSE is similar to the elbow of a SSE-K relation diagram, and the corresponding number is determined as the size of the aggregation number K;
step 2.1.3, selecting an initial clustering center; the method comprises the following specific steps:
step 2.1.3.1, randomly selecting a point as an initial polymerization center;
step 2.1.3.2, calculating the Euclidean distance D between each point in the sample and the nearest initial aggregation center;
step 2.1.3.3, increasing the probability that the point with the farthest distance is used as the next aggregation center;
step 2.1.3.4, repeating step 2.1.3.2 and step 2.1.3.3 until K initial polymerization centers are selected;
2.2, a light storage polymerization model based on a K-means + + algorithm; the peak regulation capacity, speed and cost of the aggregated optical storage cluster are included;
light reservoir group aggregate capacity and climbing constraint:
Figure SMS_11
Figure SMS_12
wherein the content of the first and second substances,
Figure SMS_13
is an adjustable up-regulation capacity of the light store, including the discharge capacity of the light store>
Figure SMS_14
Is an adjustable turndown capacity of the light store, including a charging capacity of the light store->
Figure SMS_15
And a maximum allowed amount of light discard->
Figure SMS_16
Figure SMS_17
Represents the maximum ramp for light storage regulation;
peak shaving cost and conditioning response time of the aggregated optical storage clusters:
Figure SMS_18
Figure SMS_19
wherein, C m And t m The peak shaving cost and the conditioning response time of each optical storage cluster after aggregation.
In the above optical storage polymerization peak shaving method based on the K-means + + algorithm, the implementation of step 3 includes:
step 3.1, an objective function;
comprehensively considering the peak regulation cost and the fluctuation cost of the power system, establishing an objective function of the distributed optical storage participating in peak regulation optimization of the power system:
Figure SMS_20
wherein, F 1 ，F 2 ，F 3 ，F 4 ，F 5 Respectively, the peak regulation cost of the light storage, the fluctuation cost of the net load, the environmental pollution cost, the power generation cost of the light storage and the thermal power generating unit, C m ，C flu ，C poll ，C pv ，C G Is the corresponding unit cost coefficient;
step 3.2, constraint conditions;
step 3.2.1, optical storage system constraint:
Figure SMS_21
wherein m =1,2.. K,
Figure SMS_22
and &>
Figure SMS_23
A maximum of a reliable up-regulation capacity and a reliable down-regulation capacity, respectively, for the light reservoir group>
Figure SMS_24
Is the maximum climbing rate of the light storage system, eta is the energy storage charging and discharging efficiency, and->
Figure SMS_25
In order to store the current SOC state,
Figure SMS_26
the energy storage charging and discharging power;
step 3.2.2, constraining the thermal generator set;
Figure SMS_27
wherein the content of the first and second substances,
Figure SMS_28
and &>
Figure SMS_29
The output limits are the upper and lower limits after the thermal power generating set is started; />
Figure SMS_30
Is the maximum climbing constraint of the thermal power unit;
step 3.2.3, energy balance constraint;
Figure SMS_31
wherein, P load,t 、P G,t And
Figure SMS_32
respectively representing electric load, thermal power generation and photovoltaic power storage and generation.
In the above optical storage polymerization peak shaving method based on the K-means + + algorithm, the implementation of step 4 includes:
step 4.1, comprehensively considering the economy and fluctuation degree of the distributed optical storage, the obtained objective function is as follows:
Figure SMS_33
wherein, C i And C flu Respectively the unit cost of the distributed optical storage and the fluctuation degree thereof.
Step 4.2, constraint conditions:
Figure SMS_34
wherein the content of the first and second substances,
Figure SMS_35
and &>
Figure SMS_36
The maximum value of the reliable up-regulation capacity and the down-regulation capacity of the optical storage cluster are respectively.
Compared with the prior art, the invention has the following beneficial effects:
(1) The invention provides a distributed optical storage-participated aggregation-peak regulation-decomposition peak regulation method, which can reduce the variable dimension and the calculation difficulty of a distributed optical storage-participated aggregated power system aiming at the high-proportion distributed optical storage-combined power system and is beneficial to participated peak regulation optimization of the power system.
(2) The distributed optical storage aggregation peak regulation method based on the K-means + + algorithm can reduce the number of decision variables and effectively reduce the complexity of calculation, and has better economic advantages under the condition of ensuring the peak regulation capability.
Drawings
FIG. 1 is a diagram of a distributed light storage aggregation process according to an embodiment of the present invention;
FIG. 2 is a diagram of distributed optical storage partitions based on spatial geographic location according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a typical power network partitioning according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a light storing peak shaving strategy considering the response time adjustment according to an embodiment of the present invention;
FIG. 5 is a SSE droop graph of the aggregation of distributed optical storage clusters according to an embodiment of the present invention;
FIG. 6 is a diagram of a distributed light storage aggregation according to an embodiment of the present invention;
FIG. 7 is a graph of the participation of a light reservoir group in power system peak shaving according to one embodiment of the present invention;
fig. 8 is a plot of peak shaver for each photo-reservoir group in accordance with one embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The present invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.
In the embodiment, firstly, the aggregation characteristic quantities affecting the distributed light storage peak shaving are extracted and analyzed, the aggregation process is divided into two steps of partitioning and layering according to the priorities of different characteristic quantities, and a light storage aggregation group with similar characteristic quantities and a corresponding aggregation model thereof are obtained by using a K-means + + algorithm. Secondly, the peak regulation cost and the fluctuation cost of the power system are comprehensively considered, meanwhile, a peak regulation model of the power system is established by utilizing the light storage aggregation group, the aggregation peak regulation process of the distributed light storage is realized by combining with an example, and an optimized scheduling result is obtained. And finally, considering both the scheduling economy and the optical storage volatility, and issuing the scheduling result of the optical storage cluster to the individual distributed optical storage system. The embodiment provides a new strategy for solving the problem that the high-proportion distributed optical storage participates in peak regulation, a distributed optical storage aggregation peak regulation model is built, a large amount of distributed optical storage is aggregated through similar peak regulation characteristic quantities, the variable dimension and the solving difficulty are reduced, and the distributed optical storage aggregation peak regulation model is easier to participate in peak regulation optimization of a power system.
According to the method, the specific aggregation characteristic quantity of the high-proportion distributed optical storage participating in peak shaving is analyzed firstly, the distributed optical storage is aggregated into different characteristic clusters, the peak shaving optimization of the power system is carried out on the basis, the task decomposition is carried out on the peak shaving result, and the problem of optimization solving of a large number of distributed optical storage participating in the peak shaving of the power system is solved effectively.
The following technical scheme is adopted in the embodiment: a light storage aggregation peak regulation method based on a K-means + + algorithm comprises the following steps:
s1, extracting and analyzing aggregation characteristic quantities influencing distributed optical storage peak shaving, and dividing an aggregation process into two steps of partitioning and layering according to priorities of different characteristic quantities;
s1.1, performing primary aggregation based on the spatial geographic position, and partitioning a large number of distributed optical storage based on the position of a bus connected with the distributed optical storage;
s1.2, performing secondary polymerization based on the light storage peak regulation characteristics, including light storage peak regulation capacity, cost and response time, and realizing layered processing of a distributed light storage cluster;
s2, obtaining a light storage polymerization group with similar characteristic quantity and a corresponding polymerization model thereof by using a K-means + + algorithm;
a clustering frequency selection method and an initial clustering center selection method of an S2.1K-mean + + algorithm;
s2.2, based on a K-means + + aggregation algorithm, aggregating a large number of distributed optical storage resources into K types, and respectively establishing optical storage aggregation models for the K types;
s3, establishing a peak regulation model of the power system by using the light storage aggregation group, realizing an aggregation peak regulation process of distributed light storage, and obtaining an optimized scheduling result;
s3.1, establishing a target function of the distributed optical storage participating in peak shaving optimization of the power system;
s3.2, establishing constraint conditions of the distributed optical storage participating in peak shaving optimization of the power system, wherein the constraint conditions comprise equipment model constraint and power balance constraint;
and S4, considering both the scheduling economy and the optical storage volatility, and issuing the scheduling result of the optical storage cluster to the individual distributed optical storage system.
S4.1, establishing an objective function considering economy and light storage volatility, and establishing a light storage group task decomposition model by combining the peak regulation characteristics of distributed light storage.
And in S2.2, the peak shaving characteristics of each aggregation cluster center represent the peak shaving characteristics of the aggregated optical storage cluster, including the peak shaving capacity of the optical storage cluster, the peak shaving speed of the optical storage cluster, and the peak shaving cost of the optical storage cluster.
In S3.1, the objective function of the distributed optical storage participating in the peak regulation optimization of the power system comprises peak regulation cost of the optical storage, fluctuation cost of net load, environmental pollution cost, and power generation cost of the optical storage and the thermal power generating unit.
In specific implementation, in the first step, the aggregation characteristic quantity influencing the distributed optical storage peak regulation is extracted and analyzed, and the aggregation process is divided into two steps of zoning and layering according to the priorities of different characteristic quantities.
With the fact that the power consumption cost of optical storage power generation in the power system is smaller and smaller, the grid-connected proportion of the optical storage power station is higher and higher, the peak shaving difficulty of the power system can be increased when the high-proportion distributed optical storage is connected into a power grid, but the optical storage system has the adjusting capacity, and needs to be considered as an adjusting main body to participate in the peak shaving of the power system. However, the number of distributed optical storage is large, the capacity is small, and if the distributed optical storage directly participates in the optimal scheduling of the power system, problems of high solving difficulty, decision variable dimension explosion and the like are caused, so that aggregation needs to be performed on the distributed optical storage firstly on the aspect that high-proportion optical storage participates in peak shaving. In this embodiment, the polymerization process is divided into two steps based on different polymerization characteristic quantities, the two steps are performed after partitioning and layering, the specific polymerization process is shown in fig. 1, and the distributed light storage polymerization indexes are as follows:
1. spatial geographical position
For distributed light storage aggregation, the spatial geographical location is an important aggregation feature quantity. The space geographic positions are close, real-time data uploading and dispatching command issuing are facilitated, uniform information acquisition and task allocation are facilitated for a power dispatching center, the illumination intensity of distributed light storage with the close geographic positions is similar to the photovoltaic power waveform, and uniform optimized dispatching of a power system is facilitated after aggregation. As shown in fig. 2, the preliminary aggregation is performed based on the spatial geographic location, and a large number of distributed optical storage devices are partitioned based on the location of the bus connected thereto, and a typical power network is partitioned as shown in fig. 3. And the partitions are carried out based on the spatial geographic positions, so that the dispatching center can conveniently carry out uniform calculation and issue dispatching commands.
2. Distributed optical storage peak shaving capacity
For different distributed light storage, the reliable peak regulation capacity is related to the energy storage capacity and the maximum light abandonment amount. For an optical storage system, the maximum up-regulation capacity depends on the energy storage capacity, and the maximum down-regulation capacity depends on the energy storage capacity and the maximum allowable light rejection, see equation (1).
Figure SMS_37
Wherein
Figure SMS_38
Is an adjustable up-regulation capacity of the light store, including the discharge capacity of the light store>
Figure SMS_39
Is an adjustable turndown capacity of the light store, including a charging capacity of the light store->
Figure SMS_40
And a maximum permissible quantity of light discarded>
Figure SMS_41
The provision of the adjustable volume should ensure that the user demand and the loss itself are minimal.
3. Distributed optical storage peak shaving cost
The peak regulation cost of different distributed optical storage systems is different due to different factors such as individual income, initial investment, electricity consumption cost, peak regulation capacity, speed and the like of each distributed optical storage system, and each distributed optical storage is used as an independent benefit main body and can adjust the peak regulation quotation according to the peak regulation cost. The main impact on the peak shaving quoted price of light stores is its cost of electricity consumption.
The cost of electricity consumption is the cost of generating electricity per unit of the generator set considering all the costs since its investment and a certain internal discount rate. Because the capacity and investment cost of a single distributed optical storage are different, the power consumption cost is generally different, and the optical storage power consumption cost has a larger decision influence on the participation of the optical storage power consumption cost in peak shaving. After the energy storage is added to the photovoltaic, the cost increment brought by the energy storage needs to be considered. The total cost comprises initial investment cost and operation period cost, and the total cost calculation formula is as follows:
Figure SMS_42
in which I pv-es The initial investment cost of the light storage construction can be divided into the initial investment cost I of the photovoltaic system pv Energy storage construction investment cost I bess And construction cost I b . The light storage operation cost comprises photovoltaic cell cleaning management cost O t Maintenance operation cost V of energy storage system t 。
4. Distributed optical storage peak shaving response time
The active power regulation speed difference of inverters of different manufacturers is large, and the active power regulation capacity and the regulation efficiency of the optical storage power station are affected, so that the economy of the photovoltaic power station and the stability of a power system are affected, therefore, in the aggregation process of a large number of distributed optical storages, the regulation response time of the active power needs to be taken as one of characteristic quantities of the photovoltaic power station, and the optical storage group with the response time larger than the reliable value Tc is taken as a decision variable to be finally considered, so that the reliability of peak regulation and the stability of the power system are enhanced to a certain extent, as shown in fig. 4.
Secondly, obtaining a light storage polymerization group with similar characteristic quantity and a corresponding polymerization model thereof by using a K-means + + algorithm;
1.K-means + + aggregation algorithm
1) And (5) index quantification. Because the size difference of each index data is far, normalization processing is required. Meanwhile, according to different index properties, the index is divided into a positive index and a negative index which are respectively shown as formulas (3-4). Wherein the larger the positive index represents, the better, and the smaller the inverse index represents, the better.
Figure SMS_43
Figure SMS_44
In the formula (I), the compound is shown in the specification,
Figure SMS_45
respectively representing the positive index and the inverse index after quantization; x ij Indicating index data before quantization; min i X ij 、max i X ij The minimum and maximum values of the index data are expressed. />
2) The number of clusters K is determined. The elbow method is a simple and effective method for determining the clustering number K. The core index is the sum of the squared errors:
Figure SMS_46
where p denotes all points in each cluster, m i Representing the polymeric center of each class.
With the increase of K, the aggregation degree of each aggregation cluster is gradually increased, the sum of squares of errors is gradually reduced, when K reaches the real aggregation number, the return of the aggregation degree is sharply reduced, the maximum value of the decrease of an SSE is corresponded, and the corresponding number can be determined as the size of the aggregation number K, similar to the elbow of a SSE-K relation graph.
3) And selecting an initial clustering center. The K-means + + algorithm is improved on the basis of the K-means algorithm, and the aggregation center with longer relative distance is obtained by setting the probability that different positions are used as the aggregation center, so that a better aggregation effect is achieved. The method comprises the following specific steps:
i: randomly selecting a point as an initial aggregation center;
ii: for each point in the sample, its euclidean distance D iii from the nearest initial aggregation center is calculated: increasing the probability that the point with the farthest distance is used as the next aggregation center;
iv: repeating steps ii and iii until K initial polymerization centers are selected.
2. Light storage polymerization model based on K-means + + algorithm
Based on a K-means + + aggregation algorithm, a large number of distributed optical storage resources can be aggregated into K classes, and an optical storage aggregation model is established for each class, wherein the optical storage aggregation model comprises the peak regulation capacity, speed and cost of the aggregated optical storage cluster. The aggregation treatment greatly reduces the number of decision variables, and is beneficial to the peak shaving of the high-proportion distributed optical storage participating power system.
Light reservoir group aggregate capacity and climbing constraint:
Figure SMS_47
Figure SMS_48
wherein
Figure SMS_49
Is an adjustable up-regulation capacity of the light store, including the discharge capacity of the light store>
Figure SMS_50
Is an adjustable turndown capacity of a light store, comprising a charging capacity of the light store>
Figure SMS_51
And a maximum allowed amount of light discard->
Figure SMS_52
Indicating the maximum ramp for light storage adjustment.
Peak shaving cost and conditioning response time of the aggregated optical storage clusters:
Figure SMS_53
Figure SMS_54
wherein C m And t m The peak shaving cost and the conditioning response time of each optical storage cluster after aggregation.
And thirdly, establishing a peak regulation model of the power system by using the light storage aggregation group to realize the aggregation peak regulation process of the distributed light storage and obtain an optimized scheduling result.
1. Objective function
Comprehensively considering the peak regulation cost and the fluctuation cost of the power system, establishing an objective function of the distributed optical storage participating in peak regulation optimization of the power system:
Figure SMS_55
wherein F 1 ，F 2 ，F 3 ，F 4 ，F 5 Respectively, the peak regulation cost of the light storage, the fluctuation cost of the net load, the environmental pollution cost, the power generation cost of the light storage and the thermal power generating unit, C m ，C flu ，C poll ，C pv ，C G Is the corresponding unit costAnd (4) the coefficient.
2. Constraint conditions
1) Light storage system constraints
Figure SMS_56
Wherein m =1,2.. K,
Figure SMS_57
and &>
Figure SMS_58
A maximum of a reliable capacity up-regulation and a capacity down-regulation of the light reservoir group, respectively>
Figure SMS_59
Is the maximum climbing rate of the light storage system, eta is the energy storage charging and discharging efficiency, and->
Figure SMS_60
In order to store the current SOC state,
Figure SMS_61
the energy storage charging and discharging power.
2) Thermal generator set constraint
Figure SMS_62
Wherein
Figure SMS_63
And &>
Figure SMS_64
The output power is the upper and lower limits of the thermal power generating set after starting. />
Figure SMS_65
Is the maximum climbing constraint of the thermal power generating unit.
3) Energy balance constraint
In the energy balance constraint, the system output power should be balanced with the load absorption power.
Figure SMS_66
/>
Wherein P is load,t 、P G,t And
Figure SMS_67
respectively representing electric load, thermal power generation and light storage power generation.
And fourthly, considering both the scheduling economy and the optical storage volatility, and issuing the scheduling result of the optical storage cluster to the distributed optical storage system individuals.
In the step, under the condition of combining self-constraint and peak regulation characteristics of each distributed light reservoir, a light reservoir group task decomposition model is established by considering economy and volatility.
Comprehensively considering the economy and fluctuation degree of the distributed optical storage, the obtained objective function is as follows:
Figure SMS_68
wherein C is i And C flu Respectively the unit cost of the distributed optical storage and the fluctuation degree thereof.
Constraint conditions are as follows:
Figure SMS_69
wherein
Figure SMS_70
And &>
Figure SMS_71
The maximum value of the reliable up-regulation capacity and the down-regulation capacity of the optical storage cluster are respectively.
The feasibility and the economy of the process of this example are demonstrated below.
Examples
And (4) carrying out light storage polymerization peak regulation based on a K-means + + algorithm.
Taking a regional power grid as an example, the power grid is merged into an optical storage yard group with the total installed capacity of 1200MW, and the optical storage yard group comprises 200 distributed optical storage power stations, and parameters of the optical storage power stations are shown in table 1.
Table 1 optical storage station parameters.
Figure SMS_72
High-proportion distributed light storage polymerization:
aiming at the high-proportion distributed optical storage access power grid, the distributed optical storage aggregation model is established based on the K-means + + algorithm. Within each partition, the selected aggregate characteristic quantities include peak shaving capacity, peak shaving cost, and conditioning response time.
And analyzing the number of the optimal cluster clusters by using an inflection point method. As shown in fig. 5, when the number of clusters is selected as 4, the slope of the SSE decreasing curve changes most, and the return due to the increase of the aggregation level decreases rapidly, so that the optimal aggregation effect can be achieved when the number of clusters is 4.
All distributed optical storage is aggregated into 4 types by using a K-means + + algorithm, and the specific aggregation process is shown in FIG. 6. In the embodiment, the peak shaving cost corresponding to the aggregation center of each cluster is used as the peak shaving cost of the whole optical storage cluster, so that the number of decision variables is greatly reduced, and the peak shaving of a high-proportion distributed optical storage power system is facilitated. The peak shaver characteristics of each light stock cluster after polymerization are shown in table 2.
Table 2 light reservoir group peak shaver characteristics.
Figure SMS_73
Figure SMS_74
The light reservoir group participates in power system peak regulation:
in combination with the above, the high-proportion distributed optical storage can be aggregated into 4 types, and based on the peak shaving characteristics of each optical storage cluster, the fluctuation cost of the net load curve is combined, so that the optical storage clusters participate in the peak shaving of the power system, and the purpose of stabilizing the fluctuation of the load curve is achieved, as shown in fig. 7.
As shown in fig. 7, when the photovoltaic output is 0, the energy storage stabilizes the random fluctuation of the net load curve through charging and discharging, in the range of 0; 8-00-11, the load is applied to a first peak of power utilization, at the moment, the light storage output is increased along with the increase of the illumination intensity, and meanwhile, the light storage system is properly increased in output under the maximum constraint condition, namely, peak clipping is realized through energy storage and discharge; 15-16, considering the electricity utilization law of a user, a net load curve comes to a valley, and the illumination intensity is highest at the moment, the light storage needs to reduce the output properly, namely, the fluctuation of the load curve is restrained by a method of energy storage charging or light abandoning, and valley filling is realized; 16-20, load meets a second peak of power utilization, but the light storage output level is lower at the moment, the output of the thermal power generating unit needs to be increased as much as possible through energy storage and discharge, and the peak regulation pressure of the thermal power generating unit is reduced on the premise of meeting the load demand, so that the peak clipping is realized again; 20-00-24, similar to the first 8 hours, the requirement of the net load curve for its surge suppression is met by the energy storage discharge.
The maximum spatial limit which can be absorbed in the light storage valley period obtained by adopting the peak regulation optimization model is 341.8MW & h, the maximum up-regulation peak regulation capacity is 58.6MW, the maximum down-regulation peak regulation capacity is 95.1MW, and the maximum peak-valley difference of the system is reduced from 884.5MW to 780.7MW after peak regulation. Therefore, after the distributed optical storage participates in peak shaving, the fluctuation of the net load curve is effectively stabilized, and the peak shaving pressure of the thermal power generating unit is relieved. The specific peak shaver for each light reservoir group is shown in fig. 7.
As can be seen from fig. 8, since the peak shaving cost of the optical storage cluster 1 is low, the peak shaving amount is the largest, and the maximum peak shaving capability is up to 29.1 MW, and then the optical storage cluster 2 and the optical storage cluster 3, the adjustment response time of the optical storage cluster 4 is long, and the reliability is poor, the optical storage cluster is finally adjusted as a backup peak shaving cluster in necessary cases, and the maximum peak shaving amount is only 12.5MW.
And finally, performing task decomposition inside each light storage cluster based on the research content, selecting the peak regulation cost and the fluctuation cost of each distributed light storage inside each light storage cluster as decomposition standards, and reasonably issuing the scheduling task to each distributed light storage by taking the individual peak regulation capacity and the peak regulation speed as constraints.
And (3) comparing and analyzing the economy and the peak regulation capacity:
the photovoltaic power station mostly adopts an average distribution Algorithm (AVE) to adjust power, and then the research method is compared with the average distribution method to verify the feasibility of the method on economy and peak regulation capacity. As shown in table 3:
TABLE 3 comparison of economics and Peak shaving Capacity
Figure SMS_75
As can be seen from table 3, if the average distribution algorithm is adopted, the total peak-shaving cost is 81227.4 yuan, the maximum peak-shaving capacity is 90.3MW, the maximum peak-shaving capacity is 50.5MW, and the maximum peak-valley difference is 793.6MW; if a polymerization peak regulation mode is adopted, the total peak regulation cost is 77742.8 yuan, 3484.6 yuan is saved, meanwhile, the maximum down-regulation peak regulation capacity is 95.1MW, the maximum up-regulation peak regulation capacity is 58.6MW, and the maximum peak-valley difference is 780.7MW.
Therefore, the distributed light storage aggregation peak regulation method can reduce the number of decision variables, effectively reduce the complexity of calculation and has better economic advantages under the condition of ensuring the peak regulation capability.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.

Claims (4)

1. A light storage polymerization peak regulation method based on a K-means + + algorithm is characterized in that: the method comprises the following steps:
step 1, extracting and analyzing aggregation characteristic quantity influencing distributed light storage peak shaving, and dividing an aggregation process into two steps of partitioning and layering according to priorities of different characteristic quantities;
step 2, obtaining a light storage aggregation group with similar characteristic quantities and a corresponding aggregation model thereof by using a K-means + + algorithm;
step 3, establishing a peak shaving model of the power system by using the light storage aggregation group to realize the aggregation peak shaving process of the distributed light storage and obtain an optimized scheduling result;
step 4, considering both the scheduling economy and the optical storage volatility, and issuing the scheduling result of the optical storage cluster to the individual distributed optical storage system;
the implementation of step 1 comprises:
step 1.1, performing preliminary aggregation according to the spatial geographic position, and partitioning a large number of distributed optical storage based on the position of a bus connected with the distributed optical storage;
step 1.2, performing secondary aggregation according to the light storage peak regulation characteristics, including distributed light storage peak regulation capacity, distributed light storage peak regulation cost and response time, and realizing layered processing of a distributed light storage cluster;
step 1.2.1, a distributed light storage peak regulation capacity formula:
Figure FDA0004072123810000011
wherein the content of the first and second substances,
Figure FDA0004072123810000012
is an adjustable up-regulation capacity of the light store, including the discharge capacity of the light store>
Figure FDA0004072123810000013
Figure FDA0004072123810000014
Is an adjustable turndown capacity of the light store, including a charging capacity of the light store->
Figure FDA0004072123810000015
And a maximum allowed amount of light discard->
Figure FDA0004072123810000016
Step 1.2.2, distributed light storage peak regulation cost; the total cost comprises initial investment cost and operation period cost, and the calculation formula is as follows:
Figure FDA0004072123810000017
wherein, I pv-es Dividing the initial investment cost of the light storage construction into the initial investment cost I of the photovoltaic system pv Energy storage construction investment cost I bess And construction cost I b (ii) a The light storage operation cost comprises photovoltaic cell cleaning management cost O t Maintenance operation cost V of energy storage system t ；
Step 1.2.3, distributed optical storage peak shaving response time; setting different light storage clusters to adjust response time t, taking the light storage clusters with the response time t larger than the reliable value Tc as reliable peak regulation clusters to preferentially participate in peak regulation, and if the peak regulation capacity does not meet the peak regulation requirement, using the standby clusters to participate in peak regulation.
2. The K-means + + algorithm-based optical storage aggregation peak shaving method according to claim 1, wherein: the implementation of the step 2 comprises the following steps:
step 2.1, K-means + + polymerization algorithm;
step 2.1.1, index quantification; firstly, normalizing index data; dividing the indexes into positive indexes and negative indexes according to different index properties, wherein the positive indexes represent the larger indexes, the better indexes are, and the negative indexes represent the smaller indexes, the better indexes are;
Figure FDA0004072123810000021
/>
Figure FDA0004072123810000022
in the formula (I), the compound is shown in the specification,
Figure FDA0004072123810000023
respectively representing the quantized positive index and the quantized inverse index; x ij Indicating index data before quantization; min i X ij 、max i X ij Minimum and maximum values representing index data;
step 2.1.2, determining a clustering number K; and (3) determining a clustering number K by adopting an elbow method, wherein the core index is the sum of squares of errors:
Figure FDA0004072123810000024
where p denotes all points in each cluster, m i Represents the polymerization center of each class;
with the increase of K, the aggregation degree of each aggregation cluster is gradually improved, the sum of squared errors is gradually reduced, when K reaches the real aggregation number, the return of the aggregation degree is sharply reduced, the maximum value of the decrease of one corresponding SSE is similar to the elbow of a SSE-K relation diagram, and the corresponding number is determined as the size of the aggregation number K;
step 2.1.3, selecting an initial clustering center; the method comprises the following specific steps:
step 2.1.3.1, randomly selecting a point as an initial polymerization center;
step 2.1.3.2, calculating the Euclidean distance D between each point in the sample and the nearest initial aggregation center;
step 2.1.3.3, increasing the probability that the point with the farthest distance is used as the next aggregation center;
step 2.1.3.4, repeating step 2.1.3.2 and step 2.1.3.3 until K initial polymerization centers are selected;
2.2, a light storage polymerization model based on a K-means + + algorithm; the peak shaving capacity, speed and cost of the aggregated optical storage cluster are included;
light reservoir group aggregate capacity and climbing constraint:
Figure FDA0004072123810000025
Figure FDA0004072123810000026
wherein the content of the first and second substances,
Figure FDA0004072123810000031
is the adjustable up-regulation capacity of the light storage cluster, including the discharge capacity of the light storage>
Figure FDA0004072123810000032
Figure FDA0004072123810000033
Is an adjustable turndown capacity of a light store cluster, comprising a charging capacity of the light store->
Figure FDA0004072123810000034
And a maximum allowed amount of light discard->
Figure FDA0004072123810000035
Figure FDA0004072123810000036
Represents a maximum hill climb for light storage regulation>
Figure FDA0004072123810000037
Light stores and generates electricity;
peak shaving cost and conditioning response time of the aggregated optical storage clusters:
Figure FDA0004072123810000038
Figure FDA0004072123810000039
wherein, C m And t m The peak shaving cost and the conditioning response time of each light storage cluster after polymerization.
3. The K-means + + algorithm-based optical storage aggregation peak shaving method according to claim 1, wherein: the implementation of the step 3 comprises the following steps:
step 3.1, an objective function;
comprehensively considering the peak regulation cost and the fluctuation cost of the power system, establishing an objective function of the distributed optical storage participating in peak regulation optimization of the power system:
Figure FDA00040721238100000310
wherein, F 1 ，F 2 ，F 3 ，F 4 ，F 5 Respectively, the peak regulation cost of the light storage, the fluctuation cost of the net load, the environmental pollution cost, the power generation cost of the light storage and the thermal power generating unit, C m ，C flu ，C poll ，C pv ，C G Is the corresponding unit cost coefficient;
step 3.2, constraint conditions;
step 3.2.1, optical storage system constraint:
Figure FDA0004072123810000041
wherein m =1,2.. K,
Figure FDA0004072123810000042
and &>
Figure FDA0004072123810000043
The maximum value of the reliable up-regulation capacity and the down-regulation capacity of the light reservoir group respectively,
Figure FDA0004072123810000044
is the maximum climbing rate of the light storage system, eta is the energy storage charging and discharging efficiency, and->
Figure FDA0004072123810000045
For storing the current SOC state>
Figure FDA0004072123810000046
The energy storage charging and discharging power;
step 3.2.2, constraining the thermal generator set;
Figure FDA0004072123810000047
wherein the content of the first and second substances,
Figure FDA0004072123810000048
and &>
Figure FDA0004072123810000049
The output limits are the upper and lower limits after the thermal power generating set is started; />
Figure FDA00040721238100000410
Is the maximum climbing constraint of the thermal power generating unit;
step 3.2.3, energy balance constraint;
Figure FDA00040721238100000411
wherein, P load,t 、P G,t And
Figure FDA00040721238100000412
respectively representing electric load, thermal power generation and light storage power generation.
4. The K-means + + algorithm-based optical storage aggregation peak shaving method according to claim 1, wherein: the implementation of the step 4 comprises the following steps:
step 4.1, comprehensively considering the economy and fluctuation degree of the distributed optical storage, the obtained objective function is as follows:
Figure FDA00040721238100000413
wherein, C i And C flu Respectively unit cost of distributed optical storage and fluctuation degree thereof;
step 4.2, constraint conditions:
Figure FDA00040721238100000414
wherein the content of the first and second substances,
Figure FDA00040721238100000415
and &>
Figure FDA00040721238100000416
The maximum value of the reliable up-regulation volume and the down-regulation volume of the light storage cluster, respectively>
Figure FDA00040721238100000417
Represents a maximum hill climb for light storage conditioning>
Figure FDA00040721238100000418
And (4) light storage and power generation. />
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