CN113361117B - Distributed energy storage polymerization degree evaluation index model based on polymerization condition - Google Patents

Distributed energy storage polymerization degree evaluation index model based on polymerization condition Download PDF

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CN113361117B
CN113361117B CN202110667986.8A CN202110667986A CN113361117B CN 113361117 B CN113361117 B CN 113361117B CN 202110667986 A CN202110667986 A CN 202110667986A CN 113361117 B CN113361117 B CN 113361117B
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叶鹏
刘思奇
关多娇
屈科明
杨硕
王士元
王枫淇
李天岳
魏靖晓
张政斌
杨宏宇
王子赫
邵旸棣
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Abstract

The invention relates to a distributed energy storage polymerization evaluation model, in particular to a distributed energy storage polymerization degree evaluation index model based on polymerization conditions. The method provides technical basis and practical method for the aggregation of distributed energy storage which participates in auxiliary service in a large scale. The method comprises the following steps: building a structural framework for participating in polymerization of distributed energy storage under the polymerization condition; acquiring operation parameters of each part of the energy storage monomer based on the established frame; and setting the output/input power of each energy storage monomer based on the acquired operation parameters of each part of the energy storage monomer. Establishing polymerization degree indexes of each energy storage monomer: calculating the energy storage polymerization degree of the energy storage monomer according to the three extracted polymerization degree index data, and calculating 40 energy storage polymerization degree data: and establishing a distributed energy storage polymerization degree evaluation index model based on the polymerization condition. And carrying out example result analysis on the effectiveness of the distributed energy storage polymerization degree evaluation index model.

Description

Distributed energy storage polymerization degree evaluation index model based on polymerization condition
Technical Field
The invention relates to a distributed energy storage polymerization evaluation model, in particular to a distributed energy storage polymerization degree evaluation index model based on polymerization conditions.
Background
The distributed energy storage power and capacity are small, the access position is flexible, the distributed energy storage power and capacity is usually concentrated on a user side, a distributed micro-grid and a medium-low voltage distribution network, and the distributed micro-grid can be used for peak shaving, frequency modulation and power quality and reliability of power supply of the grid. However, the distributed energy storage is widely dispersed and difficult to uniformly control, and the waste of energy storage resources is easily caused. And the large-scale distributed energy storage participation area electric auxiliary service compensation (market) work is advanced in various places, and the convergence, integration and aggregation of energy storage resources become important actions of the power grid for effectively regulating and controlling energy storage objects.
At present, research on distributed energy storage polymerization at home and abroad mainly has two aspects of emphasis: the energy storage with similar regulation potential is aggregated into groups or the relative equalization of the charge states of the energy storage is realized. The expert focuses on aggregation grouping, firstly, energy storage is clustered or evaluated and grouped through static indexes of energy storage operation characteristics based on an improved K-means algorithm and a panoramic theory, and then an aggregation potential regulation model of the grouped energy storage is constructed; the expert focuses on realizing equalization, firstly setting energy storage parameters and charge-discharge equalization functions, and then integrating all energy storage into a whole based on an aggregation effect and a complete distributed control algorithm, and carrying out optimization regulation and control. However, the above-mentioned studies are limited to a single aspect and have poor effects.
Disclosure of Invention
The invention provides a distributed energy storage polymerization evaluation model aiming at the defects in the prior art, and particularly relates to a distributed energy storage polymerization degree evaluation index model based on polymerization conditions. Aiming at the problems of wide-area distribution of distributed energy storage, dispersed resources, incapability of efficient polymerization and the like, the method can further realize the polymerization of the energy storage monomers to the energy storage polymer through smaller in-vivo variability and higher in-vivo polymerization degree, and provides technical basis and practical method for the polymerization of the distributed energy storage which participates in auxiliary service on a large scale.
In order to achieve the above purpose, the invention adopts the following technical scheme that the method comprises the following steps:
building a structural framework for participating in polymerization of distributed energy storage under the polymerization condition;
acquiring operation parameters of each part of the energy storage monomer based on the established frame;
setting the output/input power of each energy storage monomer i relative to the instant state of charge s based on the acquired operation parameters of each part of the energy storage monomers i Is a function of the charge and discharge p(s) i ) c 、p(s i ) d The following are provided:
Figure BDA0003117681720000021
Figure BDA0003117681720000022
wherein: p(s) i ) c 、p(s i ) d The input/output power for each energy storage cell i is related to the instantaneous state of charge s i Is a charge-discharge function of (2); c (C) i The rated capacity of the energy storage monomer i; c (C) ∑ The total rated volume of all energy storage monomers entering the regulating layer is calculated; p (P) i·rated Rated power of the energy storage monomer i; a is that i Is an adaptive factor thereof; a is that i0 Adaptive prime factors for the same; d, d i Self-assigning coefficients thereto; s is(s) i Is an immediate state of charge; s is(s) shc =0.4 is the shallow charge point; s is(s) shd =0.6 is the shallow charge point; s is(s) max =0.9 is the maximum charge point; s is(s) min =0.1 is the minimum charge point; s is(s) mid =0.5 is the charge midpoint;
establishing polymerization degree indexes of each energy storage monomer i: degree of stored energy power regulation P i The method comprises the steps of carrying out a first treatment on the surface of the Energy storage adaptive equalization degree P i * The method comprises the steps of carrying out a first treatment on the surface of the Degree of contribution R of energy storage capacity dev ；
Calculating the energy storage polymerization degree A of the energy storage monomer i according to the extracted three polymerization degree index data i 40 energy storage polymerization degrees A are calculated i Data:
A i ＝μ 1 ·P i +μ 2 ·P i *+μ 3 ·R dev
wherein: mu (mu) x Weights representing polymerization degree indexes of three energy storage monomers, and selecting mu according to a 9-level scale method 1 ＝0.5；μ 2 ＝0.3；μ 3 ＝0.2；
And establishing a distributed energy storage polymerization degree evaluation index model based on the polymerization condition.
Further, the effectiveness of the distributed energy storage polymerization degree evaluation index model is subjected to example result analysis, and the distributed energy storage polymerization degree evaluation index model based on the polymerization condition is verified to be capable of achieving efficient polymerization of energy storage resources.
Further, the operation parameters comprise rated capacity C of each energy storage monomer i The method comprises the steps of carrying out a first treatment on the surface of the Rated power P i·rated The method comprises the steps of carrying out a first treatment on the surface of the Adaptive factor A i The method comprises the steps of carrying out a first treatment on the surface of the Adaptive elementary factor A i0 The method comprises the steps of carrying out a first treatment on the surface of the Self-distribution coefficient d i The method comprises the steps of carrying out a first treatment on the surface of the Immediate state of charge s i The method comprises the steps of carrying out a first treatment on the surface of the Immediate zone control deviation a i 。
Further, the building of the structural framework for participating in aggregation of the distributed energy storage under the aggregation condition comprises the following steps:
establishing an upper-layer and lower-layer distributed energy storage polymerization framework: the upper layer is a polymerization layer, and the lower layer is a response layer;
upper polymeric layer: based on a distributed energy storage polymerization degree evaluation index model under a polymerization condition, the method is used for polymerizing energy storage monomers with similar polymerization degrees into an energy storage polymer; to achieve efficient aggregation of energy storage resources.
The lower layer is a response layer and is used for the energy storage polymer to complete the demand response issued by the power grid.
Further, the established stored energy power adjustment degree P i The characterization in the index of polymerization degree is: during the operation of the energy storage polymer auxiliary power grid, along with the continuous change of the adjustment degree, the adjustment capability of the output/input power of each energy storage monomer in the polymer body; p (P) i The calculation method comprises the following steps:
Figure BDA0003117681720000031
Figure BDA0003117681720000032
wherein: p (a) i ) c 、p(a i ) d The instantaneous area control deviation a of the energy storage release/absorption power for the area requirement of the individual energy storage cells i during the control process i Is used for self-adaptive equalization charge-discharge function; p (P) i·rated Rated power of the energy storage monomer i; a is that i Is an adaptive factor thereof; a is that i0 Adaptive prime factors for the same; d, d i Self-assigning coefficients thereto; a, a i Controlling deviation for the immediate area; a, a max =0.9 is the emergency action point; a, a min =0.1 is the minimum action point; a, a mid =0.5 is the normal action point;
Figure BDA0003117681720000041
/>
Figure BDA0003117681720000042
wherein: p'(s) i ) c 、p′(s i ) d The input/output power of each energy storage monomer i after removing the capacity factor is related to the instant state of charge s i Is a charge-discharge function of (2); p(s) i ) c 、p(s i ) d The input/output power for each energy storage cell i is related to the instantaneous state of charge s i Is a charge-discharge function of (2); c (C) i The rated capacity of the energy storage monomer i; c (C) ∑ The total rated volume of all energy storage monomers entering the regulating layer is calculated; p (P) i The energy storage power adjustment degree; p (a) i ) c 、p(a i ) d The instantaneous area control deviation a of the energy storage release/absorption power for the area requirement of the individual energy storage cells i during the control process i Is used for self-adaptive equalization charge-discharge function; s is(s) i Is an immediate state of charge; s is(s) mid =0.5 is the charge midpoint.
Further, the energy storage self-adaptive balance degree established is characterized in the polymerization degree index as follows: during the operation of the energy storage polymer and the power grid, along with the continuous change of the adjustment degree, the self-adaptive equalization capability of each energy storage monomer in the polymer body; which can be regulated by the energy storage power of the energy storage monomer i i A per-unit representation based on an adaptive equalization technique; self-adaptive equilibrium degree P of energy storage monomer i i * Can be expressed as:
Figure BDA0003117681720000043
wherein: p (P) i * Is self-adaptive equilibrium degree; p (P) i The energy storage power adjustment degree; p (P) B Is an auto-allocation coefficient d=15; elementary adaptive factor A 0 =0.01; adaptive factor a=10·a 0 /P rated Degree of stored energy power adjustment P i 。
Further, the established energy storage capacity contribution degree is characterized in the polymerization degree index as follows: during the operation of the energy storage polymer and the power grid, the capacity contribution capability of each energy storage monomer in the polymer body is changed along with the continuous change of the required capacity; therefore, the energy storage monomer i capacity contribution degree R dev Expressed as:
Figure BDA0003117681720000051
Figure BDA0003117681720000052
wherein: c (C) c A schedulable capacity that varies with stored energy output; p (P) t (s i ) The input/output power for each energy storage cell i is related to the instantaneous state of charge s i Is a function of (2); t is a scheduling period; r is R dev A contribution to capacity; c (C) d The capacity is required for the system;
after the polymerization degree index of each energy storage monomer i is established, the three polymerization degree indexes are simulated by the energy storage monomers configured in a typical industrial park, and data are extracted.
Further, the establishing the distributed energy storage polymerization degree evaluation index model based on the polymerization condition comprises the following steps: 40 energy storage polymerization degree data A obtained by calculation i As cloud droplet variable in distributed energy storage polymerization degree evaluation index model, A i The following distribution is satisfied:
Figure BDA0003117681720000053
cloud expectation curve y (A i ) Describing geometric form of distributed energy storage polymerization degree evaluation index model, y (A i ) The expression is as follows:
Figure BDA0003117681720000054
the quantitative data y (A) i ) Digital characteristic C (E Ai ,En,He)，(E Ai En, he) is expressed as follows:
Figure BDA0003117681720000055
Figure BDA0003117681720000056
Figure BDA0003117681720000057
wherein E is Ai Representing the expectation of the polymerization degree evaluation index model, wherein En represents the entropy of the polymerization degree evaluation index model, he represents the super entropy of the polymerization degree evaluation index model, n represents the cloud droplet number, and S represents the variance;
digital features C (E Ai En, he) generates n=1500 cloud droplets, and the above steps are repeatedly generated until a sufficient calculation result of the distributed energy storage polymerization degree evaluation index model is generated.
Further, the distributed energy storage device which is taken as an independent object and participates in the regulation of the energy storage polymerizer is called an energy storage monomer; the energy storage monomer completes the aggregation and integration of the self-resource energy under the control of an energy storage polymerizer, which is called polymerization, and a large-scale energy storage monomer object formed by polymerizing the energy storage monomer is called an energy storage polymer.
Further, A i Is an adaptive factor thereof; the value range is between 0.01 and 1, and is related to the type of the energy storage battery, and the nickel-cadmium, all-vanadium liquid flow and lithium battery are 0.01; taking 0.5 of sodium-sulfur and zinc-bromine batteries; taking 1 of a lead-acid battery; a is that i0 For its self-adapting elementary factor A i0 ＝A·P rated /10；d i The self-allocation coefficient is selected, the value range is between 15 and 20, and the value range is related to the type of the energy storage battery, and the sodium-sulfur battery and the lithium battery are 13; taking 15 of an all-vanadium redox flow battery; 18 of nickel-cadmium and zinc-bromine batteries; the lead acid battery was 20.
Further, the energy storage monomers of a typical industrial park configuration are: lead-acid batteries, all-vanadium redox flow batteries, nickel-cadmium batteries, sodium-sulfur batteries, zinc-bromine batteries, and lithium batteries; the configuration parameters are respectively as follows: 4MW/10 MW-h, 50MW/100 MW-h, 8MW/20 MW-h, 25MW/80 MW-h, 15MW/40 MW-h, 30MW/60 MW-h.
Specifically, the polymerization potential among the energy storage monomers is defined as the polymerization degree of the energy storage monomers, and three polymerization degree indexes are set under the energy storage polymerization degree, and are respectively as follows: the energy storage power adjustment degree of the power adjustment factors is calculated; considering the energy storage self-adaptive balance degree of the charge state adjusting factors; and taking into account the energy storage capacity contribution of the capacity adjustment factors.
Specifically, if the results of the distributed energy storage polymerization degree evaluation index model of a certain energy storage monomer are closer, the probability that the energy storage monomers are polymerized into an energy storage polymer is proved to be higher.
Specifically, the energy storage polymerization degree A of the energy storage monomer i is calculated i Three polymerization degree indices P i 、P i *、R dev The data of the (2) is random extraction of the data on the operation curve, and the extracted data can be maximally attached to the dynamic trend of the curve; and calculating corresponding polymerization degree data according to the extracted index data, and pre-generating 40 polymerization degree data for each energy storage monomer.
Compared with the prior art, the invention has the beneficial effects.
The invention is based on the distributed energy storage polymerization degree evaluation index model under the polymerization condition, can balance the difference in the energy storage monomer groups to a greater extent and realize the relative balance among the energy storage monomer groups. The traditional distributed energy storage polymerization evaluation model only considers a single polymerization group or single polymerization regulation and control, and can not effectively combine the two, so that the difference in the energy storage monomer groups and the unbalance among the energy storage monomer groups are caused, and the situation of energy storage resource waste exists. The energy storage monomers with similar polymerization degree are screened out through the model and polymerized into the energy storage polymer, so that the energy storage monomers can be polymerized to the energy storage polymer through smaller in-vivo difference and higher in-vivo polymerization degree, and the energy storage resource can be efficiently utilized.
The method is easy to implement, and based on polymerization conditions, the energy storage polymerization degree is calculated by using three innovative energy storage polymerization degree indexes, so that the polymerization potential of the energy storage monomers can be more intuitively expressed, and the method is easy to implement from the aspect of calculation; meanwhile, the distributed energy storage polymerization degree evaluation index model is adopted for solving, and the calculation result is easy to observe.
The method is convenient for commercialized development, and along with the promotion of large-scale distributed energy storage participation area electric auxiliary service compensation (market) work in various places, the development of the distributed energy storage polymerization degree evaluation index model based on the polymerization condition necessarily has larger requirements, and has better commercial development prospect.
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The invention is further described below with reference to the drawings and the detailed description. The scope of the present invention is not limited to the following description.
Fig. 1 is a structural framework of distributed energy storage participation in aggregation.
FIG. 2 is a flow chart of a distributed energy storage polymerization degree evaluation index model based on polymerization conditions.
Fig. 3 is energy storage monomer parameters for 6 typical industrial park configurations in the Liaoning province.
Fig. 4 is a simulated operating curve of the power regulation of 6 energy storage monomers.
Fig. 5 is a simulated operating curve of the adaptive equalization of 6 energy storage cells.
Fig. 6 is a simulated run curve of the 6 energy storage cell capacity contributions.
FIG. 7 is a simulation result of the distributed energy storage polymerization degree evaluation index model.
Fig. 8 is a 10-time fit residual plot of the simulation evaluation result of the distributed energy storage polymerization degree evaluation index model.
FIGS. 9-15 are data extracted from three polymerization degree index simulation curves of energy storage monomers configured in 6 typical industrial parks in Liaoning province and calculated 40 energy storage polymerization degrees A i Data.
Detailed Description
The invention provides a distributed energy storage polymerization degree evaluation index model based on polymerization conditions. The energy storage monomers with similar polymerization degree are screened out through the model and polymerized into an energy storage polymer, so that the energy storage monomers can be polymerized into the energy storage polymer through smaller in-vivo difference and higher in-vivo polymerization degree, and the energy storage resource can be efficiently utilized. The basic idea is as follows: the polymerization layer is based on a distributed energy storage polymerization degree evaluation index model under the polymerization condition and is used for polymerizing energy storage monomers with similar polymerization degrees into an energy storage polymer. The idea of this layer is: and the energy storage resources are efficiently aggregated. The idea of the overall model is: and the wide-area dispersed energy storage resources are aggregated and regulated, so that the power grid demand and the dispatching are conveniently responded, and a practical reference is provided for the aggregation of the distributed energy storage which participates in the auxiliary service in a large scale.
The specific technical scheme is as follows: based on a polymerization condition framework, three dynamic regulation indexes of energy storage power regulation degree, self-adaptive balance degree and capacity contribution degree are firstly established, and the three dynamic regulation indexes can fully represent the dynamic regulation capability of energy storage monomers when the energy storage monomers participate in regional power grid response so as to realize that the energy storage monomers with similar dynamic regulation capability are grouped into one group. Then, the energy storage polymerization degree is calculated according to the three dynamic regulation indexes, the energy storage polymerization degree is used as input data of a distributed energy storage polymerization degree evaluation index model, and energy storage monomers with similar polymerization degrees are screened out through the model and polymerized into an energy storage polymer.
Based on a polymerization condition framework, three dynamic adjustment indexes of energy storage power adjustment degree, self-adaptive balance degree and capacity contribution degree are firstly established, and the three dynamic adjustment indexes can fully represent the dynamic adjustment capability of energy storage monomers when participating in regional power grid response so as to realize that the energy storage monomers with similar dynamic adjustment capability are grouped. Then, the energy storage polymerization degree is calculated according to the three dynamic regulation indexes, the energy storage polymerization degree is used as input data of a distributed energy storage polymerization degree evaluation index model, and energy storage monomers with similar polymerization degrees are screened out through the model and polymerized into an energy storage polymer.
A distributed energy storage polymerization degree evaluation index model based on polymerization conditions comprises the following steps:
1. a distributed energy storage polymerization degree evaluation index model based on a polymerization condition is characterized in that three dynamic adjustment indexes of energy storage power adjustment degree, self-adaptive equalization degree and capacity contribution degree are firstly established based on a polymerization condition framework. Then, selecting energy storage monomers configured in 6 typical industrial parks in Liaoning province, performing simulation verification on operation curves of polymerization degree indexes of the energy storage monomers configured in 6 typical industrial parks in Liaoning province, randomly extracting operation curves of all indexes as data, and enabling the extracted data to be maximally attached to dynamic trend of the curves. And calculating corresponding polymerization degree data according to the extracted index data, pre-generating 40 polymerization degree data for each energy storage monomer, taking the data as input data of a distributed energy storage polymerization degree evaluation index model, screening the energy storage monomers with similar polymerization degrees through the model, and polymerizing the energy storage monomers into an energy storage polymer. The method is characterized in that: the method comprises the following steps:
step 1), firstly, building a structural framework of the distributed energy storage participating in polymerization under the polymerization condition;
(1) Establishing an upper-layer and lower-layer distributed energy storage polymerization framework: the upper layer is a polymeric layer and the lower layer is a responsive layer.
(2) And the upper polymerization layer is used for polymerizing the energy storage monomers with similar polymerization degrees into an energy storage polymer based on a distributed energy storage polymerization degree evaluation index model under the polymerization condition. The purpose of this layer is: and the energy storage resources are efficiently aggregated.
(3) The lower layer is a response layer which is mainly used for the energy storage polymer to complete the demand response issued by the power grid.
Step 2) based on the established framework, acquiring operation parameters of each part of energy storage monomers configured in 6 typical industrial parks in Liaoning province, wherein the operation parameters comprise: rated capacity C of each energy storage monomer i The method comprises the steps of carrying out a first treatment on the surface of the Rated power P i·rated The method comprises the steps of carrying out a first treatment on the surface of the Adaptive factor A i The method comprises the steps of carrying out a first treatment on the surface of the Adaptive elementary factor A i0 The method comprises the steps of carrying out a first treatment on the surface of the Self-distribution coefficient d i The method comprises the steps of carrying out a first treatment on the surface of the Immediate state of charge s i The method comprises the steps of carrying out a first treatment on the surface of the Immediate zone control deviation a i ；
Step 3) based on the obtained operation parameters, setting the output (input) power of the energy storage monomers configured in 6 typical industrial parks in Liaoning province to the instant state of charge s i Is a function of the charge and discharge p(s) i ) c 、p(s i ) d The following are provided:
Figure BDA0003117681720000101
Figure BDA0003117681720000102
wherein: p(s) i ) c 、p(s i ) d The output (input) power for each energy storage cell i is related to the instantaneous state of charge s i Is a charge-discharge function of (2); c (C) i The rated capacity of the energy storage monomer i; c (C) ∑ The total rated volume of all energy storage monomers entering the regulating layer is calculated; p (P) i·rated Rated power of the energy storage monomer i; a is that i Is an adaptive factor thereof; a is that i0 Adaptive prime factors for the same; d, d i Self-assigning coefficients thereto; s is(s) i Is an immediate state of charge; s is(s) shc =0.4 is the shallow charge point; s is(s) shd =0.6 is the shallow charge point; s is(s) max =0.9 is the maximum charge point; s is(s) min =0.1 is the minimum charge point; s is(s) mid =0.5 is the charge midpoint.
Step 4) based on the set charge-discharge function of the energy storage monomer, establishing polymerization degree indexes of the energy storage monomer configured in 6 typical industrial parks in Liaoning province: degree of stored energy power regulation P i The method comprises the steps of carrying out a first treatment on the surface of the Energy storage adaptive equalization degree P i * The method comprises the steps of carrying out a first treatment on the surface of the Degree of contribution R of energy storage capacity dev 。
(1) Established energy storage power regulation degree P i The characterization in the index of polymerization degree is: during the operation of the auxiliary power grid of the energy storage polymer, along with the continuous change of the adjustment degree, the adjustment capability of the output (input) power of each energy storage monomer in the polymer body. P (P) i The calculation method comprises the following steps:
Figure BDA0003117681720000111
/>
Figure BDA0003117681720000112
wherein: p (a) i ) c 、p(a i ) d Energy storage and release (absorption) power for the area requirement of each energy storage monomer i in the adjustment processControl deviation a with respect to immediate area i Is used for self-adaptive equalization charge-discharge function; p (P) i·rated Rated power of the energy storage monomer i; a is that i Is an adaptive factor thereof; a is that i0 Adaptive prime factors for the same; d, d i Self-assigning coefficients thereto; a, a i Controlling deviation for the immediate area; a, a max =0.9 is the emergency action point; a, a min =0.1 is the minimum action point; a, a mid =0.5 is the normal action point.
Figure BDA0003117681720000113
Figure BDA0003117681720000114
Wherein: p'(s) i ) c 、p′(s i ) d The output (input) power of each energy storage monomer i after removing the capacity factor is related to the instant state of charge s i Is a charge-discharge function of (2); p(s) i ) c 、p(s i ) d The output (input) power for each energy storage cell i is related to the instantaneous state of charge s i Is a charge-discharge function of (2); c (C) i The rated capacity of the energy storage monomer i; c (C) ∑ The total rated volume of all energy storage monomers entering the regulating layer is calculated; p (P) i The energy storage power adjustment degree; p (a) i ) c 、p(a i ) d The instantaneous area control deviation a of the energy storage release (absorption) power for the area demand of the individual energy storage cells i during the regulation process i Is used for self-adaptive equalization charge-discharge function; s is(s) i Is an immediate state of charge; s is(s) mid =0.5 is the charge midpoint.
(2) The energy storage self-adaptive balance degree is characterized in the polymerization degree index as follows: and during the operation of the energy storage polymer and the power grid, along with the continuous change of the adjustment degree, the self-adaptive equalization capability of each energy storage monomer in the polymer body. Which can be regulated by the energy storage power of the energy storage monomer i i Per-unit representation based on adaptive equalization techniques. Self-adaptive equilibrium degree P of energy storage monomer i i * Can be expressed as:
Figure BDA0003117681720000121
wherein: p (P) i * Is self-adaptive equilibrium degree; p (P) i The energy storage power adjustment degree; p (P) B Is an auto-allocation coefficient d=15; elementary adaptive factor A 0 =0.01; adaptive factor a=10·a 0 /P rated Degree of stored energy power adjustment P i 。
(3) The energy storage capacity contribution degree is characterized in the polymerization degree index as follows: the energy storage polymer cooperates with the capacity contribution capability of each energy storage monomer in the power grid along with the continuous change of the required capacity during the operation of the power grid. Therefore, the energy storage monomer i capacity contribution degree R dev Represented as;
Figure BDA0003117681720000122
Figure BDA0003117681720000123
wherein: c (C) c A schedulable capacity that varies with stored energy output; p (P) t (s i ) The output (input) power for each energy storage cell i is related to the instantaneous state of charge s i Is a function of (2); t is a scheduling period; r is R dev A contribution to capacity; c (C) d Capacity is required for the system.
(4) Based on the established index of degree of polymerization of the energy storage monomer: the three polymerization degree indexes are simulated by using energy storage monomers configured in 6 typical industrial parks in Liaoning province, and data are extracted, wherein the extracted data are shown in figure 9.
Step 5) calculating the energy storage polymerization degree A of the energy storage monomers configured in 6 typical industrial parks in Liaoning province according to the extracted three polymerization degree index data i 40 energy storage polymerization degrees A are calculated i Data, as shown in fig. 9:
A i ＝μ 1 ·P i +μ 2 ·P i *+μ 3 ·R dev
wherein: mu (mu) x Weights representing polymerization degree indexes of three energy storage monomers, and selecting mu according to a 9-level scale method 1 ＝0.5；μ 2 ＝0.3；μ 3 ＝0.2。
Step 6), establishing a polymerization degree evaluation index model based on energy storage monomers configured in 6 typical industrial parks in Liaoning province under the polymerization condition;
(1) 40 energy storage polymerization degree data A obtained by calculation i Cloud variable in index model for evaluation of polymerization degree of energy storage monomers configured in 6 typical industrial parks in Liaoning province, A i The following distribution is satisfied:
Figure BDA0003117681720000131
(2) Cloud expectation curve y (A i ) Geometric shape of evaluation index model describing degree of polymerization of energy storage monomers configured in 6 typical industrial parks in Liaoning province, y (A) i ) The expression is as follows:
Figure BDA0003117681720000132
(3) The quantitative data y (A) i ) Digital characteristics C (E) of the index model for evaluating degree of polymerization converted into 6 energy storage monomers configured in typical industrial parks in Liaoning province Ai ,En,He)，(E Ai En, he) is expressed as follows:
Figure BDA0003117681720000133
Figure BDA0003117681720000134
Figure BDA0003117681720000135
wherein E is Ai Representing the expectation of the polymerization degree evaluation index model, en represents the entropy of the polymerization degree evaluation index model, he represents the super entropy of the polymerization degree evaluation index model, n represents the cloud droplet count, and S represents the variance.
(4) Digital feature C (E) Ai En, he) generates n=1500 cloud droplets, and the above steps are repeatedly generated until the polymerization degree evaluation index model calculation results of the energy storage monomers configured in 6 typical industrial parks in the Liaoning province are generated.
And 7) carrying out example result analysis on the effectiveness of the polymerization degree evaluation index model of the energy storage monomers configured in 6 typical industrial parks in Liaoning province, and verifying that the distributed energy storage polymerization degree evaluation index model based on the polymerization condition can realize efficient polymerization of energy storage resources.
Fig. 1 is a structural framework of distributed energy storage participation in aggregation. The upper layer is a polymerization layer, and the layer is based on a distributed energy storage polymerization degree evaluation index model under the polymerization condition and is used for polymerizing energy storage monomers with similar polymerization degrees into an energy storage polymer. The lower layer is a response layer which is mainly used for the energy storage polymer to complete the demand response issued by the power grid.
FIG. 2 is a flow chart of a distributed energy storage polymerization degree evaluation index model based on polymerization conditions. A distributed energy storage polymerization degree evaluation index model based on polymerization conditions is characterized in that three dynamic adjustment indexes of energy storage power adjustment degree, self-adaptive equalization degree and capacity contribution degree are firstly established based on a polymerization condition framework, and the three dynamic adjustment indexes can fully represent the dynamic adjustment capability of energy storage monomers when the energy storage monomers participate in regional power grid response so as to realize that the energy storage monomers with similar dynamic adjustment capability are grouped. Then, the energy storage polymerization degree is calculated according to the three dynamic regulation indexes, the energy storage polymerization degree is used as input data of a distributed energy storage polymerization degree evaluation index model, and energy storage monomers with similar polymerization degrees are screened out through the model and polymerized into an energy storage polymer.
Fig. 3 illustrates energy storage monomer parameters for 6 typical industrial park configurations in the Liaoning province.
Fig. 4 is a simulated operating curve of the stored energy power regulation of 6 stored energy monomers. The figure shows that the adjustment depth of the energy storage power adjustment degrees of the 6 energy storage monomers is consistent (longitudinal trend is positioned between [ -1,1 ]) the main adjustment ranges are different (transverse trend), and the shorter the main adjustment range interval is, the earlier the energy storage monomers participate in adjustment is proved. Wherein the energy storage 2, 6 is mainly located in the early stage of regulation; the energy storage 4 and 3 is mainly positioned in the middle period of regulation; the stored energy 5, 1 is mainly located in the late phase of regulation, as explained in connection with fig. 2.
Fig. 5 is a simulated operating curve of the adaptive equalization of 6 energy storage cells. From the figure, the adjustment range of the self-adaptive equalization degree of each energy storage monomer is approximately locked between [0,0.5], and the adjustment range is the range of the self-adaptive equalization of each energy storage monomer. When the adjustment requirement is urgent, the self-adaptive balance degree of the energy storage 1, 3, 4 and 5 has a small section of climbing through the self-adaptive balance range, which indicates that the relative balance of the adjustment capability is realized on the basis that the self-adjustment capability of each energy storage monomer is not weakened.
Fig. 6 is a simulated run curve of the 6 energy storage cell capacity contributions. In the figure, when the required capacity is greater than zero, the required energy storage discharge (output) is represented; when the demand capacity is less than zero, it represents the demand stored energy charge (input). As can be seen from the graph, the capacity contribution of each energy storage monomer is represented by the plane size when the capacity contribution is equal to 1, and the capacity contribution of each energy storage monomer is sequentially as follows: the energy storage units 2, 4, 6, 5, 3 and 1 correspond to the capacity of each energy storage monomer.
FIG. 7 is a simulation result of the distributed energy storage polymerization degree evaluation index model. Fig. 8 is a 10-time fit residual plot of the simulation evaluation result of the distributed energy storage polymerization degree evaluation index model. As shown in fig. 7 and 8, the black dispersion point is characterized by: and estimating the polymerization degree of the energy storage monomer according to the inputted polymerization degree data. The red curve is characterized by: and fitting a ten-time fitting curve according to the polymerization data. The residual plot of each fitted curve is shown in fig. 8. As can be seen from the figure, the energy storage monomers 1, 2 and 3 have similar polymerization degree evaluation index models and fitting curves; the energy storage monomers 4, 5 and 6 have similar polymerization degree evaluation index models and fitting curves, and residual data of the energy storage monomers 1, 2 and 3 are similar; the residual data of the energy storage monomers 4, 5 and 6 are close. It can thus be stated that the energy-storing monomers 1, 2, 3 are suitable for polymerization into the polymer 1 and that the energy-storing monomers 4, 5, 6 are suitable for polymerization into the polymer 2.
FIGS. 9-15 are data extracted from three polymerization degree index simulation curves of energy storage monomers configured in 6 typical industrial parks in Liaoning province and calculated 40 energy storage polymerization degrees A i Data.
It should be understood that the foregoing detailed description of the present invention is provided for illustration only and is not limited to the technical solutions described in the embodiments of the present invention, and those skilled in the art should understand that the present invention may be modified or substituted for the same technical effects; as long as the use requirement is met, the invention is within the protection scope of the invention.

Claims (10)

1. The distributed energy storage polymerization degree evaluation index model based on the polymerization condition is characterized by comprising the following steps of:
building a structural framework for participating in polymerization of distributed energy storage under the polymerization condition;
acquiring operation parameters of each part of the energy storage monomer based on the established frame;
setting the output/input power of each energy storage monomer i relative to the instant state of charge s based on the acquired operation parameters of each part of the energy storage monomers i Is a function of the charge and discharge p(s) i ) c 、p(s i ) d The following are provided:
Figure QLYQS_1
Figure QLYQS_2
wherein: p(s) i ) c 、p(s i ) d The input/output power for each energy storage cell i is related to the instantaneous state of charge s i Is a charge-discharge function of (2); c (C) i The rated capacity of the energy storage monomer i; c (C) ∑ The total rated volume of all energy storage monomers entering the regulating layer is calculated; p (P) i·rated Rated power of the energy storage monomer i; a is that i Is an adaptive factor thereof; a is that i0 Adaptive prime factors for the same; d, d i Self-assigning coefficients thereto; s is(s) i Is an immediate state of charge; s is(s) shc =0.4 is the shallow charge point; s is(s) shd =0.6 is the shallow charge point; s is(s) max =0.9 is the maximum charge point; s is(s) min =0.1 is the minimum charge point; s is(s) mid =0.5 is the charge midpoint;
establishing polymerization degree indexes of each energy storage monomer i: degree of stored energy power regulation P i The method comprises the steps of carrying out a first treatment on the surface of the Energy storage adaptive equalization degree P i * The method comprises the steps of carrying out a first treatment on the surface of the Degree of contribution R of energy storage capacity dev ；
Calculating the energy storage polymerization degree A of the energy storage monomer i according to the extracted three polymerization degree index data i 40 energy storage polymerization degrees A are calculated i Data:
A i ＝μ 1 ·P i +μ 2 ·P i *+μ 3 ·R dev
wherein: mu (mu) x Weights representing polymerization degree indexes of three energy storage monomers, and selecting mu according to a 9-level scale method 1 ＝0.5；μ 2 ＝0.3；μ 3 ＝0.2；
And establishing a distributed energy storage polymerization degree evaluation index model based on the polymerization condition.
2. The distributed energy storage polymerization degree evaluation index model based on the polymerization condition according to claim 1, wherein the distributed energy storage polymerization degree evaluation index model is characterized in that: and carrying out example result analysis on the effectiveness of the distributed energy storage polymerization degree evaluation index model, and verifying that the distributed energy storage polymerization degree evaluation index model based on the polymerization condition can realize efficient polymerization of energy storage resources.
3. A polymeric strip-based paint according to claim 1The distributed energy storage polymerization degree evaluation index model under the piece is characterized in that: the operation parameters comprise rated capacity C of each energy storage monomer i The method comprises the steps of carrying out a first treatment on the surface of the Rated power P i·rated The method comprises the steps of carrying out a first treatment on the surface of the Adaptive factor A i The method comprises the steps of carrying out a first treatment on the surface of the Adaptive elementary factor A i0 The method comprises the steps of carrying out a first treatment on the surface of the Self-distribution coefficient d i The method comprises the steps of carrying out a first treatment on the surface of the Immediate state of charge s i The method comprises the steps of carrying out a first treatment on the surface of the Immediate zone control deviation a i 。
4. The distributed energy storage polymerization degree evaluation index model based on the polymerization condition according to claim 1, wherein the distributed energy storage polymerization degree evaluation index model is characterized in that: the construction framework for participating in polymerization of distributed energy storage under the polymerization condition comprises the following steps:
establishing an upper-layer and lower-layer distributed energy storage polymerization framework: the upper layer is a polymerization layer, and the lower layer is a response layer;
upper polymeric layer: based on a distributed energy storage polymerization degree evaluation index model under a polymerization condition, the method is used for polymerizing energy storage monomers with similar polymerization degrees into an energy storage polymer; to achieve efficient aggregation of energy storage resources;
the lower layer is a response layer and is used for the energy storage polymer to complete the demand response issued by the power grid.
5. The distributed energy storage polymerization degree evaluation index model based on the polymerization condition according to claim 1, wherein the distributed energy storage polymerization degree evaluation index model is characterized in that: established energy storage power regulation degree P i The characterization in the index of polymerization degree is: during the operation of the energy storage polymer auxiliary power grid, along with the continuous change of the adjustment degree, the adjustment capability of the output/input power of each energy storage monomer in the polymer body; p (P) i The calculation method comprises the following steps:
Figure QLYQS_3
Figure QLYQS_4
wherein: p (a) i ) c 、p(a i ) d The instantaneous area control deviation a of the energy storage release/absorption power for the area requirement of the individual energy storage cells i during the control process i Is used for self-adaptive equalization charge-discharge function; p (P) i·rated Rated power of the energy storage monomer i; a is that i Is an adaptive factor thereof; a is that i0 Adaptive prime factors for the same; d, d i Self-assigning coefficients thereto; a, a i Controlling deviation for the immediate area; a, a max =0.9 is the emergency action point; a, a min =0.1 is the minimum action point; a, a mid =0.5 is the normal action point;
Figure QLYQS_5
Figure QLYQS_6
wherein: p'(s) i ) c 、p′(s i ) d The input/output power of each energy storage monomer i after removing the capacity factor is related to the instant state of charge s i Is a charge-discharge function of (2); p(s) i ) c 、p(s i ) d The input/output power for each energy storage cell i is related to the instantaneous state of charge s i Is a charge-discharge function of (2); c (C) i The rated capacity of the energy storage monomer i; c (C) ∑ The total rated volume of all energy storage monomers entering the regulating layer is calculated; p (P) i The energy storage power adjustment degree; p (a) i ) c 、p(a i ) d The instantaneous area control deviation a of the energy storage release/absorption power for the area requirement of the individual energy storage cells i during the control process i Is used for self-adaptive equalization charge-discharge function; s is(s) i Is an immediate state of charge; s is(s) mid =0.5 is the charge midpoint.
6. The distributed energy storage polymerization degree evaluation index model based on the polymerization condition according to claim 1, wherein the distributed energy storage polymerization degree evaluation index model is characterized in that: the characterization of the established energy storage self-adaptive balance in the polymerization degree index is as follows: during the operation of the energy storage polymer and the power grid, the energy storage polymer is not regulatedThe self-adaptive equalization capability of each energy storage monomer in the polymer body is changed continuously; which can be regulated by the energy storage power of the energy storage monomer i i A per-unit representation based on an adaptive equalization technique; self-adaptive equilibrium degree P of energy storage monomer i i * Can be expressed as:
Figure QLYQS_7
wherein: p (P) i * Is self-adaptive equilibrium degree; p (P) i The energy storage power adjustment degree; p (P) B Is an auto-allocation coefficient d=15; elementary adaptive factor A 0 =0.01; adaptive factor a=10·a 0 /P rated Degree of stored energy power adjustment P i 。
7. The distributed energy storage polymerization degree evaluation index model based on the polymerization condition according to claim 1, wherein the distributed energy storage polymerization degree evaluation index model is characterized in that: the established energy storage capacity contribution degree is characterized in the polymerization degree index as follows: during the operation of the energy storage polymer and the power grid, the capacity contribution capability of each energy storage monomer in the polymer body is changed along with the continuous change of the required capacity; therefore, the energy storage monomer i capacity contribution degree R dev Expressed as:
Figure QLYQS_8
Figure QLYQS_9
wherein: c (C) c A schedulable capacity that varies with stored energy output; p (P) t (s i ) The input/output power for each energy storage cell i is related to the instantaneous state of charge s i Is a function of (2); t is a scheduling period; r is R dev A contribution to capacity; c (C) d The capacity is required for the system;
after the polymerization degree index of each energy storage monomer i is established, the three polymerization degree indexes are simulated by the energy storage monomers configured in a typical industrial park, and data are extracted.
8. The distributed energy storage polymerization degree evaluation index model based on the polymerization condition according to claim 1, wherein the distributed energy storage polymerization degree evaluation index model is characterized in that: the establishing of the distributed energy storage polymerization degree evaluation index model based on the polymerization condition comprises the following steps: 40 energy storage polymerization degree data A obtained by calculation i As cloud droplet variable in distributed energy storage polymerization degree evaluation index model, A i The following distribution is satisfied:
Figure QLYQS_10
cloud expectation curve y (A i ) Describing geometric form of distributed energy storage polymerization degree evaluation index model, y (A i ) The expression is as follows:
Figure QLYQS_11
the quantitative data y (A) i ) Digital characteristic C (E Ai ,En,He)，(E Ai En, he) is expressed as follows:
Figure QLYQS_12
Figure QLYQS_13
Figure QLYQS_14
wherein E is Ai Representing the expectation of the polymerization degree evaluation index model, en represents the entropy of the polymerization degree evaluation index model, he represents the super of the polymerization degree evaluation index modelEntropy, n represents the cloud droplet count, and S represents the variance;
digital features C (E Ai En, he) generates n=1500 cloud droplets, and the above steps are repeatedly generated until a sufficient calculation result of the distributed energy storage polymerization degree evaluation index model is generated.
9. The distributed energy storage polymerization degree evaluation index model based on the polymerization condition according to claim 1, wherein the distributed energy storage polymerization degree evaluation index model is characterized in that: the distributed energy storage equipment which is taken as an independent object and participates in the regulation and control of an energy storage polymer is called an energy storage monomer; the energy storage monomer completes the aggregation and integration of the self-resource energy under the control of an energy storage polymerizer, which is called polymerization, and a large-scale energy storage monomer object formed by polymerizing the energy storage monomer is called an energy storage polymer.
10. The distributed energy storage polymerization degree evaluation index model based on the polymerization condition according to claim 1, wherein the distributed energy storage polymerization degree evaluation index model is characterized in that: a is that i Is an adaptive factor thereof; the value range is between 0.01 and 1, and is related to the type of the energy storage battery, and the nickel-cadmium, all-vanadium liquid flow and lithium battery are 0.01; taking 0.5 of sodium-sulfur and zinc-bromine batteries; taking 1 of a lead-acid battery; a is that i0 For its self-adapting elementary factor A i0 ＝A·P rated /10；d i The self-allocation coefficient is selected, the value range is between 15 and 20, and the value range is related to the type of the energy storage battery, and the sodium-sulfur battery and the lithium battery are 13; taking 15 of an all-vanadium redox flow battery; 18 of nickel-cadmium and zinc-bromine batteries; taking 20 of a lead-acid battery;
typical energy storage monomers for industrial park configuration are: lead-acid batteries, all-vanadium redox flow batteries, nickel-cadmium batteries, sodium-sulfur batteries, zinc-bromine batteries, and lithium batteries; the configuration parameters are respectively as follows: 4MW/10 MW-h, 50MW/100 MW-h, 8MW/20 MW-h, 25MW/80 MW-h, 15MW/40 MW-h, 30MW/60 MW-h.
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