CN113364018B - Distributed energy storage online learning aggregation control method for secondary frequency modulation - Google Patents

Distributed energy storage online learning aggregation control method for secondary frequency modulation Download PDF

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CN113364018B
CN113364018B CN202110691618.7A CN202110691618A CN113364018B CN 113364018 B CN113364018 B CN 113364018B CN 202110691618 A CN202110691618 A CN 202110691618A CN 113364018 B CN113364018 B CN 113364018B
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frequency modulation
energy storage
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response
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CN113364018A (en
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胡秦然
陈心宜
全相军
窦晓波
吴在军
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Southeast University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
    • H02J7/00302—Overcharge protection
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
    • H02J7/00306—Overdischarge protection

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Abstract

The invention discloses a distributed energy storage online learning aggregation control method for secondary frequency modulation, which comprises the following steps: judging the type of frequency modulation according to the frequency deviation of the power grid, calculating the energy storage capacity required by frequency modulation, and determining a polymerization control target; according to the frequency modulation type, establishing a response behavior model of the distributed energy storage users to the aggregation control signal; considering the influence of the charge quantity of the energy storage equipment on the response behavior of the user, and establishing a charge quantity correlation coefficient model; based on a multi-arm learning machine frame, a user selection method for aggregation control distributed energy storage to participate in secondary frequency modulation is provided, online learning is carried out, an estimated value of the real response probability of each user is continuously updated, and a proper user is selected to participate in secondary frequency modulation. The method is used for processing the uncertainty of the response behavior of the user to the aggregation control signal, and considering the actual influence of the charge amount of the energy storage equipment on the response behavior of the user, so that smaller aggregation deviation of the energy storage capacity and more reliable frequency modulation effect can be obtained.

Description

Distributed energy storage online learning aggregation control method for secondary frequency modulation
Technical Field
The invention relates to the field of power systems, in particular to a distributed energy storage online learning aggregation control method for secondary frequency modulation.
Background
With the gradual increase of the proportion of new energy accessed by the power grid, the safe and stable operation of the power grid is greatly challenged, such as the unstable condition of the power grid frequency caused by uncertainty of output of new energy. In recent years, distributed energy storage has been developed, and devices such as household energy storage and plug-in electric vehicle storage batteries are widely popularized. The distributed energy storage has a quick response characteristic and a flexible bidirectional power output capability, so that the problem of short-time power unbalance of a power grid and one of important resources for assisting safe and stable operation of the power grid gradually becomes solved, and powerful support can be provided for frequency modulation of the power grid.
However, unlike conventional fixed large-capacity energy storage devices, the capacity of a single distributed energy storage device is small, and is not enough to participate in frequency modulation of a power grid, and the single distributed energy storage device needs to participate in frequency modulation after being aggregated to obtain an appreciable capacity in the form of an aggregator. However, the aggregated control of distributed energy storage devices faces serious user behavior uncertainty issues. The user may quit responding to the aggregation control signal due to factors such as own energy demand, electricity price information, personal preference, and energy storage device charge amount. This results in the energy storage capacity obtained by the polymerization being too different from the required energy storage capacity to achieve a reliable frequency modulation. Therefore, the key point for realizing the reliable aggregation control of the distributed energy storage and participating in the secondary frequency modulation is to solve the uncertainty problem of the user behavior. However, at present, it is difficult to essentially solve the problem of uncertainty of user behavior by starting with exploring behavior characteristics of a user and learning the true probability distribution of user response behavior.
Disclosure of Invention
In order to solve the defects mentioned in the background art, the invention aims to provide a distributed energy storage online learning aggregation control method facing secondary frequency modulation, which learns the real probability distribution of the distributed energy storage user response behavior online through a multi-arm learning machine frame to process the uncertainty of the user response behavior and take the objective actual influence of the energy storage equipment charge on the user response behavior into account, thereby obtaining more reliable two frequency modulation services of upward frequency modulation and downward frequency modulation.
The purpose of the invention can be realized by the following technical scheme:
a distributed energy storage online learning aggregation control method for secondary frequency modulation comprises the following steps:
(1) judging the type of frequency modulation according to the frequency deviation of the power grid, calculating the energy storage capacity required by frequency modulation, and determining a polymerization control target;
(2) according to the frequency modulation type, establishing a response behavior model of the distributed energy storage users to the aggregation control signal;
(3) considering the influence of the charge quantity of the energy storage equipment on the response behavior of the user, and establishing a charge quantity correlation coefficient model;
(4) based on a multi-arm learning machine frame, a user selection method for aggregation control distributed energy storage to participate in secondary frequency modulation is provided, online learning is carried out, an estimated value of the real response probability of each user is continuously updated, and a proper user is selected to participate in secondary frequency modulation.
Further preferably, in the step (1), when the power grid frequency is lower than the allowable lower limit value, upward frequency modulation needs to be implemented; when the frequency of the power grid is higher than the allowable upper limit value, downward frequency modulation needs to be implemented;
if the upward frequency modulation is implemented at the time t, the energy storage capacity required by the frequency modulation is as follows:
Figure BDA0003126991470000021
if downward frequency modulation is implemented at the time t, the energy storage capacity required by the frequency modulation is as follows:
Figure BDA0003126991470000022
in the formula, kd、kcRepresenting the frequency modulation coefficient; f. oft、fnRespectively representing the power grid frequency and the rated frequency at the moment t;
Figure BDA0003126991470000031
representing the existing frequency modulation reserve capacity of the power grid;
the aggregation control target is the minimum aggregation deviation of the energy storage capacity and can be expressed as:
Figure BDA0003126991470000032
in the formula, RtRepresenting the energy storage capacity aggregation deviation at the time t;
Figure BDA0003126991470000033
representing the energy storage capacity actually aggregated at time t.
Further preferably, in the step (2), the aggregator controls the distributed energy storage through aggregation, so that the distributed energy storage is discharged, and an upward frequency modulation service can be provided; charging the battery to provide down-modulation service; the aggregator generates two aggregation control signals corresponding to the two types of frequency modulation: the signal A starts to discharge to participate in upward frequency modulation, and the signal B starts to charge to participate in downward frequency modulation; the response behavior model of the distributed energy storage user i to the aggregation control signal can be represented as:
Figure BDA0003126991470000034
Figure BDA0003126991470000035
The response behavior model represents that the response probability of the user to the signal A is pA,iThe response means that the discharge is willing to start, and the probability of exiting the response signal A is 1-pA,i(ii) a The probability of response to signal B is pB,iThe response means that the charging is willing to be started, and the probability of rejecting the response signal B is 1-pB,iIn practice, pA,i、pB,iUnknown, influenced by user preference, self energy demand and other factors, limited by the charge capacity of the energy storage equipment, XA,i、XB,iRespectively representing the response of the user to the signal a and the signal B.
Further preferably, in the step (3), when the charge of the energy storage device is too low, the energy available for discharging is limited, and excessive discharging may affect the battery life, the probability of the user responding to the signal a will decrease, and the probability of the user responding to the signal B will increase; when the charge capacity of the energy storage device is too high, the energy which can be stored is limited, the service life of the battery is influenced by overcharging, the probability of responding to the signal A by a user is increased, and the probability of responding to the signal B is reduced;
considering the actual influence of the charge quantity of the energy storage equipment on the response behavior of the user, establishing a charge quantity correlation coefficient model as
Figure BDA0003126991470000041
In the formula (I), the compound is shown in the specification,
Figure BDA0003126991470000042
respectively representing charge quantity correlation coefficients of upward frequency modulation and downward frequency modulation; SOCi,tRepresenting the charge of the energy storage equipment at the moment t; beta is a A、βBAnd charge quantity correlation constant parameters respectively representing upward frequency modulation and downward frequency modulation are obtained through a statistical method.
Further preferably, in step (4), under the frame of the dobby learning machine, the response behavior of the user i to the signal a and the signal B is considered as one rocker arm, so that the total N distributed energy storage users correspond to the N rocker arms A, N rocker arms B; the user selection method for aggregation control distributed energy storage to participate in secondary frequency modulation specifically comprises the following steps:
1) input parameter alpha1、α2、XA,i、XB,i、
Figure BDA0003126991470000043
2) Initialization: initializing nA,i,t、nB,i,tEqual to 1; initializing estimated values of true response probabilities of users to signals A and B according to empirical knowledge of aggregators
Figure BDA0003126991470000044
3) Calculating the sequencing index of each user for any time T cycle in the aggregation control period T:
Figure BDA0003126991470000045
4) considering the actual influence of the energy storage device charge amount on the user response behavior, the ranking index of the user is replaced by:
Figure BDA0003126991470000051
5) according to the frequency modulation type, all users are arranged according to vi,tAnd (3) descending order arrangement:
Figure BDA0003126991470000052
6) and sequentially selecting M distributed energy storage users to participate in frequency modulation according to a descending order until the following conditions are met:
Figure BDA0003126991470000053
if all selected users still cannot reach the energy storage capacity required by frequency modulation, all selected users are selected, and M is equal to N:
Figure BDA0003126991470000054
7) selecting a set S of usersM,t＝{σ1,σ2…,σMAnd sending a signal A or a signal B to finish the aggregation control;
8) Updating the estimated value of true response probability for each user
Figure BDA0003126991470000055
Updating nA,i,t、nB,i,t；
9) The loop is ended.
The invention has the beneficial effects that:
the invention provides a distributed energy storage online learning aggregation control method facing secondary frequency modulation, which can obtain more accurate energy storage aggregation capacity and provide more reliable frequency up-modulation and frequency down-modulation services for a power grid. In addition, the method considers the objective and actual influence of the charge quantity of the energy storage equipment on the user behavior, can improve the reliability of the aggregation control effect, and can effectively prevent the damage of overcharge and overdischarge to the service life of the energy storage equipment of the user.
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The invention will be further described with reference to the accompanying drawings.
Fig. 1 is a flow chart of a distributed energy storage online learning aggregation control method for secondary frequency modulation according to the present invention;
FIG. 2 is a frame diagram of a distributed energy storage participating grid frequency modulation provided by the invention;
fig. 3 is a diagram of a distributed energy storage online learning aggregation control framework based on a multi-arm learning machine framework provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in 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 of the 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.
As shown in fig. 1, the invention provides a distributed energy storage online learning aggregation control method facing to secondary frequency modulation, which comprises the following steps:
(1) judging the type of frequency modulation according to the frequency deviation of the power grid, calculating the energy storage capacity required by frequency modulation, and determining a polymerization control target;
specifically, when the grid frequency is lower than the allowable lower limit value, upward frequency modulation needs to be implemented; when the grid frequency is higher than the allowable upper limit value, down-modulation needs to be implemented.
If an upward frequency modulation is carried out at time t, the energy storage capacity required for the frequency modulation is
Figure BDA0003126991470000061
If a downward frequency modulation is carried out at time t, the energy storage capacity required for the frequency modulation is
Figure BDA0003126991470000071
In the formula, kd、kcRepresenting the frequency modulation coefficient; f. oft、fnRespectively representing the power grid frequency and the rated frequency at the moment t;
Figure BDA0003126991470000072
representing the existing frequency modulation reserve capacity of the power grid.
The aggregation control target is that the aggregation deviation of the energy storage capacity is minimum and can be expressed as
Figure BDA0003126991470000073
In the formula, RtRepresenting the energy storage capacity aggregation deviation at the time t;
Figure BDA0003126991470000074
representing the energy storage capacity actually aggregated at time t.
(2) According to the frequency modulation type, establishing a response behavior model of the distributed energy storage users to the aggregation control signal;
specifically, as shown in fig. 2, the aggregator controls the distributed energy storage to discharge through aggregation, and can provide the frequency-up service; charging it can provide the down-modulation service. The aggregator generates two aggregate control signals, signal a: start discharging to participate in the up-modulation, signal B: charging is started to participate in the down-modulation. The response behavior model of the distributed energy storage user i to the aggregation control signal can be expressed as
Figure BDA0003126991470000075
Figure BDA0003126991470000076
The response behavior model represents that the probability of the response of the user to the signal A is pA,iThe response means that the discharge is willing to start, and the probability of exiting the response signal A is 1-pA,i(ii) a The probability of response to signal B is pB,iThe response means that the charging is willing to be started, and the probability of rejecting the response signal B is 1-pB,i. In practice, pA,i、pB,iUnknown, subject to user's preference, self-energy demandAnd the like, and is limited by the charge capacity of the energy storage equipment. XA,i、XB,iRespectively representing the response of the user to the signal A and the signal B.
(3) Considering the influence of the charge quantity of the energy storage equipment on the response behavior of the user, and establishing a charge quantity correlation coefficient model;
specifically, when the charge of the energy storage device is too low, the energy available for discharging is limited, and excessive discharge affects the battery life, the probability of the user responding to the signal a will decrease, and the probability of the user responding to the signal B will increase; when the charge of the energy storage device is too high, the energy that can be stored is limited, and overcharging affects the battery life, and the probability that the user responds to the signal a will rise, and the probability that the user responds to the signal B will fall. Considering the actual influence of the charge quantity of the energy storage equipment on the response behavior of the user, establishing a charge quantity correlation coefficient model as
Figure BDA0003126991470000081
In the formula (I), the compound is shown in the specification,
Figure BDA0003126991470000082
Respectively representing charge quantity correlation coefficients of upward frequency modulation and downward frequency modulation; SOC (system on chip)i,tRepresenting the charge of the energy storage equipment at the moment t; beta is aA、βBAnd charge quantity correlation constant parameters respectively representing upward frequency modulation and downward frequency modulation are obtained through a statistical method.
(4) Based on a multi-arm learning machine frame, a user selection method for aggregation control distributed energy storage participation secondary frequency modulation is provided, online learning is carried out, an estimated value of real response probability of each user is continuously updated, and a proper user is selected to participate in secondary frequency modulation;
specifically, as shown in fig. 3, under the multi-arm learning machine frame, the response behavior of the user i to the signals a and B is considered as one rocker, and then the total N distributed energy storage users correspond to the N rockers A, N rocker B. The user selection method for aggregation control distributed energy storage to participate in secondary frequency modulation specifically comprises the following steps:
1) transfusion systemParameter alpha1、α2、XA,i、XB,i、
Figure BDA0003126991470000083
2) Initialization: initializing nA,i,t、nB,i,tEqual to 1; initializing estimated values of true response probabilities of users to signals A and B according to empirical knowledge of aggregators
Figure BDA0003126991470000084
3) Calculating the sequencing index of each user for any time T cycle in the aggregation control period T:
Figure BDA0003126991470000091
4) considering the actual influence of the energy storage device charge amount on the user response behavior, the ranking index of the user is replaced by:
Figure BDA0003126991470000092
5) According to the frequency modulation type, all users are arranged according to vi,tAnd (3) descending order arrangement:
Figure BDA0003126991470000093
6) and sequentially selecting M distributed energy storage users to participate in frequency modulation according to a descending order until the following conditions are met:
Figure BDA0003126991470000094
if all selected users still cannot reach the energy storage capacity required by frequency modulation, all selected users are selected, and M is equal to N:
Figure BDA0003126991470000095
7) selecting a set S of usersM,t＝{σ1,σ2…,σMAnd sending a signal A or a signal B to finish the aggregation control;
8) updating the estimated value of true response probability for each user
Figure BDA0003126991470000096
Updating nA,i,t、nB,i,t；
9) The loop is ended.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.

Claims (4)

1. A distributed energy storage online learning aggregation control method for secondary frequency modulation is characterized by comprising the following steps:
(1) judging the type of frequency modulation according to the frequency deviation of the power grid, calculating the energy storage capacity required by frequency modulation, and determining a polymerization control target;
(2) according to the frequency modulation type, establishing a response behavior model of the distributed energy storage users to the aggregation control signal;
(3) considering the influence of the charge quantity of the energy storage equipment on the response behavior of the user, and establishing a charge quantity correlation coefficient model;
(4) based on a multi-arm learning machine frame, a user selection method for aggregation control distributed energy storage participation secondary frequency modulation is provided, online learning is carried out, an estimated value of real response probability of each user is continuously updated, and a proper user is selected to participate in secondary frequency modulation;
in the step (4), under the multi-arm learning machine frame, the response behaviors of the user i to the signal a and the signal B are both regarded as one rocker arm, and then the whole N distributed energy storage users correspond to the N rocker arms A, N rocker arms B; the user selection method for aggregation control distributed energy storage to participate in secondary frequency modulation specifically comprises the following steps:
1) inputting parameters:
Figure 65853DEST_PATH_IMAGE001
；
2) initialization: initialization
Figure 649238DEST_PATH_IMAGE002
Equal to 1; initializing estimated values of true response probabilities of users to signals A and B according to empirical knowledge of aggregators
Figure 897817DEST_PATH_IMAGE003
；
3) For any time T cycle in the aggregation control period T, calculating the sequencing index of each user:
Figure 27447DEST_PATH_IMAGE004
4) considering the actual influence of the energy storage device charge amount on the user response behavior, the ranking index of the user is replaced by:
Figure 310661DEST_PATH_IMAGE005
5) according to the frequency modulation type, all users are assigned
Figure 805227DEST_PATH_IMAGE006
In descending order
Figure 541102DEST_PATH_IMAGE007
6) And sequentially selecting M distributed energy storage users to participate in frequency modulation according to a descending order until the following conditions are met:
Figure 208844DEST_PATH_IMAGE008
if all selected users still cannot reach the energy storage capacity required by frequency modulation, all selected users are selected, and M is equal to N:
Figure 346564DEST_PATH_IMAGE009
7) selecting a set of users
Figure 808769DEST_PATH_IMAGE010
And sends signal A or signal B to complete the aggregation control;
8) updating the estimated value of true response probability for each user
Figure 31940DEST_PATH_IMAGE011
(ii) a Updating
Figure 237794DEST_PATH_IMAGE012
；
9) The loop is ended.
2. The distributed energy storage online learning and aggregation control method for secondary frequency modulation according to claim 1, wherein in the step (1), when the grid frequency is lower than an allowable lower limit value, upward frequency modulation needs to be implemented; when the frequency of the power grid is higher than an allowable upper limit value, downward frequency modulation needs to be implemented;
if the upward frequency modulation is implemented at the time t, the energy storage capacity required by the frequency modulation is as follows:
Figure 26758DEST_PATH_IMAGE013
if downward frequency modulation is implemented at the time t, the energy storage capacity required by the frequency modulation is as follows:
Figure 925444DEST_PATH_IMAGE014
in the formula (I), the compound is shown in the specification,
Figure 635911DEST_PATH_IMAGE015
Representing the frequency modulation coefficient;
Figure 379876DEST_PATH_IMAGE016
respectively representing the power grid frequency and the rated frequency at the moment t;
Figure 492189DEST_PATH_IMAGE017
representing the existing frequency modulation reserve capacity of the power grid;
the aggregation control target is the minimum aggregation deviation of the energy storage capacity and can be expressed as:
Figure 296197DEST_PATH_IMAGE018
in the formula (I), the compound is shown in the specification,
Figure 493960DEST_PATH_IMAGE019
representing the energy storage capacity aggregation deviation at the time t;
Figure 41616DEST_PATH_IMAGE020
representing the energy storage capacity actually aggregated at time t.
3. The distributed energy storage online learning aggregation control method for secondary frequency modulation according to claim 1, wherein in the step (2), an aggregator controls distributed energy storage through aggregation to discharge, so that an upward frequency modulation service can be provided; charging the battery to provide down frequency modulation service; the aggregator generates two aggregation control signals corresponding to the two types of frequency modulation: the signal A starts to discharge to participate in upward frequency modulation, and the signal B starts to charge to participate in downward frequency modulation; the response behavior model of the distributed energy storage user i to the aggregation control signal can be represented as:
Figure 8435DEST_PATH_IMAGE021
the response behavior model represents the probability that the user responds to the signal A as
Figure 983344DEST_PATH_IMAGE022
The response means that the discharge is willing to start, and the probability of exiting the response signal A is
Figure 402824DEST_PATH_IMAGE023
(ii) a The probability of response to signal B is
Figure 754171DEST_PATH_IMAGE024
The response means that the charging is willing to be started, and the probability of rejecting the response signal B is
Figure 575496DEST_PATH_IMAGE025
In practice, the amount of the solvent is,
Figure 986886DEST_PATH_IMAGE026
unknown, influenced by user preference and self energy utilization demand factors, limited by the charge quantity of the energy storage equipment,
Figure 893662DEST_PATH_IMAGE027
respectively representing the response of the user to the signal a and the signal B.
4. The distributed energy storage online learning aggregation control method for secondary frequency modulation according to claim 1, wherein in step (3), when the charge amount of the energy storage device is too low, the energy available for discharging is limited, and excessive discharging may affect the battery life, the probability of the user responding to the signal a will decrease, and the probability of the user responding to the signal B will increase; when the charge amount of the energy storage equipment is too high, the energy which can be stored is limited, the service life of the battery is influenced by overcharging, the probability of responding to the signal A by a user is increased, and the probability of responding to the signal B is reduced;
considering the actual influence of the charge quantity of the energy storage equipment on the response behavior of the user, establishing a charge quantity correlation coefficient model as follows:
Figure 783121DEST_PATH_IMAGE028
in the formula (I), the compound is shown in the specification,
Figure 724532DEST_PATH_IMAGE029
respectively representing charge quantity correlation coefficients of upward frequency modulation and downward frequency modulation;
Figure 306823DEST_PATH_IMAGE030
representing the charge of the energy storage equipment at the moment t;
Figure 700895DEST_PATH_IMAGE031
and charge quantity correlation constant parameters respectively representing upward frequency modulation and downward frequency modulation are obtained through a statistical method.
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