CN114914942A - Polymorphic distributed energy storage primary frequency modulation control method based on consistency algorithm - Google Patents

Polymorphic distributed energy storage primary frequency modulation control method based on consistency algorithm Download PDF

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CN114914942A
CN114914942A CN202210554380.8A CN202210554380A CN114914942A CN 114914942 A CN114914942 A CN 114914942A CN 202210554380 A CN202210554380 A CN 202210554380A CN 114914942 A CN114914942 A CN 114914942A
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钱一民
王易
郑剑
丁凯
黄曾睿
陈乔
李伟
汪蓓
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Hubei Electric Power Co Ltd
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Abstract

The invention provides a polymorphic distributed energy storage primary frequency modulation control method based on a consistency algorithm, which comprises the following steps: a: constructing a power system frequency modulation model containing a thermal power generating unit and power grid side distributed energy storage; b: analyzing the frequency response characteristic of the system, and designing a multi-scale morphological frequency division filter; c: establishing a frequency modulation consumption model of the stored energy, and determining related constraints in the frequency modulation process; d: and optimally distributing the frequency modulation output of the distributed energy storage to finish a distributed energy storage primary frequency modulation control strategy. The invention divides the frequency signal into high and low frequency bands through the morphological filter, and the frequency bands are respectively borne by the polymorphic distributed energy storage and thermal power generating units, takes the lowest primary frequency modulation consumption of the system as an optimization target, and converts the optimization problem into the optimization problem of consistency of frequency modulation margin cost of each energy storage, so that each energy storage bears the frequency modulation responsibility conforming to the self frequency modulation capability, the thermal power mechanical abrasion can be reduced, the over-charging and over-discharging of the energy storage can be avoided, and the frequency response capability of the system can be improved.

Description

Polymorphic distributed energy storage primary frequency modulation control method based on consistency algorithm
Technical Field
The invention relates to the technical field of distributed energy storage primary frequency modulation control, in particular to a polymorphic distributed energy storage primary frequency modulation control method based on a consistency algorithm.
Background
The distributed energy storage of the power grid side in the polymorphic form is used as a novel frequency modulation resource, has the advantages of high response speed, accurate and flexible control, short construction period, flexible site selection, strong expandability and the like, is an effective means for effectively solving the problem of safe and stable frequency of a power system caused by high new energy permeability, promoting new energy power generation absorption and improving the new energy power generation scale, and is also one of important ways for green energy transformation in China. However, as the types and the number of the distributed energy storages on the power grid side are gradually increased, the performance of the frequency modulation capability is restricted by the control mode of distributed energy storage decentralized layout and unified scheduling in the system.
The traditional distributed energy storage participation primary frequency modulation method generally adopts a fixed setting for the frequency modulation responsibility distribution of each energy storage, when the load fluctuation suddenly changes, the primary frequency modulation pressure of part of the energy storage is too large, the residual energy storage wastes the frequency modulation capability, the primary frequency modulation potential of each energy storage cannot be fully exerted, the frequency modulation cost is increased, the problem of overcharge and overdischarge of part of the energy storage is caused, and the energy storage loss is increased. Distributed energy storage types are numerous, frequency modulation technical characteristics are different, how to depict the dynamic response characteristics of energy storage and distinguish the frequency modulation characteristics of various types of energy storage, and the frequency modulation control of the traditional thermal power generating unit and the distributed energy storage is economically and effectively coordinated, which is a problem to be solved urgently at present.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a polymorphic distributed energy storage primary frequency modulation control method based on a consistency algorithm, which divides a power grid frequency signal into a high frequency band and a low frequency band through a morphological filter, and the high frequency band and the low frequency band are respectively borne by a polymorphic distributed energy storage unit and a thermal power unit, takes the lowest primary frequency modulation consumption of a system as an optimization target, and converts the optimization problem into the optimization problem of consistency of frequency modulation marginal cost of each energy storage, so that each energy storage bears the frequency modulation responsibility conforming to the self frequency modulation capability, thereby accurately tracking the primary frequency modulation signal, reducing the mechanical abrasion of thermal power, avoiding the overcharge and overdischarge of the energy storage, and improving the frequency response capability of the system.
A polymorphic distributed energy storage primary frequency modulation control method based on a consistency algorithm comprises the following steps:
A. establishing a polymorphic distributed energy storage frequency modulation model and a thermal power generating unit frequency modulation model, and further establishing a power system frequency modulation model containing the thermal power generating unit and the power grid side distributed energy storage;
B. analyzing the frequency response characteristic of a power system containing a thermal power generating unit and power grid side distributed energy storage, and designing a multi-scale morphological frequency division filter, wherein the multi-scale morphological frequency division filter is used for decomposing a frequency signal into a high frequency band and a low frequency band;
C. establishing an energy storage frequency modulation consumption model, and determining frequency modulation capacity range constraints in the frequency modulation process, wherein the energy storage frequency modulation capacity range constraints comprise energy storage real-time residual capacity constraints, frequency modulation power standby constraints and climbing rate constraints;
D. and C, optimally distributing the frequency modulation output of the distributed energy storage according to the multi-scale morphological frequency division filter designed in the step B and the frequency modulation consumption model of the energy storage established in the step C, and finishing a primary frequency modulation control strategy of the distributed energy storage.
Further, the step a specifically includes:
step A1: obtaining the key frequency modulation coefficient of each type of energy storage through parameter identification according to the frequency modulation characteristic of each energy storage, and establishing a frequency modulation model of the polymorphic distributed energy storage, wherein the parameters identified in the parameter identification comprise: time constant T for expressing energy storage time delay characteristic S (ii) a A droop coefficient r; real-time power weight coefficient alpha of stored energy s (ii) a Weight coefficient beta of energy storage residual capacity s (ii) a Energy storage charging and discharging efficiency eta c 、η d (ii) a Rated capacity of stored energy E rated (ii) a Maximum and minimum limit of available capacity of energy storage
Figure BDA0003651796680000021
i E(ii) a Energy storage frequency modulation output maximum and minimum limit
Figure BDA0003651796680000022
i P(ii) a Maximum and minimum limits of energy storage climbing speed
Figure BDA0003651796680000023
i R；
Step A2: according to historical data of the thermal power generating unit, obtaining a frequency modulation key coefficient of the thermal power generating unit through parameter identification, and establishing a frequency modulation model of the thermal power generating unit, wherein parameters identified in the parameter identification comprise: set feedback time constant T s0 、T s1 (ii) a A unit difference adjustment coefficient R; a valve characteristic curve model; high pressure steam chamber steam volume time constant T CH (ii) a Reheat steam volume time constant T RH (ii) a Power coefficient F of high pressure cylinder HP (ii) a Low power coefficient F of cylinder LP ；
Step A3: establishing thermal power-containing power-point according to the frequency modulation key coefficients of the various types of energy storage obtained in the step A1 and the step A2 and the frequency modulation key coefficient of the thermal power generating unitThe model parameters of the distributed energy storage combined frequency modulation model further comprise a generator-power grid equivalent inertia coefficient M s (ii) a Damping coefficient D of the system s (ii) a A frequency modulation coefficient beta; frequency modulation participation degree alpha; frequency modulation ratio parameter K P (ii) a Frequency modulation integral parameter K I ；
Through the steps, the modeling of the frequency modulation system containing thermal power-distributed energy storage is completed, and the frequency modulation model of the power system containing the thermal power generating unit and the power grid side distributed energy storage is constructed and obtained.
Further, the step B specifically includes the following steps:
step B1: obtaining a frequency response transfer function of the power system containing the thermal power-distributed energy storage through the model parameters obtained in the step A, and analyzing the frequency response characteristics of the system, wherein
The system frequency response transfer function h(s) containing the thermal power-distributed energy storage is:
Figure BDA0003651796680000031
wherein Δ f(s) is a system frequency deviation, Δ P D (s) is the system load disturbance deviation;
respectively analyzing the baud graphs of the system frequency response transfer function under the following two conditions:
(1)H 1 (s): only the traditional thermal power generating unit participates in the primary frequency modulation of the system;
(2)H 2 (s): and participating in primary frequency modulation of the system only through distributed energy storage.
Calculate H 1 (s) and H 2 (s) demarcation frequency value f at intersection of amplitude-frequency curve d When the frequency of load fluctuation in the system is higher than f d When the system is in use, only the distributed energy storage participates in the gain delta f (s)/delta P of the primary frequency modulation of the system D (s) is smaller, which shows that the system frequency in the frequency band has stronger regulation capability, so that the demarcation frequency value f d Can be used as the frequency division critical parameter of the frequency division filter;
step B2: designing a frequency division filter through a decomposition algorithm of the multi-scale morphological filter:
setting an input signal as x (N), wherein N is epsilon {0,1, …, N-1 }; the structural element is gamma (M), M belongs to {0,1, …, M-1}, and the expansion operation and the corrosion operation of the function are respectively as follows:
Figure BDA0003651796680000032
(xΘγ)(n)＝min[x(n+m)-γ(m)](3) in the formula:
Figure BDA0003651796680000033
is the inflation operator; Θ is the corrosion operator;
the specific operation processes of the opening and closing operation are respectively as follows:
Figure BDA0003651796680000034
Figure BDA0003651796680000035
in the formula (I), the compound is shown in the specification,
Figure BDA0003651796680000036
an on operator; is the closed operator; s is a time scale;
the multi-scale morphology filter (MMF) is obtained by weighting and summing mixed morphology filters of different scales, and the specific expression is as follows:
Figure BDA0003651796680000041
Figure BDA0003651796680000042
in the formula: gamma is a structural element; k is the number of timescales;
Figure BDA0003651796680000043
the expression of the weighted value of each scale structural element is as follows:
Figure BDA0003651796680000044
in the formula:
Figure BDA0003651796680000045
for filtering the mean square error, the expression is:
Figure BDA0003651796680000046
adaptively selecting structural elements under different scales through an algorithm, and calculating the boundary frequency value f according to the step B1 d And decomposing the frequency signal of the primary frequency modulation feedback channel of the system frequency response model into high and low 2 frequency bands.
Further, the step C specifically includes the following steps:
step C1: defining the frequency modulation consumption of stored energy:
referring to the output consumption cost concept in the economic dispatching of the traditional generating set, citing the concept of frequency modulation cost function, and taking the lowest output consumption cost as the target of the frequency modulation control of the system,
frequency modulation cost function of energy storage i at time t
Figure BDA0003651796680000047
Comprises the following steps:
Figure BDA0003651796680000048
Figure BDA0003651796680000049
in the formula, P i,t Real-time power for stored energy i; e i,t For storing the real-time residual capacity Delta of the energy i at tt is the frequency modulation control period;
modulating frequency consumption
Figure BDA00036517966800000410
Written as relating to charge and discharge power P i,t The established frequency modulation consumption model of the stored energy is as follows:
Figure BDA00036517966800000411
coefficient a in formula (11) i 、b i,t 、c i,t The specific expression of (A) is as follows:
Figure BDA0003651796680000051
Figure BDA0003651796680000052
Figure BDA0003651796680000058
step C2: determining the frequency modulation capacity range constraint of energy storage:
the energy storage frequency modulation capacity range constraint comprises energy storage real-time residual capacity constraint, frequency modulation power standby constraint and climbing rate constraint, and specifically comprises the following steps:
Figure BDA0003651796680000053
under the restriction of the range, the upper limit and the lower limit of the energy storage charging and discharging power
Figure BDA0003651796680000054
Comprises the following steps:
Figure BDA0003651796680000055
further, the step D specifically includes the following steps:
d1: dividing the frequency signal into high and low frequency bands by a morphological filter, wherein the low frequency signal delta f low Borne by thermal power generating units and having high-frequency signals Deltaf high The output requirement of primary frequency modulation at the moment t is determined by bearing a plurality of distributed energy storages
Figure BDA0003651796680000056
Comprises the following steps:
Figure BDA0003651796680000057
in order to obtain the optimal primary frequency modulation output distribution scheme at the moment t, the total frequency modulation consumption Z is used t And (3) establishing a mathematical optimization model by taking the minimum as an objective function:
Figure BDA0003651796680000061
step D2: defining a consistency variable of distributed energy storage, and converting an optimization problem of an optimal primary frequency modulation output distribution scheme into an optimization problem of frequency modulation consumption margin cost consistency:
defining a partial derivative of the energy storage frequency modulation consumption to the frequency modulation output, namely frequency modulation marginal cost, as a consistency variable lambda, wherein the consistency variables of the distributed energy storage are respectively as follows:
Figure BDA0003651796680000062
converting the optimization problem of equation (16) to an optimization problem as shown in equation (18):
Figure BDA0003651796680000063
step D3: discretizing the optimization problem, optimizing and distributing the frequency modulation output of the distributed energy storage through a discrete distributed consistency algorithm,
the distributed energy storage frequency modulation output is optimized and distributed through a discrete distributed consistency algorithm, and the specific flow is as follows:
1) first, each variable is initialized. Initializing the initial output of each stored energy will
Figure BDA0003651796680000064
The initial distribution to each distributed energy storage is as follows:
Figure BDA0003651796680000065
in the formula (I), the compound is shown in the specification,
Figure BDA0003651796680000066
initial frequency-modulated output, rho, for each stored energy i The preliminary allocation proportion of the frequency-modulated signal is assigned.
Initializing consistency variables of each stored energy:
Figure BDA0003651796680000067
in the formula (I), the compound is shown in the specification,
Figure BDA0003651796680000068
an initial consistency variable for each stored energy;
2) the scheme for optimally calculating the frequency-modulated output distribution of each stored energy at the time t is divided into four steps (1) to (4), and specifically comprises the following steps:
(1) judging the consistency of adjacent stored energy: each distributed energy storage obtains a consistency variable lambda of adjacent energy storage through a communication network t And judges whether the consistency variable is consistent with the consistency variable,
the conditions that specify near agreement are:
Figure BDA0003651796680000071
in the formula, k is the number of iterations,
Figure BDA0003651796680000072
representing that the stored energy m is adjacent to and in communication with the power source n;
each distributed energy storage obtains the consistency variable lambda of the adjacent energy storage through the communication network t Judging whether the consistency variable is consistent with the consistency variable, if the consistency variable meets the consistency condition, skipping to the step (4), and if not, continuing to perform;
(2) iterative consistency variables: for making the consistency variable lambda of the stored energy n n,t The method is approximately consistent with an adjacent power supply, the frequency modulation output of the stored energy needs to be adjusted, and the method is divided into four small steps from (i) to (iv), and the iterative calculation method specifically comprises the following steps:
calculating a virtual consistency variable of the energy storage n
Figure BDA0003651796680000073
Consistency variables for the (k-1) th iteration
Figure BDA0003651796680000074
And correcting the power constraint, specifically:
Figure BDA0003651796680000075
in the formula, τ 1 ，τ 2 To correct the ratio, the first part of the correction is to store the consistency variable of the nth (k-1) iteration
Figure BDA0003651796680000076
Respectively with adjacent power supplies
Figure BDA0003651796680000077
Calculating and accumulating difference values; the second part of the correction is a correction of the power constraint. This is to ensure that the frequency modulation scheme always meets the basic requirements of control;
secondly, calculating a consistency variable, namely calculating the consistency variable,by aligning virtual consistency variables
Figure BDA0003651796680000078
And (3) constraining, further defining the consistency variable correction value, and ensuring the convergence of the iterative process:
Figure BDA0003651796680000079
calculating theoretical power
Figure BDA00036517966800000710
And virtual power
Figure BDA00036517966800000711
Wherein the theoretical power
Figure BDA00036517966800000712
According to the definition of the consistency variable, the following results are obtained:
Figure BDA0003651796680000081
for theoretical power
Figure BDA0003651796680000082
Corrected to obtain virtual power
Figure BDA0003651796680000083
Comprises the following steps:
Figure BDA0003651796680000084
in the formula, τ 3 To correct the ratio;
fourthly, calculating the distribution value of the frequency modulation power
Figure BDA0003651796680000085
The power distribution result of the stored energy n of the kth iteration is as follows:
Figure BDA0003651796680000086
(3) judging whether the maximum iteration number is exceeded: if the frequency modulation control operation is within the specified maximum iteration times, namely within the frequency modulation period delta t, returning to the step (1), otherwise, entering the next step;
(4) taking the frequency modulation power distribution value of each stored energy obtained by the last iteration as frequency modulation output;
and further completing a distributed energy storage primary frequency modulation control strategy.
The invention has the following advantages and beneficial effects:
1. the frequency modulation output of each stored energy is reasonably distributed, the excessive utilization of a single stored energy is avoided, the frequency modulation potential of each stored energy is fully exerted, the thermal power mechanical abrasion is reduced, and the overall frequency modulation capability of the system is improved;
2. the distributed consistency algorithm disperses the operation burden of the centralized control method to each frequency modulation resource, so that the calculation pressure of a control center can be relieved, and the distributed consistency algorithm is more suitable for the development trend that the types and the quantity of the distributed frequency modulation resources on the power grid side are increased continuously in the future;
the control method can give certain guidance to the primary frequency modulation control of the polymorphic distributed energy storage under high new energy penetration efficiency.
Drawings
FIG. 1 is a schematic flow chart of a polymorphic distributed energy storage primary frequency modulation control method based on a consistency algorithm;
FIG. 2 is a schematic diagram of a power system frequency modulation control model containing thermal power-distributed energy storage;
FIG. 3 is a Bode plot of the system frequency response transfer function for two cases;
FIG. 4 is a flow diagram of an adaptive generalized morphological filter;
FIG. 5 is a flow chart of a frequency modulated output distribution method based on a consistency algorithm;
FIG. 6 is a 3-machine 9-node simulation model with polymorphic distributed energy storage;
FIG. 7 is load power disturbance data in a power grid;
FIG. 8 is a graph of a grid frequency deviation under three conditions of a distributed energy storage frequency modulation control method of the present invention, a conventional frequency modulation control method, and no energy storage participating in frequency modulation;
FIG. 9 is a graph of the system frequency deviation between the high frequency and the low frequency divided by the multi-scale frequency division filter according to the present invention;
FIG. 10 is a frequency modulation output curve diagram of three typical stored energy under the distributed energy storage frequency modulation control method of the present invention;
fig. 11 is a remaining capacity ratio graph of three typical energy storages under the distributed energy storage frequency modulation control method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Fig. 1 is a schematic flow chart of a polymorphic distributed energy storage primary frequency modulation control method based on a consistency algorithm according to an embodiment of the present invention, where the method includes the following steps:
A. establishing a polymorphic distributed energy storage frequency modulation model, and establishing a power system frequency modulation model containing a thermal power generating unit and power grid side distributed energy storage, wherein the step A specifically comprises the following steps:
a1: and according to the frequency modulation characteristics of each energy storage, obtaining the frequency modulation key coefficient of each type of energy storage through parameter identification, and establishing a uniform frequency modulation model of the polymorphic distributed energy storage. The main parameters include: time constant T for expressing energy storage time delay characteristic S (ii) a A droop coefficient r; real-time power weight coefficient alpha of stored energy s (ii) a Energy storage residual capacity weight coefficient beta s (ii) a Energy storage punch,Discharge efficiency η c 、η d (ii) a Rated capacity of stored energy E rated (ii) a Maximum and minimum limit of available capacity of energy storage
Figure BDA0003651796680000091
i E(ii) a Energy storage frequency modulation output maximum and minimum limit
Figure BDA0003651796680000092
i P(ii) a Maximum and minimum limits of energy storage climbing speed
Figure BDA0003651796680000093
i R。
A2: and obtaining a frequency modulation key coefficient of the thermal power generating unit through parameter identification according to historical data of the thermal power generating unit. The main parameters include: set feedback time constant T s0 、T s1 (ii) a A unit difference adjustment coefficient R; a valve characteristic curve model; high pressure steam chamber steam volume time constant T CH (ii) a Reheat steam volume time constant T RH (ii) a Power coefficient F of high pressure cylinder HP (ii) a Power coefficient F of low pressure cylinder LP 。
A3: according to the data of the steps, a combined frequency modulation model containing thermal power-distributed energy storage is established, and the model parameters further comprise a generator-power grid equivalent inertia coefficient M s (ii) a Damping coefficient D of the system s (ii) a A frequency modulation coefficient beta; frequency modulation participation degree alpha; frequency modulation ratio parameter K P (ii) a Frequency modulation integral parameter K I 。
Through the steps, the modeling of the frequency modulation system containing the thermal power-distributed energy storage is completed. The obtained power system frequency modulation control model containing thermal power-distributed energy storage is shown in fig. 2.
B. Analyzing the frequency response characteristic of the system, and designing a frequency division filter, wherein the step B specifically comprises the following steps:
b1: and obtaining a system frequency response transfer function containing thermal power-distributed energy storage through the model parameters of the steps, and analyzing the system frequency response characteristics.
The system frequency response transfer function h(s) containing the thermal power-distributed energy storage is:
Figure BDA0003651796680000101
wherein Δ f(s) is the system frequency deviation, Δ P D And(s) is the system load disturbance deviation.
As shown in fig. 3, bode plots of the system frequency response transfer function are analyzed for the following two cases:
(1)H 1 (s): only the traditional thermal power generating unit participates in the primary frequency modulation of the system;
(2)H 2 (s): and participating in primary frequency modulation of the system only through distributed energy storage.
Calculate H 1 (s) and H 2 (s) demarcation frequency value f at intersection of amplitude-frequency curve d . When the frequency of load fluctuation in the system is higher than f d When the system is in use, only the distributed energy storage participates in the gain delta f (s)/delta P of the primary frequency modulation of the system D And(s) is smaller, which shows that the system frequency adjustment capability in the frequency band is stronger. Thereby dividing the frequency value f d Can be used as the frequency dividing critical parameter of the frequency dividing filter.
B2: and designing a frequency division filter through a decomposition algorithm of the multi-scale morphological filter.
Setting an input signal as x (N), wherein N belongs to {0,1, …, N-1 }; the structural element is gamma (M), and M belongs to {0,1, …, M-1 }. The functions are respectively:
Figure BDA0003651796680000111
Figure BDA0003651796680000112
in the formula:
Figure BDA0003651796680000113
is the inflation operator; Θ is the erosion operator.
The specific operation processes of the opening and closing operation are respectively as follows:
Figure BDA0003651796680000114
Figure BDA0003651796680000115
in the formula (I), the compound is shown in the specification,
Figure BDA0003651796680000116
an on operator; is the closed operator; and s is a time scale.
The multi-scale morphology filter (MMF) is obtained by weighting and summing mixed morphology filters of different scales, and the specific expression is as follows:
Figure BDA0003651796680000117
Figure BDA0003651796680000118
in the formula: gamma is a structural element; k is the number of time scales;
Figure BDA0003651796680000119
the expression of the weighted value of the structural element of each scale is as follows:
Figure BDA00036517966800001110
in the formula:
Figure BDA00036517966800001111
for filtering the mean square error, the expression is:
Figure BDA00036517966800001112
as shown in fig. 4, the structural elements under different scales are adaptively selected through an algorithm, and the frequency signal of the primary frequency modulation feedback channel of the system frequency response model is decomposed into high and low 2 frequency bands.
C. Establishing an energy storage frequency modulation consumption model, and determining related constraints in the frequency modulation process, wherein the step C specifically comprises the following steps:
c1: the frequency modulation consumption of the stored energy is defined.
Referring to the concept of output consumption cost in the economic dispatching of the traditional generating set, the concept of frequency modulation cost function (frequency modulation consumption) is introduced, and the lowest value is taken as the target of system frequency modulation control.
Frequency modulation cost function (frequency modulation consumption) of energy storage i at time t
Figure BDA0003651796680000121
Comprises the following steps:
Figure BDA0003651796680000122
Figure BDA0003651796680000123
in the formula, P i,t Real-time power for stored energy i; e i,t And the real-time residual capacity delta t of the energy storage i at t is a frequency modulation control period.
Further, the frequency modulation consumption can be reduced
Figure BDA0003651796680000124
Written as relating to charge-discharge power P i,t The quadratic function of (d):
Figure BDA0003651796680000125
the specific expression values of the above coefficients are:
Figure BDA0003651796680000126
Figure BDA0003651796680000127
Figure BDA0003651796680000128
step C2: and determining the frequency modulation capacity range constraint of the stored energy.
The energy storage frequency modulation capacity range constraint comprises energy storage real-time residual capacity constraint, frequency modulation power standby constraint and climbing rate constraint, and specifically comprises the following steps:
Figure BDA0003651796680000129
under the restriction of the range, the upper limit and the lower limit of the energy storage charging and discharging power
Figure BDA00036517966800001210
Comprises the following steps:
Figure BDA0003651796680000131
D. optimizing and distributing the frequency modulation output of the distributed energy storage, and finishing a distributed energy storage primary frequency modulation control strategy, wherein the step D specifically comprises the following steps:
d1: dividing the frequency signal into high and low frequency bands by a multi-scale morphological filter, wherein the low frequency signal delta f low Borne by thermal power generating units and having high-frequency signals Deltaf high The output requirement of primary frequency modulation at the moment t is determined by bearing a plurality of distributed energy storages
Figure BDA0003651796680000132
Comprises the following steps:
Figure BDA0003651796680000133
in order to obtain the optimal primary frequency modulation output distribution scheme at the moment t, the total frequency modulation consumption Z is used t And (3) establishing a mathematical optimization model by taking the minimum as an objective function:
Figure BDA0003651796680000134
d2: and defining consistency variables of distributed energy storage, and converting the optimization problem of the optimal primary frequency modulation output distribution scheme into the optimization problem of frequency modulation consumption margin cost consistency.
Defining a partial derivative (frequency modulation marginal cost) of the stored energy frequency modulation consumption to the frequency modulation output as a consistency variable lambda, wherein the consistency variables of the distributed stored energy are respectively as follows:
Figure BDA0003651796680000135
converting the optimization problem of equation (16) to the optimization problem of equation (18)
Figure BDA0003651796680000136
D3: and discretizing the optimization problem, and optimizing and distributing the frequency modulation output of the distributed energy storage through a discrete distributed consistency algorithm.
As shown in fig. 5, the energy storage frequency modulation output distribution method based on the consistency algorithm has the following flow:
1) first, each variable is initialized. Initializing the initial output of each stored energy will
Figure BDA0003651796680000141
The initial distribution to each distributed energy storage is as follows:
Figure BDA0003651796680000142
in the formula (I), the compound is shown in the specification,
Figure BDA0003651796680000143
initial frequency-modulated output, rho, for each stored energy i The preliminary allocation proportion of the frequency-modulated signal is assigned.
Initializing consistency variables of each stored energy:
Figure BDA0003651796680000144
in the formula (I), the compound is shown in the specification,
Figure BDA0003651796680000145
an initial consistency variable for each stored energy.
This step requires setting the key FM voltage as a proxy node (agent) for the consistency algorithm, i.e. the stored energy in the local grid in connection with the control center. And calculating to obtain an initial value of the initial frequency modulation output and the consistency variable of each stored energy at the key stored energy position, and distributing the initial value signals to all stored energy in a signal bidirectional conduction mode between adjacent stored energy.
2) The scheme for optimally calculating the frequency-modulated output distribution of each stored energy at the time t is divided into four steps (1) to (4), and specifically comprises the following steps:
(1) judging the consistency of adjacent stored energy: each distributed energy storage obtains a consistency variable lambda of adjacent energy storage through a communication network t And judges whether the consistency variable is consistent with the consistency variable.
The conditions that specify near agreement are:
Figure BDA0003651796680000146
wherein k is the number of iterations,
Figure BDA0003651796680000147
representing that the stored energy m is adjacent to and in communication with the power source n.
Each distributed energy storage obtains the consistency variable lambda of the adjacent energy storage through the communication network t And judges whether the consistency variable is consistent with the consistency variable. And (4) if the consistency condition is met, jumping to the step (4), otherwise, continuing.
(2) Iterative consistency variables: for making the consistency variable lambda of the stored energy n n,t The method is approximately consistent with an adjacent power supply, the frequency modulation output of the stored energy needs to be adjusted, and the method is divided into four small steps from (i) to (iv), and the iterative calculation method specifically comprises the following steps:
calculating a virtual consistency variable of the stored energy n
Figure BDA0003651796680000151
Consistency variables for the (k-1) th iteration
Figure BDA0003651796680000152
And correcting the power constraint, specifically:
Figure BDA0003651796680000153
in the formula, τ 1 ，τ 2 To correct the ratio, the first part of the correction is to store the consistency variable of the nth (k-1) iteration
Figure BDA0003651796680000154
Respectively with adjacent power supplies
Figure BDA0003651796680000155
Calculating and accumulating difference values; the second part of the correction is a correction of the power constraint. This is to ensure that the frequency modulation scheme always meets the basic requirements of control.
Calculating consistency variable by aiming at virtual consistency variable
Figure BDA0003651796680000156
And (5) constraining, further defining the consistency variable correction value, and ensuring the convergence of the iterative process.
Figure BDA0003651796680000157
Calculating theoretical power
Figure BDA0003651796680000158
And virtual power
Figure BDA0003651796680000159
Wherein the theoretical power
Figure BDA00036517966800001510
According to the definition of the consistency variable, the following results are obtained:
Figure BDA00036517966800001511
for theoretical power
Figure BDA00036517966800001512
Corrected to obtain virtual power
Figure BDA00036517966800001513
Comprises the following steps:
Figure BDA00036517966800001514
in the formula, τ 3 To correct the scale.
Fourthly, calculating the distribution value of the frequency modulation power
Figure BDA00036517966800001515
The power distribution result of the stored energy n of the kth iteration is as follows:
Figure BDA00036517966800001516
(3) judging whether the maximum iteration number is exceeded: and (4) if the frequency modulation control operation is within the specified maximum iteration number, namely within the frequency modulation period delta t, returning to the step (1), otherwise, entering the next step.
(4) And taking the frequency modulation power distribution value of each stored energy obtained by the last iteration as the frequency modulation output.
And further completing a distributed energy storage primary frequency modulation control strategy.
Taking the modified 3-machine 9-node power system shown in fig. 6 as an example, the power system with 3-machine 9 nodes of a wind, light, thermal power and power grid side distributed energy storage power station built in the MATLAB/Simulink environment is provided. The grid load disturbance data is shown in fig. 7.
As shown in fig. 8, compared with the conventional method of fixedly setting the energy storage primary frequency modulation responsibility, when the distributed energy storage primary frequency modulation control strategy of the present invention is adopted, the frequency fluctuation of the system is significantly reduced.
As shown in fig. 9, the frequency signal is passed through a multi-scale morphological filter, and can be divided into high and low frequency bands quickly and reasonably. The low-frequency signal is taken charge of by the thermal power generating unit, so that the mechanical abrasion of the thermal power generating unit can be effectively reduced, and the frequency modulation burden of the thermal power generating unit is lightened; the high-frequency signal is borne by the distributed energy storage cluster, and the energy storage rapid frequency adjusting capability is exerted.
For convenience of observation and analysis, representative energy storages of various types are selected, namely power type energy storage S1 (super capacitor), hybrid type energy storage S5 (lithium battery pack) and capacity type energy storage S9 (lead storage battery pack). The frequency modulation output curve and the residual capacity ratio curve of the three stored energies are respectively shown in fig. 10 and fig. 11.
As can be seen from fig. 10, the power type energy storage S1 can quickly respond to the frequency modulation signal at the initial stage of frequency modulation, and exhibits the characteristic of fast ramp-up, but the output power is gradually reduced due to the small capacity; the capacity type energy storage S9 has slow response speed at the initial stage of frequency modulation due to slow climbing, but has larger capacity, and can maintain high-power output; the frequency modulation response of the hybrid energy storage S5 is intermediate between that of the power type energy storage and that of the capacity type energy storage. Therefore, the distributed energy storage primary frequency modulation control method can reasonably arrange the frequency modulation output of each energy storage according to the frequency modulation characteristic of each energy storage, exert the respective advantages of the energy storage primary frequency modulation output and improve the overall frequency modulation capability of the system.
As can be seen from fig. 11, the remaining capacity ratios of the stored energy are all within the specified range, and the set primary frequency modulation consumption is in positive correlation with the deviation of the remaining capacity of the stored energy, so that the control method can effectively avoid overcharging and overdischarging of a single stored energy, avoid the exceeding of the stored energy capacity, and enable each stored energy to be in a "shallow charging and shallow discharging" state, thereby prolonging the charging and discharging life of the stored energy.
In order to prove the advantage of the distributed consistency algorithm in the aspect of optimizing speed, a Quadratic Programming (QP) method and a multi-object with particle swarm optimization (MOPSO) optimization algorithm are selected, the characteristics of a centralized control method and the distributed consistency control method in the aspects of optimizing speed, optimizing precision and the like are compared, and specific simulation comparison data are shown in table 1.
TABLE 1 comparison of distributed Algorithm with centralized control method
Figure BDA0003651796680000161
Figure BDA0003651796680000171
Compared with QP and MOPSO methods adopting a centralized control method, the distributed consistency algorithm adopted by the invention has great advantages on both single average optimization time and single maximum optimization time, and although the average tracking error is greater than the former, the average tracking error is within a controllable range. When the maximum iteration number of the consistency algorithm is increased from 80 to 120, the average tracking error is reduced, but the optimization time is also increased, so that 100 times are selected as the maximum iteration number; when the connectivity of the communication topology of the energy storage is increased from 2 to 6, the optimization time and the tracking error are reduced, because the increase of the connectivity improves the communication link between the energy storage, accelerates the convergence of optimization iteration, but increases the communication cost.
The centralized control method takes a control center as a core, the control center needs to keep two-way communication with each energy storage, when the geographic distance between the control center and the distributed energy storage is too far, the communication delay is considerable, and the communication cost is large; the distributed consistency algorithm disperses the operation burden of the centralized control method to each energy storage, so that the calculation pressure of the control center can be relieved, and the distributed consistency algorithm is more suitable for the development trend that the types and the quantity of the distributed energy storage on the power grid side are increased continuously in the future.
The invention has the following advantages and beneficial effects: 1. the frequency modulation output of each energy storage is reasonably distributed, the excessive utilization of a single energy storage is avoided, the frequency modulation potential of each energy storage is fully exerted, the thermal power mechanical abrasion is reduced, and the overall frequency modulation capability of the system is improved. 2. The distributed consistency algorithm disperses the operation burden of the centralized control method to each frequency modulation resource, so that the calculation pressure of the control center can be relieved, and the distributed consistency algorithm is more suitable for the development trend that the types and the quantity of the distributed frequency modulation resources on the power grid side are increased continuously in the future. The control method can give certain guidance to the primary frequency modulation control of the polymorphic distributed energy storage under the high new energy permeation efficiency.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (5)

1. A polymorphic distributed energy storage primary frequency modulation control method based on a consistency algorithm is characterized in that: the method comprises the following steps:
A. establishing a polymorphic distributed energy storage frequency modulation model and a thermal power generating unit frequency modulation model, and further establishing a power system frequency modulation model containing the thermal power generating unit and the power grid side distributed energy storage;
B. analyzing the frequency response characteristic of a power system containing a thermal power generating unit and power grid side distributed energy storage, and designing a multi-scale morphological frequency division filter, wherein the multi-scale morphological frequency division filter is used for decomposing a frequency signal into a high frequency band and a low frequency band;
C. establishing an energy storage frequency modulation consumption model, and determining frequency modulation capacity range constraints in the frequency modulation process, wherein the energy storage frequency modulation capacity range constraints comprise energy storage real-time residual capacity constraints, frequency modulation power standby constraints and climbing rate constraints;
D. and C, optimally distributing the frequency modulation output of the distributed energy storage according to the multi-scale morphological frequency division filter designed in the step B and the frequency modulation consumption model of the energy storage established in the step C, and finishing a distributed energy storage primary frequency modulation control strategy.
2. The multimodal distributed energy storage primary frequency modulation control method based on the consistency algorithm as claimed in claim 1, characterized in that: the step A specifically comprises the following steps:
step A1: obtaining the key frequency modulation coefficient of each type of energy storage through parameter identification according to the frequency modulation characteristic of each energy storage, and establishing a frequency modulation model of polymorphic distributed energy storage, wherein the parameters identified in the parameter identification comprise: time constant T for expressing energy storage time delay characteristic S (ii) a A droop coefficient r; real-time power weight coefficient alpha of stored energy s (ii) a Weight coefficient beta of energy storage residual capacity s (ii) a Energy storage charging and discharging efficiency eta c 、η d (ii) a Rated capacity of stored energy E rated (ii) a Maximum and minimum limit of available capacity of energy storage
Figure FDA0003651796670000011
i E(ii) a Energy storage frequency modulation output maximum and minimum limit
Figure FDA0003651796670000012
i P(ii) a Maximum and minimum limits of energy storage climbing speed
Figure FDA0003651796670000013
i R；
Step A2: according to historical data of the thermal power generating unit, obtaining a frequency modulation key coefficient of the thermal power generating unit through parameter identification, and establishing a frequency modulation model of the thermal power generating unit, wherein parameters identified in the parameter identification comprise: set feedback time constant T s0 、T s1 (ii) a A unit difference adjustment coefficient R; a valve characteristic curve model; high pressure steam chamber steam volume time constant T CH (ii) a Reheat steam volume time constant T RH (ii) a Power coefficient F of high pressure cylinder HP (ii) a Low power coefficient F of cylinder LP ；
Step A3: establishing a combined frequency modulation model containing thermal power-distributed energy storage according to the frequency modulation key coefficients of the various types of energy storage and the frequency modulation key coefficients of the thermal power generating unit obtained in the steps A1 and A2, wherein the model parameters further comprise a generator-power grid equivalent inertia coefficient M s (ii) a Damping coefficient D of the system s (ii) a A frequency modulation coefficient beta; frequency modulation participation degree alpha; frequency modulation proportional parameter K P (ii) a Frequency modulation integral parameter K I ；
Through the steps, the modeling of the frequency modulation system containing thermal power-distributed energy storage is completed, and the frequency modulation model of the power system containing the thermal power generating unit and the power grid side distributed energy storage is constructed and obtained.
3. The polymorphic distributed energy storage primary frequency modulation control method based on the consistency algorithm according to claim 2, characterized in that: the step B specifically comprises the following steps:
step B1: obtaining a frequency response transfer function of the power system containing the thermal power-distributed energy storage through the model parameters obtained in the step A, and analyzing the frequency response characteristics of the system, wherein
The system frequency response transfer function H(s) containing the thermal power-distributed energy storage is as follows:
Figure FDA0003651796670000021
wherein Δ f(s) is the system frequency deviation, Δ P D (s) is the system load disturbance deviation;
respectively analyzing the baud graphs of the system frequency response transfer function under the following two conditions:
(1)H 1 (s): only the traditional thermal power generating unit participates in the primary frequency modulation of the system;
(2)H 2 (s): by distributed energy storage onlyPrimary frequency modulation with a system;
calculate H 1 (s) and H 2 (s) demarcation frequency value f at intersection of amplitude-frequency curve d When the frequency of load fluctuation in the system is higher than f d When the system is in use, only the distributed energy storage participates in the gain delta f (s)/delta P of the primary frequency modulation of the system D (s) is smaller, which shows that the system frequency in the frequency band has stronger regulation capability, so that the demarcation frequency value f d Can be used as the frequency division critical parameter of the frequency division filter;
step B2: designing a frequency division filter through a decomposition algorithm of the multi-scale morphological filter:
setting an input signal as x (N), wherein N belongs to {0,1, …, N-1 }; the structural element is gamma (M), M belongs to {0,1, …, M-1}, and the expansion operation and the corrosion operation of the function are respectively as follows:
Figure FDA0003651796670000022
(xΘγ)(n)＝min[x(n+m)-γ(m)] (3)
in the formula:
Figure FDA0003651796670000023
is the inflation operator; Θ is the corrosion operator;
the specific operation processes of the opening and closing operation are respectively as follows:
Figure FDA0003651796670000031
Figure FDA0003651796670000032
in the formula (I), the compound is shown in the specification,
Figure FDA0003651796670000033
an on operator; is the close operator; s is a time scale;
the multi-scale morphology filter (MMF) is obtained by weighting and summing mixed morphology filters of different scales, and the specific expression is as follows:
Figure FDA0003651796670000034
Figure FDA0003651796670000035
in the formula: gamma is a structural element; k is the number of timescales;
Figure FDA0003651796670000036
the expression of the weighted value of each scale structural element is as follows:
Figure FDA0003651796670000037
in the formula:
Figure FDA0003651796670000038
for filtering the mean square error, the expression is:
Figure FDA0003651796670000039
adaptively selecting structural elements under different scales through an algorithm, and calculating the boundary frequency value f according to the step B1 d And decomposing the frequency signal of the primary frequency modulation feedback channel of the system frequency response model into high and low 2 frequency bands.
4. The polymorphic distributed energy storage primary frequency modulation control method based on the consistency algorithm according to claim 3, characterized in that: the step C specifically comprises the following steps:
step C1: defining the frequency modulation consumption of stored energy:
referring to the output consumption cost concept in the economic dispatching of the traditional generating set, citing the concept of frequency modulation cost function, and taking the lowest output consumption cost as the target of the frequency modulation control of the system,
frequency modulation cost function of energy storage i at time t
Figure FDA00036517966700000310
Comprises the following steps:
Figure FDA0003651796670000041
in the formula, P i,t Real-time power for stored energy i; e i,t The real-time residual capacity delta t of the energy storage i at t is a frequency modulation control period;
modulating frequency consumption
Figure FDA0003651796670000042
Written as relating to charge and discharge power P i,t The established frequency modulation consumption model of the stored energy is as follows:
Figure FDA0003651796670000043
coefficient a in formula (11) i 、b i,t 、c i,t The specific expression of (A) is as follows:
Figure FDA0003651796670000044
step C2: determining the frequency modulation capacity range constraint of energy storage:
the energy storage frequency modulation capacity range constraint comprises energy storage real-time residual capacity constraint, frequency modulation power standby constraint and climbing rate constraint, and specifically comprises the following steps:
Figure FDA0003651796670000045
under the restriction of the range, the upper limit and the lower limit of the energy storage charging and discharging power
Figure FDA0003651796670000046
Comprises the following steps:
Figure FDA0003651796670000047
5. the multimodal distributed energy storage primary frequency modulation control method based on the consistency algorithm as claimed in claim 4, characterized in that: the step D specifically comprises the following steps:
d1: dividing the frequency signal into high and low frequency bands by a morphological filter, wherein the low frequency signal delta f low Borne by thermal power generating units and having high-frequency signals Deltaf high The output requirement of primary frequency modulation at the moment t is determined by bearing a plurality of distributed energy storages
Figure FDA0003651796670000051
Comprises the following steps:
Figure FDA0003651796670000052
in order to obtain the optimal primary frequency modulation output distribution scheme at the moment t, the total frequency modulation consumption Z is used t And establishing a mathematical optimization model with the minimum as an objective function:
Figure FDA0003651796670000053
step D2: defining a consistency variable of distributed energy storage, and converting an optimization problem of an optimal primary frequency modulation output distribution scheme into an optimization problem of frequency modulation consumption margin cost consistency:
defining a partial derivative of the energy storage frequency modulation consumption to the frequency modulation output, namely frequency modulation marginal cost, as a consistency variable lambda, wherein the consistency variables of the distributed energy storage are respectively as follows:
Figure FDA0003651796670000054
converting the optimization problem of equation (16) to an optimization problem as shown in equation (18):
Figure FDA0003651796670000055
step D3: discretizing the optimization problem, optimizing and distributing the frequency modulation output of the distributed energy storage through a discrete distributed consistency algorithm,
the distributed energy storage frequency modulation output is optimized and distributed through a discrete distributed consistency algorithm, and the specific flow is as follows:
1) firstly, initializing each variable, initializing initial output of each energy storage, and enabling each energy storage to be initialized
Figure FDA0003651796670000061
The initial distribution to each distributed energy storage is as follows:
Figure FDA0003651796670000062
in the formula (I), the compound is shown in the specification,
Figure FDA0003651796670000063
initial frequency-modulated output, rho, for each stored energy i For the preliminary assignment of the proportion of the frequency-modulated signal,
initializing consistency variables of each stored energy:
Figure FDA0003651796670000064
in the formula (I), the compound is shown in the specification,
Figure FDA0003651796670000065
an initial consistency variable for each stored energy;
2) the scheme for optimally calculating the frequency-modulated output distribution of each stored energy at the time t is divided into four steps (1) to (4), and specifically comprises the following steps:
(1) judging the consistency of adjacent stored energy: each distributed energy storage obtains a consistency variable lambda of adjacent energy storage through a communication network t And judges whether the consistency variable is consistent with the consistency variable,
the conditions that specify near agreement are:
Figure FDA0003651796670000066
wherein k is the number of iterations,
Figure FDA0003651796670000067
representing that the stored energy m is adjacent to and in communication with the power source n;
each distributed energy storage obtains the consistency variable lambda of the adjacent energy storage through the communication network t Judging whether the consistency variable is consistent with the consistency variable, if the consistency variable meets the consistency condition, jumping to the step (4), and if not, continuing to perform;
(2) iterative consistency variables: for making the consistency variable lambda of the stored energy n n,t The method is approximately consistent with an adjacent power supply, the frequency modulation output of the stored energy needs to be adjusted, and the method is divided into four small steps from (i) to (iv), and the iterative calculation method specifically comprises the following steps:
calculating a virtual consistency variable of the stored energy n
Figure FDA0003651796670000068
Consistency variables for the (k-1) th iteration
Figure FDA0003651796670000069
And correcting the power constraint, specifically:
Figure FDA00036517966700000610
in the formula, τ 1 ，τ 2 To correct the ratio, the first part of the correction is to store the consistency variable of the nth (k-1) iteration
Figure FDA0003651796670000071
Respectively with adjacent power supplies
Figure FDA0003651796670000072
Calculating and accumulating difference values; the second part of the modification is a modification of the power constraint;
calculating consistency variable by aiming at virtual consistency variable
Figure FDA0003651796670000073
And (3) constraining, further defining the consistency variable correction value, and ensuring the convergence of the iterative process:
Figure FDA0003651796670000074
calculating theoretical power
Figure FDA0003651796670000075
And virtual power
Figure FDA0003651796670000076
Wherein the theoretical power
Figure FDA0003651796670000077
According to the definition of the consistency variable, the following results are obtained:
Figure FDA0003651796670000078
for theoretical power
Figure FDA0003651796670000079
Corrected to obtain virtual power
Figure FDA00036517966700000710
Comprises the following steps:
Figure FDA00036517966700000711
in the formula, τ 3 To correct the ratio;
fourthly, calculating the distribution value of the frequency modulation power
Figure FDA00036517966700000712
The power distribution result of the stored energy n of the kth iteration is as follows:
Figure FDA00036517966700000713
(3) judging whether the maximum iteration number is exceeded: if the frequency modulation control operation is within the specified maximum iteration times, namely within the frequency modulation period delta t, returning to the step (1), otherwise, entering the next step;
(4) taking the frequency modulation power distribution value of each stored energy obtained by the last iteration as frequency modulation output;
and further completing a distributed energy storage primary frequency modulation control strategy.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117117973A (en) * 2023-10-24 2023-11-24 国网浙江省电力有限公司宁波供电公司 Distributed power supply scheduling method and device based on time scale and storage medium

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180329383A1 (en) * 2015-07-28 2018-11-15 Battelle Memorial Institute Extracting maximal frequency response potential in controllable loads
CN112366727A (en) * 2020-10-23 2021-02-12 国网湖北省电力有限公司电力科学研究院 Primary frequency modulation control method, device and equipment for thermal power generating unit and storage medium
CN112531792A (en) * 2020-12-03 2021-03-19 江苏方天电力技术有限公司 Frequency control method and system for interconnected power system containing energy storage resources
CN112615399A (en) * 2020-11-24 2021-04-06 国网江苏省电力有限公司 Energy storage system participating power grid frequency modulation optimization control method and system and storage medium
CN114243727A (en) * 2021-12-21 2022-03-25 国网河北省电力有限公司电力科学研究院 Source network load self-optimization intelligent regulation and control system based on wide area perception prediction and real-time control optimization technical method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180329383A1 (en) * 2015-07-28 2018-11-15 Battelle Memorial Institute Extracting maximal frequency response potential in controllable loads
CN112366727A (en) * 2020-10-23 2021-02-12 国网湖北省电力有限公司电力科学研究院 Primary frequency modulation control method, device and equipment for thermal power generating unit and storage medium
CN112615399A (en) * 2020-11-24 2021-04-06 国网江苏省电力有限公司 Energy storage system participating power grid frequency modulation optimization control method and system and storage medium
CN112531792A (en) * 2020-12-03 2021-03-19 江苏方天电力技术有限公司 Frequency control method and system for interconnected power system containing energy storage resources
CN114243727A (en) * 2021-12-21 2022-03-25 国网河北省电力有限公司电力科学研究院 Source network load self-optimization intelligent regulation and control system based on wide area perception prediction and real-time control optimization technical method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117117973A (en) * 2023-10-24 2023-11-24 国网浙江省电力有限公司宁波供电公司 Distributed power supply scheduling method and device based on time scale and storage medium
CN117117973B (en) * 2023-10-24 2024-01-12 国网浙江省电力有限公司宁波供电公司 Distributed power supply scheduling method and device based on time scale and storage medium

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