CN116780587A - Frequency modulation capacity optimization method and system for hybrid energy storage auxiliary unit - Google Patents
Frequency modulation capacity optimization method and system for hybrid energy storage auxiliary unit Download PDFInfo
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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
- H02J3/30—Arrangements for balancing of the load in a network by storage of energy using dynamo-electric machines coupled to flywheels
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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
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
- H02J3/241—The oscillation concerning frequency
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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
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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
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Abstract
The invention discloses a method and a system for optimizing frequency modulation capacity of a hybrid energy storage auxiliary unit, which belong to the technical field of power grid regulation, wherein the method comprises the following steps: constructing an objective function for optimizing the capacity of each energy storage device of the hybrid energy storage auxiliary unit; determining constraint conditions for optimizing the frequency modulation capacity of the hybrid energy storage auxiliary unit; and solving the objective function by adopting a self-adaptive chaotic particle swarm algorithm based on the constraint condition, and determining the optimal capacity of each energy storage device of the hybrid energy storage auxiliary unit. According to the invention, under the constraint condition of the energy storage device, the comprehensive performance of the system under different energy storage capacities is considered, the optimal configuration of the capacities of the energy storage devices for hybrid energy storage is realized, the maximization of return on the power generation side is realized, and the resource waste is avoided.
Description
Technical Field
The invention relates to the technical field of power grid regulation, in particular to a frequency modulation capacity optimization method and system of a hybrid energy storage auxiliary unit.
Background
Renewable energy sources such as wind power, photovoltaic and the like have the characteristics of randomness, intermittence and uncertainty, and can have great influence on the frequency of a power grid when large-scale grid connection is performed, so that the safe and stable operation of the power grid is seriously influenced. The original frequency modulation means can not stabilize the fluctuation of the power grid frequency, and a new frequency modulation mode is urgently needed to solve the problem of frequency stabilization caused by large-scale uncertainty new energy grid connection. The energy storage system has the advantages of high instantaneous power throughput capacity, high response speed, high regulation precision and the like, and the energy storage system is introduced into the power system to perform power-frequency control, so that the frequency stability of the power grid can be improved. The energy storage frequency modulation capacity is set to be too large, so that the cost is increased, the resource is wasted, the capacity is too small, the thermal power unit can frequently respond to the AGC command, the loss of the thermal power unit is increased, the operation and maintenance cost is increased, and meanwhile, the operation efficiency of the thermal power unit is also reduced. Therefore, on the basis of guaranteeing the frequency modulation performance, the capacity of the energy storage system is reasonably configured, and the method has important practical significance for improving the running stability of the power grid.
Disclosure of Invention
The invention aims to provide a frequency modulation capacity optimization method and system for a hybrid energy storage auxiliary unit, so as to realize optimal configuration of capacities of energy storage devices of hybrid energy storage and maximization of return on a power generation side on the basis of guaranteeing frequency modulation performance and stability of power grid operation.
In order to achieve the above object, the present invention provides the following solutions:
the invention provides a frequency modulation capacity optimization method of a hybrid energy storage auxiliary unit, which comprises the following steps:
-constructing an objective function for optimizing the capacity of each energy storage device of the hybrid energy storage auxiliary unit, the objective function comprising: building punishment, running loss and taking into consideration the return of the frequency modulation performance index;
determining constraint conditions for optimizing the frequency modulation capacity of the hybrid energy storage auxiliary unit;
and solving the objective function by adopting a self-adaptive chaotic particle swarm algorithm based on the constraint condition, and determining the optimal capacity of each energy storage device of the hybrid energy storage auxiliary unit.
Optionally, the objective function is:
f＝M 1 -M 2 -M 3 ；
wherein f is an objective function, M 1 To consider the return of FM performance indicators, M 2 To build punishment, M 3 Is the running loss;
M 2 ＝α i V i ；
M 3 ＝M 31 +M 32 ；
M 31 ＝γ i m i ；
M 32 ＝δ i n i ；
Y AGC for AGC adjustment performance reporting, k p For AGC to adjust performance reporting index, D n The method is characterized in that the method contributes to the stabilization of the power grid frequency in the nth AGC frequency modulation of the hybrid energy storage auxiliary unit, N is the number of the AGC frequency modulation and alpha i Penalty coefficient for construction of the ith energy storage device, V i For the capacity of the ith energy storage device, M 31 Run penalty for deep charging, M 32 Gamma, an operation penalty term for overdischarge i Penalty factor, m, for deep charge and discharge operation of the ith energy storage device i Is the depth charge and discharge times delta of the ith energy storage device i Excessive for the ith energy storage deviceCharge-discharge run penalty factor, n i The number of times of overcharge and discharge of the ith energy storage device.
Optionally, the manner of determining the AGC adjustment performance report indicator is:
k p ＝k 1 ×k 2 ×k 3 ；
wherein ,k1 To adjust the speed index, k 2 To adjust the accuracy index, k 3 Is a response time index;
v n for the standard regulation rate of the hybrid energy storage auxiliary unit, v is the speed of the hybrid energy storage auxiliary unit responding to the AGC command, deltaP is the deviation after one AGC regulation, and P n For the rated power of the hybrid energy storage auxiliary unit, deltaT is the average value of the response time of the hybrid energy storage auxiliary unit, T n Is a response time reference value.
Optionally, the constraint condition includes: the charge and discharge power constraint, the SOC level constraint, the power constraint and the capacity constraint of each energy storage device.
Optionally, the charge-discharge power constraint is:
P i，dmin ≤P i，d (t)≤P i，dmax ；
P i，cmin ≤P i，c (t)≤P i，cmax ；
wherein ,Pi，d(t) and Pi，c (t) is the discharge power and the charge power of the ith energy storage device at the moment t respectively, P i，dmin and Pi，dmax Respectively the minimum discharge power and the maximum discharge power stored by the ith energy storage device, P i，cmin and Pi，cmax Respectively storing the minimum charging power and the maximum charging power of the ith energy storage device;
the SOC level constraint is:
SOC i，min ≤SOC i (t)≤SOC i，max ；
wherein ,SOCi (t) is the ith energy storage deviceState of charge, SOC at time t i，min and SOCi，max The minimum charge state and the maximum charge state of the ith energy storage device respectively;
the power constraint is:
wherein ,Pi，d and Pi，c Rated discharge power and rated charge power, beta, respectively, of the ith energy storage device i，d and βi，c The discharging efficiency and the charging efficiency of the ith energy storage device are respectively;
the capacity constraint is:
wherein ,Vi For the capacity of the ith energy storage device, P i (t) is the operating power of the ith energy storage device at the moment t, and the soc i，0 And the SOC value is the SOC value at the operation starting time of the ith energy storage device, and delta t is the sampling period.
Optionally, the energy storage device of the hybrid energy storage auxiliary unit includes a flywheel energy storage device and a storage battery energy storage device, and the manner of determining the discharging power and the charging power of each energy storage device at the time t is as follows:
calculating a power difference value between an AGC instruction at the time t and the current output power of the hybrid energy storage auxiliary unit;
performing modal decomposition on the power difference value to obtain a plurality of intrinsic modal components and a margin;
reconstructing the plurality of eigenmode components and the allowance to obtain a high-frequency component and a low-frequency component;
based on the high-frequency component and the low-frequency component, determining the charging power and the discharging power of the flywheel energy storage device and the charging power and the discharging power of the storage battery energy storage device are respectively as follows:
P Ac (t)＝min(P Ac (t-1)+P L (t)，P Acmax )；
P Ad (t)＝min(P Ad (t-1)+P L (t)，P Admax )；
P Fc (t)＝min(P Fc (t-1)+P H (t)，P Fcmax )；
P Fc (t)＝min(P Fd (t-1)+P H (t)，P Fdmax )；
wherein ,PAc(t) and PAd (t) represents the charging power and the discharging power of the accumulator energy storage device at the time t, P Acmax and PAdmax The maximum charging power and the maximum discharging power of the storage battery energy storage device are respectively; p (P) Fc(t) and PFd (t) the charge power and the discharge power of the flywheel energy storage device respectively, P Fcmax and PFdmax The maximum charging power and the maximum discharging power of the flywheel energy storage device are respectively.
Optionally, in the adaptive chaotic particle swarm algorithm, an updating formula of the inertia weight of the particles is as follows:
wherein ,is the inertia weight of the jth particle at the kth+1th iteration, +.> and />Respectively represent the maximum value and the minimum value of the inertia weight of each particle at the kth iteration, +.>An objective function value representing the jth particle at the kth iteration,> and />Respectively representing the average objective function value and the minimum objective function value of all particles at the kth iteration;
the updated formula for the velocity and position of the particles is:
wherein , and />The speed of the jth particle at the kth and the (k+1) th iteration, respectively,/-> and />The position of the jth particle at the kth and the (k+1) th iteration, respectively,/->For the individual extremum of the jth particle at the kth iteration,>for the global extremum at the kth iteration, c1, c2 are learning factors, r 1 、r 2 Are all distributed in [0,1 ]]Random numbers of intervals.
A hybrid energy storage auxiliary unit frequency modulation capacity optimization system, the system being applied to the above method, the system comprising:
an objective function construction module, configured to construct an objective function for optimizing a capacity of each energy storage device of the hybrid energy storage auxiliary unit, where the objective function includes: building punishment, running loss and taking into consideration the return of the frequency modulation performance index;
the constraint condition determining module is used for determining constraint conditions for optimizing the frequency modulation capacity of the hybrid energy storage auxiliary unit;
and the objective function solving module is used for solving the objective function by adopting a self-adaptive chaotic particle swarm algorithm based on the constraint condition and determining the optimal capacity of each energy storage device for frequency modulation of the hybrid energy storage auxiliary unit.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method described above when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method described above.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the embodiment of the invention provides a method and a system for optimizing the frequency modulation capacity of a hybrid energy storage auxiliary unit, wherein the method comprises the following steps: constructing an objective function for optimizing the capacity of each energy storage device of the hybrid energy storage auxiliary unit; determining constraint conditions for optimizing the frequency modulation capacity of the hybrid energy storage auxiliary unit; and solving the objective function by adopting a self-adaptive chaotic particle swarm algorithm based on the constraint condition, and determining the optimal capacity of each energy storage device of the hybrid energy storage auxiliary unit. Under the constraint condition of the energy storage device, the comprehensive performance of the system under different energy storage capacities is considered, the optimal configuration of the capacities of the energy storage devices for hybrid energy storage is realized, and the maximization of the return on the power generation side is realized.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for optimizing frequency modulation capacity of a hybrid energy storage auxiliary unit according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a frequency modulation capacity optimization method and system for a hybrid energy storage auxiliary unit, so as to avoid resource waste on the basis of guaranteeing frequency modulation performance and guaranteeing the running stability of a power grid.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
The embodiment of the invention provides a frequency modulation capacity optimization method of a hybrid energy storage auxiliary unit, which comprises the following steps:
-constructing an objective function for optimizing the capacity of each energy storage device of the hybrid energy storage auxiliary unit, the objective function comprising: construction penalty, running loss and return taking into account frequency modulation performance metrics.
And determining constraint conditions for optimizing the frequency modulation capacity of the hybrid energy storage auxiliary unit.
And solving the objective function by adopting a self-adaptive chaotic particle swarm algorithm based on the constraint condition, and determining the optimal capacity of each energy storage device of the hybrid energy storage auxiliary unit.
The objective function provided by the embodiment of the invention includes construction penalty, operation loss term, and return considering frequency modulation performance index, specifically:
f＝M 1 -M 2 -M 3 ；
wherein f is an objective function, M 1 To consider the return of FM performance indicators, M 2 To build punishment, M 3 Is the running loss;
the construction penalty refers to the construction penalty term of purchasing the initial investment of the energy storage device and the energy storage, and is related to the capacity of the energy storage, M 2 ＝α i V i ；
The operation loss refers to the loss corresponding to the life loss of the energy storage system due to deep charge and over discharge in the production operation process. The calculation formula is as follows:
M 3 ＝M 31 +M 32 ；
M 31 ＝γ i m i ；
M 32 ＝δ i n i ；
when the return of the frequency modulation performance index is considered to indicate that the energy storage system participates in frequency modulation, compensating frequency modulation is carried out on the AGC unit, and the calculation formula is as follows:
wherein ,YAGC For AGC adjustment performance reporting, k p For AGC to adjust performance reporting index, D n The method is characterized in that the method contributes to the stabilization of the power grid frequency in the nth AGC frequency modulation of the hybrid energy storage auxiliary unit, N is the number of the AGC frequency modulation and alpha i Penalty coefficient for construction of the ith energy storage device, V i For the capacity of the ith energy storage device, M 31 Run penalty for deep charging, M 32 Gamma, an operation penalty term for overdischarge i Penalty factor, m, for deep charge and discharge operation of the ith energy storage device i Depth of the ith energy storage deviceNumber of charge and discharge, delta i An excessive charge and discharge operation penalty factor of the ith energy storage device, n i The number of times of overcharge and discharge of the ith energy storage device.
Wherein, the frequency modulation performance is mainly evaluated by AGC regulation performance return index of a unit, and mainly comprises regulation speed index k 1 Adjusting accuracy k 2 And a response time index k 3 The mode of determining the AGC adjustment performance return index is:
k p ＝k 1 ×k 2 ×k 3 ；
wherein ,k1 To adjust the speed index, k 2 To adjust the accuracy index, k 3 Is a response time index;
v n for the standard regulation rate of the hybrid energy storage auxiliary unit, v is the speed of the hybrid energy storage auxiliary unit responding to the AGC command, deltaP is the deviation after one AGC regulation, and P n For the rated power of the hybrid energy storage auxiliary unit, deltaT is the average value of the response time of the hybrid energy storage auxiliary unit, T n Is a response time reference value.
Exemplary constraints include: the charge and discharge power constraint, the SOC level constraint, the power constraint and the capacity constraint of each energy storage device.
The charge and discharge power constraint is as follows:
P i，dmin ≤P i，d (t)≤P i，dmax ；
P i，cmin ≤P i，c (t)≤P i，cmax ；
wherein ,Pi，d(t) and Pi，c (t) is the discharge power and the charge power of the ith energy storage device at the moment t respectively, P i，dmin and Pi，dmax Respectively the minimum discharge power and the maximum discharge power stored by the ith energy storage device, P i，cmin and Pi，cmax Respectively storing the minimum charging power and the maximum charging power of the ith energy storage device;
the SOC level constraint is:
SOC i，min ≤SOC i (t)≤SOC i，max ；
wherein ,SOCi (t) is the state of charge, SOC, of the ith energy storage device at time t i，min and SOCi，max The minimum charge state and the maximum charge state of the ith energy storage device respectively;
wherein ,SOCi (t-1) is the charge state of the ith energy storage device at the time t-1, V i For the capacity of the ith energy storage device, P i (t-1) is the operating power of the ith energy storage device at time t-1;
the power constraint is:
wherein ,Pi，d and Pi，c Rated discharge power and rated charge power, beta, respectively, of the ith energy storage device i，d and βi，c The discharging efficiency and the charging efficiency of the ith energy storage device are respectively;
the capacity constraint is:
wherein ,Vi For the capacity of the ith energy storage device, P i (t) is the operating power of the ith energy storage device at time t, P when charging i (t)＝P i，c (t) when discharging, P i (t)＝P i，d (t)，soc i，0 For the operation starting time of the ith energy storage deviceAnd Δt is the sampling period.
The energy storage device of the hybrid energy storage auxiliary unit comprises a flywheel energy storage device and a storage battery energy storage device, and the discharging power and the charging power of each energy storage device at the time t are determined by the following modes:
step 1: calculating a power difference delta P (t) between an AGC instruction at a moment t and the current unit output power by using the operation data of the unit;
step 2: decomposing the power difference delta P (t) calculated in the step 1 into 10 intrinsic mode components (intrinsic mode function, IMF) and a margin r (t) by using empirical mode decomposition (empirical mode decomposition, EMD), specifically as shown in the formula (1):
step 3: reconstructing the 10 IMF components obtained by decomposition in the step 2 to obtain a high-frequency component P H (t) and mid-low frequency component P L (t) as shown in the formulas (2) and (3):
wherein the value range of a in the above formula is [1,9 ]]There are 10 reconstruction schemes, IMF m (t) is the mth eigenmode component.
Step 4: determining the current running state of the energy storage device according to the self SOC state and the charge-discharge state of the energy storage at the current t moment, and reconstructing the high-frequency component P H (t) and mid-low frequency component P L And (t) respectively born by the flywheel energy storage device and the storage battery energy storage device, and respectively calculating the output of the energy storage device. The specific charge and discharge power is expressed by the following formula:
P Ac (t)＝min(P Ac (t one)1)+P L (t)，P Acmax ) (4)
P Ad (t)＝min(P Ad (t-1)+P L (t)，P Admax ) (5)
P Fc (t)＝min(P Fc (t-1)+P H (t)，P Fcmax ) (6)
P Fc (t)＝min(P Fd (t-1)+P H (t)，P Fdmax ) (7)
wherein ,PAc(t) and PAd (t) represents the charging power and the discharging power of the accumulator energy storage device at the time t, P Acmax and PAdmax The maximum charging power and the maximum discharging power of the storage battery energy storage device are respectively; PF (physical filter) c(t) and PFd (t) the charge power and the discharge power of the flywheel energy storage device respectively, P Fcmax and PFdmax The maximum charging power and the maximum discharging power of the flywheel energy storage device are respectively.
On the basis of the steps 1-4, the embodiment of the invention further comprises the following steps:
step 5: and constructing an energy storage device capacity optimization objective function f aiming at maximizing the yield of the power generation side on the basis of guaranteeing the improvement of the AGC frequency modulation effect index according to the energy storage device charge and discharge power constraint, the energy storage device SOC constraint condition power and the capacity constraint.
Step 6: and solving the capacity and the power of the corresponding flywheel energy storage device and the corresponding storage battery energy storage device when the objective function maxf is solved by using an Adaptive Chaotic particle swarm Algorithm (ACPSO).
Illustratively, as shown in fig. 1, the capacities of the flywheel energy storage device and the battery energy storage device when the Adaptive Chaotic particle swarm Algorithm (ACPSO) is used to solve the objective function maxf include the following steps:
step 6-1: initializing and setting related parameters of an objective function, and setting related parameters of ACPSO: maximum allowable iteration number N, population scale M, inertia weight omega, learning factors c1 and c2, and giving flywheel energy storage device and storage battery energy storageInitial capacity V of the device f 、V b 。
Step 6-2: chaos initializes particle position and velocity. Randomly generating a vector z between 0 and 1 1 The next particle is generated according to equation (8). The fitness of each particle was calculated, producing J initial velocities randomly.
Where μ is a control parameter.
Step 6-3: updating inertial weights of the updated particles according to equation (9):
wherein ,is the inertia weight of the jth particle at the kth+1th iteration, +.> and />Respectively represent the maximum value and the minimum value of the inertia weight of each particle at the kth iteration, +.>An objective function value representing the jth particle at the kth iteration,> and />The average objective function value and the minimum objective function value of all particles at the kth iteration are represented, respectively.
Step 6-4: the velocity and position of the particles are updated according to the formulas (10) and (11).
wherein , and />The speed of the jth particle at the kth and the (k+1) th iteration, respectively,/-> and />The position of the jth particle at the kth and the (k+1) th iteration, respectively,/->For the individual extremum of the jth particle at the kth iteration,>for the global extremum at the kth iteration, c1, c2 are learning factors, r 1 、r 2 Are all distributed in [0,1 ]]Random numbers of intervals.
Step 6-5: and comparing the fitness value of each particle with the individual extremum gbest and the global extremum zbest of each particle, and if the fitness value is smaller, updating the individual extremum gbest and the global extremum zbest of each particle.
Step 6-6: and performing chaotic optimization on the optimal position to obtain a feasible solution with the best performance, and replacing the position of any particle in the current population with the feasible solution.
Step 6-7: and (3) whether the precision requirement is met or the maximum iteration number is reached, if the stopping condition is met, searching is stopped, and outputting a global optimal position, otherwise, returning to the step (6-3).
Step 7: and outputting the global optimal capacity of the flywheel energy storage device and the storage battery energy storage device.
Example 2
The embodiment 2 of the invention provides a frequency modulation capacity optimization system of a hybrid energy storage auxiliary unit, the system is applied to the method of the embodiment 1, and the system comprises:
an objective function construction module for constructing an objective function for optimizing a capacity of each energy storage device of the hybrid energy storage auxiliary unit, the objective function comprising: building punishment, running loss and taking into consideration the return of the frequency modulation performance index;
the constraint condition determining module is used for determining constraint conditions for optimizing the frequency modulation capacity of the hybrid energy storage auxiliary unit;
and the objective function solving module is used for solving the objective function by adopting a self-adaptive chaotic particle swarm algorithm based on the constraint condition and determining the optimal capacity of each energy storage device of the hybrid energy storage auxiliary unit.
Example 3
Embodiment 3 of the present invention provides an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method of embodiment 1 when executing the computer program.
Example 4
Embodiment 4 of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of embodiment 1.
Based on the embodiment, the technical scheme of the invention has the beneficial effects that:
according to the invention, the shortage between the AGC command of the thermal power unit and the unit output power is subjected to signal decomposition to obtain a plurality of sub-signals, the sub-signals are reconstructed to obtain a high-frequency component and a low-frequency component, the high-frequency component and the low-frequency component are respectively born by the flywheel energy storage device and the storage battery energy storage device, under the constraint condition of the energy storage device, the comprehensive performance of the system under different energy storage capacities is considered, the optimal configuration of the capacity of the hybrid energy storage is realized, and the maximization of the generating side income is realized.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (10)
1. The frequency modulation capacity optimization method of the hybrid energy storage auxiliary unit is characterized by comprising the following steps of:
-constructing an objective function for optimizing the capacity of each energy storage device of the hybrid energy storage auxiliary unit, the objective function comprising: building punishment, running loss and taking into consideration the return of the frequency modulation performance index;
determining constraint conditions for optimizing the frequency modulation capacity of the hybrid energy storage auxiliary unit;
and solving the objective function by adopting a self-adaptive chaotic particle swarm algorithm based on the constraint condition, and determining the optimal capacity of each energy storage device of the hybrid energy storage auxiliary unit.
2. The hybrid energy storage auxiliary unit frequency modulation capacity optimization method according to claim 1, wherein the objective function is:
f＝M 1 -M 2 -M 3 ；
wherein f isObjective function, M 1 To consider the return of FM performance indicators, M 2 To build punishment, M 3 Is the running loss;
M 2 ＝α i V i ；
M 3 ＝M 31 +M 32 ；
M 31 ＝γ i m i ；
M 32 ＝δ i n i ；
Y AGC for AGC adjustment performance reporting, k p For AGC to adjust performance reporting index, D n The method is characterized in that the method contributes to the stabilization of the power grid frequency in the nth AGC frequency modulation of the hybrid energy storage auxiliary unit, N is the number of the AGC frequency modulation and alpha i Penalty coefficient for construction of the ith energy storage device, V i For the capacity of the ith energy storage device, M 31 Run penalty for deep charging, M 32 Gamma, an operation penalty term for overdischarge i Penalty factor, m, for deep charge and discharge operation of the ith energy storage device i Is the depth charge and discharge times delta of the ith energy storage device i An excessive charge and discharge operation penalty factor of the ith energy storage device, n i The number of times of overcharge and discharge of the ith energy storage device.
3. The method for optimizing the frequency modulation capacity of a hybrid energy storage auxiliary unit according to claim 2, wherein the method for determining the AGC adjustment performance reporting index is as follows:
k p ＝k 1 ×k 2 ×k 3 ；
wherein ,k1 To adjust the speed index, k 2 To adjust the accuracy index, k 3 Is a response time index;
v n for the standard regulation rate of the hybrid energy storage auxiliary unit, v is the speed of the hybrid energy storage auxiliary unit responding to the AGC command, deltaP is the deviation after one AGC regulation, and P n For the rated power of the hybrid energy storage auxiliary unit, deltaT is the average value of the response time of the hybrid energy storage auxiliary unit, T n Is a response time reference value.
4. The hybrid energy storage auxiliary unit frequency modulation capacity optimization method according to claim 1, wherein the constraint conditions comprise: the charge and discharge power constraint, the SOC level constraint, the power constraint and the capacity constraint of each energy storage device.
5. The hybrid energy storage auxiliary unit frequency modulation capacity optimization method according to claim 4, wherein the charge-discharge power constraint is:
P i，dmin ≤P i，d (t)≤P i，dmax ；
P i，cmin ≤P i，c (t)≤P i，cmax ；
wherein ,Pi，d(t) and Pi，c (t) is the discharge power and the charge power of the ith energy storage device at the moment t respectively, P i，dmin and Pi，dmax Respectively the minimum discharge power and the maximum discharge power stored by the ith energy storage device, P i，cmin and Pi，cmax Respectively storing the minimum charging power and the maximum charging power of the ith energy storage device;
the SOC level constraint is:
SOC i，min ≤SOC i (t)≤SOC i，max ；
wherein ,SOCi (t) is the state of charge, SOC, of the ith energy storage device at time t i，min and SOCi，max The minimum charge state and the maximum charge state of the ith energy storage device respectively;
the power constraint is:
wherein ,Pi，d and Pi，c Rated discharge power and rated charge power, beta, respectively, of the ith energy storage device i，d and βi，c The discharging efficiency and the charging efficiency of the ith energy storage device are respectively;
the capacity constraint is:
wherein ,Vi For the capacity of the ith energy storage device, P i (t) is the operating power of the ith energy storage device at the moment t, and the soc i，0 And the SOC value is the SOC value at the operation starting time of the ith energy storage device, and delta t is the sampling period.
6. The method for optimizing the frequency modulation capacity of a hybrid energy storage auxiliary unit according to claim 5, wherein the energy storage device of the hybrid energy storage auxiliary unit comprises a flywheel energy storage device and a storage battery energy storage device, and the manner of determining the discharging power and the charging power of each energy storage device at the time t is as follows:
calculating a power difference value between an AGC instruction at the time t and the current output power of the hybrid energy storage auxiliary unit;
performing modal decomposition on the power difference value to obtain a plurality of intrinsic modal components and a margin;
reconstructing the plurality of eigenmode components and the allowance to obtain a high-frequency component and a low-frequency component;
based on the high-frequency component and the low-frequency component, determining the charging power and the discharging power of the flywheel energy storage device and the charging power and the discharging power of the storage battery energy storage device are respectively as follows:
P Ac (t)＝min(P Ac (t-1)+P L (t)，P Acmax )；
P Ad (t)＝min(P Ad (t-1)+P L (t)，P Admax )；
P Fc (t)＝min(P Fc (t-1)+P H (t)，P Fcmax )；
P Fc (t)＝min(P Fd (t-1)+P H (t)，P Fdmax )；
wherein ,PAc(t) and PAd (t) represents the charging power and the discharging power of the accumulator energy storage device at the time t, P Acmax and PAdmax The maximum charging power and the maximum discharging power of the storage battery energy storage device are respectively; p (P) Fc(t) and PFd (t) the charge power and the discharge power of the flywheel energy storage device respectively, P Fcmax and PFdmax The maximum charging power and the maximum discharging power of the flywheel energy storage device are respectively.
7. The method for optimizing the frequency modulation capacity of the hybrid energy storage auxiliary unit according to claim 1, wherein in the adaptive chaotic particle swarm algorithm, an updating formula of the inertia weight of the particles is as follows:
wherein ,is the inertia weight of the jth particle at the kth+1th iteration, +.> and />Respectively represent the maximum value and the minimum value of the inertia weight of each particle at the kth iteration, +.>Represents the objective function value of the jth particle at the kth iteration, and />Respectively representing the average objective function value and the minimum objective function value of all particles at the kth iteration;
the updated formula for the velocity and position of the particles is:
wherein , and />The speed of the jth particle at the kth and the (k+1) th iteration, respectively,/-> and />The position of the jth particle at the kth and the (k+1) th iteration, respectively,/->Is the individual extremum of the jth particle at the kth iteration,is the global extremum at the kth iteration, c 1 、c 2 R is the learning factor 1 、r 2 Are all distributed in [0,1 ]]Random numbers of intervals.
8. A hybrid energy storage auxiliary unit frequency modulation capacity optimization system, characterized in that the system is applied to the method of any one of claims 1-7, the system comprising:
an objective function construction module for constructing an objective function for optimizing a capacity of each energy storage device of the hybrid energy storage auxiliary unit, the objective function comprising: building punishment, running loss and taking into consideration the return of the frequency modulation performance index;
the constraint condition determining module is used for determining constraint conditions for optimizing the frequency modulation capacity of the hybrid energy storage auxiliary unit;
and the objective function solving module is used for solving the objective function by adopting a self-adaptive chaotic particle swarm algorithm based on the constraint condition and determining the optimal capacity of each energy storage device of the hybrid energy storage auxiliary unit.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, characterized in that a computer program is stored thereon, which program, when being executed by a processor, implements the method of any of claims 1 to 7.
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| CN117878974B (en) * | 2024-03-13 | 2024-06-11 | 西安热工研究院有限公司 | Frequency modulation method and system for fused salt energy storage coupling thermal power generating unit based on error feedback |
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