CN115459303A - Self-adaptive control method for participating in primary frequency modulation of power grid by battery energy storage - Google Patents

Self-adaptive control method for participating in primary frequency modulation of power grid by battery energy storage Download PDF

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CN115459303A
CN115459303A CN202211154915.9A CN202211154915A CN115459303A CN 115459303 A CN115459303 A CN 115459303A CN 202211154915 A CN202211154915 A CN 202211154915A CN 115459303 A CN115459303 A CN 115459303A
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energy storage
frequency
frequency modulation
control
virtual
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段建东
李志凡
秦博
陈宝桥
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Xian University of Technology
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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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/10—Flexible AC transmission systems [FACTS]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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Abstract

The invention discloses a self-adaptive control method for participating in primary frequency modulation of a power grid by battery energy storage, and provides a self-adaptive control strategy based on fuzzy logic control. The method comprises the steps of establishing a battery energy storage participation power grid primary frequency modulation equivalent model, analyzing the change rule of the power grid frequency deviation and the change rate of the frequency deviation in the battery energy storage primary frequency modulation process, reasonably distributing the weight occupied by virtual inertia control and virtual droop control in the frequency modulation process, and providing adaptive virtual droop control and adaptive virtual inertia control based on fuzzy logic control. The self-adaptive control method for the battery energy storage to participate in the primary frequency modulation of the power grid is beneficial to researching the problem that the battery energy storage participates in the primary frequency modulation of the power grid under the background of high-proportion renewable energy sources, improves the frequency safety of the power grid, and prolongs the service life of an energy storage battery.

Description

Self-adaptive control method for participating in primary frequency modulation of power grid by battery energy storage
Technical Field
The invention belongs to the field of energy storage operation and control of a smart power grid, and particularly relates to a self-adaptive control method for participating in primary frequency modulation of the power grid by battery energy storage.
Background
Because the traditional fossil energy can cause pollution to the environment, the power generation proportion of new energy such as wind power, photovoltaic and the like is continuously improved, but the new energy is greatly influenced by natural conditions, the output has volatility and randomness, and the challenge is brought to the stability of a power grid. Along with the rapid development of energy storage technology, the energy storage is used on the power supply side to participate in the auxiliary frequency modulation service of the power grid, and the quality of electric energy is improved by utilizing the characteristics of short energy storage response time, high energy density, flexible configuration and the like. How to fully utilize the advantage of energy storage to participate in primary frequency modulation of a power grid becomes a hotspot of industrial research. The control strategy for the battery energy storage to participate in the primary frequency modulation mainly comprises virtual inertia control and virtual droop control, and the virtual inertia control and the virtual droop control have advantages respectively. The virtual inertia control may reduce the rate of change of the frequency deviation, and the virtual droop control may reduce the steady-state frequency deviation.
Disclosure of Invention
The invention aims to provide a self-adaptive control method for participating in primary frequency modulation of a power grid by battery energy storage, which makes full use of the advantages of participating in the primary frequency modulation of the power grid by the battery energy storage, better coordinates virtual inertia control and virtual droop control of participating in the primary frequency modulation by the battery energy storage, combines the advantages of the virtual droop control and the virtual inertia control in the frequency modulation process, improves the frequency safety of the power grid, and prolongs the service life of an energy storage battery.
The technical scheme adopted by the invention is as follows: the self-adaptive control method for the battery energy storage to participate in the primary frequency modulation of the power grid comprises the following specific operation steps of:
step 1: collecting system frequency, and judging whether the frequency deviation meets the condition that the battery energy storage participates in primary frequency modulation of the power grid, namely whether delta f is more than or equal to f t In the formula f t Designing a fuzzy rule of the fuzzy logic controller for a frequency modulation dead zone according to the characteristics of the frequency deviation and the frequency deviation change rate (shown in figures 2-3) of the power grid in the frequency modulation process;
step 2: the method comprises the steps that the frequency deviation and the frequency deviation change rate of a system are subjected to amplitude limiting and then serve as the input of a fuzzy logic controller, the output of the fuzzy logic controller serves as a self-adaptive virtual droop control weight factor, and the self-adaptive virtual inertia control weight factor is obtained according to the weight factor of virtual droop control;
and 3, step 3: superposing the virtual droop control weight factor and the virtual inertia control weight factor on a virtual droop control and a virtual inertia control respectively, superposing the outputs of the virtual droop control and the virtual inertia control to be used as a reference power value of battery energy storage, and outputting corresponding power after being controlled by a converter;
and 4, step 4: establishing a battery energy storage participation power grid primary frequency modulation equivalent model, and inputting a load change curve, a battery energy storage inertia time constant, a system inertia time constant, a load damping coefficient, a speed regulator time constant of a thermal power generating unit, a steam turbine time constant and a droop coefficient of the thermal power generating unit into the equivalent model to be used as a system parameter of the equivalent model;
and 5: the established equivalent models are used for respectively verifying the frequency modulation effect under the step load disturbance and the continuous load disturbance, and the comparison is carried out in a K-varying method of the traditional control method, so as to prove the superiority of the method.
The invention is also characterized in that:
the input and output of the fuzzy logic controller adopt triangular membership function, the input of the fuzzy logic controller is respectively frequency deviation delta f and frequency deviation change rate d delta f/dt, the input quantity is per unit processed by taking 50Hz as reference value, and the input domain of the fuzzy logic controller is [ -0.005,0.005].
The input variables Δ f and d Δ f/dt are classified into 5 levels { NB, NS, Z0, PS, PB },
wherein NB is negative and large, NS is negative and small, Z0 is zero, PS is positive and small, and PB is positive and large;
the output domain is divided into 5 levels, the domain is { Z0, S, M, L, VL },
wherein Z0 is zero, S is small, M is medium, L is large, and VL is very large;
according to theoretical analysis and practical experience of energy storage participating in primary frequency modulation, 25 fuzzy rules are designed, and are specifically shown in table 1.
TABLE 1 adaptive control fuzzy rules
Figure BDA0003856573770000021
Figure BDA0003856573770000031
Step 1, the specific derivation process of the fuzzy rule of the fuzzy logic controller is designed as follows:
the control strategy for the battery energy storage to participate in the primary frequency modulation mainly comprises virtual inertia control and virtual droop control, and the virtual inertia control and the virtual droop control have advantages respectively. The virtual inertia control simulates the inertia response process of the synchronous generator, so that the frequency deviation change rate can be reduced; the virtual droop control simulates the droop characteristic of the synchronous generator, and can reduce the steady-state frequency deviation.
The virtual droop control is used for simulating the droop characteristic of the synchronous generator participating in primary frequency modulation, and the active power increment of the link is as shown in a formula (1):
ΔP B1 ＝-xK B Δf (1)
wherein Δ f is the system frequency deviation; k B The larger the droop coefficient is, the larger the energy storage output is under the same frequency deviation; as shown in FIG. 4, x is an adaptive droop factor whose magnitude satisfies 0 ≦ x ≦ 1;
the virtual droop control is mainly used for reducing frequency deviation and improving the steady-state performance of the system, and the advantages of the virtual droop control can be fully exerted by changing the droop factors at different stages after the load disturbance of the system occurs.
The virtual inertia control simulates the inertia response process of the synchronous generator, and the active power increment in the process is
ΔP B2 ＝-yM B dΔf/dt (2)
In the formula, M B Is a coefficient of inertia, and M B >0; y is an adaptive inertia factor, the size of y is more than or equal to-1 and less than or equal to 1, and x + | y | =1.
Taking Δ f <0, energy storage cell discharge as an example: when the system frequency is in a descending stage, y is more than or equal to 0, the energy storage battery adopts positive virtual inertia control to increase output, further frequency deterioration can be effectively inhibited, and dynamic performance at the initial disturbance stage is improved. When the system frequency is in the recovery stage, if the positive virtual inertia control strategy is still adopted to store energy and be in the charging state, the recovery of the frequency can be hindered. Therefore, in order to accelerate frequency recovery, the inertia factor y is less than or equal to 0 in the frequency recovery stage, the energy storage battery adopts negative virtual inertia control to increase output, and the frequency recovery time is shortened.
The fuzzy rule design idea of the fuzzy logic controller is as follows: when the system load increases, d Δ f/dt increases and then decreases in the frequency decreasing phase, and Δ f gradually increases. Therefore, in this stage, the positive virtual inertia control is adopted, the inertia factor is gradually decreased, the droop factor is gradually increased, the frequency change rate is suppressed in the initial stage of the frequency drop, and the system frequency deviation is decreased in the latter stage of the frequency drop. During the frequency recovery phase, d Δ f/dt increases and then decreases, and Δ f gradually decreases to a steady-state value. At this stage, the inertia factor is increased and then decreased, and the droop factor is decreased and then increased, so that the frequency recovery process is accelerated, and the energy storage frequency modulation potential is exerted.
In the step 2, because the instantaneous frequency change rate of the disturbance is large, the situation that the disturbance exceeds the measurement range of the sampling circuit may occur, and therefore, an amplitude limiting link needs to be added. The amplitude limiting link can be realized through software, and if the deviation between the frequency change rate sampling value of this time and the frequency change rate sampling value of the last time is larger than the set maximum deviation value, the sampling of this time is abandoned.
In the step 3, the energy storage alternating current side converter adopts a feedforward decoupling PQ control strategy, decoupling control of active power and reactive power is achieved through coordinate transformation, and the output power in the step 3 is used as a reference value of the power outer loop active power of the energy storage alternating current side converter.
The system frequency response in the primary frequency modulation equivalent model established in the step 4 satisfies the following relation:
(2H eq s+D eq )Δf(s)＝ΔP G +ΔP B -ΔP L (3)
in the formula,. DELTA.P G For active power increment of thermal power generating units, delta P B Storing active energy increment, Δ P, for a battery L For load changes, H eq Is the equivalent time constant of inertia of the system, D eq Is an equivalent load damping coefficient.
The frequency response expression of the thermal power generating unit is
Figure BDA0003856573770000041
In the formula, R H Difference coefficient, T, of thermal power generating unit GT Is the time constant of the speed regulator, F HP Is the reheat constant, T, of the steam turbine RH Is a reheat time constant, T CH Is the turbine time constant.
The invention has the beneficial effects that:
the self-adaptive control strategy for the battery energy storage to participate in the primary frequency modulation of the power grid is combined with the self characteristics of virtual droop control and virtual inertia control, so that the battery energy storage can better participate in the primary frequency modulation of the power grid. In the initial stage of frequency response, the advantages of virtual inertia control are fully exerted, further deterioration of frequency can be restrained, and the maximum frequency deviation of a system is reduced; in the frequency recovery phase, the advantages of the virtual droop control should be fully exerted, so as to shorten the frequency recovery time. Because there is no definite function relation between the frequency deviation and the frequency deviation change rate, the complexity of the control system can be reduced by adopting fuzzy logic control, the energy storage output is smoother, and the service life of the energy storage is prolonged.
The control strategy provided by the invention realizes the self-adaptive adjustment of the virtual droop factor by considering the frequency deviation and the frequency deviation change rate, avoids the frequency fluctuation caused by the switching of the virtual droop control and the virtual inertia control, and improves the transient performance of the system. By considering the frequency deviation change frequency and the frequency deviation in the frequency modulation process, the system frequency deviation can be reduced by using a proper self-adaptive factor through fuzzy logic control, and the system performance is improved. Simulation results show that in the aspect of energy storage participation in primary frequency modulation, compared with the traditional method, the adaptive control strategy based on fuzzy logic control provided by the invention has better stability and robustness under various load disturbance conditions.
Drawings
FIG. 1 is a flow chart of an implementation of an adaptive control strategy for participation of battery energy storage in primary frequency modulation of a power grid according to the present invention;
FIG. 2 is a fuzzy logic controller input delta f membership function in the adaptive control strategy of the battery energy storage participating in the primary frequency modulation of the power grid according to the invention;
FIG. 3 is a membership function of d Δ f/dt input of a fuzzy logic controller in the adaptive control strategy of the battery energy storage participating in the primary frequency modulation of the power grid according to the invention;
FIG. 4 is a fuzzy logic controller output x membership function in the adaptive control strategy of the invention for battery energy storage to participate in primary frequency modulation of the power grid;
FIG. 5 is a step load disturbance change curve when the battery energy storage of the present invention participates in the simulation verification of the adaptive control strategy of the primary frequency modulation of the power grid;
FIG. 6 is a diagram of system frequency under step load disturbance in simulation verification of a self-adaptive control strategy for participation of battery energy storage in primary frequency modulation of a power grid according to the invention;
FIG. 7 is a continuous load disturbance variation curve of the adaptive control strategy simulation verification of the battery energy storage participating in the primary frequency modulation of the power grid according to the present invention;
FIG. 8 is a system frequency under continuous load disturbance when the battery energy storage participates in the adaptive control strategy simulation verification of the primary frequency modulation of the power grid in accordance with the present invention;
FIG. 9 is a self-adaptive control strategy adaptive factor variation curve of the invention for participation of battery energy storage in primary frequency modulation of a power grid.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention relates to a self-adaptive control method for participating in primary frequency modulation of a power grid by battery energy storage, which comprises the following specific operation steps of:
step 1: collecting system frequency, judging whether the frequency deviation meets the condition that the battery energy storage participates in primary frequency modulation of the power grid, and designing a fuzzy rule of a fuzzy logic controller according to the characteristics of the frequency deviation of the power grid and the frequency deviation change rate in the frequency modulation process;
step 1, the specific derivation process of the fuzzy rule of the fuzzy logic controller is designed as follows:
the control strategy for the battery energy storage to participate in the primary frequency modulation mainly comprises virtual inertia control and virtual droop control, and the two control strategies have advantages respectively. The virtual inertia control simulates the inertia response process of the synchronous generator, so that the frequency deviation change rate can be reduced; the virtual droop control simulates the droop characteristic of the synchronous generator, and can reduce the steady-state frequency deviation.
The virtual droop control is used for simulating the droop characteristic of the synchronous generator participating in primary frequency modulation, and the active power increment of the link is as shown in a formula (1):
ΔP B1 ＝-xK B Δf (1)
wherein Δ f is the system frequency deviation; k is B The larger the droop coefficient is, the larger the energy storage output is under the same frequency deviation; x is an adaptive droop factor, and the size of x is more than or equal to 0 and less than or equal to 1.
The virtual droop control is mainly used for reducing frequency deviation and improving the steady-state performance of the system, and the advantages of the virtual droop control can be fully exerted by changing the droop factors at different stages after the load disturbance of the system occurs.
The virtual inertia control simulates the inertia response process of the synchronous generator, and the active power increment in the process is
ΔP B2 ＝-yM B dΔf/dt (2)
In the formula, M B Is a coefficient of inertia, and M B >0; y is an adaptive inertia factor, the size of y is more than or equal to-1 and less than or equal to 1, and x + | y | =1.
Taking Δ f <0, energy storage cell discharge as an example: when the system frequency is in a descending stage, y is more than or equal to 0, the energy storage battery adopts positive virtual inertia control to increase output, further frequency deterioration can be effectively inhibited, and dynamic performance at the initial disturbance stage is improved. When the system frequency is in the recovery stage, if the positive virtual inertia control strategy is still adopted to store energy and be in the charging state, the recovery of the frequency is hindered. Therefore, in order to accelerate frequency recovery, the inertia factor y is less than or equal to 0 in the frequency recovery stage, the energy storage battery adopts negative virtual inertia control to increase output, and the frequency recovery time is shortened.
The fuzzy rule design idea of the fuzzy logic controller is as follows: when the system load is increased, in the frequency decreasing stage, d delta f/dt is increased and then decreased, and delta f is gradually increased. Therefore, the positive virtual inertia control is adopted at this stage, the inertia factor is gradually decreased, the droop factor is gradually increased, the frequency change rate is suppressed at the initial stage of the frequency drop, and the system frequency deviation is reduced at the latter stage of the frequency drop. During the frequency recovery phase, d Δ f/dt increases and then decreases, and Δ f gradually decreases to a steady-state value. In the stage, the inertia factor is increased and then reduced, and the droop factor is decreased and then increased, so that the frequency recovery process is accelerated, and the energy storage frequency modulation potential is exerted.
The input and output of the fuzzy logic controller adopt a triangular membership function, the input of the fuzzy logic controller is respectively frequency deviation delta f and frequency deviation change rate d delta f/dt, the input quantity is subjected to per unit treatment by taking 50Hz as a reference value, and the input domain of the fuzzy logic controller is [ -0.005,0.005].
The input variables Δ f and d Δ f/dt are categorized into 5 levels { NB, NS, Z0, PS, PB },
wherein NB is negative and large, NS is negative and small, Z0 is zero, PS is positive and small, and PB is positive and large;
the output domain is divided into 5 levels, the domain is { Z0, S, M, L, VL },
wherein Z0 is zero, S is small, M is medium, L is large, and VL is very large;
according to theoretical analysis and practical experience of energy storage participating in primary frequency modulation, 25 fuzzy rules are designed, and are specifically shown in table 1.
TABLE 1 adaptive control fuzzy rules
Figure BDA0003856573770000071
Step 2: the method comprises the steps that system frequency deviation and frequency deviation change rate are subjected to amplitude limiting and then serve as input of a fuzzy logic controller, output of the fuzzy logic controller serves as a self-adaptive virtual droop control weight factor, and the self-adaptive virtual inertia control weight factor is obtained according to the weight factor of virtual droop control;
and step 3: superposing the virtual droop control weight factor and the virtual inertia control weight factor on a virtual droop control and a virtual inertia control respectively, superposing the outputs of the virtual droop control and the virtual inertia control to be used as a reference power value of battery energy storage, and outputting corresponding power after being controlled by an energy storage AC side converter;
and 3, decoupling control of active power and reactive power is realized by the energy storage alternating current side converter through coordinate transformation by adopting a feedforward decoupling PQ control strategy, and the output power of the step 3 is used as a reference value of the power outer loop active power of the energy storage alternating current side converter.
And 4, step 4: establishing a battery energy storage participation power grid primary frequency modulation equivalent model, and inputting a load change curve, a battery energy storage inertia time constant, a system inertia time constant, a load damping coefficient, a speed regulator time constant of a thermal power generating unit, a steam turbine time constant and a droop coefficient of the thermal power generating unit into the equivalent model to be used as a system parameter of the equivalent model;
the system frequency response in the primary frequency modulation equivalent model established in the step 4 satisfies the following relation:
(2H eq s+D eq )Δf(s)＝ΔP G +ΔP B -ΔP L (3)
in the formula,. DELTA.P G For active power increment of thermal power generating units, delta P B Storing active delta, Δ P, for a battery L For load changes, H eq Is the system equivalent inertia time constant, D eq Is an equivalent load damping coefficient.
The frequency response expression of the thermal power generating unit is
Figure BDA0003856573770000081
In the formula, R H For the difference coefficient, T, of thermal power generating units GT Is the time constant of the speed regulator, F HP Is the turbine reheat constant, T RH Is a reheat time constant, T CH Is the turbine time constant.
And 5: the established equivalent models are used for respectively verifying the frequency modulation effect under step load disturbance and continuous load disturbance, and the comparison is carried out in a traditional control method K-varying method to prove the superiority of the method
As shown in fig. 5-6, the adaptive control strategy of the present invention is a step load disturbance change curve and a system frequency curve when the battery energy storage participates in the simulation verification of the primary frequency modulation of the power grid, so that it can be seen that the present strategy can reduce the maximum frequency deviation and accelerate the recovery of the system frequency compared with the K-varying method.
FIG. 1 shows the general implementation process of the adaptive control strategy proposed by the present invention, f t In order to store energy and frequency modulation dead zones, the frequency modulation dead zone is 0.033Hz, and the stored energy participates in frequency modulation when the frequency modulation condition is met. At Δ f>0 is taken as an example, in the frequency response stage, the stored energy is controlled by adopting positive virtual inertia, and the inertia factor y =1-x; in the frequency recovery phase, the energy storage adopts negative virtual inertia control, and the inertia factor y = x-1. And (3) respectively calculating the energy storage output of the virtual droop control and the virtual inertia control according to the formula (1) and the formula (2), and superposing the energy storage output and the energy storage output to obtain the total energy storage output through an amplitude limiting link. And when the primary frequency modulation is finished, the energy storage exits the frequency modulation.
Examples
According to the invention, a regional power grid frequency modulation simulation model containing thermal power and energy storage is built in MATLAB/Simulink software. The stored energy is installed at the outlet of a thermal power plant in a centralized arrangement mode, and a power electronic converter is used for being connected into a system. The rated capacity of the thermal power generating unit is 600MW, the battery energy storage rated parameter is 1MW/500 kW.h, and the rated frequency is 50Hz.
In order to verify the effectiveness of the strategy provided by the invention, simulation verification is carried out under the condition of step load typical disturbance, and the frequency modulation characteristics of the method provided by the invention and a K-varying method (common type) under the condition are compared, as shown in FIGS. 5-6. It can be seen that the present strategy can reduce the maximum frequency deviation and speed up the recovery of the system frequency compared to the K-variant method. In order to simulate the frequency modulation characteristics of the proposed control strategy against continuous load disturbances, load fluctuations varying in the range-0.025 pu to 0.025pu were randomly generated and set to vary every 10s, simulating as much as possible the load fluctuations in the actual situation, as shown in fig. 7. Fig. 8 is a system frequency variation curve under continuous load disturbance, and it can be known from simulation results that the maximum frequency deviation of the system can be reduced and the transient performance of the system can be improved under the condition that different sizes of the adaptive control strategy provided by the present invention meet the disturbance according to the variable K method; in the frequency recovery phase, the system steady-state frequency deviation can be reduced. Fig. 9 is a graph showing the variation of the virtual droop factor x and the virtual inertia factor y in the entire frequency modulation process according to the method of the present invention, which is determined by the frequency deviation variation rate and the frequency deviation. In the frequency response stage, the virtual inertia factor y is a positive value, is controlled by virtual positive inertia, and reduces the frequency change rate; in the frequency recovery stage, the virtual inertia factor y is a negative value, and is virtual negative inertia control, so that the frequency recovery is accelerated. In the whole process of primary frequency modulation, the virtual droop factor x is constantly larger than or equal to 0 so as to reduce frequency deviation.

Claims (6)

1. The self-adaptive control method for the battery energy storage to participate in the primary frequency modulation of the power grid is characterized by comprising the following specific operation steps of:
step 1: collecting system frequency, judging whether the frequency deviation meets the primary frequency modulation condition of the battery energy storage participation power grid, and if the frequency deviation meets the primary frequency modulation condition of the battery energy storage participation power grid, designing a fuzzy rule of a fuzzy logic controller according to the characteristics of the frequency deviation and the frequency deviation change rate of the power grid in the frequency modulation process;
step 2: the method comprises the steps that the frequency deviation and the frequency deviation change rate of a system are subjected to amplitude limiting and then serve as the input of a fuzzy logic controller, the output of the fuzzy logic controller serves as a self-adaptive virtual droop control weight factor, and the self-adaptive virtual inertia control weight factor is obtained according to the weight factor of virtual droop control;
and step 3: superposing the virtual droop control weight factor and the virtual inertia control weight factor on virtual droop control and virtual inertia control respectively, superposing the outputs of the virtual droop control and the virtual inertia control to be used as a reference power value of battery energy storage, and outputting corresponding power after being controlled by an energy storage AC side converter;
and 4, step 4: establishing a battery energy storage participation power grid primary frequency modulation equivalent model, and inputting a load change curve, a battery energy storage inertia time constant, a system inertia time constant, a load damping coefficient, a speed regulator time constant of a thermal power generating unit, a steam turbine time constant and a droop coefficient of the thermal power generating unit into the equivalent model to be used as a system parameter of the equivalent model;
and 5: the established equivalent models are used for respectively verifying the frequency modulation effect under the step load disturbance and the continuous load disturbance, and the comparison with the traditional control method K-varying method is carried out to prove the superiority of the method.
2. The adaptive control method for participation in primary frequency modulation of the power grid in the battery energy storage according to claim 1, wherein the conditions for participation in the primary frequency modulation of the power grid in the battery energy storage in step 1 are as follows: the frequency deviation is larger than the frequency modulation dead zone.
3. The adaptive control method for participating in primary frequency modulation of the power grid through battery energy storage according to claim 1, wherein the specific method for designing the fuzzy rule of the fuzzy logic controller in the step 1 is as follows:
the input and the output of the fuzzy logic controller adopt a triangular membership function, the input of the fuzzy logic controller is respectively frequency deviation delta f and frequency deviation change rate d delta f/dt, the input quantity is subjected to per unit treatment by taking 50Hz as a reference value, and the input domain of the fuzzy logic controller is [ -0.005,0.005];
the input variables Δ f and d Δ f/dt are categorized into 5 levels { NB, NS, Z0, PS, PB },
wherein NB is negative and large, NS is negative and small, Z0 is zero, PS is positive and small, and PB is positive and large;
the output domain is divided into 5 levels, the domain is { Z0, S, M, L, VL },
wherein Z0 is zero, S is small, M is medium, L is large, and VL is very large;
according to theoretical analysis and practical experience of energy storage participating in primary frequency modulation, the following 25 fuzzy rules are designed:
TABLE 1 adaptive control fuzzy rules
Figure FDA0003856573760000021
4. The adaptive control method for participating in primary frequency modulation of the power grid through battery energy storage according to claim 3, wherein the fuzzy rule of the fuzzy logic controller designed in step 1 is derived as follows:
the virtual droop control is used for simulating the droop characteristic of the synchronous generator participating in primary frequency modulation, and the active power increment of the link is as shown in a formula (1):
ΔP B1 ＝-xK B Δf (1)
wherein Δ f is the system frequency deviation; k B The larger the droop coefficient is, the larger the energy storage output force is under the same frequency deviation; x is a self-adaptive droop factor, and x is more than or equal to 0 and less than or equal to 1;
the virtual droop control is mainly used for reducing frequency deviation and improving the steady-state performance of the system, and the advantages of the virtual droop control can be fully exerted by changing droop factors at different stages after the load disturbance of the system occurs;
the virtual inertia control simulates an inertia response process of a synchronous generator, and the active power increment in the process is as follows:
ΔP B2 ＝-yM B dΔf/dt (2)
in the formula, M B Is a coefficient of inertia, and M B >0; y is an adaptive inertia factor, the size of y is more than or equal to-1 and less than or equal to 1, and x + | y | =1;
taking Δ f <0, energy storage cell discharge as an example: when the system frequency is in a descending stage, y is more than or equal to 0, the energy storage battery adopts positive virtual inertia to control and increase output, further frequency deterioration can be effectively inhibited, and the dynamic performance of the disturbance initial stage is improved; when the system frequency is in a recovery stage, if the positive virtual inertia control strategy is still adopted to store energy and is in a charging state, the recovery of the frequency is hindered; therefore, in order to accelerate frequency recovery, the inertia factor y is less than or equal to 0 in the frequency recovery stage, the energy storage battery adopts negative virtual inertia control to increase output, and the frequency recovery time is shortened;
the fuzzy rule design idea of the fuzzy logic controller is as follows: when the system load is increased, in the frequency reduction stage, d delta f/dt is increased firstly and then decreased, and delta f is gradually increased, so that positive virtual inertia control is adopted in the stage, the inertia factor is gradually decreased, and the droop factor is gradually increased; suppressing the frequency change rate at the initial stage of frequency reduction, and reducing the system frequency deviation at the later stage of frequency reduction; in the frequency recovery stage, d delta f/dt is increased and then decreased, delta f is gradually decreased to a steady-state value, in the stage, the inertia factor is increased and then decreased, the droop factor is decreased and then increased, the frequency recovery process is accelerated, and the energy storage frequency modulation potential is exerted.
5. The adaptive control method for the battery energy storage-participated grid primary frequency modulation according to claim 1, wherein the energy storage AC side converter adopts a feedforward decoupling PQ control strategy, active power and reactive power are decoupled and controlled through coordinate transformation, and the output power in step 3 is used as a reference value of the power outer loop active power of the energy storage AC side converter.
6. The adaptive control method for participation of battery energy in primary frequency modulation of the power grid according to claim 1, wherein the system frequency response in the equivalent model for participation of battery energy in primary frequency modulation of the power grid in the step 4 needs to satisfy the following relation:
(2H eq s+D eq )Δf(s)＝ΔP G +ΔP B -ΔP L (3)
in the formula,. DELTA.P G For active power increment of thermal power generating units, delta P B Storing active energy increment, Δ P, for a battery L For load changes, H eq Is the system equivalent inertia time constant, D eq Is an equivalent load damping coefficient;
the frequency response expression of the thermal power generating unit is
Figure FDA0003856573760000031
In the formula, R H For the difference coefficient, T, of thermal power generating units GT Is the time constant of the speed governor, F HP Is the turbine reheat constant, T RH Is the reheat time constant, T CH Is the turbine time constant.
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CN116231709A (en) * 2023-03-10 2023-06-06 南京理工大学 Energy storage system primary frequency modulation inertia control strategy based on fuzzy control theory
CN116742699A (en) * 2023-05-19 2023-09-12 国网湖北省电力有限公司随州供电公司 Wind-solar energy storage station centralized frequency modulation control method and system considering power grid frequency characteristics
CN116760072A (en) * 2023-08-17 2023-09-15 长江三峡集团实业发展(北京)有限公司 Frequency adjusting method, device, equipment and medium of multi-energy complementary system
CN117117913A (en) * 2023-07-18 2023-11-24 北京盛藏技术有限公司 Hybrid energy storage frequency modulation control method, system, medium and equipment
CN118017553A (en) * 2024-04-10 2024-05-10 浙江大学 Energy storage frequency modulation distribution coefficient calculation method and system based on frequency deviation characteristics

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116231709A (en) * 2023-03-10 2023-06-06 南京理工大学 Energy storage system primary frequency modulation inertia control strategy based on fuzzy control theory
CN116742699A (en) * 2023-05-19 2023-09-12 国网湖北省电力有限公司随州供电公司 Wind-solar energy storage station centralized frequency modulation control method and system considering power grid frequency characteristics
CN117117913A (en) * 2023-07-18 2023-11-24 北京盛藏技术有限公司 Hybrid energy storage frequency modulation control method, system, medium and equipment
CN117117913B (en) * 2023-07-18 2024-05-03 北京盛藏技术有限公司 Hybrid energy storage frequency modulation control method, system, medium and equipment
CN116760072A (en) * 2023-08-17 2023-09-15 长江三峡集团实业发展(北京)有限公司 Frequency adjusting method, device, equipment and medium of multi-energy complementary system
CN116760072B (en) * 2023-08-17 2023-11-03 长江三峡集团实业发展(北京)有限公司 Frequency adjusting method, device, equipment and medium of multi-energy complementary system
CN118017553A (en) * 2024-04-10 2024-05-10 浙江大学 Energy storage frequency modulation distribution coefficient calculation method and system based on frequency deviation characteristics

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