CN113972676B - Distributed BESS coordination control method for improving transient stability of power system - Google Patents
Distributed BESS coordination control method for improving transient stability of power system Download PDFInfo
- Publication number
- CN113972676B CN113972676B CN202111262586.5A CN202111262586A CN113972676B CN 113972676 B CN113972676 B CN 113972676B CN 202111262586 A CN202111262586 A CN 202111262586A CN 113972676 B CN113972676 B CN 113972676B
- Authority
- CN
- China
- Prior art keywords
- bess
- distributed
- controller
- parameters
- voltage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/22—Current control, e.g. using a current control loop
-
- 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]
-
- 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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Feedback Control In General (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The application relates to a distributed BESS coordination control method for improving transient stability of a power system, which comprises the following steps of, step 1, establishing a dynamic model of the power system containing the distributed BESS; step 2, designing a single BESS distributed control double closed loop decoupling controller based on the BESS grid-connected mathematical model established in the step 1; step 3, designing objective functions and constraint conditions to optimize control parameters of a plurality of scattered BESS controllers so as to achieve the effect of mutual coordination of the controllers; step 4: solving multi-constraint optimization of the distributed BESS controller parameters by using the objective function and the constraint condition established in the step 3; and 5, carrying out coordination control on the distributed BESS according to the controller parameters obtained by solving in the step 4. The application can rapidly and accurately optimize the parameters of the distributed controller, and avoids the complicated process of setting the parameters of the controller in the past.
Description
Technical Field
The application relates to the field of energy storage control, in particular to a distributed BESS coordination control method for improving transient stability of a power system.
Background
The energy storage is an important technology for realizing the application of the distributed renewable energy sources, and the energy storage can be used for realizing the smooth fluctuation, peak regulation, frequency modulation and the like of the renewable energy sources, so that the requirement of large-scale access grid connection of the renewable energy source power is met. Energy storage is also an important technology of an intelligent micro-grid, and because the load fluctuation rate and the fault rate of the micro-grid are relatively high, distributed energy storage is a necessary key technology for improving the power supply reliability, the electric energy quality and the like. The energy storage technology has great strategic requirements, important research value and great development potential in the fields of distributed renewable energy application and intelligent micro-networks.
Transient stability control of a power system has been an important topic in power system stability research. Along with the rapid development of energy storage technology, the energy storage element provides a new research thought for transient stability control of the power system, BESS (Battery Energy Storage System) is a typical representative of the energy storage element, and has the characteristics of high rated power (up to 100kW-10 MW), high response speed (less than 1 s), long discharge time (in hours) and the like, so that the BESS has incomparable advantages of the traditional element in the aspect of improving the transient stability of the power system. Related researches prove that the feasibility of improving the transient stability of the power system by using the BESS is high, but the single BESS capacity required for effectively improving the transient stability of the power system is high; and the improvement effect on the transient stability of the system is obviously influenced by site selection and fault positions.
The above-mentioned problems can be solved by distributing a plurality of BESS, but if only the distributed independent actions of a plurality of BESS controllers are considered, and the interaction influence among the controllers is not considered, the dynamic controllers may be inconsistent with each other, so that the dynamic performance of the system is deteriorated, and even the stability of the system is destroyed. At present, the research in the distributed BESS field is relatively few, the coordination control method is not clear, and the existing method is used for adjusting parameters through a trial and error method, and is blind and inefficient.
Disclosure of Invention
In order to solve the defects in the prior art, the application aims to provide a distributed BESS coordinated control method for improving the transient stability of a power system, so as to optimize parameters of the distributed BESS coordinated controller, ensure mutual coordination among various controllers, improve the transient stability of the power system, greatly improve the control efficiency and overcome blindness and inefficiency caused by adjusting the parameters of the controllers by a traditional trial-and-error method.
The application adopts the following technical scheme:
a distributed BESS coordination control method for improving transient stability of an electric power system comprises the following steps,
step 1, establishing a dynamic model of an electric power system containing distributed BESS;
step 2, designing a single BESS distributed control double closed loop decoupling controller based on the BESS grid-connected mathematical model established in the step 1;
step 3, designing objective functions and constraint conditions to optimize control parameters of a plurality of scattered BESS controllers so as to achieve the effect of mutual coordination of the controllers;
step 4: solving multi-constraint optimization of the distributed BESS controller parameters by using the objective function and the constraint condition established in the step 3;
and 5, carrying out coordination control on the distributed BESS according to the controller parameters obtained by solving in the step 4.
Preferably, the circuit model of the power system with the distributed BESS mainly comprises a battery primary model, a voltage source type converter and a low-pass filter; the BESS is provided with a voltage stabilizing capacitor at the direct current side.
Preferably, the BESS grid-connected mathematical model based on dq0 rotation coordinate system is expressed as
Wherein C is the voltage stabilizing capacitor of BESS at the direct current side, V dc R is the voltage value of two ends of the voltage stabilizing capacitor s Representing the equivalent internal resistance of the storage battery; r is the equivalent resistance of the line and the transformer, L is the equivalent inductance of the line inductance and the transformer leakage inductance; omega is the angular frequency of the alternating current; i.e d And i q Ac current i, respectively, of the grid connection abc Components of d axis and q axis after park transformation; m and theta are the amplitude modulation ratio and the triggering phase angle of PWM of the voltage source type converter respectively; v (V) s For the output voltage of BESS, R s Is the equivalent internal resistance of the BESS.
Preferably, in step 2, the BESS controller adopts a dual closed-loop vector control strategy, and for each BESS unit, a distributed controller is designed according to local related information thereof, so as to perform independent dual closed-loop decoupling control.
Preferably, in step 2, the BESS controller includes an outer ring controller and an inner ring controller;
node voltage amplitude V of BESS in the outer ring controller is an input variable; the rotating speed of the BESS access point near-end generator is used as an active class instruction, the voltage amplitude of the BESS access bus is used as a reactive class instruction, and the outer ring controller outputs a current reference value i dref And i qref ；
The inner loop controller adopts decoupling control of current, and makes d-axis current i of alternating-current side current of the voltage source type converter through control action d And q-axis current i q Tracking the current reference value i output by the outer loop controller dref And i qref 。
Preferably, the inner loop controller satisfies the following relationship:
e dref ＝v d +(k p1 +k i1 )(i dref -i d )-ωLi q
e qref ＝(k p2 +k i2 )(i qref -i q )+ωLi d
wherein k is p1 、k i1 、k p2 、k i2 PI parameters of an inner ring controller in the BESS double closed-loop controller; i.e dref 、i qref D-axis current and q-axis current reference values output by an outer ring of the double closed-loop control strategy; i.e d 、i q The voltage source type converter is a true value of d-axis current and q-axis current on the alternating current side of the voltage source type converter; v d Is the voltage v of the alternating current side of the voltage source type converter a,b,c The d-axis voltage v is obtained after park transformation d 。
Preferably, in step 3, with the objective of improving transient stability of the system, the power angle oscillation amplitude of the generator is reduced, the period oscillation time is shortened, and the sum of the integral of the relative power angle differences of the generator is selected as an objective function:
meanwhile, constraint conditions of given optimization parameters are as follows:
wherein J is an objective function, M is the total number of generators in the objective power system, delta i (t) is the power angle deviation of an ith generator in the system, and t is a target period; k (k) p,g 、k i,g And for the PI control parameters of the g-th inner ring controller, min and max respectively represent the minimum value and the maximum value of each PI control parameter, and the total number of controllers in the target power system is N.
Preferably, in step 4, a particle swarm optimization PSO is applied to realize multi-constraint optimization of the distributed controller parameters.
Preferably, step 4 further comprises:
step 4.1: initializing a population of particles, randomly assigning individual particles in the populationInitial position variable x= (X) 1 ,X 2 ,…X n ) And speed variable V i ＝(V i1 ,V i2 ,…V iD ) T Simultaneously generating individual extremum P of ions i ＝(P i1 ,P i2 ,…P iD ) T Sum population extremum g= (G) 1 ,G 2 ,…G D ) T ；
Step 4.2: substituting the controller parameters represented by the particles into a transient stability program according to an objective functionCalculating fitness values of particles, and determining individual extremum and population extremum by comparing the fitness values of the particles;
step 4.3: updating the speed and the position of the particles according to the individual extremum and the group extremum;
step 4.4: repeating the steps 4.2 to 4.3 for iteration until the maximum iteration number is reached.
Preferably, in step 4.3, the speed and position of the particles themselves are updated according to the individual extremum and the population extremum, namely:
wherein w is inertial weight, r 1 、r 2 Is two mutually independent random numbers in the range of (0, 1); c 1 、c 2 Is an acceleration constant;for the individual extremum of the particle in the d-th dimension in the t-th iteration, < >>The particle is the d in the t iterationPopulation extremum of dimension,/->Represents the speed variable of the particle in the d-th dimension in the t and t+1 iterations, respectively,>the position variables representing the d-th dimension of the particles in the t and t+1 iterations, respectively, are limited to a certain range.
Compared with the prior art, the distributed BESS coordination control method has the advantages that the parameters of the distributed controller can be optimized rapidly and accurately, and the complicated process of setting the parameters of the controller in the past is avoided; the controllers are coordinated mutually, so that the problem that a plurality of dynamic controllers are uncoordinated and the dynamic performance of the system is deteriorated is effectively avoided. The PSO algorithm can effectively optimize the control parameters of the distributed BESS controller, is favorable for realizing the mutual coordination of the distributed BESS and effectively improves the transient stability of the power system.
Drawings
FIG. 1 is a flow chart of a distributed BESS coordination control method for improving transient stability of a power system according to the present application;
FIG. 2 is a topology of a grid-connected circuit of the BESS of the present application;
FIG. 3 is a schematic diagram of a dual closed loop controller according to the present application;
FIG. 4 is a control schematic of the outer loop controller of the present application;
FIG. 5 is a schematic diagram of a control structure of the inner loop controller for controlling the grid-connected circuit according to the present application;
FIG. 6 is a schematic topology diagram of an IEEE-3 set 9 node power system in accordance with one embodiment of the application;
FIG. 7 is a graph comparing the power curves before and after optimization of the distributed BESS controller in one embodiment of the present application.
Detailed Description
The application is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present application, and are not intended to limit the scope of the present application.
As shown in fig. 1, the distributed BESS coordination control method for improving transient stability of a power system of the present application mainly includes the following steps:
step 1, establishing a dynamic model of an electric power system containing distributed BESS;
step 2, designing a single BESS distributed control double closed loop decoupling controller based on the BESS grid-connected mathematical model established in the step 1;
step 3, designing objective functions and constraint conditions to optimize control parameters of a plurality of scattered BESS controllers so as to achieve the effect of mutual coordination of the controllers;
step 4: solving multi-constraint optimization of the distributed BESS controller parameters by using the objective function and the constraint condition established in the step 3;
and 5, carrying out coordination control on the distributed BESS according to the controller parameters obtained by solving in the step 4.
Specific steps are described in detail below.
Step 1: and establishing a dynamic model of the power system containing the distributed BESS.
The circuit model of the power system including the distributed BESS mainly includes: the equivalent circuit of the battery elementary model, the Voltage Source Converter (VSC) and the low-pass filter is shown in fig. 2. BESS is provided with a voltage stabilizing capacitor C, V at the DC side dc R is the voltage value of two ends of the voltage stabilizing capacitor s Representing the equivalent internal resistance of the storage battery; r is the equivalent resistance of the line and the transformer, L is the equivalent inductance of the line inductance and the transformer leakage inductance; v abc Bus voltage of AC system e abc Is the grid-connected alternating voltage of the converter, i abc Is a grid-connected alternating current; p is the active power injected into the alternating current system by the BESS, and Q is the reactive power injected into the alternating current system by the BESS.
After Park conversion, the BESS AC side, i.e., the VSC AC side, locks the d-axis of the synchronous rotation dq0 coordinate system to v by the action of a phase-locked loop (PLL of FIG. 3) d On, i.e. d-axis and voltage v d Included angle of 0, v d ＝V，v q =0, where v d And v q Bus voltage v of AC system abc The components of the d-axis and q-axis after Park change, V, is the amplitude of the ac side voltage. The steady state equation of the VSC ac side in dq0 coordinate system can be obtained:
where ω is the angular frequency of the alternating current, e d And e q The converter grid-connected alternating voltage e abc Components of d-axis and q-axis after Park conversion, i d And i q Ac current i, respectively, of the grid connection abc The components of the d-axis and q-axis after Park transformation.
Based on instantaneous power theory, active power P injected into network side g And reactive power Q g Can be expressed as
For ac systems on the ac side, the VSC corresponds to a voltage source with a controllable phase and amplitude of the ac voltage. The amplitude and the phase of the modulation wave can be controlled by adjusting the amplitude modulation ratio M and the triggering phase angle theta of the PWM of the VSC, so that the independent decoupling control of the amplitude and the phase of the voltage at the alternating-current side of the VSC is realized. The relationship between the VSC control amount and the controlled amount can be expressed as:
for the direct current side, BESS adopts elementary model, V s For the output voltage of BESS, R s Is equivalent internal resistance of BESS, I s Is the output current of the BESS.
Wherein P is dc Refers to the output active power of the battery, I dc The current is truly output by the battery after the voltage stabilizing capacitor.
The energy of the DC side and the AC network side is kept equal, namely P, without regard to the loss of the converter and the consumption of active power on the line dc ＝P g Substituting the formula (2) and (4) respectively, can obtain:
in summary, the BESS grid-connected mathematical model based on the dq0 rotation coordinate system can be expressed as
The BESS grid-connected mathematical model is followed by the basis of design controllers and simulation analysis.
Step 2: and (3) designing a single BESS distributed control double-closed-loop decoupling controller based on the BESS grid-connected mathematical model established in the step (1).
As shown in fig. 3, the BESS controller adopts a dual closed-loop vector control strategy, and for each BESS unit, a distributed controller is designed according to local related information thereof to perform independent dual closed-loop decoupling control. Wherein the voltage v of the VSC AC side a,b,c After passing through a phase-locked loop PLL, park transformation from abc coordinates to dq coordinates is carried out, and voltage v of VSC AC side a,b,c And current i a,b,c Also performs park transformation to obtain voltage v d,q And current i d,q 。
In the outer loop controller, node voltage magnitude V of the BESS is an input variable. To improve transient stability of the system, BESS is arranged near the generator, i.e. the BESS near the generator is regulated to better improve transient stabilitySex. The rotation speed of the BESS access point near-end generator is used as an active class instruction, the voltage amplitude of the BESS access bus is used as a reactive class instruction, and as shown in figure 4, the outer ring controller outputs a current reference value i dref And i qref 。
The inner loop control adopts decoupling control of current, as shown in fig. 5, the d-axis current i of the VSC alternating current side current is controlled d And q-axis current i q Tracking the current reference value i output by the outer loop controller dref And i qref . The inner loop controller should satisfy the following relationship:
e dref ＝v d +(k p1 +k i1 )(i dref -i d )-ωLi q (7)
e qref ＝(k p2 +k i2 )(i qref -i q )+ωLi d (8)
wherein k is p1 、k i1 、k p2 、k i2 PI parameters of an inner ring controller in the BESS double closed-loop controller; i.e dref 、i qref D-axis current and q-axis current reference values output by an outer ring of the double closed-loop control strategy; i.e d 、i q Is the true value of the d-axis current and q-axis current on the VSC ac side.
Equations (7) and (8) yield the output value e of the inner loop controller dref 、e qref D-axis current e d And q-axis current e q Tracking the current reference value e output by the outer loop controller dref And e dref By changing the above, according to the expression (3), the trigger phase angle θ can be obtained, thereby realizing control.
And step 3, designing objective functions and constraint conditions to optimize control parameters of a plurality of scattered BESS controllers so as to achieve the effect of mutual coordination of the controllers.
The control of the BESS is realized by controlling the amplitude modulation ratio M and the trigger phase angle theta of the PWM of the VSC in the mode (3). The particle swarm optimization algorithm is an optimization algorithm, the optimization algorithm needs an objective function, the objective function is selected to be the integral of the power angle difference of the generator, namely the optimization purpose is to reduce the power angle difference oscillation of the generator in the transient process by adjusting the output of the BESS, and the power angle oscillation of the generator is an important index for measuring the transient stability.
Aiming at improving transient stability of the system, reducing power angle oscillation amplitude and period oscillation time of the generator, and selecting the sum of relative power angle difference integral of the generator as an objective function:
meanwhile, constraint conditions of given optimization parameters are as follows:
wherein J is an objective function, M is the total number of generators in the objective power system, delta i (t) is the power angle deviation of an ith generator in the system, and t is a target period; k (k) p,g 、k i,g And for the PI control parameters of the g-th inner ring controller, min and max respectively represent the minimum value and the maximum value of each PI control parameter, and the total number of controllers in the target power system is N.
Step 4: and (3) solving multi-constraint optimization of the distributed BESS controller parameters by using the objective function and the constraint condition established in the step (3).
And a particle swarm algorithm (PSO) with strong universality and searching capability is applied to realize multi-constraint optimization of the distributed controller parameters.
In a D-dimensional search space, a population x= (X) of n particles 1 ,X 2 ,…X n ) Wherein the ith particle represents a vector X in D-dimension i ＝(x i1 ,x i2 ,…x iD ) T It represents the position of the ith particle in the search space, i.e., one potential solution to the distributed BESS controller parameters. Calculation of each particle position X from the objective function i Corresponding fitness value, the velocity of each particle is V i ＝(V i1 ,V i2 ,…V iD ) T Its individual extremum is P i ＝(P i1 ,P i2 ,…P iD ) T Population extremum of population is g= (G) 1 ,G 2 ,…G D ) T . In each iteration process, the particles update the speed and the position of the particles through the individual extremum and the group extremum, and the updated particles are used as new potential solutions of the controller parameters and are substituted into a transient program to carry out simulation calculation.
Specifically, step 4 further includes:
step 4.1: initializing a particle population, randomly giving each particle in the population an initial position variable x= (X) 1 ,X 2 ,…X n ) And speed variable V i ＝(V i1 ,V i2 ,…V iD ) T Simultaneously generating individual extremum P of ions i ＝(P i1 ,P i2 ,…P iD ) T Sum population extremum g= (G) 1 ,G 2 ,…G D ) T ；
Step 4.2: substituting the controller parameters represented by the particles into a transient stability program according to an objective functionCalculating fitness values of particles, and determining individual extremum and population extremum by comparing the fitness values of the particles;
step 4.3: updating the speed and position of the particles according to the individual extremum and the group extremum, namely:
wherein w is inertial weight, r 1 、r 2 Is two mutually independent random numbers in the range of (0, 1); c 1 、c 2 Is an acceleration constant;for the individual extremum of the particle in the d-th dimension in the t-th iteration, < >>For population extremum of the particles in the d-th dimension in the t-th iteration +.>Represents the speed variable of the particle in the d-th dimension in the t and t+1 iterations, respectively,>the position variables representing the d-th dimension of the particles in the t and t+1 iterations, respectively, are limited to a certain range.
The updated particles represent a new set of potential solutions to the controller parameters.
Step 4.4: repeating the steps 4.2 to 4.3 for iteration until the maximum iteration number is reached.
In one embodiment of the present application, the maximum number of iterations is set to 50, and when the maximum number of iterations is reached, the iteration is stopped.
And 5, carrying out coordination control on the distributed BESS according to the controller parameters obtained by solving in the step 4.
The distributed BESS coordination control method for improving the transient stability of the power system is introduced.
The application is further described below in connection with a specific embodiment for better embodying the application.
In this embodiment, the transient stability simulation test is performed by using an IEEE-3 machine 9 node power system including a distributed BESS, and fig. 6 shows the IEEE-3 machine 9 node power system. Because the load of the 9 node system of the IEEE-3 machine is basically and uniformly distributed near the buses No. 5, no. 6 and No. 8, the distributed BESS also adopts a uniformly configuration method, namely, 3 groups of BESS with the capacity of 10MW are respectively connected into an alternating current system through buses No. 5, no. 6 and No. 8.
Taking the generator No. 1 as a reference, and taking the sum of the integral of the relative power angle differences of the generator No. 2 and the generator No. 3 as an objective function:
the constraints for a given optimization parameter are:
considering the most serious fault condition of a system in a transient program, and setting that when t=1s, a three-phase grounding short circuit fault occurs at the tail end of a certain line; after the fault, the set time t passes 1 =0.1 s, three-phase tripping of the circuit breaker at the head end and the tail end of the line cuts off faults; then pass t 2 =0.5 s, the device recloses and the system reverts to the topology before the failure occurred.
Fig. 7 shows the comparison of the power curves before and after the parameter optimization of the distributed BESS controller by using the particle swarm algorithm when the line fault No. 2 and the line fault No. 4 are provided. From the figure, the distributed BESS controllers optimized by the particle swarm optimization can be better coordinated with each other, and the transient stability of the system is obviously improved.
Compared with the prior art, the distributed BESS coordination control method has the advantages that the parameters of the distributed controller can be optimized rapidly and accurately, and the complicated process of setting the parameters of the controller in the past is avoided; the controllers are coordinated mutually, so that the problem that a plurality of dynamic controllers are uncoordinated and the dynamic performance of the system is deteriorated is effectively avoided. The PSO algorithm can effectively optimize the control parameters of the distributed BESS controller, is favorable for realizing the mutual coordination of the distributed BESS and effectively improves the transient stability of the power system.
While the applicant has described and illustrated the embodiments of the present application in detail with reference to the drawings, it should be understood by those skilled in the art that the above embodiments are only preferred embodiments of the present application, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present application, and not to limit the scope of the present application, but any improvements or modifications based on the spirit of the present application should fall within the scope of the present application.
Claims (7)
1. A distributed BESS coordination control method for improving transient stability of an electric power system is characterized by comprising the following steps,
step 1, establishing a dynamic model of an electric power system containing distributed BESS;
step 2, designing a single BESS distributed control double closed loop decoupling controller based on the BESS grid-connected mathematical model established in the step 1; the BESS controller comprises an outer ring controller and an inner ring controller; node voltage amplitude V of BESS in the outer ring controller is an input variable; the rotating speed of the BESS access point near-end generator is used as an active class instruction, the voltage amplitude of the BESS access bus is used as a reactive class instruction, and the outer ring controller outputs a current reference value i dref And i qref The method comprises the steps of carrying out a first treatment on the surface of the The inner loop controller adopts decoupling control of current, and makes d-axis current i of alternating-current side current of the voltage source type converter through control action d And q-axis current i q Tracking the current reference value i output by the outer loop controller dref And i qref The method comprises the steps of carrying out a first treatment on the surface of the The inner loop controller satisfies the following relationship:
e dref ＝v d +(k p1 +k i1 )(i dref -i d )-ωLi q
e qref ＝(k p2 +k i2 )(i qref -i q )+ωLi d
wherein k is p1 、k i1 、k p2 、k i2 PI parameters of an inner ring controller in the BESS double closed-loop controller; i.e dref 、i qref D-axis current and q-axis current reference values output by an outer ring of the double closed-loop control strategy; i.e d 、i q The voltage source type converter is a true value of d-axis current and q-axis current on the alternating current side of the voltage source type converter; v d Is the voltage v of the alternating current side of the voltage source type converter a,b,c The d-axis voltage v is obtained after park transformation d ；
Step 3, designing objective functions and constraint conditions to optimize control parameters of a plurality of scattered BESS controllers so as to achieve the effect of mutual coordination of the controllers; aiming at improving transient stability of the system, reducing power angle oscillation amplitude and period oscillation time of the generator, and selecting the sum of relative power angle difference integral of the generator as an objective function:
meanwhile, constraint conditions of given optimization parameters are as follows:
wherein J is an objective function, M is the total number of generators in the objective power system, delta i (t) is the power angle deviation of an ith generator in the system, and t is a target period; k (k) p,g 、k i,g The min and max represent the minimum value and the maximum value of each PI control parameter respectively for the PI control parameter of the g-th inner ring controller, and the total number of controllers in a target power system is N;
step 4: solving multi-constraint optimization of the distributed BESS controller parameters by using the objective function and the constraint condition established in the step 3;
and 5, carrying out coordination control on the distributed BESS according to the controller parameters obtained by solving in the step 4.
2. The distributed BESS coordination control method of claim 1, wherein,
the circuit model of the power system comprising the distributed BESS mainly comprises a battery primary model, a voltage source type converter and a low-pass filter; the BESS is provided with a voltage stabilizing capacitor at the direct current side.
3. The distributed BESS coordination control method of claim 2, wherein,
BESS grid-connected mathematical model based on dq0 rotation coordinate system is expressed as
Wherein C is the voltage stabilizing capacitor of BESS at the direct current side, V dc R is the voltage value of two ends of the voltage stabilizing capacitor s Representing the equivalent internal resistance of the storage battery; r is the equivalent resistance of the line and the transformer, L is the equivalent inductance of the line inductance and the transformer leakage inductance; omega is the angular frequency of the alternating current; i.e d And i q Ac current i, respectively, of the grid connection abc Components of d axis and q axis after park transformation; m and theta are the amplitude modulation ratio and the triggering phase angle of PWM of the voltage source type converter respectively; v (V) s For the output voltage of BESS, R s Is the equivalent internal resistance of the BESS.
4. The distributed BESS coordination control method of claim 3, wherein,
in step 2, the BESS controller adopts a double closed-loop vector control strategy, and a distributed controller is designed for each BESS unit according to local related information of the BESS unit to perform independent double closed-loop decoupling control.
5. The distributed BESS coordination control method of claim 1, wherein,
in step 4, a particle swarm optimization PSO is applied to realize multi-constraint optimization of the distributed controller parameters.
6. The distributed BESS coordination control method of claim 5, wherein,
step 4 further comprises:
step 4.1: initializing a particle population, randomly giving each particle in the population an initial position variable x= (X) 1 ,X 2 ,…X n ) And speed variable V i ＝(V i1 ,V i2 ,…V iD ) T Simultaneously generating ionsIndividual extremum P i ＝(P i1 ,P i2 ,…P iD ) T Sum population extremum g= (G) 1 ,G 2 ,…G D ) T ；
Step 4.2: substituting the controller parameters represented by the particles into a transient stability program according to an objective functionCalculating fitness values of particles, and determining individual extremum and population extremum by comparing the fitness values of the particles;
step 4.3: updating the speed and the position of the particles according to the individual extremum and the group extremum;
step 4.4: repeating the steps 4.2 to 4.3 for iteration until the maximum iteration number is reached.
7. The distributed BESS coordination control method of claim 6, wherein,
in step 4.3, the speed and position of the particles themselves are updated according to the individual extremum and the population extremum, namely:
wherein w is inertial weight, r 1 、r 2 Is two mutually independent random numbers in the range of (0, 1); c 1 、c 2 Is an acceleration constant;for the individual extremum of the particle in the d-th dimension in the t-th iteration, < >>For the particles at the t th timePopulation extremum of d-th dimension in generation, +.>Represents the speed variable of the particle in the d-th dimension in the t and t+1 iterations, respectively,>the position variables representing the d-th dimension of the particles in the t and t+1 iterations, respectively, are limited to a certain range.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111262586.5A CN113972676B (en) | 2021-10-28 | 2021-10-28 | Distributed BESS coordination control method for improving transient stability of power system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111262586.5A CN113972676B (en) | 2021-10-28 | 2021-10-28 | Distributed BESS coordination control method for improving transient stability of power system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113972676A CN113972676A (en) | 2022-01-25 |
| CN113972676B true CN113972676B (en) | 2023-09-01 |
Family
ID=79588768
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111262586.5A Active CN113972676B (en) | 2021-10-28 | 2021-10-28 | Distributed BESS coordination control method for improving transient stability of power system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113972676B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20180031184A (en) * | 2016-09-19 | 2018-03-28 | 연암공과대학교산학협력단 | Adaptive Reclosing Method using Second-Order Difference of THD in Distribution System with BESS used as Uninterruptible Power Supply |
| CN112003322A (en) * | 2020-08-12 | 2020-11-27 | 北方工业大学 | Method for designing control parameters of grid-connected converter of micro-grid system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8606424B2 (en) * | 2011-04-05 | 2013-12-10 | King Fahd University Of Petroleum And Minerals | Particle swarm optimization system and method for microgrids |
-
2021
- 2021-10-28 CN CN202111262586.5A patent/CN113972676B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20180031184A (en) * | 2016-09-19 | 2018-03-28 | 연암공과대학교산학협력단 | Adaptive Reclosing Method using Second-Order Difference of THD in Distribution System with BESS used as Uninterruptible Power Supply |
| CN112003322A (en) * | 2020-08-12 | 2020-11-27 | 北方工业大学 | Method for designing control parameters of grid-connected converter of micro-grid system |
Non-Patent Citations (1)
| Title |
|---|
| 含储能系统的配电网电压调节深度强化学习算法;史景坚;周文涛;张宁;陈桥;刘金涛;曹振博;陈懿;宋航;刘友波;;电力建设(第03期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113972676A (en) | 2022-01-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhu et al. | An inertia and damping control method of DC–DC converter in DC microgrids | |
| Zhang et al. | Active power reserve photovoltaic virtual synchronization control technology | |
| WO2024021206A1 (en) | Method and system for energy storage system control based on grid-forming converter, storage medium, and device | |
| CN106712552B (en) | A kind of aviation more electric engin VIENNA rectifier control method | |
| CN113131521A (en) | Virtual synchronous machine multi-machine parallel stable control and inertia matching method thereof | |
| Khatibi et al. | Impact of distributed energy resources on frequency regulation of the bulk power system | |
| CN110768239A (en) | Virtual direct current motor control method based on P-U droop characteristic | |
| CN111064232A (en) | Virtual synchronous generator-based microgrid system inverter secondary frequency control method | |
| CN115764989A (en) | Virtual synchronous generator system based on network-building type current converter | |
| CN115912405A (en) | Adaptive control strategy for virtual synchronous generator in complex oscillation environment | |
| CN109698512B (en) | Method for analyzing migration characteristics of out-of-step oscillation center of photovoltaic centralized grid-connected system | |
| CN113972676B (en) | Distributed BESS coordination control method for improving transient stability of power system | |
| Hu et al. | State-space model of an inverter-based micro-grid | |
| CN115912450A (en) | Flexible direct-current power transmission system control method and device based on virtual synchronous control | |
| CN115764912A (en) | Networking type SVG control method taking stability and active support into consideration | |
| Liu et al. | Control loop stability criterion and interaction law analysis for grid-connected inverter in weak grid | |
| Keshtkar et al. | Multi-agent based control of a microgrid power system in case of cyber intrusions | |
| Bao et al. | Cooperative control strategy of multiple VSGs in microgrid based on adjacent information | |
| Li et al. | The small-signal stability of offshore wind power transmission inspired by particle swarm optimization | |
| Shao et al. | Adaptive damping coefficient control of virtual synchronous generator of microgrid inverter | |
| Genwang et al. | Single-phase sinusoidal inverter based on fuzzy PID control for small wind power system | |
| Zhang et al. | An Improved SoC Balancing Strategy for Battery Energy Storage System in All-Electric Propulsion Ships Current Sharing Effect | |
| Lu et al. | Small-Signal Stability Research of Grid-Connected Virtual Synchronous Generators. Energies 2022, 15, 7158 | |
| Li et al. | Research on hybrid microgrid control system based on improved dynamic virtual synchronous machine | |
| Fuyin et al. | Research on UPQC harmonic control strategy based on optimized QPIR controller of beetle antennae search algorithm in microgrid |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |