CN108448586B - Micro-grid power supply quality evaluation and load balance simulation control system and method - Google Patents
Micro-grid power supply quality evaluation and load balance simulation control system and method 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
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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- 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
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- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
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- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
Abstract
The invention discloses a micro-grid power supply quality evaluation and load balancing simulation control system and method. By utilizing the intelligent evaluation device for the power supply quality of the micro-grid, the problems that the evaluation indexes of the power supply quality of the micro-grid are many, the evaluation method is complicated and laggard, and the evaluation effectiveness is poor are solved. A power equalizer is adopted, a power autonomous equalization algorithm is provided, a power set value is distributed to a simulation load unit in an equalization mode, and the problem that the follow-up treatment effect of micro-grid power supply quality evaluation is poor is solved. A robust droop controller is added in decoupling control of the analog load, so that the influence of the voltage and frequency fluctuation of the alternating-current bus of the micro-grid on the operation of the parallel system of the analog load is balanced, and the robustness of the system power following the operation of a set value is improved. In the charge/discharge control of the energy storage equipment, two constraint conditions of stabilizing the power fluctuation of the micro-grid and protecting the energy storage equipment are comprehensively utilized, constant-power charge and discharge of the energy storage equipment are maintained to the maximum extent, the energy storage equipment is protected, the service life of the equipment is prolonged, and the operation cost of the system is reduced.
Description
Technical Field
The invention belongs to the technical field of power systems, and particularly relates to a micro-grid power supply quality intelligent evaluation system and a simulation load balance control system and method.
Background
With the increasing consumption of non-renewable energy sources such as coal, petroleum and natural gas, from sustainable development and environmental protection, clean distributed generation such as solar energy and wind energy develops rapidly, and the micro-grid plays an important role in solving the problems of electric energy quality such as grid frequency fluctuation and voltage deviation caused by intermittency and randomness of distributed generation. The invention provides a micro-grid power supply quality intelligent evaluation method based on national power supply quality standards, which can well solve the problems existing in micro-grid power supply quality evaluation, and simultaneously provides a simulated load balance control device and a power autonomous balance algorithm to well solve the problem of unsatisfactory follow-up treatment effect of micro-grid power supply quality evaluation, and has positive influence on the improvement of micro-grid power supply quality, the friendly interaction of the micro-grid and the large power grid and the development of 'internet plus' intelligent energy.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a micro-grid power supply quality evaluation and analog load balance control system and method, so as to achieve the purposes of improving the micro-grid power supply quality evaluation efficiency and effectiveness, improving the subsequent treatment effect of power supply quality evaluation, effectively stabilizing the micro-grid power fluctuation, improving the power supply quality problems of voltage, frequency offset and the like, and improving the micro-grid power supply reliability.
The utility model provides a little electric wire netting power supply quality aassessment and load balancing control system simulates thereof, this system includes little electric wire netting voltage, electric current, frequency acquisition module, little electric wire netting power supply quality intelligent assessment ware and simulation load balancing controller, wherein:
the micro-grid voltage, current and frequency acquisition module comprises: the intelligent evaluation device is used for acquiring real-time microgrid voltage, current and frequency values and transmitting the real-time microgrid voltage, current and frequency values to the microgrid power supply quality intelligent evaluation device;
little electric wire netting power supply quality intelligence evaluator: the system is used for analyzing and calculating the voltage, the current and the frequency of the real-time microgrid, evaluating whether the power supply quality of the microgrid meets the requirements and judging whether the simulation load balancing controller needs to be put into operation;
the analog load balancing controller comprises a power equalizer and an analog load system;
a power equalizer: the system comprises a load simulation module, a load calculation module and a load calculation module, wherein the load calculation module is used for calculating the absorption and emission values of active power and reactive power of the load simulation module and distributing the absorption and emission values of the active power and the reactive power for each energy storage device of the load simulation in a balanced manner;
a load simulation system: the system is used for absorbing or emitting active power or reactive power, stabilizing the power fluctuation of the microgrid and improving the power supply quality of the microgrid, and a plurality of analog load units are connected in parallel to form a multi-analog load parallel operation system; the analog load unit comprises a bidirectional AC-DC converter, a bidirectional DC-DC converter, a bidirectional AC-DC converter power controller, a bidirectional DC-DC converter controller and an energy storage device. The bidirectional DC-DC converter control unit automatically switches three charging/discharging modes of constant current, constant power and constant voltage according to the SOC of the energy storage equipment, and achieves the aims of protecting the energy storage equipment, prolonging the service life of the energy storage equipment and reducing the system cost.
The method for evaluating the power supply quality of the micro-grid and simulating the load balancing control system comprises the following steps:
the method comprises the following steps: calculating and analyzing real-time microgrid voltage, current and frequency data acquired by a microgrid voltage, current and frequency acquisition module by using a microgrid power supply quality intelligent evaluator, evaluating whether the real-time microgrid power supply quality meets the standard according to the national power quality standard, and simulating that a load balancing controller needs to be put into use if the real-time microgrid power supply quality does not meet the standard;
step two: when the load balancing controller is required to be put into use, the power equalizer obtains a set value of the charging and discharging power of the system by using a power autonomous balancing algorithm according to the voltage and the frequency in the intelligent evaluation device for the power supply quality of the microgrid, calculates the maximum allowable charging and discharging power of the simulation load unit and the system by combining the SOC of the energy storage device of each simulation load unit in the simulation load system, and calculates the set value of the charging and discharging power which is required to be distributed by each simulation load unit according to the SOC of the energy storage device.
Further, the first step specifically includes the following steps:
step 1: the sampling module processes the data to obtain a sample characteristic set containing 9 characteristics of voltage deviation, voltage fluctuation and flicker, harmonic content, three-phase voltage unbalance, temporary overvoltage, transient overvoltage, current harmonic content and frequency deviation;
step 2: normalizing each characteristic of the micro-grid power supply quality characteristic set;
and step 3: marking the sample data according to the national standard of power supply quality;
and 4, step 4: randomly dividing the training samples and the test samples according to the proportion, and labeling the sample data according to the step 3;
and 5: training each training sample selected in the step 4 by adopting a bottom-up method, dividing the training process into four layers of an input layer, a training layer, an adjusting layer and an output layer, and training and optimizing layer by layer;
step 6: and evaluating the final output value of the training sample by using the test sample, evaluating the accuracy of system training, and if the sample does not meet the power supply quality requirement, putting the simulation load balance control device into use.
Further, step 5 in the first step specifically includes the following steps:
step 5-1: assigning initial values to parameters of each layer at the beginning of training, wherein the parameters of each layer comprise training layer bias gammahOutput layer bias θjInput layer ith unit and training layer h unit connection weight vihThe connection weight w of the h unit of the training layer and the j unit of the output layerhjTuning layer η1、η2、η3、η44 learning rate parameters, which are 4 constant positive quantities; input value calculation of training layer and output layer according to formula
Calculating, in the formula, the input layer input value of each sample is xi(i is 1,2, … d), d is the number of input layer units, and the training layer input value is eh(h is 1,2, …, q), q is the number of training layer units, and the input value of the output layer is yj(j ═ 1,2, …, l), where l is the number of output layer cells;
step 5-2: taking the output of the training layer as the input of the next training layer, repeating the step 5-1, and inputting the parameters into the optimization layer from top to bottom for parameter optimization after the number of the set training layers is reached;
step 5-3: and performing parameter tuning processing on the evaluation process.
Calculating the mean square error E of each sample;
step 5-3-1: adjusting output layer bias θ in a layerjThe tuning formula is
In the formula (I), the compound is shown in the specification,
expected for the output value of each sample of the output layer.
Step 5-3-2: connection weight w of h unit of training layer and j unit of output layerhjThe tuning formula is
Step 5-3-3: tuning training layer bias gamma in layerhThe tuning formula is
Therefore, it is
Step 5-3-4: adjusting connection weight v of ith unit of input layer and h unit of training layer in optimization layerihThe tuning formula is
Therefore, it is
Further, the power autonomous equalization algorithm of the power equalizer in the second step includes the following steps:
step 1): according to the evaluation result of the intelligent evaluation device for the power supply quality of the microgrid, if the simulation load does not need to be put into use, the step is executed in a circulating way; if the simulation load needs to be put into use, executing the step 2);
step 2): calculating the maximum allowable charge-discharge power of the simulation load unit and the system according to the SOC and the energy storage equipment parameters;
step 3): calculating a set value of charging/discharging power of the system according to the voltage and frequency values of the intelligent evaluation device for the power supply quality of the microgrid and parameters of each power generation device;
step 4): taking the power set value in the step 3 and the smaller value in the maximum allowable charging and discharging power of the system in the step 2) as a new system power set value;
step 5): and obtaining a power set value of each analog load unit according to the battery SOC and a power weight distribution formula, and sending the power set value to the power controller of the bidirectional AC-DC converter.
Further, the maximum allowable charge/discharge power calculation formula in the step 2) is
In the formula, N represents the number of analog load units, namely energy storage devices, each analog load unit is represented by a letter i, and the state of charge of each energy storage device is SOCi(i＝1,2，……N)，Pi mcRepresents the maximum allowable charging active power, Qi mcRepresenting maximum allowable charging reactive power, Pi mdIndicating maximum allowable discharge active power、Qi mdThe maximum allowable discharge reactive power is shown, the maximum charge/discharge power of each energy storage device is determined by the equipment when leaving the factory according to the self characteristics of the energy storage device, and the switching value ui1 denotes the use of the dummy load cell, uiTaking 0 to indicate that the analog load unit is not in use, uiThe value of (a) depends on the SOC of the energy storage equipment, namely u when the SOC of the energy storage equipment is in the allowable working range of the equipment i1, when the SOC of the energy storage device is out of the allowable working range of the device uiTake 0.
Further, the system charge/discharge power set value in the step 3) is calculated, and the relation between the power set value of the simulation load system and the rotating speed of the generator set is obtained by the motion equation of the generator set rotor
Written in the form of a state equation
Wherein, ω isNAt rated angular velocity, delta is the power angle, PT、PE、PDMechanical power, output power and loss power, T, of the generator setJFor equivalent inertia time constant, the calculation formula is
TJiContinuously deducing the relationship between the power set value and the frequency of the analog load system for the inertia time constant of each generator set
Where ω and f are the inertial center angular velocity and the inertial center frequency, respectively, of
And calculating.
Further, the step 5) calculates the power set value of each analog load unit according to the battery SOC and the power weight distribution formula
Wherein the charging power of each analog load unit is Pi c(Qi c) Discharge power of Pi d(Qi d) And sending the calculation result to a power controller of the bidirectional AC-DC converter.
Further, the power control unit of the bidirectional AC-DC converter in the step 5) controls the bidirectional AC-DC converter according to the power set value obtained by the power equalizer, so that the decoupling control of the power of the microgrid is completed, and the aim of improving the power supply quality of the microgrid is fulfilled; meanwhile, a robust droop controller is added in the power decoupling control to balance the frequency and voltage deviation of the microgrid, so that the robustness of the system is improved, and the method specifically comprises the following steps: the frequency deviation and the voltage deviation are added into the deviation of active power and reactive power after passing through the proportional action of a robust droop coefficient and an amplitude limiting module, the robustness of the system is improved, the effectiveness analysis of the robust droop controller is embodied by frequency relative errors, and the power relative errors are obtained according to the droop relation
In the formula, P and P are respectively an active power value and an active power set value of an actual microgrid alternating-current bus, Q and Q are respectively a reactive power value and a reactive power set value of the actual microgrid alternating-current bus, and U0Measured micro-grid AC bus voltage and bus voltage standard values, f and f respectively0Respectively the actually measured AC bus frequency of the microgridStandard values (50Hz) of the rate and the frequency, wherein m and n are droop coefficients; micro-grid AC bus frequency and voltage relative error
And
and if the change is not large in a certain period, the larger the droop coefficients m and n are, the larger the relative error of the active power on the alternating current bus side of the micro-grid is. The relative error of power after adding the robust droop controller is
By robust droop coefficient Kp、KqThe influence of the droop coefficients m and n on the relative power error is balanced, and the power control precision is improved.
The invention has the beneficial effects that:
the invention relates to a micro-grid power supply quality evaluation and simulation load balance control system and method, which coordinate a plurality of indexes of micro-grid power supply quality evaluation, provide an intelligent power supply quality evaluation method, solve the problems of complexity and lagging of the evaluation method and poor evaluation effect, and realize the intelligence and effectiveness of micro-grid power supply quality evaluation; by the autonomous balance control of the power of the energy storage equipment, the coordination among the protection of the energy storage equipment, the prolonging of the service life of the energy storage equipment, the reduction of the system operation cost, the stabilization of the power fluctuation of the micro-grid and the improvement of the power supply quality of the micro-grid is realized to the greatest extent; multiple simulation loads run in parallel, the requirement of the diversity of the loads of the micro-grid is met, and the reliability of the operation of the simulation loads is improved; a robust droop controller is added in the power control of the bidirectional AC-DC converter, so that the frequency and voltage deviation of the micro-grid is balanced, and the robustness of the system is improved.
Drawings
FIG. 1 is a block diagram of the system components of an embodiment of the present invention;
fig. 2 is a flowchart of an intelligent estimator of the power supply quality of a microgrid according to an embodiment of the present invention;
FIG. 3 is a block diagram of an intelligent evaluator in accordance with an embodiment of the present invention;
FIG. 4 is a flow chart of the operation of the power equalizer controller according to the embodiment of the present invention;
FIG. 5 is a block diagram of a simulated load cell assembly according to an embodiment of the invention;
FIG. 6 is a diagram of a simulated load cell topology according to an embodiment of the present invention;
FIG. 7 is a block diagram of a bidirectional AC-DC converter control according to an embodiment of the present invention;
FIG. 8 is a block diagram of a bidirectional DC to DC converter control according to an embodiment of the present invention;
FIG. 9 is a diagram illustrating the switching of the charging power, voltage and current modes of the energy storage device according to an embodiment of the present invention;
FIG. 10 is a diagram illustrating discharge power, voltage, and current mode switching of an energy storage device according to an embodiment of the invention
FIG. 11 is a schematic diagram of a microgrid voltage waveform according to an embodiment of the present invention;
FIG. 12 is a diagram of a microgrid current waveform according to an embodiment of the present invention;
FIG. 13 is a frequency waveform of a microgrid according to an embodiment of the present invention;
FIG. 14 is a graph of an active power waveform at the AC side of a simulated load according to an embodiment of the present invention;
fig. 15 is a waveform diagram of the reactive power at the ac side of the analog load according to the embodiment of the present invention.
Detailed Description
An embodiment of the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of a system according to an embodiment of the present invention, which includes a microgrid voltage, current, and frequency acquisition module, a microgrid power supply quality intelligent evaluator, a power equalizer, and a simulated load parallel system, wherein the microgrid voltage, current, and frequency acquisition module is capable of acquiring a real-time ac microgrid bus voltage, a bus current, and a bus frequency, and transmitting acquired information to the microgrid power supply quality intelligent evaluator, the microgrid power supply quality intelligent evaluator transmits an evaluation result, i.e., whether a simulated load needs to be put into use, to the power equalizer, and if the current microgrid power supply quality is not within a standard range, the power equalizer combines the current energy storage device SOC with the real-time microgrid ac bus voltage, bus current, and bus frequency of the acquisition module, and in each energy storage device, the analog load parallel system finishes the operation of the power at the side of the alternating current bus of the micro-grid according to a set value through the control of a bidirectional AC-DC converter and a bidirectional DC-DC converter according to the distribution value of a power equalizer, thereby finishing the stabilization of the power fluctuation of the micro-grid and further improving the power supply quality of the micro-grid.
Fig. 2 and fig. 3 are a flowchart and a structure diagram of an intelligent estimator for power supply quality of a microgrid according to an embodiment of the present invention, and an implementation of the intelligent estimator for power supply quality of a microgrid includes the following steps:
step 1: the data after the calculation of the sampling module is used for obtaining four aspects of data of voltage, current, frequency and power factor, the voltage comprises six characteristics of voltage deviation, voltage fluctuation and flicker, harmonic content, three-phase voltage unbalance, temporary overvoltage, transient overvoltage and the like, the current comprises one characteristic of current harmonic content, the frequency comprises one characteristic of frequency deviation, the power factor comprises one characteristic of power factor size, namely each variable of the sample set comprises 9 characteristics, and the sample set is represented as a matrix
N is the sample size.
Step 2: normalizing each characteristic of the micro-grid power supply quality characteristic set by adopting a 0-mean value normalization formula, wherein the normalization formula is
In the formula, mean (x) is the average value of each feature in each sample feature set, and σ is the standard deviation of the feature, and the training speed and the accuracy of the model can be improved through normalization processing.
And step 3: marking the sample data according to the national standard, and marking the data set
The sample which satisfies the power supply quality requirement in the sample characteristic set is marked as (1, 0)TSamples not meeting the quality criteria for power supply are labeled (0, 1)T。
The national standard of the specific power supply quality refers to allowable deviation of power supply voltage of power quality (GB/T12325-2003), < harmonic of public power grid > < GB/T14549- > 1993 > < allowable unbalance of three-phase voltage (GB/T15543- > 2008 >, < allowable deviation of frequency of electric power system > < GB/T15949- > 1995 >, < voltage fluctuation and flicker > < GB/T12326- > 2008 >, < temporary overvoltage and transient overvoltage > < GB/T18481- > 2001 >, respectively.
And 4, step 4: and (4) randomly dividing the training sample and the test sample according to the proportion, and labeling the sample data according to the step (3).
And 5: training each training sample selected in the step 4 by adopting a bottom-up method, dividing the training process into four layers of an input layer, a training layer, an adjusting layer and an output layer, wherein the input value of the input layer of each sample is xi(i-1, 2, … 9) and training layer input value eh(h is 1,2, …, q), q is the number of training layer units, 5 training layers are selected in the embodiment of the invention, the number of each training layer is 45,36,18,9,2 in sequence, and the input value of the output layer is yj(j ═ 1,2), the training layer is biased by γhOutput layer bias θjInput layer ith unit and training layer h unitihThe connection weight w of the h unit of the training layer and the j unit of the output layerhjAssigning random values to the 4 parameters, adjusting the layer parameters η1、η2、η3、η4The 4 learning rate parameters are assigned to an initial value of 0.1.
Step 5-1: input value calculation of training layer and output layer according to formula
And (4) calculating.
Step 5-2: and (5) taking the output of the training layer as the input of the next training layer, repeating the step 5-1, and inputting the parameters into the optimization layer from top to bottom for parameter optimization after the number of the set training layers is reached.
Step 5-3: tuning base in layer data processing
The mean square error for each sample is calculated, where,
expected for the output value of each sample of the output layer.
Step 5-3-1: adjusting output layer bias θ in a layerjThe tuning formula is
In the formula
Is calculated by the formula
Step 5-3-2: connection weight w of h unit of training layer and j unit of output layerhjThe tuning formula is
In the formula
Is calculated by the formula
Step 5-3-3: tuning training layer bias gamma in layerhThe tuning formula is
The formula for solving the partial derivative is
Therefore, it is
Step 5-3-4: adjusting connection weight v of ith unit of input layer and h unit of training layer in optimization layerihThe tuning formula is
In the formula
Is solved by the formula
Therefore, it is
Step 6: and (5) evaluating the final output value of the training sample by using the test sample, returning to the step 5 if the training result does not meet the accuracy requirement, continuously adjusting and optimizing the parameters, and judging whether the analog load balancing control device needs to be put into use according to whether the power supply quality is a standard set or not if the training result meets the accuracy requirement.
Fig. 4 is a flowchart of a power autonomous balancing algorithm of a power balancing controller according to an embodiment of the present invention, where in the embodiment of the present invention, there are N energy storage devices, that is, there are N analog load units, each analog load unit is denoted by a letter i, and the power autonomous balancing algorithm in the power balancer includes the following steps:
step 1: receiving an instruction of a micro-grid power supply quality intelligent evaluator that the load needs to be simulated for use;
step 2: the power equalizer reads the voltage, the current and the frequency of the alternating-current bus of the micro-grid from the intelligent evaluation device of the power supply quality of the micro-grid, and reads the current state of charge (SOC) and the maximum charging/discharging power from each energy storage device, wherein the maximum charging power uses Pi mc(Qi mc) Indicating that the maximum discharge power is Pi md(Qi md) The maximum charging/discharging power of the energy storage device is determined by the equipment when the equipment leaves the factory according to the self characteristics of the energy storage device, and the state of charge of each energy storage device is marked as SOCi(i＝1,2，……N)；
And step 3: switching value u for use of each analog load unitiDenotes ui1 denotes the use of the dummy load cell, uiTaking 0 to indicate that the analog load unit is not in use, uiThe value of (a) depends on the SOC of the energy storage equipment, namely u when the SOC of the energy storage equipment is in the allowable working range of the equipment i1, when the SOC of the energy storage device is out of the allowable working range of the device uiTaking 0, wherein the allowable working range of the energy storage equipment is determined by the equipment when leaving the factory according to the self characteristics of the energy storage equipment;
and 4, step 4: from the maximum allowable charge/discharge power calculation formula
Separately calculating the simulated negativesMaximum allowable charging/discharging power of parallel system, wherein the maximum allowable charging power of the system is Pmc(Qmc) Maximum allowable discharge power of system is Pmd(Qmd)；
And 5: and calculating a set value of the charge/discharge power of the analog load. Relation between power set value of simulation load system and rotating speed of generator set can be obtained by generator set rotor motion equation
Written in the form of a state equation
Wherein, ω isNAt rated angular velocity, delta is the power angle, PT、PE、PDMechanical power, output power and loss power, T, of the generator setJFor equivalent inertia time constant, the calculation formula is
TJiContinuously deducing the relation between the power set value and the frequency of the obtained simulation load system for the inertia time constant of each generator set
Where ω and f are the angular velocity and the frequency of the center of inertia, respectively, of the center of inertia
And calculating.
The reactive power to be absorbed/compensated by the analog load from the grid side is calculated from Q ═ P × tan θ, where θ is the power factor angle, based on the active power set value and the power factor in step 1.
Step 6: comparing the set value of the power of the analog load parallel system calculated in the step 5 with the maximum allowable charge/discharge power of the analog load parallel system calculated in the step 4, and taking the smaller value of the two as a new set value of the power of the analog load parallel system;
and 7: according to the SOC of each energy storage device of the analog load unit, calculating the formula
Calculating the charge/discharge power to be distributed to each analog load unit, wherein the charge power of each analog load unit is Pi c(Qi c) Discharge power of Pi d(Qi d)，
And 8: and the power value distributed by each analog load unit is sent to the control unit of the bidirectional AC-DC converter and the bidirectional DC-DC converter.
Fig. 5 is a block diagram of a simulated load unit according to an embodiment of the present invention, where the simulated load unit is composed of a bidirectional AC-DC converter, a bidirectional DC-DC converter, and an energy storage device, and fig. 6 is a topology structure diagram of the simulated load unit according to an embodiment of the present invention, where a topology structure of the bidirectional AC-DC converter includes a micro-grid AC bus side filter inductor, six switching devices constitute three controllable bridge arms A, B, C and a DC side filter capacitor; the topological structure of the bidirectional DC-DC converter comprises two IGBTs and an inductor to form a bidirectional Buck/Boost circuit capable of bidirectional voltage rising/reducing.
Fig. 7 is a control block diagram of a bidirectional AC-DC converter according to an embodiment of the present invention, where after a charging/discharging power set value P (Q) of each analog load unit is obtained, a power complete decoupling control method is applied to control active power and reactive power at an interface between an analog load and a microgrid AC bus, and droop relationships between the active power and frequency and between the reactive power and voltage are utilized to control the active power and the reactive power at the interface between the analog load and the microgrid AC bus
Finally, the aim of regulating the voltage and the frequency of the micro-grid within a standard range is achieved, wherein U and U0Measured micro-grid AC bus voltage and bus voltage standard value (380V), f and f respectively0The actual measured frequency and the frequency standard value (50Hz) of the alternating current bus of the microgrid are respectively, P and P are respectively an active power value and an active power set value of the alternating current bus of the actual microgrid, Q and Q are respectively a reactive power value and a reactive power set value of the alternating current bus of the actual microgrid, m and n are droop coefficients, and the initial droop coefficient is set to be 0.11.
According to the concepts of rectification and inversion and kirchhoff's law, in combination with the power decoupling control of the PWM rectifier, the current equation under the rotating dq coordinate system can be obtained as
Wherein L issIn the embodiment of the invention, the filter inductance is 0.5mH, e for the side of the AC bus of the micro-gridd、eqFor transforming the microgrid AC busbar voltage, i, into the dq coordinate systemd、iqFor transforming the alternating bus current of the microgrid under the dq coordinate system, R is the side line resistance of the alternating bus of the microgrid, omega is the angular frequency of the alternating bus of the microgrid, ud、uqIs a switching voltage vector in dq coordinate system.
Selecting the d axis in the dq rotating coordinate system to coincide with the voltage vector, neglecting the switching loss and the alternating current side resistance loss, and combining the current and instantaneous power equation under the rotating coordinate system
To obtain
Setting the power to be adjusted in a closed loop manner
On the basis of decoupling control, a robust droop controller for frequency and voltage deviation is added according to droop relations of active power and frequency and reactive power and voltage, namely the frequency deviation and the voltage deviation are added into the deviation of the active power and the reactive power after passing through a proportional action of a robust droop coefficient and an amplitude limiting module, then power is decoupled, and the robustness of a system is improved. The effectiveness analysis of the robust droop controller comprises the following steps:
step 1: according to the formula
Calculating the relative power regulation error of the AC bus side of the micro-grid by using eP、eQExpressing that P and P in the formula are an active power value and an active power set value of an actual microgrid alternating-current bus respectively, and Q are a reactive power value and a reactive power set value of the actual microgrid alternating-current bus respectively;
step 2: according to the sag relation
The mathematical relation between the power deviation and the frequency and voltage deviation is obtained
Substituting into the power relative error formula to obtain the power relative error
Step 2-1: micro-grid alternating current busRelative error in frequency
If the change is not large in a certain period, the larger the droop coefficient m is, the larger the relative error of the active power at the side of the alternating-current bus of the micro-grid is;
step 2-2: relative error of voltages of alternating current buses of micro-grid
If the change is not large in a certain period, the larger the droop coefficient n is, the larger the relative error of the reactive power at the side of the alternating current bus of the micro-grid is;
and step 3: after the frequency and voltage deviation of the alternating current bus of the micro-grid is subjected to robust droop control, the relative error of power regulation is changed into
In the formula Kp、KqFor the droop coefficient in the robust droop controller of fig. 7, the initial value is set to 0.1, with the robust droop coefficient Kp、KqThe influence of the droop coefficients m and n on the relative power error is balanced, and therefore the power control precision is improved.
Fig. 8 is a control block diagram of a bidirectional DC-DC converter according to an embodiment of the present invention, and the implementation of the control part of the bidirectional DC-DC converter includes the following steps:
step 1: comparing the power of the alternating current bus side of the micro-grid with a power set value;
step 2: when the power of the alternating current bus side of the micro-grid is larger than a power set value, a simulation load charging mode is selected to absorb the redundant power of the micro-grid, the current, the voltage and the SOC of the energy storage equipment are detected through the sensor, under two constraint conditions of stabilizing the power fluctuation of the micro-grid and protecting the energy storage equipment,
step 2-1: when the SOC of the energy storage equipment is lower than 10%, in order to prevent the damage to the energy storage equipment caused by overlarge initial charging current, the energy storage equipment is charged in a constant current charging mode;
step 2-2: when the SOC of the energy storage equipment reaches 20%, the charging mode is switched to be a constant-power charging mode, in the charging mode, the power absorbed by the energy storage equipment is just balanced with the redundant power of the microgrid, and at the moment, the energy storage equipment can play a role in stabilizing the power fluctuation of the microgrid most effectively;
step 2-3: when the charging voltage reaches the nominal voltage of the energy storage equipment, switching to a constant voltage charging mode to prevent the energy storage equipment from being damaged due to overhigh charging voltage;
step 2-4: when the SOC of the energy storage equipment is higher than 90%, in order to protect the energy storage equipment and avoid overcharging of the energy storage equipment, the switching function u is usediAnd taking 0, namely the energy storage equipment in the state is not put into use any more.
And step 3: when the power of the AC bus side of the micro-grid is smaller than a power set value, a simulated load discharge mode is selected to compensate the power missing from the micro-grid, the current, the voltage and the SOC of the energy storage equipment are detected through the sensor, under two constraint conditions of stabilizing the power fluctuation of the micro-grid and protecting the energy storage equipment,
step 3-1: when the discharge voltage of the energy storage equipment is higher than the lowest discharge voltage, the discharge mode is selected as a constant-power discharge mode, the power emitted by the energy storage equipment is just balanced with the power lacking in the microgrid in the discharge mode, and the energy storage equipment can play a role of stabilizing the power fluctuation of the microgrid most effectively;
step 3-2: when the SOC of the energy storage equipment is lower than 30%, along with the reduction of the internal resistance of the energy storage equipment, in order to prevent the discharge current from being overlarge, a constant current discharge mode of a simulation load is selected for discharge treatment;
step 3-3: when the discharge voltage reaches the lowest discharge voltage of the energy storage equipment, in order to protect the energy storage equipment and avoid causing over discharge of the energy storage equipment, the switching function u is usediAnd taking 0, namely the energy storage equipment in the state is not put into use any more.
With reference to the control block diagram and the step description of fig. 8, fig. 9 is a diagram illustrating switching of charging power, voltage, and current modes of the energy storage device according to the embodiment of the present invention, and fig. 10 is a diagram illustrating switching of discharging power, voltage, and current modes of the energy storage device according to the embodiment of the present invention, so that it can be clearly seen when the energy storage device switches the charging/discharging modes.
In an embodiment of the present invention, fig. 11 is a voltage waveform diagram of a microgrid, fig. 12 is a current waveform diagram of the microgrid, and fig. 13 is a frequency waveform diagram of the microgrid, so as to satisfy power supply quality requirements on the voltage, current and frequency of an alternating current bus of the microgrid.
Fig. 14 and fig. 15 are waveform diagrams of active power and reactive power at the ac side of the simulated load according to the embodiment of the present invention, where the initial value of the active power setting is 10kW, the system is switched to 15kW when running for 0.4s, and voltage sag and frequency fluctuation of the microgrid occur at 0.7s, and the power setting value of the simulated load system is adjusted in time to maintain 10 kW; the reactive power setting initial value is 0kVar, the system is switched to 10kVar when running to 0.4s, voltage sag and frequency fluctuation of the microgrid occur in 0.7s, the power setting value of the analog load system is adjusted in time to be maintained at 0.9kVar, the power of the alternating-current side of the analog load is controlled according to the requirement of the power fluctuation of the alternating-current bus of the microgrid from the tracking control of the active power and the reactive power of the alternating-current bus side of the microgrid, the power fluctuation of the microgrid is stabilized, and finally the aim of improving the power supply quality of the microgrid is achieved.
Claims (8)
1. A method for evaluating the power supply quality of a micro-grid and simulating a load balance control system of the micro-grid is characterized by comprising a micro-grid voltage, current and frequency acquisition module, a micro-grid power supply quality intelligent evaluator and a simulation load balance controller, wherein:
the micro-grid voltage, current and frequency acquisition module comprises: the intelligent evaluation device is used for acquiring real-time microgrid voltage, current and frequency values and transmitting the real-time microgrid voltage, current and frequency values to the microgrid power supply quality intelligent evaluation device;
little electric wire netting power supply quality intelligence evaluator: the system is used for analyzing and calculating the voltage, the current and the frequency of the real-time microgrid, evaluating whether the power supply quality of the microgrid meets the requirements and judging whether the simulation load balancing controller needs to be put into operation;
the analog load balancing controller comprises a power equalizer and an analog load system;
a power equalizer: the system comprises a load simulation module, a load calculation module and a load calculation module, wherein the load calculation module is used for calculating the absorption and emission values of active power and reactive power of the load simulation module and distributing the absorption and emission values of the active power and the reactive power for each energy storage device of the load simulation in a balanced manner;
a load simulation system: the system is used for absorbing or emitting active power or reactive power, stabilizing the power fluctuation of the microgrid and improving the power supply quality of the microgrid, and a plurality of analog load units are connected in parallel to form a multi-analog load parallel operation system; the analog load unit comprises a bidirectional AC-DC converter, a bidirectional DC-DC converter, a bidirectional AC-DC converter power controller, a bidirectional DC-DC converter controller and energy storage equipment;
the method comprises the following steps:
the method comprises the following steps: calculating and analyzing real-time microgrid voltage, current and frequency data acquired by a microgrid voltage, current and frequency acquisition module by using a microgrid power supply quality intelligent evaluator, evaluating whether the real-time microgrid power supply quality meets the standard according to the national power quality standard, and simulating that a load balancing controller needs to be put into use if the real-time microgrid power supply quality does not meet the standard; the method specifically comprises the following steps:
step 1: the sampling module processes the data to obtain a sample characteristic set containing 9 characteristics of voltage deviation, voltage fluctuation and flicker, harmonic content, three-phase voltage unbalance, temporary overvoltage, transient overvoltage, current harmonic content and frequency deviation;
step 2: normalizing each characteristic of the micro-grid power supply quality characteristic set;
and step 3: marking the sample data according to the national standard of power supply quality;
and 4, step 4: randomly dividing the training samples and the test samples according to the proportion, and labeling the sample data according to the step 3;
and 5: training each training sample selected in the step 4 by adopting a bottom-up method, dividing the training process into four layers of an input layer, a training layer, an adjusting layer and an output layer, and training and optimizing layer by layer;
step 6: evaluating the final output value of the training sample by using the test sample, evaluating the accuracy of system training, and if the sample does not meet the power supply quality requirement, simulating that the load balance control device needs to be put into use;
step two: when the load balancing controller is required to be put into use, the power equalizer obtains a set value of the charging and discharging power of the system by using a power autonomous balancing algorithm according to the voltage and the frequency in the intelligent evaluation device for the power supply quality of the microgrid, calculates the maximum allowable charging and discharging power of the simulation load unit and the system by combining the SOC of the energy storage device of each simulation load unit in the simulation load system, and calculates the set value of the charging and discharging power which is required to be distributed by each simulation load unit according to the SOC of the energy storage device.
2. The method for evaluating the power supply quality of the microgrid and simulating a load balancing control system according to claim 1, wherein the step 5 in the step one specifically comprises the following steps:
step 5-1: assigning corresponding initial values to the parameters of each layer when training begins; input value calculation of training layer and output layer according to formula
Calculating;
step 5-2: taking the output of the training layer as the input of the next training layer, repeating the step 5-1, and inputting the parameters into the optimization layer from top to bottom for parameter optimization after the number of the set training layers is reached;
step 5-3: and performing parameter tuning processing on the evaluation process.
3. The method for evaluating the power supply quality of the microgrid and simulating a load balancing control system according to claim 2, characterized in that the step 5-3 specifically comprises the following steps:
calculating the mean square error E of each sample;
step 5-3-1: adjusting output layer bias θ in a layerjThe tuning formula is
Step 5-3-2: connection weight w of h unit of training layer and j unit of output layerhjThe tuning formula is
Step 5-3-3: tuning training layer bias gamma in layerhThe tuning formula is
Therefore, it is
Step 5-3-4: adjusting connection weight v of ith unit of input layer and h unit of training layer in optimization layerihThe tuning formula is
Therefore, it is
4. The method for evaluating the power supply quality of the microgrid and simulating a load balancing control system according to claim 1, wherein the power autonomous balancing algorithm of the power equalizer in the second step comprises the following steps:
step 1): according to the evaluation result of the intelligent evaluation device for the power supply quality of the microgrid, if the simulation load does not need to be put into use, the step is executed in a circulating way; if the simulation load needs to be put into use, executing the step 2);
step 2): calculating the maximum allowable charge-discharge power of the simulation load unit and the system according to the SOC and the energy storage equipment parameters;
step 3): calculating a set value of charging/discharging power of the system according to the voltage and frequency values of the intelligent evaluation device for the power supply quality of the microgrid and parameters of each power generation device;
step 4): taking the power set value in the step 3 and the smaller value in the maximum allowable charging and discharging power of the system in the step 2) as a new system power set value;
step 5): and obtaining a power set value of each analog load unit according to the battery SOC and a power weight distribution formula, and sending the power set value to the power controller of the bidirectional AC-DC converter.
5. The method for evaluating the quality of power supply of the microgrid and simulating a load balancing control system according to claim 4, characterized in that the maximum allowable charge/discharge power calculation formula in the step 2) is
In the formula, N represents the number of analog load units, namely energy storage devices, each analog load unit is represented by a letter i, and the state of charge of each energy storage device is SOCi(i＝1,2，……N)，Pi mcRepresents the maximum allowable charging active power, Qi mcRepresenting maximum allowable charging reactive power, Pi mdRepresents the maximum allowable discharge active power, Qi mdThe maximum allowable discharge reactive power is shown, the maximum charge/discharge power of each energy storage device is determined by the equipment when leaving the factory according to the self characteristics of the energy storage device, and the switching value ui1 denotes the use of the dummy load cell, uiTaking 0 to indicate that the analog load unit is not in use, uiThe value of (a) depends on the SOC of the energy storage equipment, namely u when the SOC of the energy storage equipment is in the allowable working range of the equipmenti1, when the SOC of the energy storage device is out of the allowable working range of the device uiTake 0.
6. The method for evaluating the power supply quality of the microgrid and simulating a load balancing control system according to claim 4, characterized in that in the step 3), the charge/discharge power set value of the system is calculated, and the relation between the charge/discharge power of the simulated load system and the rotating speed of the generator set is obtained by the motion equation of the rotor of the generator set
Written in the form of a state equation
Wherein, ω isNAt rated angular velocity, delta is the power angle, PT、PE、PDMechanical power, output power and loss power, T, of the generator setJFor equivalent inertia time constant, the calculation formula is
TJiContinuously deducing the relationship between the charge and discharge power and the frequency of the simulation load system for the inertia time constant of each generator set
Where ω and f are the inertial center angular velocity and the inertial center frequency, respectively, of
And calculating.
7. The method for evaluating the quality of power supply of the microgrid and simulating a load balancing control system according to claim 4, characterized in that, in the step 5), the power set value of each simulated load unit is calculated by a battery SOC and a power weight distribution formula, wherein the power weight distribution formula is
Wherein the charging power of each analog load unit is Pi c(Qi c) Discharge power of Pi d(Qi d) And sending the calculation result to a power controller of the bidirectional AC-DC converter.
8. The method for evaluating the power supply quality of the microgrid and simulating a load balancing control system according to claim 4, characterized in that in the step 5), the bidirectional AC-DC converter power control unit controls the bidirectional AC-DC converter according to a power set value obtained by the power equalizer, so as to complete the power decoupling control of the microgrid and achieve the purpose of improving the power supply quality of the microgrid; meanwhile, a robust droop controller is added in the power decoupling control to balance the frequency and voltage deviation of the microgrid, so that the robustness of the system is improved, and the method specifically comprises the following steps: the frequency deviation and the voltage deviation are added into the deviation of active power and reactive power after passing through the proportional action of a robust droop coefficient and an amplitude limiting module, the robustness of the system is improved, the effectiveness analysis of the robust droop controller is embodied by frequency relative errors, and the power relative errors are obtained according to the droop relation
The relative error of power after adding the robust droop controller is
By robust droop coefficient Kp、KqThe influence of the droop coefficients m and n on the relative power error is balanced, and the power control precision is improved.
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