CN118157242A - Control method and device for electrolytic aluminum load to participate in secondary frequency modulation of power grid - Google Patents
Control method and device for electrolytic aluminum load to participate in secondary frequency modulation of power grid Download PDFInfo
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Abstract
The invention discloses a control method and a device for participation of electrolytic aluminum load in secondary frequency modulation of a power grid. The method comprises the steps of constructing a generalized predictive control model by taking unbalanced power of an electrolytic aluminum system and historical data of direct current voltage of an electrolytic tank as input objects and taking historical data of electrolytic aluminum load power and electrolytic tank temperature as output objects, wherein the unbalanced power historical data and the electrolytic aluminum load power historical data of the electrolytic aluminum system follow a first corresponding relation, and the historical data of the direct current voltage of the electrolytic tank and the historical data of the electrolytic tank temperature follow a second corresponding relation; and generating a preliminary result of the electrolytic aluminum load power and the future time of the temperature of the electrolytic tank based on the generalized predictive control model, and correcting the preliminary result based on the PID controller to obtain a predicted result of the electric network secondary frequency modulation, namely the electrolytic aluminum load power. Due to the combination of the generalized predictive control model and PID control, inaccuracy of the predicted result of the electrolytic aluminum load power is avoided.
Description
Technical Field
The invention relates to the technical field of power system control, in particular to a control method and a device for an electrolytic aluminum load to participate in secondary frequency modulation of a power grid.
Background
The active regulation characteristics of the power grids in each region in the traditional interconnected power system are not greatly different. When unbalanced power disturbances occur, the synchronous motor groups with the automatic power generation control (automatic generation control, AGC) systems in the respective zones can adjust their output power, thereby together stabilizing the power fluctuations of the inter-zone links. In contrast, high-energy-consumption industrial power grids containing high-permeability wind power have an order of magnitude obvious difference from large power grids in terms of regulation means, regulation capacity and the like. Because the frequency of the large power grid supports, fluctuation of wind power is stabilized by the large power grid at the grid-connected side through the connecting lines, and the problem of frequency stabilization of the grid-connected high-energy-consumption industrial power grid can be guaranteed. However, this mode has two disadvantages: for high-energy-consumption industrial enterprises, the large-capacity tie-line capacity can generate huge tie-line spare capacity fees, which is not beneficial to the economic operation of the enterprises; for the large power grid side, when a large amount of high-energy-consumption industrial power grids containing high-permeability renewable energy sources are accessed, huge pressure is generated for safe operation of the large power grid side, and an additional rapid adjustment generator set needs to be built.
The fluctuation of wind power is a main cause of fluctuation of tie line power between a grid-connected high-energy-consumption industrial power grid and a large power grid. Wind power exhibits varying degrees of volatility at different time scales, such as hours, minutes, seconds, and the like. The ultra-short-term second-level wind power fluctuation brings higher requirements on the active regulation rate of the power system, and is one of the technical difficulties of stabilizing the power fluctuation of the conventional tie line. However, the conventional power supply-side coal-fired thermal power unit is limited by the regulation rate of a turbine, and it is difficult to effectively track and stabilize the wind power fluctuation of the ultra-short-term second level.
In a power grid system, a model predictive controller (model predictive control, MPC) is generally used to solve the problem of electrolytic aluminum load power distribution of an electrolytic aluminum load participating in a power grid. Model predictive control has the explicit processing constraint capability, but has the defects of poor real-time performance, sensitivity to parameters, complex constraint processing and the like, and can not meet the requirements of a novel power system.
There is therefore a need for a new control method for enabling an electrolytic aluminium load to participate in the efficient control of the electrolytic aluminium load power of the grid.
Disclosure of Invention
In view of the above, the control method and the device for the electrolytic aluminum load to participate in the secondary frequency modulation of the power grid can accurately predict the electrolytic aluminum load power.
In a first aspect, the invention provides a control method for participation of electrolytic aluminum load in secondary frequency modulation of a power grid, comprising the following steps:
Constructing a first corresponding relation of electrolytic aluminum load power along with unbalanced power change of an electrolytic aluminum system;
Constructing a second corresponding relation of the temperature of the electrolytic cell along with the change of the direct-current voltage of the electrolytic cell;
Constructing a generalized predictive control model by taking the unbalanced power of the electrolytic aluminum system and the historical data of the direct current voltage of the electrolytic tank as input objects and taking the historical data of the load power of the electrolytic aluminum and the temperature of the electrolytic tank as output objects, wherein the unbalanced power historical data of the electrolytic aluminum system and the historical data of the load power of the electrolytic aluminum follow the first corresponding relation, and the historical data of the direct current voltage of the electrolytic tank and the historical data of the temperature of the electrolytic tank follow the second corresponding relation; generating a preliminary result of the electrolytic aluminum load power and the electrolytic tank temperature at future time based on the generalized predictive control model;
and correcting the primary result based on the PID controller to obtain a prediction result of the secondary frequency modulation of the power grid.
Optionally, the first correspondence is in the form of a formula,
；
In the above-mentioned method, the step of,To adjust the load power of the electrolytic aluminum before the electrolysis,/>To adjust the post-electrolytic aluminum load power,/>Is unbalanced power.
Optionally, the second correspondence is expressed in the following formula,
；
Wherein, C is the heat capacity of the device, m is the mass of the device, T 1 is the temperature of the electrolytic cell before adjustment, T 2 is the temperature of the electrolytic cell after adjustment,To adjust the direct current voltage of the electrolytic cell before/(For the adjusted cell dc voltage, t 1 is the initial time for the start of the adjustment and t 2 is the end time for the end of the adjustment.
Optionally, the unbalanced powerAs will be determined by the following description of the embodiments,
；
；
；
For the power disturbance quantity of electrolytic aluminum system,/>For a maximum up-primary frequency modulation capacity,For the upward rotation standby power of the current thermal power unit of the power grid, P up is the primary frequency modulation upper limit of the thermal power unit, f is the frequency of an electrolytic aluminum load system, t 0 represents the initial time, t represents the current time,/>For the initial rate of change of frequency of the electrolytic aluminum system, H is the inertial time constant for power generation, and min is the minimum.
Optionally, the control mode of the PID controller is expressed by the following formula,
；
；
；
；
T is a preliminary result of the future moment of the electrolytic cell temperature, P ASL is a preliminary result of the future moment of the electrolytic aluminum load power, T r is a prediction result of the future moment of the electrolytic cell temperature, P ASLr is a prediction result of the future moment of the electrolytic aluminum load power, K P is a scale factor, K I is an integral factor, K D is a differential factor, Z -1 is a backward operator, e (T) is an output error function, y r (T) is a set value of the electrolytic aluminum load output, y (T) is an actual value of the electrolytic aluminum load output, and [ (T) represents a transpose matrix.
Alternatively, the K P、KI、KD is determined by the following formula,
；
；
；
In the above-mentioned method, the step of,For the j-th electrolytic aluminum load power,/>Frequency of participation of electrolytic aluminum load in power grid before j-th adjustment,/>And the frequency of the electrolytic aluminum load participating in the power grid after the j-th adjustment is set, and N is the number of the electrolytic aluminum loads.
Optionally, the K P、KI、KD is optimized through a genetic algorithm determined by a performance index of the generalized predictive control model.
In a second aspect, the invention provides a control device for participation of electrolytic aluminum load in secondary frequency modulation of a power grid, comprising:
The first construction module is used for constructing a first corresponding relation of electrolytic aluminum load power along with unbalanced power change of an electrolytic aluminum system;
the second construction module is used for constructing a second corresponding relation of the temperature of the electrolytic tank along with the change of the direct-current voltage of the electrolytic tank;
The generation module is used for constructing a generalized predictive control model by taking the unbalanced power of the electrolytic aluminum system and the historical data of the direct current voltage of the electrolytic tank as input objects and taking the historical data of the electrolytic aluminum load power and the electrolytic tank temperature as output objects, wherein the unbalanced power historical data of the electrolytic aluminum system and the historical data of the electrolytic aluminum load power follow the first corresponding relation, and the historical data of the direct current voltage of the electrolytic tank and the historical data of the electrolytic tank temperature follow the second corresponding relation; generating a preliminary result of the electrolytic aluminum load power and the electrolytic tank temperature at future time based on the generalized predictive control model;
And the correction module is used for correcting the primary result based on the PID controller to obtain a prediction result of the secondary frequency modulation of the power grid.
In a third aspect, the present invention provides a computer readable storage medium comprising a program which, when run on a computer, causes the computer to perform a method as described above.
In a fourth aspect, the present invention provides an execution device comprising a processor and a memory, the processor being coupled to the memory;
the memory is used for storing programs;
the processor is configured to execute the program in the memory, so that the execution device executes the method as described above.
In the method disclosed by the invention, the generalized predictive control model is constructed by taking the unbalanced power of the electrolytic aluminum system and the historical data of the direct current voltage of the electrolytic tank as input objects and taking the historical data of the load power of the electrolytic aluminum and the temperature of the electrolytic tank as output objects, wherein the unbalanced power historical data of the electrolytic aluminum system and the historical data of the load power of the electrolytic aluminum follow the first corresponding relation, and the historical data of the direct current voltage of the electrolytic tank and the historical data of the temperature of the electrolytic tank follow the second corresponding relation; and generating a preliminary result of the electrolytic aluminum load power and the future time of the temperature of the electrolytic tank based on the generalized predictive control model, and correcting the preliminary result based on the PID controller to obtain a predicted result of the electric network secondary frequency modulation, namely the electrolytic aluminum load power. Due to the combination of the generalized predictive control model and PID control, inaccuracy of the predicted result of the electrolytic aluminum load power is avoided.
Drawings
The technical solution and other advantageous effects of the present invention will be made apparent by the following detailed description of the specific embodiments of the present invention with reference to the accompanying drawings.
Fig. 1 shows an operation flow chart of a control method for participation of electrolytic aluminum load in secondary frequency modulation of a power grid according to an exemplary embodiment.
FIG. 2 shows a block diagram of a control device for electrolytic aluminum load participation in secondary frequency modulation of a power grid according to an exemplary embodiment;
fig. 3 shows a block diagram of an execution apparatus according to an exemplary embodiment.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The present invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit of the invention, whereby the invention is not limited to the specific embodiments disclosed below.
The terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or modules is not necessarily limited to those steps or modules that are expressly listed or inherent to such process, method, article, or apparatus. The naming or numbering of the steps in the present invention does not mean that the steps in the method flow must be executed according to the time/logic sequence indicated by the naming or numbering, and the execution sequence of the steps in the flow that are named or numbered may be changed according to the technical purpose to be achieved, so long as the same or similar technical effects can be achieved. The division of the units in the present invention is a logical division, and may be implemented in another manner in practical application, for example, a plurality of units may be combined or integrated in another system, or some features may be omitted or not implemented, and in addition, coupling or direct coupling or communication connection between the units shown or discussed may be through some interfaces, and indirect coupling or communication connection between the units may be electrical or other similar manners, which are not limited in the present invention. The units or sub-units described as separate components may be physically separated or not, may be physical units or not, or may be distributed in a plurality of circuit units, and some or all of the units may be selected according to actual needs to achieve the purpose of the present invention.
Referring to fig. 1, fig. 1 shows a flowchart of a control method for participation of electrolytic aluminum load in secondary frequency modulation of a power grid according to an exemplary embodiment. The method is implemented by steps 101-104.
In step 101, a first correspondence relationship of electrolytic aluminum load power as a function of unbalanced power of an electrolytic aluminum system is constructed.
Power disturbance quantity received by electrolytic aluminium load power grid systemHere H is the inertial time constant for power generation.
Maximum upward frequency modulation capacity capable of being provided at current moment of thermal power generating unitUpward rotation of the current thermal power unit for standby/>And the primary frequency modulation upper limit/> of the thermal power generating unitCo-determination, i.e./>Related to the current output of the unit, the method can be implemented by/>Calculated, where/>Is the installed capacity of the thermal power generating unit,Is the current active output of the thermal power generating unit. Primary frequency modulation upper limit/>, of thermal power generating unitDetermined by the heat stored in the boiler. Only when the active adjustment quantity of primary frequency modulation of the thermal power generating unit is smaller than/>When the boiler stores heat, the pressure of the main steam can be kept constant, otherwise, the active power output of the generator can not reach the set value. /(I)Typically 5% to 10% of the rated capacity of the unit.
Therefore, it isEqual to/>And/>The smaller value of (a) is expressed as。
Unbalanced power of the systemAs determined by the following formulation,
。
Electrolytic aluminum load powerAnd unbalanced power/>The relation is:
; in this case,/> To adjust the load power of the electrolytic aluminum before the electrolysis,/>To adjust the load power of the electrolytic aluminum.
In step 102, a second correspondence of cell temperature as a function of cell dc voltage is constructed.
In this step, the cell is first modeled, V B=Id R+E, where V B is the cell DC voltage and I d is the cell DC current.
The equivalent resistance r=2.016 ohms, the back electromotive force e= 354.6V, is found from the known data. Assuming that the cell loss power is unchanged during the time period from T 1 to T 2, the relationship between cell temperature T and cell dc voltage V B can be found:
；
Will be Substituting the obtained product into the above-mentioned formula to obtain the product,
；
Wherein C is the heat capacity of the device, m is the mass of the device, T 1 is the temperature of the electrolytic cell before adjustment, T 2 is the temperature of the electrolytic cell after adjustment,To adjust the direct current voltage of the electrolytic cell before/(For regulating the direct current of the electrolytic cell before/(For the regulated direct voltage of the electrolytic cell,/>For the adjusted cell dc, t 1 is the initial time for the start of the adjustment and t 2 is the end time for the end of the adjustment.
In step 103, a generalized predictive control model is constructed by taking the unbalanced power of the electrolytic aluminum system and the historical data of the direct current voltage of the electrolytic tank as input objects and taking the historical data of the load power of the electrolytic aluminum and the temperature of the electrolytic tank as output objects, wherein the unbalanced power historical data of the electrolytic aluminum system and the historical data of the load power of the electrolytic aluminum follow the first corresponding relation, and the historical data of the direct current voltage of the electrolytic tank and the historical data of the temperature of the electrolytic tank follow the second corresponding relation; and generating a preliminary result of the electrolytic aluminum load power and the electrolytic cell temperature at a future moment based on the generalized predictive control model.
As a specific exemplary form, in this step, the generalized predictive control model is constructed as follows:
First, let the 2-dimensional input 2-dimensional output controlled object be described by the following generalized predictive control model:
(1) ；
Wherein, 、/>、/>Are 2 x 2 matrix polynomials, i.e.,
；
；
；
；
；
Wherein in the formula (1), y (t), u (t) and w (t) are respectively an output vector of 2×1 dimensions, a2×1 input vector and a zero mean white noise vector of 2×1 dimensions, Z is a complex frequency domain variable, a (), B (), C () are polynomials of a back-shift operator Z -1, a m (m represents a corner mark, m=1, 2, … n A) is a coefficient of a back-shift polynomial a (), B m (m represents a corner mark, m=0, 1,2, … n B) is a coefficient of a back-shift polynomial B (), C m (m represents a corner mark, m=0, 1,2, … n C) is a coefficient of a back-shift polynomial C (); For a diagonal differential matrix of 2 x 2, i.e./> V B is the direct current voltage of the electrolytic cell, T is the temperature of the electrolytic cell,/>For unbalanced power, t is electrolysis time, and P ASL is electrolytic aluminum load power.
Furthermore, using Diop hantine's equation, the future time output can be obtained as:
(2)；
Wherein, ；
；
；
；
；
Here, G is a matrix of the lose-figure equation, which is expressed asIn this matrix, G m (m is 0,1, …, N N-1, etc.) is an element of the matrix element of the cartographic equation G.
Then adopting rolling optimization, namely after the output of the future time system is predicted by using a generalized prediction model, obtaining the control output of the future time through optimization calculation according to the set reference track obtained by the set reference track generator, wherein the set reference track can be set as follows:
；
wherein, in the formula, y r is a true value
The task of generalized predictive control is to make the output y of the controlled object as close as possible to y r, and then at time t, the optimal performance index of the system is:
(3)；
where λ is the control weighted sequence and is constant and ζ is the conditional mathematical expectation that data is available at time t.
Substituting the formula (2) into the formula (3) can calculate the control rate of the minimum value of J as,
（4）；
Here, λ represents a control weighting constant, I represents a weighting parameter influence coefficient, G, F is F (z -1), and H is H (z -1) is the meaning given in the foregoing formula (2);
the generalized predictive control rate can be written as follows:
(5)；
(6) ；
Wherein, Here, α m (m is 0,1, …, representing a corner mark) is a coefficient of the polynomial α (z -1);
Here, β m (m is 0,1, …, representing a corner mark) is a coefficient of the polynomial β (z -1).
Here, equations (5) and (6) are generalized predictive control rates, where α and β are weight coefficients, and the matrix p T isY r, F, H are both meanings as presented in formula (2) above.
Definition of the definitionThe first line is denoted/>(N 1 is the number of elements of matrix p T); and define/>。
In step 104, the primary result is corrected based on the PID controller, and a prediction result of the secondary frequency modulation of the power grid is obtained.
The model adopted by the generalized predictive control uses the generalized correction mode of online parameter identification, so that the method has strong robustness, but has larger limitation on models with more orders. When a large error exists between the model prediction output and the actual process output, an error feedback idea in PID control is adopted, and the error is used for adjusting the output at the future moment, so that the actual output result is more approximate to a set value.
A PID controller, which can be expressed by the following formula,
（7）；
Wherein,；
；
；
Is the predicted result produced by the generalized predicted control model and corrected by the preliminary result,/>For the preliminary results produced by the generalized predictive control model, K P is the scale factor, K I is the integral factor, K D is the differential factor, Z -1 is the back shift operator, e (t) is the output error function, [ ] T represents the transpose matrix.
As a typical embodiment, here K P、 KI、 KD may be determined according to the performance index of the PID controller. The performance index function is:
(8)；
Wherein N is the prediction time domain, Represents a performance index influencing function and satisfies the following formula:
q 0 represents a performance index influence function coefficient;
Similar to the derivation of the formulas (1) to (8) and (9), the control rate for minimizing J is calculated as,
(9) In the formula (9), i is the number of stages;
Will be Expanded term is denoted/>; The following formula (9) is a variable for K P、KI、KD:
（10) ；
wherein L (-) represents the PID tuning parameter function and satisfies （11）；
The combined type (9), (10) and (11) can be calculated:
；
；
；
Here the number of the elements is the number, Frequency of participation of electrolytic aluminum load in power grid before j-th adjustment,/>For the frequency of participation of the electrolytic aluminum load into the power grid after the j-th adjustment, N is the number of the electrolytic aluminum loads,/>Is the j-th electrolytic aluminum load power and isIs a term of expansion of (1).
Because of the difficulty in setting the parameters of the speed type PID regulator and when a large deviation occurs in the system, integral terms are accumulated, which may cause the control quantity to exceed the limit of the actuator to cause integral saturation, and a genetic algorithm is used to optimize the parameter K P、KI、KD of the speed type PID regulator.
Since the operation of genetic algorithms is well known, as an exemplary process, the following is given:
first, initializing a population: randomly selecting a group of chromosomes as an initial population, wherein the population element X i is expressed as ；
Second, calculate fitness value: selecting ITAE and overshootObtaining the corresponding fitness value of each individual as/>, which is a component part of the fitness function；
Where i represents the absolute value, e (t) is the error, mp is the overshoot, w 1、w2 is the weight parameter and preferably the value w 1 is 0.3, w 2 is 0.7.
Third, selecting operation and crossing operation: selecting individuals with large fitness values for crossover operation, wherein the probability of each individual being selected is：/>To select probability/>As a new probability distribution, a new population of individuals is recombined from the current individuals.
Randomly selecting two different individuals from a population to probabilityAnd carrying out gene exchange to obtain two new individuals, and carrying out n/2 times to obtain a new population.
Fourth, chromosomal variation: randomly selecting individuals from a population to vary the probabilityChromosomal variation is performed to obtain a new population, which is used as a sub-population for performing a genetic manipulation.
Fifth step: and finally judging whether the stopping condition is met, if so, outputting the result to the speed type PID regulator, and if not, returning to the second step in the genetic algorithm to calculate the fitness value.
Referring to fig. 2, a block diagram of a control device for participation of electrolytic aluminum load in secondary frequency modulation of a power grid is shown. The apparatus 200 comprises:
a first construction module 201, configured to construct a first correspondence relationship of electrolytic aluminum load power with unbalanced power variation of an electrolytic aluminum system;
a second construction module 202 for constructing a second correspondence relationship between the temperature of the electrolytic cell and the change of the direct current voltage of the electrolytic cell;
The generating module 203 is configured to construct a generalized predictive control model by taking the unbalanced power of the electrolytic aluminum system and the historical data of the direct current voltage of the electrolytic tank as input objects and taking the historical data of the load power of the electrolytic aluminum and the temperature of the electrolytic tank as output objects, where the unbalanced power historical data of the electrolytic aluminum system and the historical data of the load power of the electrolytic aluminum follow the first corresponding relationship, and the historical data of the direct current voltage of the electrolytic tank and the historical data of the temperature of the electrolytic tank follow the second corresponding relationship; generating a preliminary result of the electrolytic aluminum load power and the electrolytic tank temperature at future time based on the generalized predictive control model;
And the correction module 204 is used for correcting the primary result based on the PID controller to obtain a prediction result of the secondary frequency modulation of the power grid.
In view of the foregoing, specific implementation of the above modules will not be repeated herein.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an execution device according to an embodiment of the present invention, and the execution device 300 may be embodied as an automatic driving vehicle, a tablet, a notebook, a desktop, a monitoring data processing device, etc., which is not limited herein. Wherein the execution device 300 is configured to implement the functions of the execution device in the corresponding embodiment of fig. 1. Specifically, the execution device 300 includes a receiver 301, a transmitter 302, a processor 303, and a memory 304 (where the number of processors 303 in the execution device 300 may be one or more, and one processor is illustrated in fig. 3), where the processor 303 may include an application processor 3031 and a communication processor 3032. In some embodiments of the invention, the receiver 301, transmitter 302, processor 303, and memory 304 may be connected by a bus or other means.
Memory 304 may include read only memory and random access memory, and provides instructions and data to processor 303. A portion of the memory 304 may also include non-volatile random access memory (non-volatile random access memory, NVRAM). The memory 304 stores a processor and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the operating instructions may include various operating instructions for performing various operations.
The processor 303 controls the operation of the execution device. In a specific application, the individual components of the execution device are coupled together by a bus system, which may include, in addition to a data bus, a power bus, a control bus, a status signal bus, etc. For clarity of illustration, however, the various buses are referred to in the figures as bus systems.
The method disclosed in the above embodiment of the present invention may be applied to the processor 303 or implemented by the processor 303. The processor 303 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 303 or instructions in the form of software. The processor 303 may be a general purpose processor, a Digital Signal Processor (DSP), a microprocessor, or a microcontroller, and may further include an Application SPECIFIC INTEGRATED Circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components. The processor 303 may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 304 and the processor 303 reads the information in the memory 304 and in combination with its hardware performs the steps of the method described above.
The receiver 301 may be used to receive input numeric or character information and to generate signal inputs related to performing relevant settings and function control of the device. The transmitter 302 may be used to output numeric or character information via a first interface; the transmitter 302 may also be configured to send instructions to the disk group through the first interface to modify data in the disk group; the transmitter 302 may also include a display device such as a display screen.
In the embodiment of the present invention, the processor 303 is configured to execute the system capacity acquisition method executed by the execution device in the corresponding embodiment of fig. 1. The specific manner in which the application processor 3031 in the processor 303 executes the above steps is based on the same concept as that of the method embodiment corresponding to fig. 1 in the present invention, and the technical effects brought by this are the same as those of the method embodiment corresponding to fig. 1 in the present invention, and the details can be referred to the descriptions in the foregoing method embodiments of the present invention, which are not repeated here.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. The control method for the participation of electrolytic aluminum load in secondary frequency modulation of the power grid is characterized by comprising the following steps of:
Constructing a first corresponding relation of electrolytic aluminum load power along with unbalanced power change of an electrolytic aluminum system;
Constructing a second corresponding relation of the temperature of the electrolytic cell along with the change of the direct-current voltage of the electrolytic cell;
Constructing a generalized predictive control model by taking the unbalanced power of the electrolytic aluminum system and the historical data of the direct current voltage of the electrolytic tank as input objects and taking the historical data of the load power of the electrolytic aluminum and the temperature of the electrolytic tank as output objects, wherein the unbalanced power historical data of the electrolytic aluminum system and the historical data of the load power of the electrolytic aluminum follow the first corresponding relation, and the historical data of the direct current voltage of the electrolytic tank and the historical data of the temperature of the electrolytic tank follow the second corresponding relation; generating a preliminary result of the electrolytic aluminum load power and the electrolytic tank temperature at future time based on the generalized predictive control model;
and correcting the primary result based on the PID controller to obtain a prediction result of the secondary frequency modulation of the power grid.
2. The method of claim 1, wherein the first correspondence is in the form of a formula,
；
In the above-mentioned method, the step of,To adjust the load power of the electrolytic aluminum before the electrolysis,/>To adjust the post-electrolytic aluminum load power,/>Is unbalanced power.
3. The method of claim 1, wherein the second correspondence is in the form of a formula,
；
Wherein, C is the heat capacity of the device, m is the mass of the device, T 1 is the temperature of the electrolytic cell before adjustment, T 2 is the temperature of the electrolytic cell after adjustment,To adjust the direct current voltage of the electrolytic cell before/(For the adjusted cell dc voltage, t 1 is the initial time for the start of the adjustment and t 2 is the end time for the end of the adjustment.
4. The method of claim 1, wherein the unbalanced powerAs will be determined by the following description of the embodiments,
；
；
；
In the above-mentioned method, the step of,For the power disturbance quantity of electrolytic aluminum system,/>For maximum up-primary frequency modulation capacity,/>For the upward rotation standby power of the current thermal power unit of the power grid, P up is the primary frequency modulation upper limit of the thermal power unit, f is the frequency of an electrolytic aluminum load system, t 0 represents the initial time, t represents the current time,/>For the initial rate of change of frequency of the electrolytic aluminum system, H is the inertial time constant for power generation, and min is the minimum.
5. The method of claim 1, wherein the PID controller is controlled by the following equation,
；
；
；
；
In the above formula, T is a preliminary result of the future moment of the electrolytic cell temperature, P ASL is a preliminary result of the future moment of the electrolytic aluminum load power, T r is a prediction result of the future moment of the electrolytic cell temperature, P ASLr is a prediction result of the future moment of the electrolytic aluminum load power, K P is a scale factor, K I is an integral factor, K D is a differential factor, Z -1 is a backward shift operator, e (T) is an output error function, y r (T) is a set value of the electrolytic aluminum load output, y (T) is an actual value of the electrolytic aluminum load output, and [ (T) represents a transpose matrix.
6. The method of claim 5, wherein K P、KI、KD is determined by the following equation,
；
；
；
In the above-mentioned method, the step of,For the j-th electrolytic aluminum load power,/>The frequency of participation of the electrolytic aluminum load in the power grid before the j-th adjustment,And the frequency of the electrolytic aluminum load participating in the power grid after the j-th adjustment is set, and N is the number of the electrolytic aluminum loads.
7. The method of claim 6, wherein the K P、KI、KD is optimized by a genetic algorithm.
8. A control device for controlling electrolytic aluminum load to participate in secondary frequency modulation of a power grid, which is characterized by comprising:
The first construction module is used for constructing a first corresponding relation of electrolytic aluminum load power along with unbalanced power change of an electrolytic aluminum system;
the second construction module is used for constructing a second corresponding relation of the temperature of the electrolytic tank along with the change of the direct-current voltage of the electrolytic tank;
The generation module is used for constructing a generalized predictive control model by taking the unbalanced power of the electrolytic aluminum system and the historical data of the direct current voltage of the electrolytic tank as input objects and taking the historical data of the electrolytic aluminum load power and the electrolytic tank temperature as output objects, wherein the unbalanced power historical data of the electrolytic aluminum system and the historical data of the electrolytic aluminum load power follow the first corresponding relation, and the historical data of the direct current voltage of the electrolytic tank and the historical data of the electrolytic tank temperature follow the second corresponding relation; generating a preliminary result of the electrolytic aluminum load power and the electrolytic tank temperature at future time based on the generalized predictive control model;
And the correction module is used for correcting the primary result based on the PID controller to obtain a prediction result of the secondary frequency modulation of the power grid.
9. A computer readable storage medium comprising a program which, when run on a computer, causes the computer to perform the method of any one of claims 1 to 7.
10. An execution device comprising a processor and a memory, the processor coupled to the memory;
the memory is used for storing programs;
The processor configured to execute a program in the memory, so that the execution device executes the method according to any one of claims 1 to 7.
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