CN112054556A - Multi-microgrid distributed interactive operation optimization control method and system - Google Patents

Multi-microgrid distributed interactive operation optimization control method and system Download PDF

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CN112054556A
CN112054556A CN202010881698.8A CN202010881698A CN112054556A CN 112054556 A CN112054556 A CN 112054556A CN 202010881698 A CN202010881698 A CN 202010881698A CN 112054556 A CN112054556 A CN 112054556A
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CN112054556B (en
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王灼
王鲁浩
程新功
宗西举
张永峰
徐航
彭放
史洁
丁广乾
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University of Jinan
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Abstract

The invention discloses a multi-microgrid distributed interactive operation optimization control method and a system, comprising the following steps: constructing a micro-grid unit combination model and constraint conditions thereof; the inter-microgrid coupling constraint of the microgrid unit combination model is relaxed by adopting an augmented Lagrange penalty function method, and a multi-microgrid distributed interactive operation optimization objective function is constructed by taking the minimized wind power storage cost, the unit combination output cost and the adjacent microgrid interactive operation cost as targets; under the constraint condition, a lagrangian penalty function method and a relaxation and approximation mechanism are adopted to solve a multi-microgrid distributed interactive operation optimization objective function to obtain an optimal interactive operation scheme, and the start-stop operation of the unit during the multi-microgrid interactive operation is controlled according to the optimal interactive operation scheme. And distributed optimization control over interactive operation among different micro-grids is realized.

Description

Multi-microgrid distributed interactive operation optimization control method and system
Technical Field
The invention relates to the technical field of optimization control of power systems, in particular to a multi-microgrid distributed interactive operation optimization control method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the popularization of new energy sources such as photovoltaic energy, wind energy and the like and renewable energy sources, the permeability of multi-main-body energy systems such as micro-grids, incremental distribution networks and the like in power systems is gradually increased. Taking the microgrid as an example, the relevance between the microgrids and the power grid and between the microgrids in the aspects of energy trading and supply and demand balance is becoming more and more compact. In the traditional centralized optimization control method, a centralized optimization control center is required to be established to collect detailed information in each microgrid, so that the communication burden is overlarge, the overall energy transaction of the system can fail once a single point of failure occurs, and the distributed autonomy of each independent microgrid cannot be realized.
The existing multi-microgrid energy source interactive operation optimization control method simplifies the combination problem of the internal units of the microgrid, and only considers the output of the internal units as a whole, so that the problems caused by starting and stopping of the units, the loss caused by charge-discharge state conversion of the energy storage equipment and the like are not fully considered. However, in a real operating environment of the power system, constraints such as minimum start-stop time of a unit inside the microgrid, charge and discharge states of energy storage facilities and the like exist, and therefore it is necessary to take such constraints with discrete variables into consideration.
However, due to the combination constraint of the units, the operation problem of each microgrid is changed into a high-dimensionality mixed integer nonlinear programming problem, and the microgrids are tightly coupled, so that independent decisions of each microgrid are more difficult, a reasonable and optimal multi-microgrid distributed interactive operation optimization control scheme is difficult to find, and the problems of resource waste, low economic efficiency and the like are easily caused; in addition, improper handling of constraints such as unit combination with discrete state variables and charging and discharging of storage batteries also causes risks such as microgrid interruption.
Disclosure of Invention
In order to solve the problems, the invention provides a multi-microgrid distributed interactive operation optimization control method and a multi-microgrid distributed interactive operation optimization control system.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a multi-microgrid distributed interactive operation optimization control method, including:
constructing a micro-grid unit combination model and constraint conditions thereof;
the inter-microgrid coupling constraint of the microgrid unit combination model is relaxed by adopting an augmented Lagrange penalty function method, and a multi-microgrid distributed interactive operation optimization objective function is constructed by taking the minimized wind power storage cost, the unit combination output cost and the adjacent microgrid interactive operation cost as targets;
under the constraint condition, a lagrangian penalty function method and a relaxation and approximation mechanism are adopted to solve a multi-microgrid distributed interactive operation optimization objective function to obtain an optimal interactive operation scheme, and the start-stop operation of the unit during the multi-microgrid interactive operation is controlled according to the optimal interactive operation scheme.
In a second aspect, the present invention provides a multi-microgrid distributed interactive operation optimization control system, including:
the model building module is used for building a micro-grid unit combination model and constraint conditions thereof;
the objective function module is used for adopting an augmented Lagrange penalty function method to perform relaxation operation on coupling constraints among the micro-grids of the micro-grid unit combination model, and constructing a multi-micro-grid distributed interactive operation optimization objective function by taking the minimized wind power storage cost, the unit combination output cost and the adjacent micro-grid interactive operation cost as targets;
and the optimization module is used for solving a multi-microgrid distributed interactive operation optimization objective function by adopting a Lagrange penalty function method and a relaxation and approximation mechanism under the constraint condition to obtain an optimal interactive operation scheme, and controlling the start-stop operation of the unit during the multi-microgrid interactive operation.
In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method of the first aspect is performed.
In a fourth aspect, the present invention provides a computer readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a distributed interactive operation optimization control method for solving the problems that the overall energy transaction process of the system is failed and the combination state of the internal units of the microgrid is changed due to the single point fault of the centralized energy transaction of the microgrid at present, and the like, and constructs a combination model of the internal units of the microgrid and an interactive model of the microgrid components, by performing relaxation and approximate optimization processing on the unit combination state variables and separating the coupling relation among the multiple microgrids by combining an augmented Lagrange penalty function method, an Alternative Optimization Control Method (AOCM) based on the Lagrange penalty function method and relaxation and approximate optimization is provided to realize complete distributed energy trading and independent decision among the multiple microgrids, and because the microgrid is completely distributed, an integrated controller is not needed, the independent decision of the operation of each microgrid can be ensured, meanwhile, risks such as failure and interruption of energy trading caused by neglecting the starting and stopping states of the unit are reduced.
The invention avoids a centralized optimization control center, only needs to establish information communication among each microgrid, solves the problem of multi-microgrid distributed energy transaction optimization control under unit combination, overcomes the defects that the existing method cannot meet independent control of each microgrid and is difficult to apply to a multi-unit scene, realizes distributed autonomy of the multiple microgrids under the conditions of minimum start-stop times of the unit and reasonable charge-discharge state of the energy storage facility, effectively reduces the running cost of each microgrid, and simultaneously improves the calculation efficiency.
The invention provides a brand-new alternative optimization control method aiming at multi-microgrid energy trading in a power system, namely interactive operation among multiple microgrids, fully considers instability of combination start and stop of units in each microgrid and state conversion of energy storage facilities to the microgrid participating in the power system energy trading, improves reliability and economy of the microgrid, and realizes complete distributed energy trading.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of a multi-microgrid distributed interactive operation optimization control method according to embodiment 1 of the present invention;
fig. 2 is a schematic diagram of an internal structure and a scheduling relationship of a multi-piconet according to embodiment 1 of the present invention;
FIG. 3 is a flowchart of an Alternative Optimization Control Method (AOCM) algorithm provided in embodiment 1 of the present invention;
fig. 4 is a schematic diagram of parameters of an internal unit of the microgrid 1 according to embodiment 1 of the present invention;
FIG. 5 is a schematic diagram of load forecast data for a transaction period according to embodiment 1 of the present invention;
fig. 6 is a load curve of each microgrid provided in embodiment 1 of the present invention;
fig. 7 is an overall force diagram of the internal units of each microgrid after the optimal transaction is executed according to embodiment 1 of the present invention;
fig. 8(a) -8(c) are concrete force diagrams of the internal units of each microgrid after performing the optimal transaction according to embodiment 1 of the present invention;
fig. 9(a) -9(c) are graphs of charging and discharging power of each microgrid energy storage facility after performing an optimal transaction according to embodiment 1 of the present invention;
fig. 10 is a diagram of interaction power between the piconet 1 and an adjacent piconet after performing an optimal transaction according to embodiment 1 of the present invention;
fig. 11(a) -11(b) are graphs illustrating piconet 1 and neighboring piconet tie error values after performing an optimal transaction as provided in embodiment 1 of the invention;
fig. 12 is a diagram of the state variables of the microgrid 1 unit after the optimal transaction is executed according to embodiment 1 of the present invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation 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 elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example 1
The method is completely distributed, an integrated controller is not needed, independent decision of operation of each microgrid can be guaranteed, and risks such as interactive operation failure and interruption caused by neglecting the start-stop state of the unit are reduced. As shown in fig. 1, the method specifically includes:
s1: constructing a micro-grid unit combination model and constraint conditions thereof;
s2: relaxing coupling constraints among the micro-grids of the micro-grid unit combination model by adopting an augmented Lagrange penalty function method, and constructing a multi-micro-grid distributed interactive operation optimization objective function by taking the minimized wind power storage cost, the unit combination output cost and the adjacent micro-grid interactive operation cost as targets;
s3: under the constraint condition, a lagrangian penalty function method and a relaxation and approximation mechanism are adopted to solve a multi-microgrid distributed interactive operation optimization objective function to obtain an optimal interactive operation scheme, and the start-stop operation of the unit during the multi-microgrid interactive operation is controlled according to the optimal interactive operation scheme.
In the step S1: as shown in fig. 2, the micro-grid internal unit combination includes a micro gas turbine, a wind turbine, and an energy storage device, and establishes a model and constraint conditions of each micro-grid internal unit combination, which specifically includes: the micro-grid power load balancing method comprises a micro gas turbine operation model and constraint conditions, an energy storage device charging and discharging model and constraint conditions, a fan output model and constraint conditions, an interaction model and constraint conditions between the micro gas turbine operation model and adjacent micro-grids, and a micro-grid power load balancing model and constraint conditions.
Step S1.1: (1) micro gas turbine cost function:
Figure BDA0002651865360000061
wherein, an、bn、cnAnd respectively representing the power generation cost coefficients of the nth conventional unit.
(2) Output constraint conditions of the micro gas turbine:
Figure BDA0002651865360000062
un,t∈{0，1} (3)
wherein the continuous variable
Figure BDA0002651865360000071
Represents the output power of the nth conventional unit, un,tRepresenting the discrete state variable of starting and stopping of the unit n, the value of which is 1 represents that the unit n is in the running state, the value of which is 0 represents that the unit n is in the shutdown state,
Figure BDA0002651865360000072
respectively representing the upper limit and the lower limit of the output of the unit n.
(3) The climbing constraint conditions of the micro gas turbine are as follows:
Figure BDA0002651865360000073
Figure BDA0002651865360000074
wherein, SUn、SDnRespectively representing the starting speed and the stopping speed of the unit n; RU (RU)n、RDnRespectively representing the climbing speed and the descending speed of the unit n.
(4) Minimum operation and downtime constraint conditions of the micro gas turbine:
Figure BDA0002651865360000075
Figure BDA0002651865360000076
wherein T represents the transaction period of the microgrid, MUn、MDnRespectively representing the minimum running time and the stop time of the unit n.
In this embodiment, each time the unit enters the next start-stop adjustment period, it needs to determine whether the next start-stop stage is less than or equal to the transaction period; if so, continuing to adjust the starting and stopping scheme of the next turbine set; if not, the solution of the unit combination in the period is ended, and the current start-stop and transaction scheme is the optimal control scheme.
Step S1.2: (1) energy Storage (ES) charge-discharge model:
Figure BDA0002651865360000077
wherein the continuous variable EStThe state of charge of the energy storage facility at the moment t; continuous variable
Figure BDA0002651865360000081
Respectively representing the charging and discharging power of the energy storage facility; etac、ηdRespectively representing the charge and discharge efficiency of the energy storage facility; Δ t represents the running time interval.
(2) And (3) output restraint of the energy storage equipment:
Figure BDA0002651865360000082
Figure BDA0002651865360000083
Figure BDA0002651865360000084
Figure BDA0002651865360000085
wherein the content of the first and second substances,
Figure BDA0002651865360000086
respectively representing the discrete states of charging and discharging of the energy storage facility;
Figure BDA0002651865360000087
respectively representing the upper limit and the lower limit of the charging and discharging power of the energy storage facility.
(3) Energy storage equipment capacity constraint conditions:
ESmin≤ESt≤ESmax (13)
EST＝ES0 (14)
wherein, ESmin、ESmaxRespectively representing the minimum and maximum capacity of the energy storage facility; ES (ES)T、ES0Respectively representing the capacity of the energy storage facility at the end time and the initial time of the transaction period.
The energy storage device does not generate electricity, but serves as a temporary storage system of energy, so that the electricity balance of the energy storage device in a transaction period needs to be met to ensure the sustainability of the transaction.
Step S1.3: the fan output constraint conditions are as follows:
Figure BDA0002651865360000088
Figure BDA0002651865360000089
Figure BDA00026518653600000810
wherein the continuous variable
Figure BDA0002651865360000091
Representing the output value of the wind power system at the time t;
Figure BDA0002651865360000092
representing wind power storage of the wind power system at time t;
Figure BDA0002651865360000093
and representing the actual generated power of the wind power system.
Step S1.4: constraint conditions of power transmission of a tie line between micro grids are as follows:
Figure BDA0002651865360000094
Figure BDA0002651865360000095
Figure BDA0002651865360000096
wherein the continuous variable
Figure BDA0002651865360000097
The transmission power of a connecting line t between the microgrid i and the microgrid j at the moment is expressed when the transaction optimization problem of the microgrid i is solved; continuous variable
Figure BDA0002651865360000098
The transmission power of a connecting line t between the microgrid i and the microgrid j at the moment is expressed when the transaction optimization problem of the microgrid j is solved;
it can be seen that the coupling constraint between two piconets is mainly reflected in the constraint condition
Figure BDA0002651865360000099
Figure BDA00026518653600000910
Representing the crosstie minimum and maximum transmit power limits, respectively.
Step S1.5: the microgrid power balance constraint condition is as follows:
Figure BDA00026518653600000911
wherein the content of the first and second substances,
Figure BDA00026518653600000912
representing the load power demand of the microgrid i at the moment t.
Between the step S1 and the step S2, the method further includes: the discrete state variables of the units in each microgrid are continuous by adopting a relaxation method, namely the discrete state variables of the units shown in formulas (3) and (12) are converted into continuous state variables shown in formulas (22) to (24):
0≤un,t≤1 (22)
Figure BDA00026518653600000913
Figure BDA00026518653600000914
in the step S2: constraint on consistency based on augmented Lagrange penalty function method
Figure BDA00026518653600000915
And (3) relaxing, and adding the relaxed object into the microgrid objective function to obtain the microgrid objective function as shown in a formula (25):
Figure BDA0002651865360000101
namely, the wind power storage cost, the conventional unit output cost and the adjacent microgrid interactive operation cost, namely the power transaction cost, are minimized; wherein j belongs to i and indicates that the microgrid i is connected with the microgrid j through a connecting line, and VwcRepresenting a wind power storage penalty factor, and N representing the total number of conventional generator sets in the microgrid i，λij,t、ρij,tRespectively, representing multipliers representing the first and second order terms of the lagrange penalty function at time t.
In the step S3: a multi-microgrid distributed transaction Alternative Optimization Control Method (AOCM) based on a Lagrange penalty function method and a relaxation and approximation mechanism is adopted, and as shown in figure 3, optimization control is carried out on a multi-microgrid distributed interaction system; taking the interaction process between the microgrid i and the adjacent microgrid j as an example, the optimization control process is as follows:
step S3.1: the distributed solution of the multi-microgrid interaction system is realized by applying an alternating direction multiplier method;
due to secondary penalty term in the objective function (25)
Figure BDA0002651865360000102
The problem resolvability is damaged, so the distributed solution is performed on the relaxed multi-microgrid interaction system by using the alternating direction multiplier method, namely, each microgrid is optimized and solved, variables of other microgrids are regarded as constants during each solution, and the latest iteration result is used.
The objective function formula (26) of the nth iteration of the microgrid i is shown as follows:
Figure BDA0002651865360000103
wherein the content of the first and second substances,
Figure BDA0002651865360000104
to represent
Figure BDA0002651865360000105
The latest iteration value of (c).
Step S3.2: on an internal processor of each micro-grid telecontrol device, a power energy interaction system composed of the micro-grid internal unit models (1) - (24) and the objective function (26) mentioned in the steps is subjected to distributed optimization control by adopting an alternating direction multiplier method, and whether an optimization target meets a convergence condition and an optimization requirement is judged, wherein the method specifically comprises the following steps:
step S3.2.1: initializing parameters: setting the initial iteration number v to 0 and the maximum iteration number v max150, trading period T24 hours, convergence accuracyvIn the present embodiment, three microgrids are taken as an example, as shown in fig. 4, the microgrids 1 include:
the power system takes 24 hours as a period, and MG1 load prediction data of a certain period is shown in FIG. 5.
Step S3.2.2: in the k iteration, on a processor inside the telecontrol device, the microgrid i realizes the solution of the self optimization problem according to the internal unit models (1) - (24) and the objective function (26), and the obtained connecting line transmission power is obtained
Figure BDA0002651865360000111
Transmitting the micro-grid j to a micro-grid j through a telecontrol device execution terminal;
step S3.2.3: the microgrid j receives data transmitted from the microgrid i through a telecontrol device of the microgrid j
Figure BDA0002651865360000112
Then, while taking the data as the latest iteration result to participate in the solution of the self-optimization problem in the processor, seeking the data transmitted close to the microgrid i as the solution of the self-optimization problem, and taking the optimal solution
Figure BDA0002651865360000113
All telecontrol device execution terminals of the microgrid transmit to the microgrid i;
step S3.2.4: and (3) multi-microgrid distributed interactive convergence judgment:
coupling constraints between the piconets are mainly reflected in tie line power interaction between piconets, and therefore, the embodiment defines tie line errors of adjacent piconets
Figure BDA0002651865360000114
As a convergence criterion, convergence accuracyv0.01KW, i.e. when
Figure BDA0002651865360000115
When the iteration is stopped; otherwise, updating multipliers of the primary term and the secondary term of the lagrange penalty function, wherein the updating method is shown in the formulas (27) and (28):
Figure BDA0002651865360000121
Figure BDA0002651865360000122
wherein tau is a quadratic term multiplier updating step length, tau is generally equal to or more than 2 and equal to or less than 3 for accelerating convergence of the algorithm, and lambdaij,t、ρij,tThe initial value is generally a relatively small constant.
Step S3.3: performing approximate operation on the output value of the unit and the continuous state variable of the unit at the current moment;
after the convergence of the distributed interactive operation of the multiple microgrids is judged, approximate operation needs to be performed on the output value and the state variable of the unit on an internal processor of the telecontrol device through the following method:
if the output value of the unit n at the moment h is smaller than the preset minimum output lower limit
Figure BDA0002651865360000123
Then the unit is considered to be in a shutdown state at this time, i.e. un,h＝0、
Figure BDA0002651865360000124
On the contrary, if the output value of the unit n at the moment h meets the unit output constraint condition shown in the formula (2), the unit is considered to be in an open state at the moment, namely un,h＝1、
Figure BDA0002651865360000125
And similarly, performing traversal approximate operation on the charging and discharging power and the state variable of the energy storage facility.
Step S3.4: by farOptimizing software gurobi self-contained linear programming solver in mobile device processor, and solving unit discrete variable u meeting current constraint conditionsn,t、
Figure BDA0002651865360000126
Combining and operating state variables u of units in each microgridn,tCharge and discharge state variable of energy storage facility
Figure BDA0002651865360000127
Converting the continuous state variables of the units shown in the formulas (22) to (24) into discrete state variables shown in the formulas (3) and (12) again, and keeping the other variables unchanged;
the output of the unit after the approximate processing in the step S3.3 is processed
Figure BDA0002651865360000128
Charging and discharging power
Figure BDA0002651865360000129
As a fixed parameter, calling the optimization software gurobi self-contained linear programming solver in the telemechanical device processor again to solve the unit discrete variable u meeting the current constraint conditionn,tAnd energy storage facility charging and discharging discrete state variable
Figure BDA00026518653600001210
In this embodiment, the method further includes step S4: the discrete variable u of the unit obtained in the step S3.4n,t、
Figure BDA0002651865360000131
And (4) returning to the step (S3.2) as a fixed parameter, continuously adopting an alternative direction multiplier method to execute distributed optimization control on the multi-microgrid interactive system on the internal processor of each microgrid telecontrol device, and comparing the finally obtained unit discrete state variables un,t、
Figure BDA0002651865360000132
Whether it is the same as the previous result;
if not, turning to step S3.2, continuing to adopt an alternative direction multiplier method to execute distributed optimization control on the power energy interaction system formed by the micro-grid internal unit models (1) - (24) and the objective function (26) through each micro-grid telemechanical device processor;
if so, combining the unit at the moment into a discrete state variable un,t、
Figure BDA0002651865360000133
And interactive operation power among each microgrid
Figure BDA0002651865360000134
And storing the target values as an optimal starting and stopping scheme and an interactive operation optimization control target value in a memory inside the telecontrol device.
As can be seen from fig. 6 and 7, in the embodiment, the output of each microgrid unit can be reasonably arranged according to the microgrid load fluctuation to meet the load requirement; as can be seen from fig. 8(a) -8(c) and fig. 9(a) -9(c), the optimization control method provided in this embodiment automatically arranges the unit with higher operation cost to be shut down when the load requirement is met, so as to reduce the microgrid transaction operation cost as much as possible; as can be seen from fig. 11(a) -11(b), in the embodiment, multi-microgrid distributed interactive operation can be realized within limited calculation times, communication burden of a power system is greatly reduced, and while calculation efficiency is improved, privacy of a user is well protected; as can be seen from fig. 10 and 12, in actual operation, the optimization control method of the embodiment can meet the unit combination constraint condition with the discrete variable in the microgrid, and meets the actual requirement.
Example 2
The embodiment provides a multi-microgrid distributed interactive operation optimization control system, which comprises:
the model building module is used for building a micro-grid unit combination model and constraint conditions thereof;
the objective function module is used for adopting an augmented Lagrange penalty function method to perform relaxation operation on coupling constraints among the micro-grids of the micro-grid unit combination model, and constructing a multi-micro-grid distributed interactive operation optimization objective function by taking the minimized wind power storage cost, the unit combination output cost and the adjacent micro-grid interactive operation cost as targets;
and the optimization module is used for solving a multi-microgrid distributed interactive operation optimization objective function by adopting a Lagrange penalty function method and a relaxation and approximation mechanism under the constraint condition to obtain an optimal interactive operation scheme, and controlling the start-stop operation of the unit during the multi-microgrid interactive operation.
It should be noted that the above modules correspond to steps S1 to S3 in embodiment 1, and the above modules are the same as the examples and application scenarios realized by the corresponding steps, but are not limited to the disclosure in embodiment 1. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of embodiment 1. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.
The method in embodiment 1 may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (10)

1. A multi-microgrid distributed interactive operation optimization control method is characterized by comprising the following steps:
constructing a micro-grid unit combination model and constraint conditions thereof;
the inter-microgrid coupling constraint of the microgrid unit combination model is relaxed by adopting an augmented Lagrange penalty function method, and a multi-microgrid distributed interactive operation optimization objective function is constructed by taking the minimized wind power storage cost, the unit combination output cost and the adjacent microgrid interactive operation cost as targets;
under the constraint condition, a lagrangian penalty function method and a relaxation and approximation mechanism are adopted to solve a multi-microgrid distributed interactive operation optimization objective function to obtain an optimal interactive operation scheme, and the start-stop operation of the unit during the multi-microgrid interactive operation is controlled according to the optimal interactive operation scheme.
2. The multi-microgrid distributed interactive operation optimization control method of claim 1, wherein the microgrid unit combination model comprises a micro gas turbine operation model, an energy storage device charging and discharging model, a fan output model, an interaction model between adjacent microgrids and a microgrid power load balancing model.
3. The method for the optimal control of the distributed interactive operation of the multiple micro-grids as claimed in claim 2, wherein the micro gas turbine operation model takes the power generation cost of the gas turbine as an objective function, and takes the output of the micro gas turbine, the climbing of the micro gas turbine, the minimum operation and the downtime of the micro gas turbine as constraint conditions;
or the energy storage equipment charge-discharge model is constructed according to the charge state and the charge-discharge power of the energy storage facility, and the output of the energy storage equipment and the capacity of the energy storage equipment are taken as constraint conditions;
or the constraint condition of the fan output model is that the output value and the wind power storage at the time t are less than or equal to the actual generated power, and the sum of the output value and the wind power storage is the actual generated power;
or, the interaction model between adjacent micro-networks takes the power transmission of the tie line as a constraint condition.
4. The multi-microgrid distributed interactive operation optimization control method of claim 1, wherein discrete state variables of the microgrid unit combination model are continuous by a relaxation method.
5. The method of claim 1, wherein the objective function for optimizing the operation of the multi-microgrid distributed interactive operation is as follows:
Figure FDA0002651865350000021
wherein j belongs to i and indicates that the microgrid i is connected with the microgrid j through a connecting line, and VwcRepresenting a wind power storage penalty factor, N representing the total number of conventional generator sets in the microgrid i, and lambdaij,t、ρij,tRespectively, representing multipliers representing the first and second order terms of the lagrange penalty function at time t.
6. The method according to claim 1, wherein the solving process for solving the multi-microgrid distributed interactive operation optimization objective function by using a lagrangian penalty function method and a relaxation and approximation mechanism comprises:
performing distributed iterative solution by adopting an alternating direction multiplier method until a convergence condition is met;
performing approximate operation on the output value of the unit and the continuous state variable of the unit at the current moment;
and solving the unit combination discrete variable and the energy storage facility charging and discharging discrete state variable which meet the current constraint condition by taking the unit output and the charging and discharging power after the approximate operation as fixed parameters.
7. The method of claim 6, wherein the solving process further comprises:
taking the unit combination discrete variable and the energy storage facility charging and discharging discrete state variable as fixed parameters, performing distributed iterative solution by adopting an alternative direction multiplier method, and comparing whether the obtained new unit discrete state variable is the same as the unit combination discrete variable or not;
if the difference is not the same, continuing to iteratively solve; and if the combined discrete state variables of the current unit and the interactive operation power among the micro-grids are the same, outputting the combined discrete state variables of the current unit and the interactive operation power among the micro-grids as an optimal unit start-stop scheme and an optimal control target value.
8. The utility model provides a many microgrids distributed interaction operation optimal control system which characterized in that includes:
the model building module is used for building a micro-grid unit combination model and constraint conditions thereof;
the objective function module is used for adopting an augmented Lagrange penalty function method to perform relaxation operation on coupling constraints among the micro-grids of the micro-grid unit combination model, and constructing a multi-micro-grid distributed interactive operation optimization objective function by taking the minimized wind power storage cost, the unit combination output cost and the adjacent micro-grid interactive operation cost as targets;
and the optimization module is used for solving a multi-microgrid distributed interactive operation optimization objective function by adopting a Lagrange penalty function method and a relaxation and approximation mechanism under the constraint condition to obtain an optimal interactive operation scheme, and controlling the start-stop operation of the unit during the multi-microgrid interactive operation.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
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