CN112701729A - Micro-grid distributed cooperative control system and method based on edge calculation - Google Patents

Micro-grid distributed cooperative control system and method based on edge calculation Download PDF

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CN112701729A
CN112701729A CN202110024258.5A CN202110024258A CN112701729A CN 112701729 A CN112701729 A CN 112701729A CN 202110024258 A CN202110024258 A CN 202110024258A CN 112701729 A CN112701729 A CN 112701729A
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杨珺
董晨晨
张化光
刘鑫蕊
王迎春
杨东升
孙秋野
苏涵光
韩海晨
黄博南
周博文
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Northeastern University China
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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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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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Abstract

The invention provides a micro-grid distributed cooperative control system and method based on edge calculation, and relates to the technical field of micro-grid operation control. The system comprises a micro-grid power terminal, edge equipment, a communication network and a cloud server; the method comprises the steps that real-time data in a micro-grid power terminal are obtained by an edge device through a sensor, the data are processed by the edge device and used for local calculation and are simultaneously uploaded to a cloud server, an edge calculation node designs an edge information confidence evaluation mechanism by using local information and neighbor node information communicated with the local information, and evaluates a local information observation value and the confidence of received neighbor node information by using a confidence threshold value periodically or non-periodically issued after cloud server data analysis, and expresses the confidence as a form of a confidence factor; and the multi-edge computing node adopts a distributed cooperative control method, and sends a control decision command obtained by secondary control to droop control of the microgrid DG to realize synchronous stabilization of the frequency of the microgrid to a reference value.

Description

Micro-grid distributed cooperative control system and method based on edge calculation
Technical Field
The invention relates to the technical field of microgrid operation control, in particular to a microgrid distributed cooperative control system and method based on edge calculation.
Background
With the wide application of modern information technology and advanced communication technology in power systems, more challenges are brought about by the operation control of complex power networks, massive data, diversified requirements and the like. The edge calculation focuses on the analysis of real-time and short-period data, local calculation is carried out near a physical environment or a data source, the aim is to realize the local real-time acquisition, the instant calculation, the timely response and the accurate control, and the method has the advantages of safety, rapidness, easiness in management and the like. The research on the deep fusion technology of the edge calculation and the operation control of the power system is a necessary measure for comprehensively promoting the intelligent construction of the power grid.
The edge computing transfers information from an original mode of concentrating on cloud processing to computing equipment or terminals close to users, so that load and computing pressure of the cloud can be reduced, and communication delay is greatly reduced. Compared with non-real-time and long-period big data analysis of cloud computing, edge computing is suitable for processing real-time and short-period data analysis and local decision. At present, scholars carry out deep research on cloud computing technology, but cloud computing is difficult to process real-time tasks, and the existing distributed cooperative control method based on edge computing has less research.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a micro-grid distributed cooperative control system and method based on edge calculation.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
on one hand, the micro-grid distributed cooperative control system based on the edge calculation comprises a micro-grid power terminal, edge equipment, a communication network and a cloud server;
the micro-grid power terminal comprises Distributed Generators (DGs), sensors and actuators, wherein the distributed generators comprise micro gas turbines, fans, photovoltaic arrays and energy storage equipment, droop control is adopted, and each distributed generator is connected with the edge equipment through the sensors and the actuators;
the edge device is connected with a sensor and an actuator of the micro-grid power terminal through a downlink communication interface, acquires real-time data of the micro-grid, sends a control decision to the power terminal, is interconnected with the cloud server through an uplink communication network, and uploads the processed data to the cloud server. The edge device comprises N edge computing nodes, N is a positive integer, the edge computing nodes are communicated and cooperated with each other, and the distributed cooperative control method is executed in real time through information acquired by the edge device and interacted with the adjacent edge computing nodes.
And the cloud server analyzes the microgrid data uploaded by the edge equipment, and makes a periodic or aperiodic control decision according to task requirements and sends the periodic or aperiodic control decision to the edge equipment.
On the other hand, a micro-grid distributed cooperative control method based on edge calculation is realized based on the micro-grid distributed cooperative control system based on edge calculation, and comprises the following steps:
step 1: the edge device acquires real-time data in the micro-grid power terminal through the sensor, wherein the real-time data comprises the topological structure, the voltage amplitude, the frequency and the output power data of the micro-grid system. And the data is processed by the edge device, used for local calculation and uploaded to the cloud server.
Step 2: aiming at the frequency control of the micro-grid, the edge computing node utilizes local information and neighbor node information communicated with the local information, and the local information is observed by utilizing a state observer, wherein the state observer is as follows:
Figure BDA0002889627870000021
wherein the content of the first and second substances,
Figure BDA0002889627870000022
and
Figure BDA0002889627870000023
respectively, frequency estimation values observed by the edge computing nodes i and j,
Figure BDA0002889627870000024
is that
Figure BDA0002889627870000025
Differentiation of (1); n is a radical ofiThe method comprises the steps that all edge computing nodes adjacent to an edge computing node i in an edge computing node communication topology are collected; a isijRepresents the weight that the information is transmitted from the edge computing node j to i; when i is the leader edge compute node, gi1, following the reference frequency ω of the microgridrefWhen i is a following edge calculation node, gi0; t is time.
And step 3: designing an edge information confidence coefficient evaluation mechanism by combining the credibility of edge-edge communication information, and respectively evaluating the local information observation value and the confidence coefficient of the received neighbor node information by utilizing a confidence coefficient threshold value which is periodically or non-periodically issued after the data analysis of a cloud server, wherein the confidence coefficient is expressed in the form of a confidence factor;
the edge information confidence evaluation mechanism comprises a local information confidence evaluation mechanism and a neighbor information confidence evaluation mechanism;
the local information confidence evaluation mechanism is that a local confidence factor is set, and each edge computing node is evaluated to observe by itself
Figure BDA0002889627870000026
The local confidence factor is:
Figure BDA0002889627870000027
wherein the confidence factor 0 is less than or equal to Ti1 ≦ alpha > 0 is a weight coefficient for weighing current information against past information, and the variable diThe expression of (t) is:
Figure BDA0002889627870000028
Figure BDA0002889627870000029
Figure BDA00028896278700000210
Δiis a set threshold; parameter epsiloni(t) is the local neighbor tracking error, → ∞ time ∞ epsiloni(t) convergence to 0; parameter sigmai(t) is the deviation, | | | σ when the system is in steady-state synchronization if the local information of the edge compute node i is not distortedi-εiI | | ═ 0, at which time Ti1 is ═ 1; otherwise, | | σ when the system is in steady-state synchronizationi-εi||＞＞Δi，Ti＜1；
The neighbor information confidence evaluation mechanism is that a neighbor confidence factor is set, and the received neighbor information of each edge computing node pair is evaluated
Figure BDA0002889627870000031
The neighbor confidence factor is expressed as:
Tij(t)＝max(Tj(t),bij(t))
Figure BDA0002889627870000032
wherein the confidence factor 0 is less than or equal to Tij≤1；TjA local confidence factor for a neighbor node j; bij(T) is a confidence factor TijThe control variable of (d); beta > 0 is a weight coefficient for weighing current information against past information, and the variable sijThe expression of (t) is:
Figure BDA0002889627870000033
k is a neighbor node of the computing node i, and k is not equal to j; | NiI is the number of all neighbor edge computing nodes of the edge computing node i; thetaiIs a set threshold. If the information transmitted by the neighbor node j is not distorted, when the system is in steady synchronization
Figure BDA0002889627870000034
At this time Tij1 is ═ 1; otherwise, TjApproaching 0, the confidence with which the neighbor node j transmits information depends on
Figure BDA0002889627870000035
Figure BDA0002889627870000036
And
Figure BDA0002889627870000037
the greater the difference, the confidence factor TijThe closer to 0.
The credibility threshold value is a credibility threshold value gamma which is periodically or non-periodically issued after the cloud server data analysisiTo evaluate
Figure BDA0002889627870000038
When T is the confidence ofij＜ΓiWhen the confidence of the information transmitted on behalf of the neighbor node j is below the threshold ΓiAt this time, the distortion information transmitted by the neighbor node j is discarded, and the confidence factor TijWill be set to 0.
And 4, step 4: and 3, by utilizing the confidence factors obtained by calculation in the step 3, the multi-edge calculation nodes adopt a distributed cooperative control method to realize secondary control of the frequency of the microgrid, and a control decision command obtained by the secondary control is sent to droop control of the microgrid DG to realize that the frequency of the microgrid is synchronously stabilized to a reference value.
The control variables of the distributed cooperative control method of the multi-edge computing node are as follows:
Figure BDA0002889627870000039
by controlling the quantity uiFinding omegani＝∫(ui+mpiPi) dt, the edge device issues a control command to an actuator of the microgrid DG through a communication interface, and then controls omega through droopi＝ωni-mpiPiOmega obtainediAll converge to omega synchronouslyrefAnd the frequency of the micro-grid is synchronized to the reference value. Wherein, cωTo control the coefficient, ωiFor the ith DG inverter output frequency, PiActive power of output of i-th DG inverter, mpiDroop coefficient, ω, for active powerniIs the frequency set point.
The beneficial effects produced by adopting the technical method are as follows:
the invention provides a micro-grid distributed cooperative control system and method based on edge computing, wherein edge devices are interacted by using local information, and the distributed cooperative control method is executed, so that the computing pressure of a cloud server can be effectively reduced, the transmission delay is shortened, the requirement on real-time performance is met, the precise control on a micro-grid is realized, and the stability of the system is improved.
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Fig. 1 is a schematic structural diagram of a micro-grid distributed cooperative control system based on edge computing according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a micro-grid distributed cooperative control method based on edge computing according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
On one hand, a micro-grid distributed cooperative control system based on edge calculation, as shown in fig. 1, includes a micro-grid power terminal, an edge device, a communication network and a cloud server;
the micro-grid power terminal comprises Distributed Generators (DGs), sensors and actuators, wherein the distributed generators comprise micro gas turbines, fans, photovoltaic arrays and energy storage equipment, droop control is adopted, and each distributed generator is connected with the edge equipment through the sensors and the actuators;
the edge device is connected with a sensor and an actuator of the micro-grid power terminal through a downlink communication interface, acquires real-time data of the micro-grid, sends a control decision to the power terminal, is interconnected with the cloud server through an uplink communication network, and uploads the processed data to the cloud server. The edge device comprises N edge computing nodes, N is a positive integer, the edge computing nodes are communicated and cooperated with each other, and the distributed cooperative control method is executed in real time through information acquired by the edge device and interacted with the adjacent edge computing nodes.
The cloud server has strong calculation, analysis and processing capabilities, analyzes the microgrid data uploaded by the edge equipment, makes a periodic or aperiodic control decision according to task requirements, and sends the periodic or aperiodic control decision to the edge equipment.
On the other hand, a micro-grid distributed cooperative control method based on edge calculation is implemented based on the aforementioned micro-grid distributed cooperative control system based on edge calculation, as shown in fig. 2, and includes the following steps:
step 1: the edge device acquires real-time data in the micro-grid power terminal through the sensor, wherein the real-time data comprises the topological structure, the voltage amplitude, the frequency and the output power data of the micro-grid system. And the data is processed by the edge device, used for local calculation and uploaded to the cloud server.
According to the obtained topological structure of the microgrid system, the communication topology of the edge computing nodes corresponds to the physical topology among the distributed power sources DG in the power terminals, the network topology of the edge computing nodes is represented by G { V, E }, wherein V {1, 2.
Figure BDA0002889627870000051
Representing a set of communication edges of the edge computing node, (j, i) epsilon E represents a node i to receive information of the node j, the edge computing node j is called a neighbor node of the node i, and the set of the neighbor nodes of the node i is Ni{ j ∈ V | (j, i) ∈ E }; defining adjacency matrix a ═ aij]N×NDescribing the communication relationship among N edge computing nodes, if (j, i) belongs to E, aijIs greater than 0; otherwise aij＝0；
Step 2: aiming at the frequency control of the micro-grid, the edge computing node utilizes local information and neighbor node information communicated with the local information, and the local information is observed by utilizing a state observer, wherein the state observer is as follows:
Figure BDA0002889627870000052
wherein the content of the first and second substances,
Figure BDA0002889627870000053
and
Figure BDA0002889627870000054
respectively, frequency estimation values observed by the edge computing nodes i and j,
Figure BDA0002889627870000055
is that
Figure BDA0002889627870000056
Differentiation of (1); n is a radical ofiThe method comprises the steps that all edge computing nodes adjacent to an edge computing node i in an edge computing node communication topology are collected; a isijRepresents the weight that the information is transmitted from the edge computing node j to i; when i is the leader edge compute node, gi1, following the reference frequency ω of the microgridrefWhen i is a following edge calculation node, gi0; t is time.
And step 3: designing an edge information confidence coefficient evaluation mechanism by combining the credibility of edge-edge communication information, and respectively evaluating the local information observation value and the confidence coefficient of the received neighbor node information by utilizing a confidence coefficient threshold value which is periodically or non-periodically issued after the data analysis of a cloud server, wherein the confidence coefficient is expressed in the form of a confidence factor;
in the data transmission process, problems such as data collision or network communication can cause transmitted data distortion and destroy the real-time performance of a control system, so that the confidence of side-to-side communication information is evaluated from two aspects:
the edge information confidence evaluation mechanism comprises a local information confidence evaluation mechanism and a neighbor information confidence evaluation mechanism;
the local information confidence evaluation mechanism is that a local confidence factor is set, and each edge computing node is evaluated to observe by itself
Figure BDA0002889627870000057
The local confidence factor is:
Figure BDA0002889627870000058
wherein the confidence factor 0 is less than or equal to Ti1 ≦ alpha > 0 is a weight coefficient for weighing current information against past information, and the variable diThe expression of (t) is:
Figure BDA0002889627870000059
Figure BDA00028896278700000510
Figure BDA00028896278700000511
Δiis a set threshold; parameter epsiloni(t) is the local neighbor tracking error, → ∞ time ∞ epsiloni(t) convergence to 0; parameter sigmai(t) is the deviation, | | | σ when the system is in steady-state synchronization if the local information of the edge compute node i is not distortedi-εiI | | ═ 0, at which time Ti1 is ═ 1; otherwise, | | σ when the system is in steady-state synchronizationi-εi||＞＞Δi，Ti＜1；
The neighbor information confidence evaluation mechanism is that a neighbor confidence factor is set, and the received neighbor information of each edge computing node pair is evaluated
Figure BDA0002889627870000061
The neighbor confidence factor is expressed as:
Tij(t)＝max(Tj(t),bij(t))
Figure BDA0002889627870000062
wherein the confidence factor 0 is less than or equal to Tij≤1；TjA local confidence factor for a neighbor node j; bij(T) is a confidence factor TijThe control variable of (d); beta > 0 is a weight coefficient for weighing current information against past information, and the variable sijThe expression of (t) is:
Figure BDA0002889627870000063
k is a neighbor node of the computing node i, and k is not equal to j; | NiI is the number of all neighbor edge computing nodes of the edge computing node i; thetaiIs a set threshold. If the information transmitted by the neighbor node j is not distorted, when the system is in steady synchronization
Figure BDA0002889627870000064
At this time Tij1 is ═ 1; otherwise, TjApproaching 0, the confidence with which the neighbor node j transmits information depends on
Figure BDA0002889627870000065
Figure BDA0002889627870000066
And
Figure BDA0002889627870000067
the greater the difference, the confidence factor TijThe closer to 0.
The credibility threshold value is a credibility threshold value gamma which is periodically or non-periodically issued after the cloud server data analysisiTo evaluate
Figure BDA0002889627870000068
When T is the confidence ofij＜ΓiWhen the confidence of the information transmitted on behalf of the neighbor node j is below the threshold ΓiAt this time, the distortion information transmitted by the neighbor node j is discarded, and the confidence factor TijWill be set to 0.
And 4, step 4: and 3, by utilizing the confidence factors obtained by calculation in the step 3, the multi-edge calculation nodes adopt a distributed cooperative control method to realize secondary control of the frequency of the microgrid, and a control decision command obtained by the secondary control is sent to droop control of the microgrid DG to realize that the frequency of the microgrid is synchronously stabilized to a reference value.
The control variables of the distributed cooperative control method of the multi-edge computing node are as follows:
Figure BDA0002889627870000069
by controlling the quantity uiFinding omegani＝∫(ui+mpiPi) dt, the edge device issues a control command to an actuator of the microgrid DG through a communication interface, and then controls omega through droopi＝ωni-mpiPiOmega obtainediAll converge to omega synchronouslyrefAnd the frequency of the micro-grid is synchronized to the reference value. Wherein, cωTo control the coefficient, ωiFor the ith DG inverter output frequency, PiActive power of output of i-th DG inverter, mpiDroop coefficient, ω, for active powerniIs the frequency set point.
In the embodiment, the method comprises the following specific steps:
step 4.1: initializing parameters, setting local confidence factor Ti(0) Neighbor confidence factor Tij(0) Fixed threshold value deltaiAnd Θi(ii) a Threshold ΓiThe cloud server analyzes data according to the daily operation condition of the micro-grid and then makes the data, and the data are issued once or more times every day; omegaref50Hz, and sending the data to the leader edge computing node; local information omegai(0) Obtaining by an edge device;
step 4.2: let t be 0,1,2, …, i be 1,2, …, N;
step 4.3: judgment of Tij(t) and ΓiIf T isij＜ΓiGo to step 4.4; otherwise, turning to step 4.5;
step 4.4: setting Tij(t) is 0, go to step 4.5;
step 4.5: updating observations
Figure BDA0002889627870000071
Confidence factor Ti(T) and Tij(t) and transmitting the information to the neighbor nodes;
step 4.6: updating the control quantity ui(t), finding ωni＝∫(ui+mpiPi)dt；
Step 4.7: controlling omega according to droopi＝ωni-mpiPiTo obtain omegai；
Step 4.8: judgment of omegaiWhether all converge to ω synchronouslyrefIf yes, ending; otherwise, let t be t +1, go to step 4.3 and repeat the above steps.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions as defined in the appended claims.

Claims (6)

1. A micro-grid distributed cooperative control system based on edge calculation is characterized by comprising a micro-grid power terminal, edge equipment, a communication network and a cloud server;
the micro-grid power terminal comprises Distributed Generators (DGs), sensors and actuators, wherein the distributed generators comprise micro gas turbines, fans, photovoltaic arrays and energy storage equipment, droop control is adopted, and each distributed generator is connected with the edge equipment through the sensors and the actuators;
the edge device is connected with a sensor and an actuator of a micro-grid power terminal through a downlink communication interface, acquires micro-grid real-time data, sends a control decision to the power terminal, is interconnected with the cloud server through an uplink communication network, uploads the processed data to the cloud server, comprises N edge computing nodes, N is a positive integer, the edge computing nodes are in mutual communication and cooperation, and executes a distributed cooperative control method in real time through self acquisition and information interacted with adjacent edge computing nodes;
and the cloud server analyzes the microgrid data uploaded by the edge equipment, and makes a periodic or aperiodic control decision according to task requirements and sends the periodic or aperiodic control decision to the edge equipment.
2. A micro-grid distributed cooperative control method based on edge calculation is realized based on the micro-grid distributed cooperative control system based on the edge calculation, and is characterized by comprising the following steps of:
step 1: the method comprises the steps that real-time data in a micro-grid power terminal are obtained by an edge device through a sensor, the data comprise a topological structure, a voltage amplitude value, a frequency and output power data of a micro-grid system, and the data are processed by the edge device and are used for local calculation and are uploaded to a cloud server;
step 2: aiming at the frequency control of the micro-grid, the edge computing node utilizes local information and neighbor node information communicated with the local information, and the local information is observed by utilizing a state observer;
and step 3: designing an edge information confidence coefficient evaluation mechanism by combining the credibility of edge-edge communication information, and respectively evaluating the local information observation value and the confidence coefficient of the received neighbor node information by utilizing a confidence coefficient threshold value which is periodically or non-periodically issued after the data analysis of a cloud server, wherein the confidence coefficient is expressed in the form of a confidence factor;
and 4, step 4: and 3, by utilizing the confidence factors obtained by calculation in the step 3, the multi-edge calculation nodes adopt a distributed cooperative control method to realize secondary control of the frequency of the microgrid, and a control decision command obtained by the secondary control is sent to droop control of the microgrid DG to realize that the frequency of the microgrid is synchronously stabilized to a reference value.
3. The microgrid distributed cooperative control method based on edge computing of claim 2, characterized in that in step 2, the state observer is:
Figure FDA0002889627860000011
wherein the content of the first and second substances,
Figure FDA0002889627860000012
and
Figure FDA0002889627860000013
respectively, frequency estimation values observed by the edge computing nodes i and j,
Figure FDA0002889627860000014
is that
Figure FDA0002889627860000015
Differentiation of (1); n is a radical ofiThe method comprises the steps that all edge computing nodes adjacent to an edge computing node i in an edge computing node communication topology are collected; a isijRepresents the weight that the information is transmitted from the edge computing node j to i; when i is the leader edge compute node, gi1, following the reference frequency ω of the microgridrefWhen i is a following edge calculation node, gi0; t is time.
4. The microgrid distributed cooperative control method based on edge computing is characterized in that in the step 3, the edge information confidence evaluation mechanism comprises a local information confidence evaluation mechanism and a neighbor information confidence evaluation mechanism;
the local information confidence evaluation mechanism is that a local confidence factor is set, and each edge computing node is evaluated to observe by itself
Figure FDA0002889627860000028
The local confidence factor is:
Figure FDA0002889627860000021
wherein the confidence factor 0 is less than or equal to Ti1 or less, alpha is more than 0The number is used to weigh the current information against the past information, and the variable diThe expression of (t) is:
Figure FDA0002889627860000022
Figure FDA0002889627860000023
Figure FDA0002889627860000024
Δiis a set threshold; parameter epsiloni(t) is the local neighbor tracking error, → ∞ time ∞ epsiloni(t) convergence to 0; parameter sigmai(t) is the deviation, | | | σ when the system is in steady-state synchronization if the local information of the edge compute node i is not distortedi-εiI | | ═ 0, at which time Ti1 is ═ 1; otherwise, | | σ when the system is in steady-state synchronizationi-εi||＞＞Δi，Ti＜1；
The neighbor information confidence evaluation mechanism is that a neighbor confidence factor is set, and the received neighbor information of each edge computing node pair is evaluated
Figure FDA0002889627860000027
The neighbor confidence factor is expressed as:
Tij(t)＝max(Tj(t),bij(t))
Figure FDA0002889627860000025
wherein the confidence factor 0 is less than or equal to Tij≤1；TjA local confidence factor for a neighbor node j; bij(T) is a confidence factor TijThe control variable of (d); beta > 0 for weighting coefficientsIn weighing current information and past information, and variable sijThe expression of (t) is:
Figure FDA0002889627860000026
k is a neighbor node of the computing node i, and k is not equal to j; | NiI is the number of all neighbor edge computing nodes of the edge computing node i; thetaiIs a set threshold value, if the information transmitted by the neighbor node j has no distortion, when the system is in steady synchronization
Figure FDA0002889627860000031
At this time Tij1 is ═ 1; otherwise, TjApproaching 0, the confidence with which the neighbor node j transmits information depends on
Figure FDA0002889627860000032
Figure FDA0002889627860000033
And
Figure FDA0002889627860000034
the greater the difference, the confidence factor TijThe closer to 0.
5. The microgrid distributed cooperative control method based on edge computing of claim 2, characterized in that in step 3, the cloud server data is analyzed and then a credibility threshold is issued periodically or aperiodically, specifically, the credibility threshold ΓiTo evaluate
Figure FDA0002889627860000036
When T is the confidence ofij＜ΓiWhen the confidence of the information transmitted on behalf of the neighbor node j is below the threshold ΓiAt this time, the distortion information transmitted by the neighbor node j is discarded, and the confidence factor TijWill be set to 0.
6. The microgrid distributed cooperative control method based on edge computing of claim 2 is characterized in that the control variables of the distributed cooperative control method of the multi-edge computing node in step 4 are as follows:
Figure FDA0002889627860000035
by controlling the quantity uiFinding omegani＝∫(ui+mpiPi) dt, the edge device issues a control command to an actuator of the microgrid DG through a communication interface, and then controls omega through droopi＝ωni-mpiPiOmega obtainediAll converge to omega synchronouslyrefSynchronizing the frequency of the microgrid to a reference value, wherein cωTo control the coefficient, ωiFor the ith DG inverter output frequency, PiActive power of output of i-th DG inverter, mpiDroop coefficient, ω, for active powerniIs the frequency set point.
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CN113285496A (en) * 2021-06-09 2021-08-20 东南大学 Micro-grid distributed elastic control method based on confidence factors
CN113675880A (en) * 2021-07-19 2021-11-19 武汉大学 Active time-lag compensation cooperative control method based on distributed inversion system
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