CN109066707B - Micro-grid energy management method based on NARMA-L2 model - Google Patents

Micro-grid energy management method based on NARMA-L2 model Download PDF

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CN109066707B
CN109066707B CN201811054866.5A CN201811054866A CN109066707B CN 109066707 B CN109066707 B CN 109066707B CN 201811054866 A CN201811054866 A CN 201811054866A CN 109066707 B CN109066707 B CN 109066707B
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distributed power
power supply
node
output
control signal
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CN109066707A (en
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顾伟
胡子健
周苏洋
吴志
窦晓波
吴在军
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Yangzhou Power Supply Branch Of State Grid Jiangsu Electric Power Co ltd
Southeast University
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State Grid Jiangsu Electric Power Co ltd Yangzhou Power Supply Branch
Southeast University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/30—Reactive power compensation

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Abstract

The invention discloses a microgrid energy management method based on a NARMA-L2 model, which comprises the following steps: step 10), establishing a relational expression between distributed power supply output and control input by utilizing a NARMA-L2 model; step 20) based on a containment control concept, aiming at reactive power equalization, generating a control signal input and voltage output data set aiming at a distributed power supply in a system; step 30) training a neural network used for representing a complex nonlinear function in the expression in the step 10) by using the data set obtained in the step 20), and fitting nonlinear output dynamic characteristics of the distributed power supply under the condition of uniform reactive power; and step 40) generating a reference value by using a gradient descent search algorithm to complete reactive power cooperative control of the microgrid. The method utilizes a neural network to fit a complex nonlinear function in input and output characteristics to realize the model-removing control of the microgrid; meanwhile, based on a containment control concept, cooperative control of the micro-grid is achieved, and control response speed and accuracy are improved.

Description

Micro-grid energy management method based on NARMA-L2 model
Technical Field
The invention relates to the technical field of energy management of a microgrid, in particular to a microgrid energy management method based on a NARMA-L2 model.
Background
In recent years, the economic development of China is rapid, the total electricity consumption is rapidly increased, and the contradiction between the increase of energy demand and the shortage of non-renewable energy, the low utilization rate of renewable energy and sustainable development is increasingly highlighted. Therefore, it is imperative to improve the utilization rate of renewable energy, develop new energy utilization techniques, and enhance the utilization of renewable energy.
Distributed power generation is mature day by day, and the increasing new energy power supplies bring new challenges to the stable operation of a power system. The most important problems are: the new energy is often uncontrollable and difficult to control, and has great influence on power supply stability. Since conventional power distribution system architectures and operating strategies cannot accommodate the requirements of large-scale distributed power access, the concept of microgrid was introduced as a solution to reliably utilize new energy. Microgrid versus conventional power distribution systems: the scale is small, the topological structure is easy to change, and the operation can be independent of a large power grid. The aim that a microgrid manager wants to achieve is to complete microgrid control economically, stably and quickly.
The distributed micro-grid cooperative control research is used, the micro-grid is modeled into a complex network by using a containment control concept, voltage cooperative control is converted into the problems of complex network cooperation and neighbor error tracking, and the voltage convergence and the running stability of a system are guaranteed. However, a distributed power supply dynamic mathematical model is adopted, the problem that the mathematical model of the high-frequency inverter is difficult to model is ignored, and control errors can be brought; meanwhile, the containment concept is that a reference value is introduced into a few nodes, and the containment is continuously transmitted among the neighbor nodes to form containment, so that the whole system is coordinated and consistent, and the stability speed is relatively slow. The traditional constraint concept micro-grid cooperative control is based on a distributed power supply dynamic mathematical model, the problem of inaccurate modeling exists, and the constraint control causes the system convergence speed to be slow.
Disclosure of Invention
The invention provides a microgrid energy management method based on an NARMA-L2 model, aiming at solving the problem of modeling errors of a distributed power supply, the NARMA-L2 model is used for replacing a power supply dynamic mathematical model, and based on a containment concept, a data set is generated to train an artificial neural network in the NARMA-L2 model, and a proper nonlinear expression is generated; and aiming at the used model, a gradient descent search algorithm is adopted to give a proper reference value, so that the rapid and accurate model removal control is realized, and the control targets of reactive load equalization and rapid voltage recovery are achieved.
The invention adopts the following technical scheme for solving the technical problems:
the invention provides a microgrid energy management method based on a NARMA-L2 model, which comprises the following steps:
step 10, establishing a relational expression between distributed power supply output and control signal input by utilizing a NARMA-L2 model;
step 20, based on a containment control concept, taking reactive power sharing as a target to obtain a data set of control signal input and voltage output;
step 30, training a neural network used for representing a complex nonlinear function in the relational expression established in the step 10 by using the data set obtained in the step 20, and fitting the nonlinear output dynamic characteristic of the distributed power supply under the condition of uniform reactive power, so as to obtain a relational expression between the trained distributed power supply output and the trained control signal input; constructing a distributed power supply controller based on NARMA-L2 according to a relation between the trained distributed power supply output and the trained control signal input;
and step 40, providing a target output voltage reference value for the distributed power supply controller constructed in the step 30 by using a gradient descent search algorithm, and finishing the reactive power cooperative control of the microgrid.
As a further optimization scheme of the microgrid energy management method based on the NARMA-L2 model, in step 10, the NARMA-L2 model is used for replacing a distributed power supply dynamic mathematical model, a complex nonlinear function part in a distributed power supply system is approximately replaced through an artificial neural network, and the relational expression between the output of the distributed power supply and the input of a control signal is as follows:
y(k+d)＝f[y(k),y(k-1),…,y(k-n+1),u(k),u(k-1),u(k-z+1)]+g[y(k),y(k-1),…,y(k-n+1),u(k),u(k-1),u(k-z+1)]·u(k) (1)
wherein u (k) represents the input of the distributed power supply control signal at the k moment, y (k) represents the output voltage signal of the distributed power supply at the k moment, y (k + d) represents the output voltage signal of the distributed power supply at the k + d moment, and f and g represent complex nonlinear functions of the nonlinear part in the distributed power generation system; collecting the distributed power supply output at the k-n +1 moment in total, wherein y (k-n +1) represents the distributed power supply output at the (k-n +1) th moment; and c, acquiring the control signal input at the k-z +1 moment, wherein u (k-z +1) is the control signal input at the (k-z +1) th moment.
As a further optimization scheme of the microgrid energy management method based on the NARMA-L2 model, the specific process of the step 20 is as follows:
step 201, for the distributed control micro-grid topology, converting the cooperative target into neighbor information error tracking:
Figure BDA0001795523890000021
in the formula, eiRepresenting the error between the i-th distributed power supply node output voltage and a reference value, yiRepresents the output voltage of the ith distributed power supply node, and j belongs to NiIndicating that the jth node is a neighbor node of the ith node, NiRepresents a set formed by all neighbor nodes of the ith distributed power node, aijRepresenting adjacency matrices in a distributed power system
Figure BDA0001795523890000031
The value of (i) th row and (j) th column in (1), giRepresenting the holdoff gain, y, of the ith distributed power nodejIndicating the output voltage value, y, of the jth node in communication topology with the ith node0Represents the output voltage value of the pinning node, i.e., the reference value;
step 202, to guarantee error eiConverging to 0, and selecting the control signal input u according to the following formula under the condition that the control target is reactive load sharingi：
Figure BDA0001795523890000032
In the formula, Q0Representing a reference reactive load, QiIndicating that the ith node outputs reactive power, omegaiIndicating whether the ith node has been pulledIn the preparation method, the raw materials are mixed,
Figure BDA0001795523890000033
representing the degree of coupling between the nodes;
step 203, replacing the reactive power in the formula (3) with the output voltage according to the droop control characteristic of the distributed power supply, namely the relational expression between the output voltage and the reactive power, wherein the control signal input expression is changed into:
Figure BDA0001795523890000034
wherein n isQiRepresenting the reactive droop coefficient, y, of the ith node0Representing the value of the output voltage of the holddown node, i.e. the reference value, u0A control signal input representing a holdover node; on the basis of the formula (4), determining corresponding control signal input by giving different distributed power supply outputs, and further forming a data set [ y ] of the control signal input and the voltage outputi,ui]。
As a further optimization scheme of the microgrid energy management method based on the NARMA-L2 model, the specific process of step 30 is as follows: from the resulting data set y in step 203i,ui]A complex non-linear function f in the relation is established for step 10]、g[*]Training an artificial neural network to replace f [. X [ ]],g[*]And on the basis of the NARMA-L2-based distributed power supply controller; the distributed power controller is a specific implementation of equation (1), and provides control signal inputs u (k) corresponding to different target voltage outputs.
As a further optimization scheme of the microgrid energy management method based on the NARMA-L2 model, the specific process of step 40 is as follows:
step 401, setting a reference value of target output voltage of the distributed power supply as yr(k + d) finding an expression of a target output voltage reference value for targeting reactive power sharing in step 2, and providing a reference value for the distributed power controller constructed in step 30; providing a target voltage output reference value by adopting a gradient descent search algorithmA generator:
Figure BDA0001795523890000035
wherein j | (i, j) ∈ DGDenotes that the ith, j distributed power node is a neighbor node, DGThe method is a set formed by combining all neighbor nodes in the distributed power system; j (y)i) Represents the average value of the sum of squares of the reactive power differences output by the ith node and the neighboring nodes, QjThe output reactive power of the jth node is represented, and m represents that the ith distributed power supply node has m neighbor nodes;
step 402, according to equation (5), J (y)i) When the minimum value exists, namely the difference between the reactive power output of all the distributed power supplies is minimum, the aim of sharing the reactive power in the step 2 is achieved; to find J (y)i) Minimum time distributed power supply target output voltage reference value yr(k + d) performing the following loop until convergence;
Figure BDA0001795523890000041
(7) formula (I) is expressed as finding the smallest J (y)i) Value, constantly iterating yiA, denotes the search step size,
Figure BDA0001795523890000042
represents J (y)i) For yiDerivative, temp, representing the current cycle yiWith the convergence condition set to two cycles yiThe variation is less than or equal to a preset value.
As a further optimization scheme of the microgrid energy management method based on the NARMA-L2 model, the preset value is 0.001.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
according to the method, a NARMA-L2 model is used for replacing a dynamic mathematical model of the distributed power supply, so that the modeling control of the microgrid is realized, and the control speed and precision are improved; and then, by utilizing the control of the drag, generating a data set of an input control signal and a voltage output signal by taking reactive power equalization and voltage rapid recovery as targets, and training an artificial neural network used for representing a complex nonlinear function in an expression so as to achieve a good fitting effect. And finally, giving a reference value generator by adopting a gradient descent search algorithm to finish the quick stabilization of the microgrid.
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FIG. 1 is a flow chart of an embodiment of the present invention.
Fig. 2 is a topology structure diagram of a microgrid according to an embodiment of the present invention.
Fig. 3 is a communication topology of a microgrid in an embodiment of the present invention.
FIG. 4 shows the variation of the power sources with sudden load increase in the system; wherein, (a) is the reactive power output change process of each distributed power supply, and (b) is the voltage change process of each distributed power supply under the condition of sudden load increase.
FIG. 5 shows a variation of the power supply remaining after the power supply is disconnected in the system; wherein, (a) is the voltage change process of the distributed power supply, and (b) is the reactive power output state change process of the distributed power supply.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
The system in the invention refers to a distributed power system, and the nodes refer to distributed power nodes.
As shown in fig. 1, the topology of the stream microgrid according to the embodiment of the present invention is shown in fig. 2. The method comprises the following steps:
step 10, establishing a relational expression between distributed power supply output and control signal input by utilizing a NARMA-L2 model;
step 20, based on a containment control concept, taking reactive power sharing as a target to obtain a data set of control signal input and voltage output;
step 30, training a neural network used for representing a complex nonlinear function in the relational expression established in the step 10 by using the data set obtained in the step 20, and fitting the nonlinear output dynamic characteristic of the distributed power supply under the condition of uniform reactive power, so as to obtain a relational expression between the trained distributed power supply output and the trained control signal input; constructing a distributed power supply controller based on NARMA-L2 according to a relation between the trained distributed power supply output and the trained control signal input;
and step 40, providing a target output voltage reference value for the distributed power supply controller constructed in the step 30 by using a gradient descent search algorithm, and finishing the reactive power cooperative control of the microgrid.
As a further optimization scheme of the microgrid energy management method based on the NARMA-L2 model, in step 10, the NARMA-L2 model is used for replacing a distributed power supply dynamic mathematical model, a complex nonlinear function part in a distributed power supply system is approximately replaced through an artificial neural network, and the relational expression between the output of the distributed power supply and the input of a control signal is as follows:
y(k+d)＝f[y(k),y(k-1),…,y(k-n+1),u(k),u(k-1),u(k-z+1)]+g[y(k),y(k-1),…,y(k-n+1),u(k),u(k-1),u(k-z+1)]·u(k) (1)
wherein u (k) represents the input of the distributed power supply control signal at the k moment, y (k) represents the output voltage signal of the distributed power supply at the k moment, y (k + d) represents the output voltage signal of the distributed power supply at the k + d moment, and f and g represent complex nonlinear functions of the nonlinear part in the distributed power generation system; collecting the distributed power supply output at the k-n +1 moment in total, wherein y (k-n +1) represents the distributed power supply output at the (k-n +1) th moment; and c, acquiring the control signal input at the k-z +1 moment, wherein u (k-z +1) is the control signal input at the (k-z +1) th moment.
As a further optimization scheme of the microgrid energy management method based on the NARMA-L2 model, the specific process of the step 20 is as follows:
step 201, for the distributed control micro-grid topology, converting the cooperative target into neighbor information error tracking:
Figure BDA0001795523890000051
in the formula, eiRepresenting the error between the i-th distributed power supply node output voltage and a reference value, yiRepresents the output voltage of the ith distributed power supply node, and j belongs to NiIndicating that the jth node is a neighbor node of the ith node, NiRepresents a set formed by all neighbor nodes of the ith distributed power node, aijRepresenting adjacency matrices in a distributed power system
Figure BDA0001795523890000061
The value of (i) th row and (j) th column in (1), giRepresenting the holdoff gain, y, of the ith distributed power nodejIndicating the output voltage value, y, of the jth node in communication topology with the ith node0Represents the output voltage value of the pinning node, i.e., the reference value;
step 202, to guarantee error eiConverging to 0, and selecting the control signal input u according to the following formula under the condition that the control target is reactive load sharingi：
Figure BDA0001795523890000062
In the formula, Q0Representing a reference reactive load, QiIndicating that the ith node outputs reactive power, omegaiIndicating whether the ith node has been pinned,
Figure BDA0001795523890000063
representing the degree of coupling between the nodes;
step 203, replacing the reactive power in the formula (3) with the output voltage according to the droop control characteristic of the distributed power supply, namely the relational expression between the output voltage and the reactive power, wherein the control signal input expression is changed into:
Figure BDA0001795523890000064
wherein n isQiRepresenting the reactive droop coefficient, y, of the ith node0Representing the value of the output voltage of the holddown node, i.e. the referenceValue u0A control signal input representing a holdover node; on the basis of the formula (4), determining corresponding control signal input by giving different distributed power supply outputs, and further forming a data set [ y ] of the control signal input and the voltage outputi,ui]。
As a further optimization scheme of the microgrid energy management method based on the NARMA-L2 model, the specific process of step 30 is as follows: from the resulting data set y in step 203i,ui]A complex non-linear function f in the relation is established for step 10]、g[*]Training an artificial neural network to replace f [. X [ ]],g[*]And on the basis of the NARMA-L2-based distributed power supply controller; the distributed power controller is a specific implementation of equation (1), and provides control signal inputs u (k) corresponding to different target voltage outputs.
As a further optimization scheme of the microgrid energy management method based on the NARMA-L2 model, the specific process of step 40 is as follows:
step 401, setting a reference value of target output voltage of the distributed power supply as yr(k + d) finding an expression of a target output voltage reference value for targeting reactive power sharing in step 2, and providing a reference value for the distributed power controller constructed in step 30; a generator for providing a target voltage output reference value by adopting a gradient descent search algorithm:
Figure BDA0001795523890000065
wherein j | (i, j) ∈ DGDenotes that the ith, j distributed power node is a neighbor node, DGThe method is a set formed by combining all neighbor nodes in the distributed power system; j (y)i) Represents the average value of the sum of squares of the reactive power differences output by the ith node and the neighboring nodes, QjThe output reactive power of the jth node is represented, and m represents that the ith distributed power supply node has m neighbor nodes;
step 402, according to equation (5), J (y)i) When the minimum value exists, all distributed power supplies output reactive powerThe difference between the two is minimum, and the aim of taking reactive power sharing as the step 2 is achieved; to find J (y)i) Minimum time distributed power supply target output voltage reference value yr(k + d) performing the following loop until convergence;
Figure BDA0001795523890000071
(7) formula (I) is expressed as finding the smallest J (y)i) Value, constantly iterating yiA, denotes the search step size,
Figure BDA0001795523890000072
represents J (y)i) For yiDerivative, temp, representing the current cycle yiWith the convergence condition set to two cycles yiThe value change is less than or equal to a preset value, which can be 0.001.
According to the method provided by the embodiment of the invention, the NAMRA-L2 model is used for replacing a distributed power supply dynamic mathematical model, so that the model removal control of the micro-grid is realized. And meanwhile, a training data set is generated by using containment control, so that the convergence and stability of the micro-grid system are ensured, and finally, a reference value is generated by using a gradient descent search algorithm based on the generated de-modeling controller, so that the system is quickly converged.
An example is illustrated below.
An independent alternating-current microgrid structure is shown in fig. 2, microgrid energy management is carried out on the microgrid, a communication topology of the microgrid is shown in fig. 3, and relevant parameters are shown in table 1
TABLE 1 System parameters
Figure BDA0001795523890000073
The application effect of the micro-grid energy management under two conditions is given below
Case 1: sudden increase in load
In this case, the coupling gain c is 4, the microgrid operates in an island mode, the simulation starts at t 0, the control strategy starts at t 0.5s, and the active load of 40kW and the reactive load of 20kVA are added inside the system at t 2 s.
The step length alpha of the gradient descent search is 0.02, the strategy execution interval is 0.1s, and the convergence condition is that the reactive power difference between the adjacent nodes is less than 100 Var.
Fig. 4 (a) shows a reactive power output change process of each distributed power supply. In fig. 4 (a), the system using the NARAMA-L2 controller can achieve reactive sharing, and the system can respond quickly when the reactive load in the system suddenly increases. The balance of reactive power distribution of the system can be ensured, and reactive power circulation is reduced. Obviously, the system can be converged within 1 s.
Fig. 4 (b) shows the voltage change process of each distributed power supply in the case of a sudden load increase, and DG1 is assumed to be a holddown node. It can be seen that even with increased load, the remaining distributed supply voltage in the system does not drop below the reference voltage. That is, the pinning node sets a voltage limit that the voltage output of all DGs will remain greater than. In the power supply range, the system does not have the voltage collapse problem.
Case 2: power supply break in system
In this case, the coupling gain c is 4, the microgrid operates in an island mode, the simulation starts at t 0, and the control strategy starts at t 0.5 s. The DG3 power supply was disconnected at t-2 s, and the rest of the setup was the same as in case 1.
As shown in fig. 5 (a), after the DG3 is disconnected, the internal load of the system is taken by the rest of the distributed power supply. The voltages of the other three power sources should be reduced accordingly according to the droop control characteristics of the power generation. The voltage is stable due to the presence of the pinning node. The rest voltage is changed within a reasonable range and does not exceed the limit value, so that the quality of the electric energy is reduced.
Fig. 5 (b) shows a reactive power output state change process of the distributed power supply, and even if DG3 is disconnected, the remaining distributed power generation still can achieve the objective of reactive power balance, achieve stability of the power supply, and reduce the influence of reactive circulating current.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (3)

1. A microgrid energy management method based on a NARMA-L2 model is characterized by comprising the following steps:
step 10, establishing a relational expression between distributed power supply output and control signal input by utilizing a NARMA-L2 model;
step 20, based on a containment control concept, taking reactive power sharing as a target to obtain a data set of control signal input and voltage output;
step 30, training a neural network used for representing a complex nonlinear function in the relational expression established in the step 10 by using the data set obtained in the step 20, and fitting the nonlinear output dynamic characteristic of the distributed power supply under the condition of uniform reactive power, so as to obtain a relational expression between the trained distributed power supply output and the trained control signal input; constructing a distributed power supply controller based on NARMA-L2 according to a relation between the trained distributed power supply output and the trained control signal input;
step 40, providing a target output voltage reference value for the distributed power supply controller constructed in the step 30 by using a gradient descent search algorithm, and finishing the reactive power cooperative control of the microgrid;
in step 10, replacing a distributed power supply dynamic mathematical model with a NARMA-L2 model, and performing approximate replacement on a complex nonlinear function part in a distributed power supply system through an artificial neural network, wherein a relational expression between distributed power supply output and control signal input is as follows:
y(k+d)＝f[y(k),y(k-1),…,y(k-n+1),u(k),u(k-1),u(k-z+1)]+g[y(k),y(k-1),…,y(k-n+1),u(k),u(k-1),u(k-z+1)]·u(k) (1)
wherein u (k) represents the input of the distributed power supply control signal at the k moment, y (k) represents the output voltage signal of the distributed power supply at the k moment, y (k + d) represents the output voltage signal of the distributed power supply at the k + d moment, and f and g represent complex nonlinear functions of the nonlinear part in the distributed power generation system; collecting the distributed power supply output at the k-n +1 moment in total, wherein y (k-n +1) represents the distributed power supply output at the (k-n +1) th moment; control signal input at the moment of k-z +1 is collected together, and u (k-z +1) is the control signal input at the (k-z +1) th moment;
the specific process of step 20 is:
step 201, for the distributed control micro-grid topology, converting the cooperative target into neighbor information error tracking:
Figure FDA0002764281590000011
in the formula, eiRepresenting the error between the i-th distributed power supply node output voltage and a reference value, yiRepresents the output voltage of the ith distributed power supply node, and j belongs to NiIndicating that the jth node is a neighbor node of the ith node, NiRepresents a set formed by all neighbor nodes of the ith distributed power node, aijRepresenting adjacency matrices in a distributed power system
Figure FDA0002764281590000021
The value of (i) th row and (j) th column in (1), giRepresenting the holdoff gain, y, of the ith distributed power nodejIndicating the output voltage value, y, of the jth node in communication topology with the ith node0Represents the output voltage value of the pinning node, i.e., the reference value;
step 202, to guarantee error eiConverging to 0, and selecting the control signal input u according to the following formula under the condition that the control target is reactive load sharingi：
Figure FDA0002764281590000022
In the formula, Q0Representing a reference reactive load, QiIndicating that the ith node outputs reactive power, omegaiIndicating whether the ith node has been pinned,
Figure FDA0002764281590000025
representing the degree of coupling between the nodes;
step 203, replacing the reactive power in the formula (3) with the output voltage according to the droop control characteristic of the distributed power supply, namely the relational expression between the output voltage and the reactive power, wherein the control signal input expression is changed into:
Figure FDA0002764281590000023
wherein n isQiRepresenting the reactive droop coefficient, y, of the ith node0Representing the value of the output voltage of the holddown node, i.e. the reference value, u0A control signal input representing a holdover node; on the basis of the formula (4), determining corresponding control signal input by giving different distributed power supply outputs, and further forming a data set [ y ] of the control signal input and the voltage outputi,ui]；
The specific process of step 40 is:
step 401, setting a reference value of target output voltage of the distributed power supply as yr(k + d) finding an expression of a target output voltage reference value for targeting reactive power sharing in step 2, and providing a reference value for the distributed power controller constructed in step 30; a generator for providing a target voltage output reference value by adopting a gradient descent search algorithm:
Figure FDA0002764281590000024
wherein j | (i, j) ∈ DGDenotes that the ith, j distributed power node is a neighbor node, DGThe method is a set formed by combining all neighbor nodes in the distributed power system; j (y)i) Represents the average value of the sum of squares of the reactive power differences output by the ith node and the neighboring nodes, QjThe output reactive power of the jth node is represented, and m represents that the ith distributed power supply node has m neighbor nodes;
step 402, according to equation (5), J (y)i) Is provided withWhen the reactive power is the minimum value, namely the difference between the reactive powers output by all the distributed power supplies is the minimum value, the aim of sharing the reactive power in the step 2 is achieved; to find J (y)i) Minimum time distributed power supply target output voltage reference value yr(k + d) performing the following loop until convergence;
Figure FDA0002764281590000031
(7) formula (I) is expressed as finding the smallest J (y)i) Value, constantly iterating yiA, denotes the search step size,
Figure FDA0002764281590000032
represents J (y)i) For yiDerivative, temp, representing the current cycle yiWith the convergence condition set to two cycles yiThe variation is less than or equal to a preset value.
2. The microgrid energy management method based on the NARMA-L2 model of claim 1, characterized in that the specific process of the step 30 is as follows: from the resulting data set y in step 203i,ui]A complex non-linear function f in the relation is established for step 10]、g[*]Training an artificial neural network to replace f [. X [ ]],g[*]And on the basis of the NARMA-L2-based distributed power supply controller; the distributed power controller is a specific implementation of equation (1), and provides control signal inputs u (k) corresponding to different target voltage outputs.
3. The energy management method for the microgrid based on the NARMA-L2 model is characterized in that the preset value is 0.001.
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