CN107947175B - Micro-grid economic dispatching method based on distributed network control - Google Patents

Micro-grid economic dispatching method based on distributed network control Download PDF

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CN107947175B
CN107947175B CN201711460098.9A CN201711460098A CN107947175B CN 107947175 B CN107947175 B CN 107947175B CN 201711460098 A CN201711460098 A CN 201711460098A CN 107947175 B CN107947175 B CN 107947175B
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夏海波
李强
王建国
徐瑞林
陈涛
陈民铀
李哲
李俊杰
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Chongqing University
Electric Power Research Institute of State Grid Chongqing Electric Power Co Ltd
State Grid Corp of China SGCC
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Electric Power Research Institute of State Grid Chongqing Electric Power Co Ltd
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Abstract

The invention discloses a micro-grid economic dispatching method based on distributed network control, which comprises the following steps of S1: deriving a theoretical optimal output formula of the controllable distributed generation DG under a constraint condition according to the economic dispatching model; s2: constructing a communication network according to the micro-grid double-layer control model; the microgrid double-layer control model comprises: a lower microgrid and an upper communication network; s3: designing a weight matrix according to an upper communication network, and obtaining a distributed control law according to the weight matrix; s4: and calculating a set value according to a distributed control law, and adjusting the output of the controllable distributed generation DG according to the calculated set value to realize the economic optimization scheduling of the microgrid. The beneficial effects obtained by the invention are as follows: the total power generation cost of the micro-grid can be minimized by realizing that the micro-increment rates of all the controllable DGs are equal; designing a weight matrix to provide convenience for algorithm design; in the iterative process, the system power balance can be ensured.

Description

Micro-grid economic dispatching method based on distributed network control
Technical Field
The invention relates to the technical field of distributed control of a microgrid, in particular to a microgrid economic dispatching method based on distributed network control.
Background
In recent years, Distributed Generation (DG) by renewable energy has been developed rapidly, and a microgrid, as an effective management form of Distributed Generation, has great significance for improving the utilization rate of renewable energy, reducing environmental pollution, alleviating energy crisis, and improving power supply reliability and stability. However, renewable energy power generation depends on the external environment, the output of the renewable energy power generation has obvious randomness, the load demand has fluctuation, meanwhile, the inertia of the microgrid is small, the tidal current flows in two directions, and the like, and the economic dispatching of the microgrid is very difficult due to the interaction of the factors.
Currently, the average consistency algorithm is widely used in economic dispatch of a microgrid, but the following two problems exist: 1) in the iterative process, the average consistency algorithm can destroy the system balance; 2) nodes with centralized functions still exist in the distributed system, and complete distribution cannot be realized.
Disclosure of Invention
In view of the above defects in the prior art, the present invention aims to provide a microgrid economic dispatching method based on distributed network control, which can minimize the total power generation cost of a microgrid by achieving equal incremental rates of all controllable DGs; and in the iterative process of the algorithm, the system balance can be always ensured.
One of the purposes of the invention is realized by the technical scheme, and the micro-grid economic dispatching method based on distributed network control comprises the following steps:
s1: deriving a theoretical optimal output formula of the controllable distributed generation DG under a constraint condition according to the economic dispatching model;
s2: constructing a communication network according to the micro-grid double-layer control model; the microgrid double-layer control model comprises: a lower microgrid and an upper communication network;
s3: designing a weight matrix according to the upper communication network, and obtaining a distributed control law according to the weight matrix;
s4: and calculating a set value according to a distributed control law, and adjusting the output of the controllable distributed generation DG according to the calculated set value to realize the economic optimization scheduling of the microgrid.
Further, in step S1, under the constraint condition, the theoretically optimal output of the controllable distributed generation DG is specifically:
s11: if m controllable distributed generation DGs exist in the microgrid, m is the number of the controllable distributed generation DGs, and each controllable DGiHas a power generation cost function of Ci(pi) The economic scheduling problem is represented as all controllable DGsiThe sum of the power generation costs of (a) is minimal, namely:
Figure GDA0002771891350000021
the constraint of equation:
Figure GDA0002771891350000022
inequality constraint conditions:
Figure GDA0002771891350000023
wherein p isiIs the output of generator i;
Figure GDA0002771891350000024
and
Figure GDA0002771891350000025
minimum and maximum output of generator i, respectively; ploadMeets the total load demand
Figure GDA0002771891350000026
Ci(pi) Is the cost function of the generation of generator i;
s12: cost function Ci(pi) Can be expressed as a function of the order of two,
Figure GDA0002771891350000027
wherein a isi，biAnd ciAll are cost parameters of the generator i;
s13: to simplify the expression, assume
Figure GDA0002771891350000028
And
Figure GDA0002771891350000029
then, the cost function is re-expressed as:
Figure GDA00027718913500000210
s14: to CiAnd solving a first partial derivative to obtain an incremental cost expression as follows:
Figure GDA00027718913500000211
s15: according to the criterion of equal micro-increment rate, when the increment cost lambda of all controllable distributed generation DGsiWhen the output of the controllable distributed generation DG is equal to the theoretical optimal output, the total generation cost is the minimum.
Further, if the constraint condition in step S1 is a constraint condition that does not include an inequality, the following is true:
s16: the optimal incremental cost is λ*Then, thenThe theoretical optimal output of the controllable distributed generation DG is:
Figure GDA0002771891350000031
further, if the constraint condition in step S1 is a constraint condition including an inequality, the following is true:
s17: setting the output value as the maximum output or the minimum output of the controllable distributed generation DG which does not meet the inequality condition; for a controllable distributed generation DG which meets the inequality condition, the theoretical optimal incremental cost is as follows:
Figure GDA0002771891350000032
wherein omegaPA set of distributed generation DGs for all inequality conditions not satisfied;
therefore, under the constraint condition of inequality, the theoretical optimal output of the controllable distributed generation DG is:
Figure GDA0002771891350000033
further, the microgrid double-layer control model in the step S2 is a typical microgrid secondary control model, that is, the lower microgrid includes: wind turbines, photovoltaics, micro gas turbines, energy storage, distributed generation DG.
Further, the distributed control law in step S3 includes a weight matrix, and the output of the controllable distributed power generation DG is adjusted according to elements in the weight matrix.
Further, the step S3 specifically includes:
s31: the communication network G (V, E) is composed of n agents, n is the number of agents, and after the communication network G (V, E) is established, an adjacency matrix A ═ a is definedij]n×nDescribing the connection relation between the agents, if the Agenti has a connection edge to the Agent j, the matrixElement aij1, otherwise aij0; matrix ATRepresenting the transpose of adjacency matrix a; matrices A and A in a directed networkTAn asymmetric matrix;
s32: defining an attribute matrix B ═ Bii]n×nRepresenting the type of Agent, B is a diagonal matrix, the diagonal elements of which are BiiIs 0 or 1, depending on the type of Agent; if Agenti is controllable Agent, then b ii1, otherwise bii＝0；
S33: defining a degree matrix D ═ Dii]n×nRepresenting the number of edges, i.e. degrees, of Agent, D matrix is a diagonal matrix, and the diagonal element D of matrix DiiAnd the relationship between the elements of matrix a is:
Figure GDA0002771891350000041
s34: defining a weight matrix W ═ Wij]n×nRepresenting the relationship between all agents; if the Agent i to the Agent j have a connecting edge, the weight value on the connecting edge is wij＝1/diiWherein d isiiIs the number of outgoing edges of Agent i; in addition, for the weight w on the self-loop ii1 is ═ 1; the sum of each row of the W matrix is 1;
s35: the power balance of a microgrid system is defined as: at two adjacent time, the sum of all the load variations is equal to the sum of the controllable distributed generation output power variations and the sum of the uncontrollable distributed generation DG output power variations, as shown in the following formula:
Figure GDA0002771891350000042
wherein p (t) ═ pi(t)]n×1And q (t) ═ qi(t)]n×1Respectively, the i-th distributed generation DG represents the active and reactive power outputs at the t-th time, P (t-1) ═ Pi(t-1)]n×1And Q (t-1) [ Q ]i(t-1)]n×1Respectively, the ith distributed generation DG at the time t-1Outputting power and reactive power; l isp(t)＝[lp i(t)]n×1And Lq(t)＝[lq i(t)]n×1Respectively representing the active power demand and the reactive power demand of the ith load at the t moment; i is an n x n order identity matrix;
s36: according to the communication network G (V, E), the control law of the distributed generation DG is given as:
Figure GDA0002771891350000043
wherein (·)TThe operation represents transposing the matrix; the matrix W is a weight matrix;
s37: according to the communication network G (V, E), giving a control law of the controllable distributed generation DG as follows:
Figure GDA0002771891350000044
wherein the content of the first and second substances,
Figure GDA0002771891350000045
representing the active output of the controlled distributed generation DG, α representing a cost parameter vector, (-)TThe operation represents transposing the matrix; the matrix H is a weight matrix and is obtained by calculation according to the following method:
Figure GDA0002771891350000046
Figure GDA0002771891350000051
wherein h isijIs the off-diagonal element, H, in the weight matrix HiiAre diagonal elements in the weight matrix H.
Further, in step S4, the economic optimization scheduling process is as follows:
in economic dispatching under the condition of inequality, a set value is calculated according to a distributed control law, when the set value is larger than the maximum capacity of a distributed generation DG, a border crossing processing rule is designed, and the control law derived in the front is modified as follows:
Figure GDA0002771891350000052
further, the distributed control law in step S3 is a completely distributed control law, that is, each Agent only needs to collect information of neighbor agents.
One of the objectives of the present invention is achieved by such a technical solution, a microgrid economic dispatching system based on distributed network control, comprising:
the output unit of the controllable distributed generation DG derives a theoretical optimal output formula of the controllable distributed generation DG under the constraint condition according to the economic dispatching model;
the micro-grid double-layer unit is used for constructing a communication network according to the micro-grid double-layer control model; the microgrid double-layer control model comprises: a lower microgrid and an upper communication network;
a weight matrix unit, which designs a weight matrix according to the communication network and obtains the distributed control law according to the weight matrix;
and the economic optimization scheduling unit of the micro-grid calculates a set value according to the distributed control law, and adjusts the output of the controllable distributed generation DG according to the calculated set value to realize economic optimization scheduling of the micro-grid.
Due to the adoption of the technical scheme, the invention has the following advantages:
(1) the total power generation cost of the micro-grid can be minimized by realizing that the micro-increment rates of all the controllable DGs are equal;
(2) giving a unified design step of the microgrid economic dispatching method, designing a weight matrix according to an upper-layer communication network, and providing convenience for designing a calculation method of economic dispatching;
(3) if the Agent controls the output of the controllable DG according to the control law, the controllable DG can be output according to the set target, and the system power is always kept balanced in the algorithm iteration process.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof.
Drawings
The drawings of the invention are illustrated as follows:
fig. 1 is a schematic flow chart of a microgrid economic dispatching method based on distributed network control.
Fig. 2 is a schematic diagram of a microgrid double-layer control model in a microgrid economic dispatching method based on distributed network control.
Fig. 3 is a schematic output diagram of active power and reactive power of a controllable DG that does not include economic optimization scheduling of inequality constraint conditions in a microgrid economic scheduling method based on distributed network control.
Fig. 4 is a schematic diagram of system frequency and line voltage of economic optimization scheduling without inequality constraint conditions in a microgrid economic scheduling system based on distributed network control.
Fig. 5 is a schematic diagram of an output result of semi-controllable DG power and a controllable DG increment cost of economic optimal scheduling without inequality constraint conditions in a microgrid economic scheduling method based on distributed network control.
Fig. 6 is a schematic output diagram of active power and reactive power of a controllable DG after economic optimization scheduling including inequality constraint conditions in a microgrid economic scheduling method based on distributed network control.
Fig. 7 is a schematic diagram of system frequency and line voltage after economic optimization scheduling including inequality constraint conditions in a microgrid economic scheduling system based on distributed network control.
Fig. 8 is a schematic diagram of an output result of the semi-controllable DG power after economic optimization scheduling including an inequality constraint condition in the microgrid economic scheduling method based on distributed network control and a controllable DG increment cost.
Detailed Description
The invention is further illustrated by the following figures and examples.
Example (b): as shown in fig. 1-8; a micro-grid economic dispatching method based on distributed network control comprises the following steps:
s1: deriving a theoretical optimal output formula of the controllable distributed generation DG under a constraint condition according to the economic dispatching model;
s2: constructing a communication network according to the micro-grid double-layer control model; the microgrid double-layer control model comprises: a lower microgrid and an upper communication network;
s3: designing a weight matrix according to the upper communication network, and obtaining a distributed control law according to the weight matrix;
s4: and calculating a set value according to a distributed control law, and adjusting the output of the controllable distributed generation DG according to the calculated set value to realize the economic optimization scheduling of the microgrid.
More specifically, step 1) in the problem of economic dispatch of the microgrid, there are:
s11: let m controllable DGs (m is the number of controllable DGs) in the microgrid be provided, and each controllable DGiHas a power generation cost function of Fi(pi) The economic scheduling problem is represented as all controllable DGsiThe sum of the power generation costs of (a) is minimal, namely:
Figure GDA0002771891350000071
the constraint of equation:
Figure GDA0002771891350000072
inequality constraint conditions:
Figure GDA0002771891350000073
wherein p isiIs the output of generator i;
Figure GDA0002771891350000074
and
Figure GDA0002771891350000075
minimum and maximum output of generator i, respectively; ploadMeets the total load demand
Figure GDA0002771891350000076
Ci(pi) Is the cost function of the generation of generator i;
s12: according to the literature, a cost function Ci(pi) Can be expressed as a function of the order of two,
Figure GDA0002771891350000077
wherein a isi，biAnd ciAll are cost parameters of the generator i;
s13: to simplify the expression, assume
Figure GDA0002771891350000078
And
Figure GDA0002771891350000079
then, the cost function is re-expressed as:
Figure GDA00027718913500000710
s14: to CiAnd solving a first partial derivative to obtain an incremental cost expression as follows:
Figure GDA00027718913500000711
s15: according to the criterion of equal micro-increment rate, when the increment cost lambda of all controllable distributed generation DGsiWhen equal, the overall power generation cost is minimal.
S16: assuming that the optimal incremental cost is λ without the inclusion of inequality*Then, the theoretical optimal output of the controllable DG at this time is:
Figure GDA0002771891350000081
s17: assuming that under the condition of containing inequality constraint, for a controllable DG which does not satisfy the inequality condition, setting an output value as the maximum output or the minimum output of the controllable DG; for a controllable DG satisfying the inequality condition, the theoretical optimal incremental cost is:
Figure GDA0002771891350000082
wherein omegaPIs the set of all inequality conditions DG are not fulfilled.
Therefore, under the constraint of inequality, the theoretical optimal output of controllable DG is:
Figure GDA0002771891350000083
the lower layer of the micro-grid double-layer control model in the step 2) is a micro-grid, and the upper layer is a communication network. The lower-layer micro-grid consists of various DGs such as a fan, a photovoltaic, a micro gas turbine, energy storage and the like.
Since a wind turbine, a photovoltaic system, and the like use a DG that is a renewable energy source, its output depends on external environmental conditions, and has uncertainty, it is defined as an uncontrollable DG. The DG of a micro gas turbine or the like is defined as a controllable DG because its output can be adjusted according to a control command.
In an island microgrid, a storage battery energy storage system working in a V/F control mode is generally used as a voltage frequency support of the whole system, and is defined as a semi-controllable DG. In order to fully utilize renewable energy sources to generate electricity, uncontrollable DGs such as a fan, a photovoltaic and the like adopt a maximum power point tracking mode, and controllable DGs such as a micro gas turbine and the like adopt a PQ control mode.
In the upper layer communication network, the agents connected to the uncontrollable DG and the semi-controllable DG are referred to as the uncontrollable Agent and the semi-controllable Agent, respectively, and the agents connected to the controllable DG are referred to as the controllable agents.
The agents can collect information of connected DGs and loads through a connection line between two layers, as shown in FIG. 2, wherein an uncontrollable Agent and a semi-controllable Agent are represented by an ellipse, a controllable Agent is represented by a diamond, and the direction of the connection line represents the direction of information transmission.
In the communication network G (V, E), an uncontrollable Agent or a semi-controllable Agent only transmits DG and load information collected by itself to a controllable Agent, and the controllable Agent can receive and transmit information. Thus, in the communication network G (V, E), an uncontrollable Agent or a semi-controllable Agent has only an outgoing edge and no incoming edge, while a controllable Agent has both an outgoing edge and an incoming edge. Meanwhile, the DG and the load information connected thereto are collected through the self-loop. It can be seen that the communication network G (V, E) is a directed graph.
In addition, in an island micro-grid, the micro-grid works in the energy storage (DG4) of a V/F control mode, and the V/F control is the control for ensuring that the output voltage is in direct proportion to the frequency, so that the magnetic flux of the motor can be kept constant, the weak magnetic and magnetic saturation phenomena are avoided, and the micro-grid is mainly used for realizing energy-saving frequency converters such as fans and pumps by using a voltage-controlled oscillator. When the voltage frequency of the microgrid deviates from a normal value, power can be injected into or absorbed by the system immediately, so that the power balance of the system is ensured, and the voltage frequency is kept stable.
However, if the stored energy is output for a long time, the state of charge (SOC) may be too low or too high, thereby affecting the next control of the system. Therefore, a parameter γ of-1 is added between the stored energy controlled by V/F and the corresponding semi-controllable Agent, and the output of the stored energy is considered as the load. Therefore, after the shortage of the energy storage instantaneous compensation system is realized, the output of the energy storage instantaneous compensation system is shared by the controllable DG, so that the output of the energy storage is gradually restored to zero.
And 3) designing a weight matrix after the upper-layer communication network is established, and giving a construction method of the distributed control law according to the weight matrix. If the Agent controls the output of the controllable DG according to the control law, the controllable DG can be output according to the set target, and the system power is always kept balanced in the iteration process.
S31: the communication network G (V, E) is composed of n agents (n is the number of agents), and after the communication network G (V, E) is established, an adjacency matrix a ═ a is definedij]n×nDescribing the connection relation between Agents, if Agenti to Agentj have a connection edge, then the matrix element aij1, otherwise aij0; matrix ATRepresenting the transpose of adjacency matrix a; matrices A and A in a directed networkTAn asymmetric matrix;
s32: defining an attribute matrix B ═ Bii]n×nRepresenting the type of Agent, B is a diagonal matrix, the diagonal elements of which are BiiIs 0 or 1, depending on the type of Agent; if Agenti is controllable Agent, then b ii1, otherwise bii＝0；
S33: defining a degree matrix D ═ Dii]n×nRepresenting the number of edges, i.e. degrees, of Agent, D matrix is a diagonal matrix, and the diagonal element D of matrix DiiAnd the relationship between the elements of matrix a is:
Figure GDA0002771891350000091
s34: defining a weight matrix W ═ Wij]n×nRepresenting the relationship between all agents; if Agenti to Agentj have a connecting edge, the weight value on the connecting edge is wij＝1/diiWherein d isiiIs the number of outgoing edges of Agent i; in addition, for the weight w on the self-loop ii1 is ═ 1; the sum of each row of the W matrix is 1;
s35: the power balance of a microgrid system is defined as: at two adjacent time instants, the sum of all load variations is equal to the sum of controllable Distributed Generation (DG) output power variations, plus the sum of uncontrollable DG output power variations, as shown in the following equation:
Figure GDA0002771891350000101
wherein p (t) ═ pi(t)]n×1And q (t) ═ qi(t)]n×1Respectively, the i-th distributed generation DG represents the active and reactive power outputs at the t-th time, P (t-1) ═ Pi(t-1)]n×1And Q (t-1) [ Q ]i(t-1)]n×1Respectively representing the active power output and the reactive power output of the ith distributed generation DG at the t-1 th moment; l isp(t)＝[lp i(t)]n×1And Lq(t)＝[lq i(t)]n×1Respectively representing the active power demand and the reactive power demand of the ith load at the t moment; i is an identity matrix of order n × n.
S36: according to the communication network G (V, E), the control law of DG is given by:
Figure GDA0002771891350000102
wherein, (.)TThe operation represents transposing the matrix; the matrix W is a weight matrix.
S37: according to the communication network G (V, E), the control law given for controllable DG is:
Figure GDA0002771891350000103
wherein, (.)TThe operation represents transposing the matrix; the matrix H is a weight matrix and is obtained by calculation according to the following method:
Figure GDA0002771891350000104
Figure GDA0002771891350000105
wherein h isijIs the off-diagonal element, H, in the weight matrix HiiAre diagonal elements in the weight matrix H.
And 4) carrying out economic dispatching under the condition of containing inequalities, and modifying the control law (13) derived in the front as follows:
Figure GDA0002771891350000106
economic dispatch of the microgrid under the condition of containing inequalities can be realized.
More specifically, in fig. 2, it is assumed that the DG and load parameter settings of the underlying microgrid are as shown in table 1:
TABLE 1 parameter settings for Distributed Generation (DG) and load
Power supply Capacity of Control mode Load(s) Maximum demand of load
DG1 50kW，40kVar PQ Load1 20kW，0kVar
DG2 30kW，0kVar MPPT Load2 35kW，0kVar
DG3 60kW，25kVar PQ Load3 10kW，20kVar
DG4 30Ah V/F Load4 30kW，0kVar
DG5 55kW，20kVar PQ Load5 20kW，20kVar
DG6 65kW，30kVar PQ Load6 10kW，10kVar
DG7 50kW，0kVar MPPT Load7 20kW，0kVar
DG8 35kW，0kVar MPPT Load8 30kW，15kVar
DG9 45kW，38kVar PQ Load9 40kW，10kVar
DG10 45kW，0kVar MPPT Load10 20kW，15kVar
DG11 70kW，28kVar PQ Load11 15kW，20kVar
DG12 50kW，0kVar MPPT Load12 20kW，0kVar
Then send outElectrical cost minimization issues, generally speaking, only the active power of the controllable DG is considered, not the generation costs of photovoltaic, wind turbines and reactive power. Cost function C without inequalityi(pi) Inner cost factor ai，biAnd ciAs shown in table 2:
TABLE 2 controllable distributed power cost parameter settings
Power supply ai bi ci
DG1 0.059 6.71 80
DG3 0.047 7.08 56
DG5 0.066 6.29 43
DG6 0.031 7.53 35
DG9 0.069 4.57 48
DG11 0.038 5.86 91
The active power output of the controllable DG is controlled by equation (13), and the simulation results are shown in fig. 3 to 5. In fig. 3, #1 is the active power and reactive power output of the controllable DG, in fig. 4, #2 and #3 are the system frequency and line voltage, respectively, and in fig. 5, #4 and #5 are the semi-controllable DG power output result and the controllable DG incremental cost, respectively.
Cost function C under the condition of containing inequalityi(pi) Inner cost factor ai，biAnd ciAs shown in table 3:
TABLE 3 controllable distributed power cost parameter settings
Power supply ai bi ci
DG1 0.059 6.71 80
DG3 0.047 7.08 56
DG5 0.066 6.29 43
DG6 0.031 7.53 35
DG9 0.05 4.57 48
DG11 0.038 5.86 91
The active power output of the controllable DG is controlled by using the formula (16), that is, economic optimization scheduling is performed, and simulation results thereof are shown in fig. 6 to 8. In fig. 6, #1 is the active power and reactive power output of the controllable DG after economic optimization scheduling, and in fig. 7, #2 and #3 are the system frequency and line voltage after economic optimization scheduling, respectively; #4 and #5 in fig. 8 are the semi-controllable DG power output result and the controllable DG incremental cost, respectively, after the economic optimal scheduling.
According to the simulation result, the minimum power generation cost of the micro-grid can be realized by designing the weight matrix H in the distributed control law.
The invention also provides a micro-grid economic dispatching system based on distributed network control, which comprises:
the output unit of the controllable distributed generation DG derives a theoretical optimal output formula of the controllable distributed generation DG under the constraint condition according to the economic dispatching model;
the micro-grid double-layer unit determines a construction rule of the communication network according to the micro-grid double-layer control model; the microgrid double-layer control model comprises: a lower microgrid and an upper communication network;
the weight matrix unit is used for designing a weight matrix after the upper-layer communication network is established, and giving a construction method of the distributed control law according to the weight matrix to obtain the distributed control law;
and the economic optimization scheduling unit of the microgrid calculates a set value according to a distributed control law, and adjusts the output of the controllable distributed generation DG when the set value is larger than the maximum capacity of the distributed generation DG, so as to realize the economic optimization scheduling of the microgrid.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (7)

1. A microgrid economic dispatching method based on distributed network control is characterized by comprising the following steps:
s1: deriving a theoretical optimal output formula of the controllable distributed generation DG under a constraint condition according to the economic dispatching model;
s2: constructing a communication network according to the micro-grid double-layer control model; the microgrid double-layer control model comprises: a lower microgrid and an upper communication network;
s3: designing a weight matrix according to the upper communication network, and obtaining a distributed control law according to the weight matrix;
s4: calculating a set value according to a distributed control law, and adjusting the output of the controllable distributed generation DG according to the calculated set value to realize economic optimization scheduling of the microgrid;
in step S1, under the constraint condition, the optimal output of the controllable distributed generation DG is specifically:
s11: if m controllable distributed generation DGs exist in the microgrid, m is the number of the controllable distributed generation DGs, and each controllable DGiHas a power generation cost function of Ci(pi) The economic scheduling problem is represented as all controllable DGsiThe sum of the power generation costs of (a) is minimal, namely:
Figure FDA0002771891340000011
the constraint of equation:
Figure FDA0002771891340000012
inequality constraint conditions:
Figure FDA0002771891340000013
wherein p isiIs the output of generator i;
Figure FDA0002771891340000014
and
Figure FDA0002771891340000015
minimum and maximum output of generator i, respectively; ploadMeets the total load demand
Figure FDA0002771891340000016
Ci(pi) Is the cost function of the generation of generator i;
s12: cost function Ci(pi) Can be expressed as a function of the order of two,
Figure FDA0002771891340000017
wherein a isi，biAnd ciAll are cost parameters of the generator i;
s13: to simplify the expression, assume
Figure FDA0002771891340000021
And
Figure FDA0002771891340000022
then, the cost function is re-expressed as:
Figure FDA0002771891340000023
s14: to CiAnd solving a first partial derivative to obtain an incremental cost expression as follows:
Figure FDA0002771891340000024
s15: according to the criterion of equal micro-increment rate, when the increment cost lambda of all controllable distributed generation DGsiWhen the output of the controllable distributed generation DG is equal, the total generation cost is the minimum, namely the output of the controllable distributed generation DG is the theoretical optimal output;
the step S3 specifically includes:
s31: the communication network G (V, E) is composed of n agents, n is the number of agents, and after the communication network G (V, E) is established, an adjacency matrix A ═ a is definedij]n×nDescribing the connection relation between Agents, if Agenti to Agentj have a connection edge, then the matrix element aij1, otherwise aij0; momentArray ATRepresenting the transpose of adjacency matrix a; matrices A and A in a directed networkTAn asymmetric matrix;
s32: defining an attribute matrix B ═ Bii]n×nRepresenting the type of Agent, B is a diagonal matrix, the diagonal elements of which are BiiIs 0 or 1, depending on the type of Agent; if Agenti is controllable Agent, then bii1, otherwise bii＝0；
S33: defining a degree matrix D ═ Dii]n×nRepresenting the number of edges, i.e. degrees, of Agent, D matrix is a diagonal matrix, and the diagonal element D of matrix DiiAnd the relationship between the elements of matrix a is:
Figure FDA0002771891340000025
s34: defining a weight matrix W ═ Wij]n×nRepresenting the relationship between all agents; if Agenti to Agentj have a connecting edge, the weight value on the connecting edge is wij＝1/diiWherein d isiiIs the number of outgoing edges of Agent i; in addition, for the weight w on the self-loopii1 is ═ 1; the sum of each row of the W matrix is 1;
s35: the power balance of a microgrid system is defined as: at two adjacent time, the sum of all the load variations is equal to the sum of the controllable distributed generation output power variations and the sum of the uncontrollable distributed generation DG output power variations, as shown in the following formula:
Figure FDA0002771891340000031
wherein p (t) ═ pi(t)]n×1And q (t) ═ qi(t)]n×1Respectively, the i-th distributed generation DG represents the active and reactive power outputs at the t-th time, P (t-1) ═ Pi(t-1)]n×1And Q (t-1) [ Q ]i(t-1)]n×1Respectively representing the active power output and the reactive power output of the ith distributed generation DG at the t-1 th moment; l isp(t)＝[lp i(t)]n×1And Lq(t)＝[lq i(t)]n×1Respectively representing the active power demand and the reactive power demand of the ith load at the t moment; i is an n x n order identity matrix;
s36: according to the communication network G (V, E), the control law of the distributed generation DG is given as:
Figure FDA0002771891340000032
wherein (·)TThe operation represents transposing the matrix; the matrix W is a weight matrix;
s37: according to the communication network G (V, E), giving a control law of the controllable distributed generation DG as follows:
Figure FDA0002771891340000033
wherein the content of the first and second substances,
Figure FDA0002771891340000034
representing the active output of the controlled distributed generation DG, α representing a cost parameter vector, (-)TThe operation represents transposing the matrix; the matrix H is a weight matrix and is obtained by calculation according to the following method:
Figure FDA0002771891340000035
Figure FDA0002771891340000036
wherein h isijIs the off-diagonal element, H, in the weight matrix HiiAre diagonal elements in the weight matrix H.
2. The economic dispatch method for a microgrid based on distributed network control as claimed in claim 1, wherein if the constraint condition in the step S1 is a constraint condition not containing inequality, then there are:
s16: the optimal incremental cost is λ*Then, the theoretical optimal output of the controllable distributed generation DG is:
Figure FDA0002771891340000037
3. the economic dispatch method for a microgrid based on distributed network control as claimed in claim 1, wherein if the constraint condition in the step S1 is a constraint condition comprising an inequality, there are:
s17: setting the output value as the maximum output or the minimum output of the controllable distributed generation DG which does not meet the inequality condition; for a controllable distributed generation DG which meets the inequality condition, the theoretical optimal incremental cost is as follows:
Figure FDA0002771891340000041
wherein omegaPA set of distributed generation DGs for all inequality conditions not satisfied;
therefore, under the constraint condition of inequality, the theoretical optimal output of the controllable distributed generation DG is:
Figure FDA0002771891340000042
4. the economic dispatch method for a microgrid based on distributed network control as claimed in claim 1, wherein the microgrid double-layer control model in the step S2 is a typical microgrid secondary control model, that is, a lower microgrid comprises: wind turbines, photovoltaics, micro gas turbines, energy storage, distributed generation DG.
5. The economic dispatching method of the microgrid based on the distributed network control as claimed in claim 1, characterized in that the distributed control law in the step S3 includes a weight matrix, and the output of the controllable distributed generation DG is adjusted according to elements in the weight matrix.
6. The economic dispatch method for microgrid based on distributed network control as claimed in claim 3, wherein in step S4, the economic optimization dispatch process is as follows:
in economic dispatching under the condition of inequality, a set value is calculated according to a distributed control law, when the set value is larger than the maximum capacity of a distributed generation DG, a border crossing processing rule is designed, and the control law derived in the front is modified as follows:
Figure FDA0002771891340000043
7. the economic dispatching method of the microgrid based on the distributed network control as claimed in claim 1, characterized in that the distributed control law in the step S3 is a fully distributed control law, that is, each Agent only needs to collect information of neighbor agents.
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