CN111340386A - AC/DC hybrid power distribution network scheduling method - Google Patents
AC/DC hybrid power distribution network scheduling method Download PDFInfo
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Abstract
The invention discloses an alternating current-direct current hybrid power distribution network scheduling method, which comprises the following steps: acquiring real-time whole network information of the alternating current-direct current hybrid power distribution network; establishing a load flow calculation and scheduling model of the alternating current-direct current hybrid power distribution network according to the real-time whole network information; establishing a cross-region distributed optimization scheduling model based on an energy router and a virtual power plant according to the load flow calculation and scheduling model of the AC/DC hybrid power distribution network; according to the method, under the large background of ubiquitous power Internet of things, the constraint of consideration of traditional power distribution network scheduling model research based on an alternating current transmission mode is broken through, and the purpose that no complete scheduling model and algorithm aiming at an alternating current-direct current hybrid power distribution network exists in the field of power distribution network scheduling and the technical blank that the influence of the addition of new equipment and new technology under ubiquitous power Internet of things such as an energy router and a virtual power plant on the scheduling regulation and control requirement of the power distribution network is not considered is achieved.
Description
Technical Field
The invention relates to a dispatching method of a power distribution network, in particular to a dispatching method of an alternating current-direct current hybrid power distribution network based on the large background of the ubiquitous power Internet of things.
Background
In 2019, a national grid company proposes to build a three-type two-network system, wherein the two-network system is a strong smart grid and a ubiquitous power internet of things: the requirements of a strong intelligent power grid indicate that high-voltage direct-current transmission is an important technical choice for long-distance trans-regional power transmission and indicate the advantages of direct-current transmission; and the digital technology for ubiquitous power internet of things enables the traditional power grid, can continuously improve the sensing capability, the interaction level and the operating efficiency of the power grid, and powerfully supports various energy access and comprehensive utilization. Under the big background of the accelerated development of a power grid, in order to accelerate the construction of the ubiquitous power internet of things, a power grid company starts to gradually construct an alternating-current and direct-current hybrid power distribution network, so that the influence of the traditional power grid constraint, the power electronic technology and a new technology in the ubiquitous power internet of things on the power distribution network scheduling is considered, and the research of a tidal current algorithm and a scheduling model under the background is an important requirement at present.
The traditional power distribution network is an alternating current power distribution network, so that the traditional power distribution network scheduling model research is usually based on the consideration of constraints under an alternating current transmission mode or the consideration of a scheduling mode of an active power distribution network under a power market environment, and a complete scheduling model and algorithm aiming at an alternating current-direct current hybrid power distribution network are not provided, and the influence of the addition of new equipment and new technology under ubiquitous power internet of things such as an energy router and a virtual power plant on the power distribution network scheduling regulation and control requirement is not considered.
Disclosure of Invention
The invention aims to provide an AC/DC hybrid power distribution network scheduling method, which is used for breaking through the consideration constraint of the traditional power distribution network scheduling model research based on an AC power transmission mode in the large background of ubiquitous power Internet of things, and filling up the technical blank that no complete scheduling model and algorithm aiming at the AC/DC hybrid power distribution network exist in the field of power distribution network scheduling, and the influence of the addition of new equipment and new technology under ubiquitous power Internet of things, such as an energy router and a virtual power plant, on the scheduling regulation and control demand of the power distribution network is not considered.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
an alternating current-direct current hybrid power distribution network scheduling method comprises the following steps:
step S1, acquiring real-time whole network information of the AC/DC hybrid power distribution network;
step S2, establishing a load flow calculation and scheduling model of the AC/DC hybrid power distribution network according to the real-time whole network information;
and S3, establishing a cross-region distributed optimization scheduling model based on an energy router and a virtual power plant according to the load flow calculation and scheduling model of the alternating current-direct current hybrid power distribution network.
Further, the step S1 includes a step S1.1 of obtaining a power grid state estimation value of the ac/dc hybrid power distribution network in real time; and S1.2, acquiring a power grid load prediction result in real time.
Further, the step S1.1 includes:
the power grid state estimation is carried out by adopting the following formula, and the measurement residual error is calculated,
in the formula, riMeasuring residual error for i moment power grid state, ziThe measured value of the state of the power grid at the moment i,
the estimated value of the power grid state at the moment i is obtained by inputting data;
calculating a regularization residual error by adopting the following formula;
in the formula, omegaiiAnd representing the power grid state measurement variance at the moment i.
Obtaining the maximum regularization residual error max r according to the formulai NJudging the maximum regularization residual max ri NWhether the grid normal state threshold value is exceeded or not; if yes, the ith measurement is bad measurement data, the ith measurement is removed from the measurement data, and the calculation process is repeated;
if not, no bad data exists or the bad data is eliminated, the bad data identification process is finished, and a power grid state estimation result and a bad data measurement result are output.
Further, the step S1.2 includes: acquiring various kinds of power grid information as incoming data of a neural network prediction model of the LSTM;
judging whether the incoming data is a stationary time sequence or not based on the neural network prediction model of the LSTM, and then carrying out stationary and regularization processing on the incoming data;
and setting an LSTM layer, a Dropout layer and a Dense layer, selecting an activation function and an optimization parameter to predict the load of the power grid in real time, and restoring data after a prediction result reaches the standard to obtain a complete prediction result of the load of the power grid.
Further, the load flow calculation and scheduling model of the alternating current-direct current hybrid power distribution network comprises: the system comprises an alternating current network model, a direct current network model and a VSC transformer substation model.
Further, the communication network model is as follows:
wherein g isijRepresenting the conductance between node i and node j, bijRepresenting susceptance between node i and node j; v is the bus voltage, viRepresenting the voltage of node i, vjRepresenting the voltage at node j, PijRepresenting the active power flow of the node i to the node j, QijRepresenting the magnitude of the reactive power, θ, flowing from node i to node jijIs the phase angle difference between node i and node j.
Further, the dc network model is as follows:
wherein the content of the first and second substances,
representing the size of the direct current flow from the node i to the node j; r isij DCRepresenting the resistance between the node i and the node j;
voltage at time t for node i;
is the voltage at node j at time t; v. ofij DCIs the voltage difference between node i and node j.
Further, the VSC substation model includes:
wherein r isjpIs a correlation matrix between the AC bus and the DC bus, acAnd bcThe AC side is actively coupled with the DC side; subscript j represents a bus ID (identity) of the AC side network connected with the VSC converter station, subscript p represents a bus ID of the DC side network connected with the VSC converter station, subscript i represents an AC side port of the VSC converter station, and subscript m represents a connection node ID of the VSC converter station side and the AC side network; i ismj,0Representing the current magnitude of the bus current flowing to the AC network bus of the AC side VSC converter station; the formula represents modeling of the VSC converter station active loss;
in the formula (I), the compound is shown in the specification,
the reactive power between a bus m and a bus j of the AC side VSC converter station is represented, and x represents a constant and is used for restraining the relation between the reactive power and the apparent power;
representing the rated apparent power of the VSC converter station i; the formula represents the reactive capacity constraint;
in the formula (I), the compound is shown in the specification,
representing the active power flow between a bus m and a bus j of the AC side VSC converter station, Imj maxIndicating the maximum value of the current flowing to its connecting AC network bus from the AC side VSC converter station bus,
the voltage of the bus m at the time t is represented; the formula represents a line flow constraint.
Further, the step S3 includes: the cross-region distributed optimization scheduling model based on the energy router and the virtual power plant is as follows:
wherein the content of the first and second substances,
in order to optimize the vector of variables,
rijfor response variables associated with the upper (i.e. i-1) sub-question(i+1)j1,…,t(i+1)jdijA target vector for connecting the (i +1) th layer child variable; pi is a penalty function of relaxation consistency constraint, and c is all vectors which do not meet continuous change of the alternating current and the direct current at two sides;
an objective function representing a basic linear programming optimization problem;
representing inequality constraints of a linear programming problem;
representing the linear programming problem equality constraints.
Further, the cross-region distributed optimization scheduling model based on the energy router and the virtual power plant is converted into a non-coupling model through an augmented Lagrange formula as follows:
wherein v isijAnd v(i+1)jIs a Lagrange multiplier, wijAnd w(i+1)jFor penalty index, O is the Hadamard product;
and then, carrying out iterative solution on the independent subproblems of the energy routers of each virtual power plant area and each link point.
Compared with the prior art, the invention has the following advantages:
the invention provides an alternating current-direct current hybrid power distribution network scheduling method, which comprises the following steps: acquiring real-time whole network information of the alternating current-direct current hybrid power distribution network; establishing a load flow calculation and scheduling model of the alternating current-direct current hybrid power distribution network according to the real-time whole network information; and establishing a cross-region distributed optimal scheduling model based on an energy router and a virtual power plant according to the load flow calculation and scheduling model of the AC/DC hybrid power distribution network. Therefore, the construction of the ubiquitous power internet of things is taken as a link, the intelligent distribution transformer terminal application and the power distribution station panoramic perception construction are combined, and after the functions of comprehensive perception of the state of a power distribution area, automatic topology recognition and the like are realized, the IoT-based comprehensive state perception big data scheduling application research is explored; the characteristics of various source-load-storage controllable resources in the power electronic transformer substation and the AC/DC distribution network are comprehensively analyzed, an optimized scheduling model is established, a planned operation curve of source-load-storage day-ahead optimized scheduling is solved, a scheme is provided for the control operation of the power electronic transformer substation, and the regional regulation and control requirements are met; the research is based on an energy router technology and a virtual power plant technology, and the electric quantity of each port is collected and controlled in real time, so that the dispatching requirement of an alternating current-direct current hybrid power distribution network is met. The invention has the advantages and effects that: firstly, compared with the traditional scheduling mode, the method combines the whole-network state monitoring technology to establish and obtain a state monitoring model, the state monitoring model can effectively eliminate bad data in the operation monitoring of the power grid and transmit reasonable data to the power grid scheduling model in real time, and the detailed description is shown in step S1 in the specific implementation mode; secondly, the invention combines the development trend of the existing power distribution network, models the load flow of the AC/DC hybrid distribution network and the VSC converter station of the power electronic facility, provides a cross-region distributed optimization scheduling model based on an energy router and a virtual power plant, and compared with the scheduling model of the traditional AC power distribution network, the scheduling model of the invention accesses the new technology of the distributed energy and ubiquitous power Internet of things, which is a research point not proposed and considered before.
Drawings
Fig. 1 is a schematic flow chart of a method for scheduling an ac/dc hybrid power distribution network according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a specific implementation flow of a method for scheduling an ac/dc hybrid power distribution network according to an embodiment of the present invention;
fig. 3 is a schematic view of a cross-region distributed optimal scheduling problem decomposition level based on an energy router and a virtual power plant in the ac/dc hybrid power distribution network scheduling method according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a network topology structure tested by an example of a method for scheduling an ac/dc hybrid power distribution network according to an embodiment of the present invention.
Detailed Description
The following describes the method for scheduling the ac/dc hybrid power distribution network according to the present invention in further detail with reference to fig. 1 to 4 and the specific embodiments. The advantages and features of the present invention will become more apparent from the following description. It is to be noted that the drawings are in a very simplified form and are all used in a non-precise scale for the purpose of facilitating and distinctly aiding in the description of the embodiments of the present invention. To make the objects, features and advantages of the present invention comprehensible, reference is made to the accompanying drawings. It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the implementation conditions of the present invention, so that the present invention has no technical significance, and any structural modification, ratio relationship change or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
As shown in fig. 1, the method for scheduling an ac/dc hybrid power distribution network in this embodiment includes:
an alternating current-direct current hybrid power distribution network scheduling method comprises the following steps:
and step S1, acquiring real-time whole network information of the alternating current and direct current hybrid power distribution network.
And S2, establishing a load flow calculation and scheduling model of the alternating current-direct current hybrid power distribution network according to the real-time whole network information.
And S3, establishing a cross-region distributed optimization scheduling model based on an energy router and a virtual power plant according to the load flow calculation and scheduling model of the alternating current-direct current hybrid power distribution network.
That is, the method for scheduling the hybrid ac/dc power distribution network in the context of the ubiquitous power internet of things and the hybrid ac/dc power distribution network in the embodiment includes three major parts: the method comprises the following steps of power distribution network state perception and big data technology, alternating current and direct current power distribution network load flow calculation and optimal scheduling, and trans-regional distributed optimal scheduling based on an energy router and a virtual power plant. As shown in fig. 2, the method firstly acquires and processes power grid information through a power distribution network state sensing and big data prediction technology module, transmits the real-time voltage data, fault information, new energy output, load size, electricity price prediction and other power grid information to a power flow calculation and optimization scheduling module, solves problems through power flow calculation models of a direct current circuit, an alternating current circuit and an AC/DC converter station and the scheduling model, applies each model including power flow calculation, state monitoring and data prediction to a cross-region distributed optimization scheduling model based on an energy router and a virtual power plant, and obtains a final scheduling problem result of the direct current hybrid power distribution network through layered solving of sub-problems.
Further, the step S1 includes a step S1.1 of obtaining a power grid state estimation value of the ac/dc hybrid power distribution network in real time; and S1.2, acquiring a power grid load prediction result in real time.
Further, the step S1.1 includes:
the power grid state estimation is carried out by adopting the following formula, and the measurement residual error is calculated,
in the formula, riMeasuring residual error for i moment power grid state, ziThe measured value of the state of the power grid at the moment i,
the estimated value of the power grid state at the moment i is obtained by inputting data;
calculating a regularization residual error by adopting the following formula;
in the formula, omegaiiRepresenting the power grid state measurement variance at the moment i;
obtaining the maximum regularization residual error max r according to the formulai NJudging the maximum regularization residual max ri NWhether the grid normal state threshold value is exceeded or not; if yes, the ith measurement is bad measurement data, the ith measurement is removed from the measurement data, and the calculation process is repeated;
if not, no bad data exists or the bad data is eliminated, the bad data identification process is finished, and a power grid state estimation result and a bad data measurement result are output.
Further, the step S1.2 includes: acquiring various kinds of power grid information as incoming data of a neural network prediction model of the LSTM based on a day-ahead load prediction method of a big data technology and an intelligent algorithm;
judging whether the incoming data is a stationary time sequence or not based on the neural network prediction model of the LSTM, and then carrying out stationary and regularization processing on the incoming data;
and setting an LSTM layer, a Dropout layer and a Dense layer, selecting an activation function and an optimization parameter to predict the load of the power grid in real time, and restoring data after a prediction result reaches the standard to obtain a complete prediction result of the load of the power grid.
Further, the load flow calculation and scheduling model of the alternating current-direct current hybrid power distribution network comprises: the system comprises an alternating current network model, a direct current network model and a VSC transformer substation model. That is, in this embodiment, one AC/DC hybrid power distribution network may be divided into three types of systems, i.e., an AC (alternating current) system and a DC (direct current) system connected to a VSC (voltage source converter) converter station, so that the three types of systems may be analyzed and modeled respectively.
Further, the communication network model (communication network power flow model) is as follows:
wherein g isijRepresenting the conductance between node i and node j, bijRepresenting susceptance between node i and node j; v is the bus voltage, viRepresenting the voltage of node i, vjRepresenting the voltage at node j, PijRepresenting the active power flow of the node i to the node j, QijRepresenting the magnitude of the reactive power, θ, flowing from node i to node jijIs the phase angle difference between node i and node j.
Further, the dc network model (dc network power flow model) is as follows:
wherein the content of the first and second substances,
representing the size of the direct current flow from the node i to the node j; r isij DCRepresenting the resistance between the node i and the node j;
voltage at time t for node i;
is the voltage at node j at time t; v. ofij DCIs the voltage difference between node i and node j.
Further, the VSC substation model (VSC network load flow model) includes:
wherein r isjpIs an AC busCorrelation matrix with DC bus, acAnd bcThe AC side is actively coupled with the DC side; subscript j represents a bus ID (identity) of the AC side network connected with the VSC converter station, subscript p represents a bus ID of the DC side network connected with the VSC converter station, subscript i represents an AC side port of the VSC converter station, and subscript m represents a connection node ID of the VSC converter station side and the AC side network; i ismj,0Representing the current magnitude of the bus current flowing to the AC network bus of the AC side VSC converter station; the formula represents modeling of the VSC converter station active loss;
in the formula (I), the compound is shown in the specification,
the reactive power between a bus m and a bus j of the AC side VSC converter station is represented, and x represents a constant and is used for restraining the relation between the reactive power and the apparent power;
representing the rated apparent power of the VSC converter station i; the formula represents the reactive capacity constraint;
in the formula (I), the compound is shown in the specification,
representing the active power flow between a bus m and a bus j of the AC side VSC converter station, Imj maxIndicating the maximum value of the current flowing to its connecting AC network bus from the AC side VSC converter station bus,
the voltage of the bus m at the time t is represented; the formula represents a line flow constraint.
Further, the step S3 includes: the cross-region distributed optimization scheduling model based on the energy router and the virtual power plant is as follows: establishing an objective function for a multi-region optimization scheduling problem based on an energy router and a virtual power plant:
wherein the content of the first and second substances,
in order to optimize the vector of variables,
rijfor response variables associated with the upper (i.e. i-1) sub-question(i+1)j1,…,t(i+1)jdijA target vector for connecting the (i +1) th layer child variable; pi is a penalty function of relaxation consistency constraint, and c is all vectors which do not meet continuous change of the alternating current and the direct current at two sides;
an objective function representing a basic linear programming optimization problem;
representing inequality constraints of a linear programming problem;
representing the linear programming problem equality constraints. Specifically, as shown in fig. 3, the AC/DC hybrid power distribution system is mainly divided into an AC system, a DC system and an AC/DC system cooperatively connected with a VSC station system according to the present inventionBased on the grid structure, the solution of the large optimization scheduling problem is divided into three levels of sub-problem solutions, a level result, namely, the level division shown in fig. 3 is made, the level structure totally contains M ═ 2M +1 elements, wherein the element at level 1 represents a DC power distribution network, the element at level 2 represents M VSC stations, and the element at level 3 corresponds to an AC power distribution network system connected from the VSC converter stations. Based on the layering, when the problem is solved, the sub-problem is solved from the 3 rd level to the top, and the original optimal scheduling problem scheme is finally obtained.
Further, the cross-region distributed optimization scheduling model based on the energy router and the virtual power plant is converted into a non-coupling model through an augmented Lagrange formula as follows:
wherein v isijAnd v(i+1)jIs a Lagrange multiplier, wijAnd w(i+1)jFor penalty index, O is the Hadamard product; and then, carrying out iterative solution on the independent subproblems of the energy routers of each virtual power plant area and each link point, namely, carrying out iterative solution on the trans-area distributed optimization scheduling problem based on the energy routers and the virtual power plants into the independent subproblems of the energy routers of each virtual power plant area and each link point through the conversion and modeling.
As shown in fig. 4, taking IEEE30 node and IEEE9 node as detection systems as examples, it is described that the embodiment relates to a ubiquitous power internet of things and ac/dc distribution network scheduling method, including the following steps:
in advance, an IEEE30 node and an IEEE9 node are adopted as detection systems;
then, establishing a power distribution network scheduling layered distributed optimization scheduling model to obtain a unit scheduling strategy and a VSC transformer substation operation state: the obtained operating state of the VSC substation is shown in table 1.
As can be seen from fig. 4, the IEEE9 node network is a dc network, and is connected to three IEEE30 node ac networks through the VSC converter station, so as to obtain a complete ac/dc hybrid power distribution network. Further, according to the collected and predicted information and the substation operation state information listed in table 1, according to the optimized scheduling objective function and constraint conditions of equation (9), according to the problem decomposition method of fig. 3, a scheduling problem solving program is written based on MATLAB, and the active power output scheduling schemes of 3 distributed power supplies and 3 virtual power plants are obtained as shown in table 2.
TABLE 1 VSC station operating State
TABLE 2 virtual power plant AC1/AC2/AC3 distributed power scheduling scheme
In summary, the present invention provides an ac/dc hybrid power distribution network scheduling method, including: acquiring real-time whole network information of the alternating current-direct current hybrid power distribution network; establishing a load flow calculation and scheduling model of the alternating current-direct current hybrid power distribution network according to the real-time whole network information; and establishing a cross-region distributed optimal scheduling model based on an energy router and a virtual power plant according to the load flow calculation and scheduling model of the AC/DC hybrid power distribution network. Therefore, the construction of the ubiquitous power internet of things is taken as a link, the intelligent distribution transformer terminal application and the power distribution station panoramic perception construction are combined, and after the functions of comprehensive perception of the state of a power distribution area, automatic topology recognition and the like are realized, the IoT-based comprehensive state perception big data scheduling application research is explored; the characteristics of various source-load-storage controllable resources in the power electronic transformer substation and the AC/DC distribution network are comprehensively analyzed, an optimized scheduling model is established, a planned operation curve of source-load-storage day-ahead optimized scheduling is solved, a scheme is provided for the control operation of the power electronic transformer substation, and the regional regulation and control requirements are met; the research is based on an energy router technology and a virtual power plant technology, and the electric quantity of each port is collected and controlled in real time, so that the dispatching requirement of an alternating current-direct current hybrid power distribution network is met.
It should be noted that the apparatuses and methods disclosed in the embodiments herein can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments herein. In this regard, each block in the flowchart or block diagrams may represent a module, a program, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments herein may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (10)
1. An alternating current-direct current hybrid power distribution network scheduling method is characterized by comprising the following steps:
step S1, acquiring real-time whole network information of the AC/DC hybrid power distribution network;
step S2, establishing a load flow calculation and scheduling model of the AC/DC hybrid power distribution network according to the real-time whole network information;
and S3, establishing a cross-region distributed optimization scheduling model based on an energy router and a virtual power plant according to the load flow calculation and scheduling model of the alternating current-direct current hybrid power distribution network.
2. The method for dispatching the alternating current-direct current hybrid power distribution network according to claim 1, wherein the step S1 comprises the steps of S1.1, acquiring a power grid state estimation value of the alternating current-direct current hybrid power distribution network in real time; and S1.2, acquiring a power grid load prediction result in real time.
3. The method for dispatching the alternating current-direct current hybrid power distribution network according to claim 2, wherein the step S1.1 comprises:
the power grid state estimation is carried out by adopting the following formula, and the measurement residual error is calculated,
in the formula, riMeasuring residual error for i moment power grid state, ziThe measured value of the state of the power grid at the moment i,
the estimated value of the power grid state at the moment i is obtained by inputting data;
calculating a regularization residual error by adopting the following formula;
in the formula, omegaiiRepresenting the power grid state measurement variance at the moment i;
obtaining the maximum regularization residual maxr according to the above formulai NJudging the maximum regularization residual maxri NWhether the grid normal state threshold value is exceeded or not; if yes, the ith measurement is bad measurement data, the ith measurement is removed from the measurement data, and the calculation process is repeated;
if not, no bad data exists or the bad data is eliminated, the bad data identification process is finished, and a power grid state estimation result and a bad data measurement result are output.
4. The method for dispatching the alternating current-direct current hybrid power distribution network according to claim 3, wherein the step S1.2 comprises: acquiring various kinds of power grid information as incoming data of a neural network prediction model of the LSTM;
judging whether the incoming data is a stationary time sequence or not based on the neural network prediction model of the LSTM, and then carrying out stationary and regularization processing on the incoming data;
and setting an LSTM layer, a Dropout layer and a Dense layer, selecting an activation function and an optimization parameter to predict the load of the power grid in real time, and restoring data after a prediction result reaches the standard to obtain a complete prediction result of the load of the power grid.
5. The method for dispatching the alternating current-direct current hybrid power distribution network according to claim 4, comprising the following steps of:
the load flow calculation and scheduling model of the alternating current-direct current hybrid power distribution network comprises the following steps: the system comprises an alternating current network model, a direct current network model and a VSC transformer substation model.
6. The method for dispatching the alternating current-direct current hybrid power distribution network according to claim 5, comprising the following steps of:
the alternating current network model is as follows:
wherein g isijRepresenting the conductance between node i and node j, bijRepresenting susceptance between node i and node j; v is the bus voltage, viRepresenting the voltage of node i, vjRepresenting the voltage at node j, PijRepresenting the active power flow of the node i to the node j, QijRepresenting the magnitude of the reactive power, θ, flowing from node i to node jijIs the phase angle difference between node i and node j.
7. The method for dispatching the alternating current-direct current hybrid power distribution network according to claim 6, wherein the direct current network model is as follows:
wherein the content of the first and second substances,
representing the size of the direct current flow from the node i to the node j; r isij DCRepresenting the resistance between the node i and the node j;
voltage at time t for node i;
is the voltage at node j at time t; v. ofij DCIs the voltage difference between node i and node j.
8. The method for dispatching the AC/DC hybrid power distribution network according to claim 7, wherein the VSC substation model comprises:
wherein r isjpIs a correlation matrix between the AC bus and the DC bus, acAnd bcThe AC side is actively coupled with the DC side; subscript j represents a bus ID (identity) of the AC side network connected with the VSC converter station, subscript p represents a bus ID of the DC side network connected with the VSC converter station, subscript i represents an AC side port of the VSC converter station, and subscript m represents a connection node ID of the VSC converter station side and the AC side network; i ismj,0Representing the current magnitude of the bus current flowing to the AC network bus of the AC side VSC converter station; the model represents modeling of the VSC converter station active loss;
in the formula (I), the compound is shown in the specification,
the reactive power between a bus m and a bus j of the AC side VSC converter station is represented, and x represents a constant and is used for restraining the relation between the reactive power and the apparent power;
representing the rated apparent power of the VSC converter station i; formulating reactive capacity constraints;
in the formula (I), the compound is shown in the specification,
representing the active power flow between a bus m and a bus j of the AC side VSC converter station, Imj maxIndicating the maximum value of the current flowing to its connecting AC network bus from the AC side VSC converter station bus,
representing bus m at time tThe voltage is large or small; the formula represents a line flow constraint.
9. The method for dispatching the alternating current-direct current hybrid power distribution network according to claim 8, wherein the step S3 comprises: the cross-region distributed optimization scheduling model based on the energy router and the virtual power plant is as follows:
min
s.t.
wherein the content of the first and second substances,
in order to optimize the vector of variables,
rijfor response variables associated with the upper (i.e. i-1) sub-question(i+1)j1,…,t(i+1)jdijA target vector for connecting the (i +1) th layer child variable; pi is a penalty function of relaxation consistency constraint, and c is all vectors which do not meet continuous change of the alternating current and the direct current at two sides;
an objective function representing a basic linear programming optimization problem;
representing linear programming questionsConstraint of the question inequality;
representing the linear programming problem equality constraints.
10. The AC/DC hybrid power distribution network scheduling method of claim 9,
converting the cross-region distributed optimization scheduling model based on the energy router and the virtual power plant into a non-coupling model through an augmented Lagrange formula as follows:
min
s.t.
wherein v isijAnd v(i+1)jIs a Lagrange multiplier, wijAnd w(i+1)jFor penalty index, O is the Hadamard product;
and then, carrying out iterative solution on the independent subproblems of the energy routers of each virtual power plant area and each link point.
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