Power system unit output determination method and system based on double-layer model

Abstract

The invention discloses a double-layer model-based power system unit output determination method and system. The method comprises the following steps of: acquiring historical data of a power system; clustering the nodes according to the sensitivity of each node in the power system to the blocking section; the clustered nodes of the same type are combined into the same virtual node; constructing a clearing upper-layer model based on the virtual nodes, and calculating the output of the virtual nodes by taking the total output of each virtual node as the total load of the system; and constructing a clearing lower-layer model of the unit, and calculating the output of the unit by taking the output of the virtual node as the sum of the outputs of all units in the class to which the virtual node belongs. Numerous actual nodes are clustered into a small number of virtual nodes through an upper-layer model, the number of section constraints is reduced, and the solving complexity of the model is reduced. The influence of the unit under the same virtual node in the lower-layer model on the network is consistent, section constraints do not need to be considered any more, the model scale is reduced, refined modeling is carried out on the unit under each virtual node in the lower-layer model, and the overall optimization effect is improved.

Description

Technical Field

[0001] The present invention relates to the field of power grid control, and in particular to a method and system for determining the output of power system units based on a double-layer model. Background Art

[0002] With the gradual opening of the energy market and the continuous advancement of technology, more and more new market players, such as distributed energy suppliers, energy storage system operators, and electric vehicle charging stations, have begun to participate in the optimization and dispatching of power grids. The emergence of these new market players has not only enriched the energy sources and load types of the power grid, but also posed new challenges to the planning, operation, and management of the power grid. It is expected that the number of power generation and consumption units in provincial power grids will surge to tens of thousands in the future. This trend will have a profound impact on the modeling and optimization of power grids.

[0003] In the traditional power grid modeling method, the generation and consumption unit is the basic modeling unit. Each generation and consumption unit represents a power source or load point and has specific parameters and properties. Based on these generation and consumption units, a model of the entire power grid can be constructed, and various simulations and analyses can be performed. However, with the increasing number of generation and consumption units, this traditional modeling method has gradually exposed some problems. First, the large number of variables to be solved is a significant problem. Since each generation and consumption unit has its own parameters and properties, a large number of variables need to be considered in the modeling process. When the number of generation and consumption units reaches tens of thousands, the number of these variables will become very large, which brings great difficulties to the solution and optimization of the model. Secondly, the difficulty of optimization is also a problem that cannot be ignored. Due to the increase in the number of variables, the scale of the optimization problem will become very large, and it is necessary to adopt efficient optimization algorithms and powerful computing power to solve it. Summary of the invention

[0004] Purpose of the invention: In view of the above shortcomings, the present invention provides a method and system for determining the output of power system units based on a double-layer model, which reduces the number of variables to be solved and improves the optimization efficiency.

[0005] Technical solution: To solve the above problems, the present invention adopts a method for determining the output of power system units based on a double-layer model, comprising the following steps:

[0006] Obtain historical data of the power system, including historical blocking section data;

[0007] Cluster the nodes according to the sensitivity of each node in the power system to the blocked section; merge the clustered nodes of the same type into the same virtual node;

[0008] Construct a clearing upper-level model based on virtual nodes, take the total output of each virtual node as the total system load, and calculate the output of the virtual nodes;

[0009] Construct the unit's output clearing lower-level model, take the virtual node output as the sum of all unit outputs under the class to which the virtual node belongs, and calculate the unit output according to the constraints of the unit output.

[0010] Furthermore, the specific steps of clustering nodes according to the sensitivity of each node in the power system to the blocked section are as follows:

[0011] (1) Randomly select M nodes as the initial cluster center set K 0 , K 0 =[F1,F2,……,F m ,……,F M ], F m Represents the node vector of the mth node;

[0012] (2) Calculate the Euclidean distances from other nodes to the nodes in the cluster center respectively, and classify them into the class to which the nodes in the nearest initial cluster center set belong;

[0013] (3) Calculate the average value of all node vectors in each cluster and update the average value to the cluster center vector. The expression is:

[0014]

[0015] Among them, F′ m is the average value of all node vectors in the class to which the mth node in the initial cluster center set belongs; N m represents the total number of nodes in the class; f sn-i represents the power flow transfer factor of node i to the nth blocking section s;

[0016] (4) Repeat steps (2) and (3) until the cluster center no longer changes.

[0017] Furthermore, the upper-level model for clearing out virtual nodes includes:

[0018] The total output of each virtual node is taken as the total system load:

[0019]

[0020] Among them, P m,t represents the output of the mth virtual node in period t, ND represents the number of bus loads, P d,t It represents the power consumption of the d-th bus load at time t;

[0021] Virtual node climbing constraints:

[0022]

[0023] in, represents the climbing ability of the mth virtual node, ΔP i U , ΔP i D represents the landslide climbing capability of the ith unit, i∈m means that the ith unit belongs to the class represented by the mth virtual node;

[0024] Virtual node output upper and lower limits:

[0025]

[0026]

[0027] Among them, α m,t Indicates the start and stop status of the mth virtual node. represents the upper limit of the output of unit i at time t, represents the lower limit of the output of unit i at time t, represents the lower limit of the output of the mth virtual node at time t, represents the upper limit of the output of the mth virtual node at time t;

[0028] Network constraints:

[0029]

[0030] in, is the power flow transmission limit of the blocking section s; f s-m is the power flow transfer factor of the mth virtual node to the blocking section s; f s-d is the flow transfer factor of the dth bus load to the blocked section s.

[0031] Furthermore, after calculating the output of the virtual node, whether there is a newly added blocking section is counted according to the output of the virtual node. If there is a newly added blocking section, the nodes are re-clustered in combination with the newly added blocking section to obtain the virtual node;

[0032] According to the output statistics of the virtual node, whether there is a new blocking section is:

[0033]

[0034] like or Then section s' is the newly added blocking section.

[0035] 5. The method for determining the output of power system units based on a double-layer model according to claim 3, characterized in that the output clearing lower-layer model of the unit comprises:

[0036] The output of a virtual node is the sum of the outputs of all units in the class to which the virtual node belongs:

[0037]

[0038] Constraints on the unit's output climbing capability:

[0039] P i,t -P i,t-1 ≤ΔP i U α i,t-1

[0040] P i,t-1 -P i,t ≤ΔP i D α i,t

[0041] Among them, P i,t represents the output of the i-th unit in period t, α i,t represents the start and stop status of the i-th unit in period t;

[0042] Unit output upper and lower limit constraints:

[0043]

[0044] in, represents the lower limit of the output of unit i at time t, Represents the lower limit of the output of unit i at time t.

[0045] The present invention also provides a system for determining the output of power system units based on a double-layer model, comprising:

[0046] A data acquisition module is used to acquire historical data of the power system, including historical blocking section data;

[0047] The clustering module is used to cluster nodes according to the sensitivity of each node in the power system to the blocking section; after clustering, nodes of the same type are merged into the same virtual node;

[0048] The upper model building module is used to build a clearing upper model based on virtual nodes, taking the total output of each virtual node as the total system load and calculating the output of the virtual node;

[0049] The lower-level model building module is used to build the unit's output-clearing lower-level model, taking the virtual node output as the sum of all unit outputs under the class to which the virtual node belongs, and calculating the unit output according to the constraints of the unit output.

[0050] The present invention also adopts a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0051] The present invention also adopts a computer-readable storage medium on which a computer program is stored, and the computer program implements the steps of the above method when executed by a processor.

[0052] Beneficial effect: Compared with the prior art, the significant advantage of the present invention is that a large number of actual nodes are clustered into a small number of virtual nodes through the Euclidean distance clustering method of the upper model, thereby reducing the number of cross-sectional constraints and greatly reducing the complexity of model solution. In addition, the units under the same virtual node in the lower model have the same impact on the network, and there is no need to consider cross-sectional constraints, which greatly reduces the scale of the model. The unit unit under each virtual node can be finely modeled in the lower model, thereby improving the overall optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the flow chart of determining the output of power system units based on the double-layer model in the present invention. DETAILED DESCRIPTION

[0054] Example 1

[0055] like Figure 1 As shown, in this embodiment, a method for determining the output of a power system unit based on a double-layer model includes the following steps:

[0056] Step 1: Obtain historical data of the power system, including historical blocking section data.

[0057] Since only the blocking section has an impact on the flow distribution, the blocking section set is determined first: the sections that have been blocked in history are counted to form a blocking section set S = [s1, s2, ..., s n ];

[0058] The influence of each node on the flow is represented by the flow transfer factor vector of each node on the blocked section, and the node i vector is:

[0059] F i = [f s1-i , f s2-i ,……, f sn-i ] (1)

[0060] In the formula, f sn-i Represents node i on section s n The flow transfer factor.

[0061] Step 2: Cluster the nodes according to their sensitivity to the blocked section in the power system; merge the clustered nodes of the same type into the same virtual node.

[0062] Step 2.1: Cluster the nodes using the Euclidean distance clustering algorithm:

[0063] (1) Randomly select M nodes as the initial cluster center set K 0 ;

[0064] K 0 =[F1,F2,……,F m ] (2)

[0065] In the formula, F m Represents the mth node vector.

[0066] (2) Calculate the Euclidean distances from other nodes i to the M nodes in the cluster center set K respectively, and classify them into the cluster with the closest distance.

[0067] The Euclidean distance from node i to the mth node:

[0068]

[0069] If the Euclidean distance from node i to the mth node in the cluster center set K is the shortest among all distances, then node i belongs to the class to which the mth cluster center belongs.

[0070] (3) Calculate the average value of all node vectors in each cluster and update the average value as the cluster center vector;

[0071] The mth cluster center vector F m is the average value of all node vectors in the cluster:

[0072]

[0073] Where N m Represents cluster F m The number of nodes that exist.

[0074] (4) Repeat (2) to (3) until the cluster center no longer changes.

[0075] Step 2.2: Merge the nodes in the same cluster into the same virtual node, and the cluster center vector F m as a virtual node vector.

[0076] The mth virtual node vector F m :

[0077] F m =[f s1-m ,f s2-m ,……,f sn-m ] (5)

[0078] Among them, f sn-m Virtual node m for section s n The power transfer factor,

[0079] Step 3: Construct a clearing upper-level model based on virtual nodes, take the total output of each virtual node as the total system load, and calculate the output of the virtual node.

[0080] (1) System load constraints:

[0081] The total output of each virtual node unit is the total system load:

[0082]

[0083] Where M represents the number of virtual nodes; P m,t represents the output of virtual node m in period t; ND represents the number of bus loads; P d,t Represents the power consumed by bus load d at time t.

[0084] (2) Virtual node climbing landslide constraint:

[0085] The landslide climbing capacity of each virtual node is equal to the sum of the landslide climbing capacity of all units under the nodes contained in the virtual node:

[0086]

[0087] In the formula, is the ability of virtual node m to climb the landslide; ΔP i U , ΔP i D is the landslide climbing capability of unit i; i∈m means that unit i belongs to virtual node m.

[0088] (3) Virtual node upper and lower limit constraints:

[0089] The output upper limit of each virtual node is equal to the sum of the output upper limits of all units under the nodes contained in the virtual node, and the output lower limit of each virtual node is equal to the minimum output lower limit of the nodes contained in the virtual node:

[0090]

[0091] (4) Network constraints:

[0092]

[0093] In the formula, are the power flow transmission limits of section s; f s1-m is the power flow transfer factor of virtual node m to section s; f s-d is the flow transfer factor of load d on section s.

[0094] Combine equations (6) to (14) to obtain the virtual node output P m,t .

[0095] Step 4: Check whether there are new blocking sections based on the output statistics of the virtual nodes. If there are new blocking sections, add the new blocking sections to the blocking section set S, and re-cluster the nodes based on the new blocking sections to obtain virtual nodes.

[0096] Among them, the power flow statistics method of section s' is:

[0097]

[0098] like or Then section s' is the newly added blocking section.

[0099] Step 5: Construct the lower-level model of the unit's output clearance, take the virtual node output as the sum of all unit outputs under the class to which the virtual node belongs, and calculate the unit output according to the constraints of the unit output.

[0100] (1) The output of a virtual node is equal to the sum of the outputs of all units under the virtual node.

[0101]

[0102] (2) Constraints on the unit output climbing capability:

[0103] P i,t -P i,t-1 ≤ΔP i U α i,t-1 (17)

[0104] P i,t-1 -P i,t ≤ΔP i D α i,t (18)

[0105] In the formula, α i,t represents the start and stop status of unit i in period t, α i,t =0 means the unit is shut down, α i,t =1 means the unit is on; ΔP i U , ΔP i D is the climbing rate of unit i.

[0106] (3) Upper and lower limits of unit output:

[0107]

[0108] in, represents the lower limit of the output of unit i at time t, It represents the upper limit of the output of unit i at time t.

[0109] Combine equations (16) to (19) to obtain the unit output P i,t .

[0110] Example 2

[0111] In this embodiment, a system for determining the output of power system units based on a double-layer model includes:

[0112] A data acquisition module is used to acquire historical data of the power system, including historical blocking section data;

[0113] The clustering module is used to cluster nodes according to the sensitivity of each node in the power system to the blocking section; after clustering, nodes of the same type are merged into the same virtual node;

[0114] The upper model building module is used to build a clearing upper model based on virtual nodes, taking the total output of each virtual node as the total system load and calculating the output of the virtual node;

[0115] The lower-level model building module is used to build the unit's output-clearing lower-level model, taking the virtual node output as the sum of all unit outputs under the class to which the virtual node belongs, and calculating the unit output according to the constraints of the unit output.

[0116] Example 3

[0117] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0118] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0121] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for determining the output of power system units based on a double-layer model, characterized in that: The following steps are involved: Obtain historical data of the power system, including historical blocking section data; Cluster the nodes according to the sensitivity of each node in the power system to the blocked section; merge the clustered nodes of the same type into the same virtual node; Construct a clearing upper-level model based on virtual nodes, take the total output of each virtual node as the total system load, and calculate the output of the virtual nodes; Construct the unit's output clearing lower-level model, take the virtual node output as the sum of all unit outputs under the class to which the virtual node belongs, and calculate the unit output according to the constraints of the unit output.

2. The method for determining the output of power system units based on a double-layer model according to claim 1, characterized in that: The specific steps of clustering nodes according to the sensitivity of each node in the power system to the blocked section are as follows: (1) Randomly select M nodes as the initial cluster center set K 0 , K 0 =[F1,F2,……,F m ,……,F M ],F m Represents the node vector of the mth node; (2) Calculate the Euclidean distances from other nodes to the nodes in the cluster center respectively, and classify them into the class to which the nodes in the nearest initial cluster center set belong; (3) Calculate the average value of all node vectors in each cluster and update the average value to the cluster center vector. The expression is: Among them, F m ' is the average value of all node vectors in the class to which the mth node in the initial cluster center set belongs; N m represents the total number of nodes in the class; f sn-i represents the power flow transfer factor of node i to the nth blocking section s; (4) Repeat steps (2) and (3) until the cluster center no longer changes.

3. The method for determining the output of power system units based on a double-layer model according to claim 2, characterized in that: The upper-level model for clearing out virtual nodes includes: The total output of each virtual node is taken as the total system load: Among them, P m,t represents the output of the mth virtual node in period t, ND represents the number of bus loads, P d,t It represents the power consumption of the d-th bus load at time t; Virtual node climbing constraints: in, represents the climbing ability of the mth virtual node, ΔP i U , ΔP i D represents the landslide climbing capability of the ith unit, i∈m means that the ith unit belongs to the class represented by the mth virtual node; Virtual node output upper and lower limits: Among them, α m,t Indicates the start and stop status of the mth virtual node. represents the upper limit of the output of unit i at time t, represents the lower limit of the output of unit i at time t, represents the lower limit of the output of the mth virtual node at time t, represents the upper limit of the output of the mth virtual node at time t; Network constraints: in, is the power flow transmission limit of the blocking section s; f s-m is the power flow transfer factor of the mth virtual node to the blocking section s; f s-d is the flow transfer factor of the dth bus load to the blocked section s.

4. The method for determining the output of power system units based on a double-layer model according to claim 3, characterized in that: After calculating the output of the virtual node, whether there is a newly added blocking section is counted according to the output of the virtual node. If there is a newly added blocking section, the node is re-clustered in combination with the newly added blocking section to obtain a virtual node; According to the output statistics of the virtual node, whether there is a new blocking section is: like or Then section s' is the newly added blocking section.

5. The method for determining the output of power system units based on a double-layer model according to claim 3, characterized in that: The lower-level model for clearing the unit includes: The output of a virtual node is the sum of the outputs of all units in the class to which the virtual node belongs: Constraints on the unit's output climbing capability: Among them, P i,t represents the output of the i-th unit in period t, α i,t represents the start and stop status of the i-th unit in period t; Unit output upper and lower limit constraints: in, represents the lower limit of the output of unit i at time t, It represents the upper limit of the output of unit i at time t.

6. A system for determining the output of power system units based on a double-layer model, characterized in that: include: A data acquisition module, used to acquire historical data of the power system; Including historical blocking section data; The clustering module is used to cluster nodes according to the sensitivity of each node in the power system to the blocking section; after clustering, nodes of the same type are merged into the same virtual node; The upper model building module is used to build a clearing upper model based on virtual nodes, taking the total output of each virtual node as the total system load and calculating the output of the virtual node; The lower-level model building module is used to build the unit's output-clearing lower-level model, taking the virtual node output as the sum of all unit outputs under the class to which the virtual node belongs, and calculating the unit output according to the constraints of the unit output.

7. The power system unit output determination system based on the double-layer model according to claim 6 is characterized in that: The upper-level model for clearing out virtual nodes includes: The total output of each virtual node is taken as the total system load: Among them, P m,t represents the output of the mth virtual node in period t, ND represents the number of bus loads, P d,t It represents the power consumption of the d-th bus load at time t; Virtual node climbing constraints: in, represents the climbing ability of the mth virtual node, ΔP i U , ΔP i D represents the landslide climbing capability of the ith unit, i∈m means that the ith unit belongs to the class represented by the mth virtual node; Virtual node output upper and lower limits: Among them, α m,t Indicates the start and stop status of the mth virtual node. represents the upper limit of the output of unit i at time t, represents the lower limit of the output of unit i at time t, represents the lower limit of the output of the mth virtual node at time t, represents the upper limit of the output of the mth virtual node at time t; Network constraints: in, is the power flow transmission limit of the blocking section s; f s-m is the power flow transfer factor of the mth virtual node to the blocking section s; f s-d is the flow transfer factor of the dth bus load to the blocked section s.

8. The power system unit output determination system based on the double-layer model according to claim 7 is characterized in that: The lower-level model for clearing the unit includes: The output of a virtual node is the sum of the outputs of all units in the class to which the virtual node belongs: Constraints on the unit's output climbing capability: P i,t -P i,t-1 ≤ΔP i U a i,t-1 P i,t-1 -P i,t ≤ΔP i D a i,t Among them, P i,t represents the output of the i-th unit in period t, α i,t represents the start and stop status of the i-th unit in period t; Unit output upper and lower limit constraints: in, represents the lower limit of the output of unit i at time t, It represents the upper limit of the output of unit i at time t.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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