A virtual power plant aggregation method based on node carbon potential and Zonotope

Abstract

This invention discloses a virtual power plant aggregation method based on node carbon potential and Zonotope, which relates to the field of virtual power plants. The method considers the second-order cone optimal power flow, designs a partitioned aggregation method for virtual power plants based on the principle of partitioned carbon balance according to node carbon potential and electrical position, and uses Zonotope to identify the external characteristics of the aggregated virtual power plants. The invention integrates carbon emission attributes into the resource aggregation process, breaking through the limitations of the single economic goal of traditional virtual power plants; it proposes the principle of partitioned carbon balance, dynamically divides regions based on modularity indicators and carbon balance potential indicators, and achieves the synergy between local carbon self-consistency and global carbon benefits.

Description

Technical Field

[0001] The present invention relates to the field of virtual power plants, and in particular to a virtual power plant aggregation method based on node carbon potential and Zonotope. Background Art

[0002] As a key technology for integrating diverse distributed resources, virtual power plants (VPPs) are becoming an important vehicle for achieving the coordinated optimization of flexible energy regulation and carbon emission reduction. However, existing VPP aggregation methods often rely on a single economic objective, ignoring the role of node carbon emission attributes in aggregation modeling. This makes it difficult to achieve a synergistic combination of resource carbon efficiency and scheduling flexibility. Summary of the Invention

[0003] The purpose of the present invention is to provide a virtual power plant aggregation method based on node carbon potential and Zonotope to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A virtual power plant aggregation method based on node carbon potential and Zonotope includes the following steps:

[0006] Step 1) Based on the calculation results of the distribution network power flow model of the second-order cone and the electrical position of each resource, the carbon potential value of each node is obtained;

[0007] Step 2) Divide the network into several carbon-balanced partitions based on the node carbon potential and electrical location, aggregate the distributed resources within each carbon-balanced partition, and form a local carbon-balanced virtual power plant unit;

[0008] Step 3) First, based on the long short-term memory network (LSTM), local training is performed on the historical operating power, state of charge, meteorological parameters, and key characteristic variables of the electricity price curve of various distributed resources. Time series prediction of resource output is performed to obtain the prediction interval at each time point. Then, based on the power constraints, energy state constraints, and ramp characteristics of various resources, the output characteristics of the aggregated resources in each partition are modeled in the form of a zonotope. The geometric characteristics of the zonotope are used to identify the external characteristics of the aggregated virtual power plant.

[0009] As a further preferred embodiment of the present invention: the distribution network power flow model based on the second-order cone in step 1) is shown as follows:

[0010]

[0011]

[0012]

[0013]

[0014] Where, is the distribution network branch set, 、 are the net injected active power and reactive power of node i, respectively; 、 are the active power and reactive power flowing through the branch with node i as the head node; 、 are the active power and reactive power flowing through the branch with node i as the tail node; 、 are the line resistance and line reactance of branch ij with nodes i and j as the head and tail nodes respectively; is the branch current flowing through branch ji; 、 are the node voltages at node j and node i respectively.

[0015] As a further preferred solution of the present invention: the node voltage and branch current constraints are:

[0016]

[0017] Where, 、 are the minimum and maximum values ​​of the node voltage at node i, respectively; 、 are the minimum and maximum branch currents of branch ij respectively;

[0018] As a further preferred solution of the present invention: the node power balance constraint is:

[0019]

[0020] Where, 、 、 、 、 They are respectively the active load of node i, the active power purchased from the main grid, the active power of the photovoltaic unit, the active power of the wind turbine unit, and the active power of the energy storage; 、 、 、 They are the reactive load of node i, the reactive power purchased from the main grid, the reactive power of the photovoltaic unit, and the reactive power of the wind turbine unit.

[0021] As a further preferred embodiment of the present invention: the carbon potential value of each node in step 1) is calculated as follows:

[0022]

[0023] Where, is the nodal carbon potential of node i; is the set of branches with active power flowing into node i; is the power flowing through branch ji; is the carbon flow density of branch ji; Connect the generators to node i, including the active output of new energy generators and energy storage; is the equivalent power generation carbon emission intensity of the unit.

[0024] As a further preferred solution of the present invention, a modularity index is used to measure the integrity of the carbon balance partition in step 2). The modularity index describes the degree of internal topological integrity after the aggregation of distributed resources. The larger the modularity index value, the closer the electrical distance connection between the internal components. The specific expression is as follows:

[0025]

[0026]

[0027]

[0028] Where, The branch matrix of the distribution network topology in the region; is the total number of branches in the distribution network topology in the region; is the number of branches connected to node i; A state variable for determining whether nodes i and j belong to the same region;

[0029] The regional carbon balance potential index is used to measure the ability of a region to achieve carbon balance. The larger the regional carbon balance potential index value, the stronger the ability of the region to achieve carbon balance. The specific expression is as follows:

[0030]

[0031] Where, is the carbon potential of the nodes in the region; Carbon emission offset quotas within the region, including free carbon quotas for units and CCERs for new energy units;

[0032] The regional division indicators are:

[0033]

[0034] Where, 、 is the weight coefficient, and ;

[0035] After determining the optimal number of partitions k through the elbow method, the hierarchical clustering method is used to complete the division of the carbon balance area to form a virtual power plant unit with local carbon balance.

[0036] As a further preferred embodiment of the present invention: the unit based on the long short-term memory network LSTM in step 3) consists of a unit, an input gate, an output gate and a forget gate. The cell will remember the value within any time interval and the three gates regulate the information flow in and out of the cell.

[0037] As a further preferred solution of the present invention: through step 3), the resource output is predicted in time series to obtain the prediction interval of each time point, and all constraints of various resources are organized into a semi-space form. ,in, is the vector of control variables; is the control variable parameter matrix; A vector of constants for the constraints;

[0038] Then the half-space polyhedron of each resource is converted into Zonotope form, which is expressed as follows:

[0039]

[0040]

[0041]

[0042]

[0043] Where, is the spatial dimension of the polyhedron; is the center point of the polyhedron; is the generator matrix; is the generator vector, indicating the extension direction of the polyhedron; is the extension length vector; is the maximum extension length.

[0044] As a further preferred embodiment of the present invention: adopting the target transformation method, constructing Normal vectors are obtained by optimizing the diameter ratio in the direction of the normal vector to quantify the approximation of the Zonotope interior to the original feasible domain. The similarity objective function is:

[0045]

[0046] Where, is the similarity index between Zonotope and the original feasible domain; 、 Zonotope and original feasible region in the normal vector Diameter in the direction.

[0047] As a further preferred solution of the present invention: constructing the same form of generator matrix for various distributed resources , the corresponding Zonotope Minkowski sum aggregation process is expressed as:

[0048]

[0049] Where, is the total zonotope after polymerization; n is the number of polymerized zonotopes; is the center point of the aggregated Zonotope; is the extension length vector of the aggregated Zonotope;

[0050] The aggregated total Zonotope can be converted into a half-space form to depict the regional feasible domain of the aggregated virtual power plant units.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] This paper pioneers a node carbon potential quantification model (combining electrical position and second-order cone optimal power flow), integrating carbon emission attributes into the resource aggregation process, breaking through the limitations of traditional virtual power plants' single economic objective. It also proposes a zoning carbon balance principle, dynamically partitioning regions based on modularity and carbon balance potential indicators to achieve local carbon self-consistency and global carbon efficiency synergy. This paper uses Zonotope geometric modeling to replace traditional polyhedron constraints, accurately describing resource power fluctuations through generator matrices and extension vectors, addressing interval conservatism. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of an embodiment;

[0054] Figure 2 This is the LSTM network structure diagram;

[0055] Figure 3 Schematic diagram of the quantification of Zonotope similarity in two-dimensional space. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] See also Figure 1 In an embodiment of the present invention, a virtual power plant aggregation method based on node carbon potential and Zonotope is provided.

[0058] Step 1): Based on the second-order cone optimal power flow calculation results and the electrical position of each resource, the carbon potential value of each node is obtained.

[0059] The distribution network power flow model based on the second-order cone is shown as follows:

[0060]

[0061]

[0062]

[0063]

[0064] Where, is the distribution network branch set, 、 are the net injected active power and reactive power of node i, respectively; 、 are the active power and reactive power flowing through the branch with node i as the head node; 、 are the active power and reactive power flowing through the branch with node i as the tail node; 、 are the line resistance and line reactance of branch ij with nodes i and j as the head and tail nodes respectively; is the branch current flowing through branch ji; 、 are the node voltages at node j and node i respectively.

[0065] The node voltage and branch current constraints are:

[0066]

[0067] Where, 、 are the minimum and maximum values ​​of the node voltage at node i, respectively; 、 are the minimum and maximum values ​​of the branch current of branch ij respectively.

[0068] The node power balance constraint is:

[0069]

[0070] Where, 、 、 、 、 They are respectively the active load of node i, the active power purchased from the main grid, the active power of the photovoltaic unit, the active power of the wind turbine unit, and the active power of the energy storage; 、 、 、 They are the reactive load of node i, the reactive power purchased from the main grid, the reactive power of the photovoltaic unit, and the reactive power of the wind turbine unit.

[0071] The calculation method of the node carbon potential considering the carbon emissions of energy storage equipment is as follows:

[0072]

[0073] Where, is the nodal carbon potential of node i; is the set of branches with active power flowing into node i; is the power flowing through branch ji; is the carbon flow density of branch ji; Connect the generators to node i, including the active output of new energy generators and energy storage; is the equivalent power generation carbon emission intensity of the unit.

[0074] Step 2): Divide the network into several carbon-balanced partitions based on the node carbon potential and electrical location, aggregate the distributed resources in the area, and form a local carbon-balanced virtual power plant unit.

[0075] To fully consider the impact of geographic location on the aggregation of flexible resources, the modularity index is used to measure the integrity of the carbon balance partition. The modularity index describes the degree of internal topological integrity after the aggregation of distributed resources. The larger the modularity index, the closer the electrical distance connection between the internal components. The specific expression is as follows:

[0076]

[0077]

[0078]

[0079] Where, The branch matrix of the distribution network topology in the region; is the total number of branches in the distribution network topology in the region; is the number of branches connected to node i; It is a state variable used to determine whether nodes i and j belong to the same region.

[0080] In order to measure the ability of a region to achieve carbon balance, a regional carbon balance potential index is proposed. The larger the value, the stronger the ability of the region to achieve carbon balance. The specific expression is as follows:

[0081]

[0082] Where, is the carbon potential of the nodes in the region; It is the carbon emission offset quota within the region, including free carbon quota of the units and CCER of new energy units.

[0083] The regional division indicators are:

[0084]

[0085] Where, 、 is the weight coefficient, and .

[0086] After determining the optimal number of partitions k through the elbow method, the hierarchical clustering method is used to complete the division of the carbon balance area to form a virtual power plant unit with local carbon balance.

[0087] Step 3): First, a long short-term memory (LSTM) network is used to perform local training on key characteristic variables such as the historical operating power, state of charge, meteorological parameters, and electricity price curves of various distributed resources. Time series forecasts are then performed on resource output to obtain prediction intervals at each time point. Based on the power constraints, energy state constraints, and ramping characteristics of each resource, the output characteristics of the aggregated resources within each partition are modeled as zonotopes. The geometric properties of the zonotopes are then used to identify the external characteristics of the aggregated virtual power plant.

[0088] The unit of LSTM network consists of a cell, an input gate, an output gate and a forget gate. The cell will remember the value of any time interval and the three gates regulate the flow of information in and out of the cell. The LSTM network structure is as follows Figure 2 shown.

[0089] Through the predicted output sequence of each resource, all constraints of each resource are organized into a half-space form. ,in, is the vector of control variables; is the control variable parameter matrix; A vector of constants for the constraints.

[0090] Then the half-space polyhedron of each resource is converted into Zonotope form. Zonotope is a special mathematical geometric polyhedron, which is expressed as:

[0091]

[0092]

[0093]

[0094]

[0095] Where, is the spatial dimension of the polyhedron; is the center point of the polyhedron; is the generator matrix; is the generator vector, indicating the extension direction of the polyhedron; is the extension length vector; is the maximum extension length.

[0096] Using the target conversion method, construct Normal vector, by optimizing the diameter ratio in the direction of the normal vector, the approximation degree of the Zonotope interior to the original feasible domain is quantified. The schematic diagram of similarity quantification in two-dimensional space is shown as follows: Figure 3 Its similarity objective function is:

[0097]

[0098] Where, is the similarity index between Zonotope and the original feasible domain; 、 Zonotope and original feasible region in the normal vector Diameter in the direction.

[0099] For various distributed resources, the same form of generator matrix can be constructed , since the extension direction is consistent, the Minkowski sum polymerization process of the corresponding Zonotope can be expressed as:

[0100]

[0101] Where, is the total zonotope after polymerization; n is the number of polymerized zonotopes; is the center point of the Zonotope after polymerization; is the extension length vector of the aggregated Zonotope.

[0102] The aggregated total Zonotope can be converted into a half-space form to depict the regional feasible domain of the aggregated virtual power plant units.

[0103] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0104] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A virtual power plant aggregation method based on node carbon potential and Zonotope, characterized in that: The steps include: Step 1) Based on the calculation results of the distribution network power flow model of the second-order cone and the electrical position of each resource, the carbon potential value of each node is obtained; Step 2) Divide the network into several carbon-balanced partitions based on the node carbon potential and electrical location, aggregate the distributed resources within each carbon-balanced partition, and form a local carbon-balanced virtual power plant unit; Step 3) First, based on the long short-term memory (LSTM) network, local training is performed on the historical operating power, state of charge, meteorological parameters, and key characteristic variables of the electricity price curve of various distributed resources. Time series forecasting of resource output is performed to obtain the prediction interval at each time point. Then, based on the power constraints, energy state constraints, and ramping characteristics of each resource, the output characteristics of the aggregated resources in each partition are modeled as a zonotope. The geometric characteristics of the zonotope are used to identify the external characteristics of the aggregated virtual power plant. The carbon potential value calculation method of each node in step 1) is shown in the following formula: Where, is the nodal carbon potential of node i; is the set of branches with active power flowing into node i; is the power flowing through branch ji; is the carbon flow density of branch ji; Connect the generators to node i, including the active output of new energy generators and energy storage; is the equivalent power generation carbon emission intensity of the unit.

2. A virtual power plant aggregation method based on node carbon potential and Zonotope according to claim 1, characterized in that: The distribution network power flow model based on the second-order cone in step 1) is shown as follows: Where, is the distribution network branch set, 、 are the net injected active power and reactive power of node i, respectively; 、 are the active power and reactive power flowing through the branch with node i as the head node; 、 are the active power and reactive power flowing through the branch with node i as the tail node; 、 are the line resistance and line reactance of branch ij with nodes i and j as the head and tail nodes respectively; is the branch current flowing through branch ji; 、 are the node voltages at node j and node i respectively.

3. A virtual power plant aggregation method based on node carbon potential and Zonotope according to claim 2, characterized in that: The node voltage and branch current constraints are: Where, 、 are the minimum and maximum values ​​of the node voltage at node i, respectively; 、 are the minimum and maximum values ​​of the branch current of branch ij respectively.

4. A virtual power plant aggregation method based on node carbon potential and Zonotope according to claim 3, characterized in that: The node power balance constraint is: Where, 、 、 、 、 They are respectively the active load of node i, the active power purchased from the main grid, the active power of the photovoltaic unit, the active power of the wind turbine unit, and the active power of the energy storage; 、 、 、 They are the reactive load of node i, the reactive power purchased from the main grid, the reactive power of the photovoltaic unit, and the reactive power of the wind turbine unit.

5. The virtual power plant aggregation method based on node carbon potential and Zonotope according to claim 1 is characterized in that: The modularity index is used to measure the integrity of the carbon balance partition in step 2). The modularity index describes the degree of internal topological integrity after the aggregation of distributed resources. The larger the modularity index, the closer the electrical distance connection between the internal components. The specific expression is as follows: Where, The branch matrix of the distribution network topology in the region; is the total number of branches in the distribution network topology in the region; is the number of branches connected to node i; A state variable for determining whether nodes i and j belong to the same region; The regional carbon balance potential index is used to measure the ability of a region to achieve carbon balance. The larger the regional carbon balance potential index value, the stronger the ability of the region to achieve carbon balance. The specific expression is as follows: Where, is the carbon potential of the nodes in the region; Carbon emission offset quotas within the region, including free carbon quotas for units and CCERs for new energy units; The regional division indicators are: Where, 、 is the weight coefficient, and ; After determining the optimal number of partitions k through the elbow method, the hierarchical clustering method is used to complete the division of the carbon balance area to form a virtual power plant unit with local carbon balance.

6. The virtual power plant aggregation method based on node carbon potential and Zonotope according to claim 1 is characterized in that: The unit based on the long short-term memory network LSTM in step 3) consists of a unit, an input gate, an output gate and a forget gate. The cell will remember the value within any time interval and the three gates regulate the flow of information into and out of the cell.

7. The virtual power plant aggregation method based on node carbon potential and Zonotope according to claim 1 is characterized in that: Through step 3), the resource output is predicted in time series to obtain the prediction interval at each time point, and all constraints of various resources are organized into a semi-space form. ,in, is the vector of control variables; is the control variable parameter matrix; A vector of constants for the constraints; Then the half-space polyhedron of each resource is converted into Zonotope form, which is expressed as follows: Where, is the spatial dimension of the polyhedron; is the center point of the polyhedron; is the generator matrix; is the generator vector, indicating the extension direction of the polyhedron; is the extension length vector; is the maximum extension length.

8. The virtual power plant aggregation method based on node carbon potential and Zonotope according to claim 7 is characterized in that: Using the target conversion method, construct Normal vectors are obtained by optimizing the diameter ratio in the direction of the normal vector to quantify the approximation of the Zonotope interior to the original feasible domain. The similarity objective function is: Where, is the similarity index between Zonotope and the original feasible domain; 、 Zonotope and original feasible region in the normal vector Diameter in the direction.

9. A virtual power plant aggregation method based on node carbon potential and Zonotope according to claim 8, characterized in that: Construct the same form of generator matrix for various distributed resources , the corresponding Zonotope Minkowski sum aggregation process is expressed as: Where, is the total zonotope after polymerization; n is the number of polymerized zonotopes; is the center point of the aggregated Zonotope; is the extension length vector of the aggregated Zonotope; The aggregated total Zonotope can be converted into a half-space form to depict the regional feasible domain of the aggregated virtual power plant units.

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