Power grid flexibility resource aggregation method and device

Abstract

The invention discloses a power grid flexibility resource aggregation method and device, and aims at user side distributed flexibility resources, a technical constraint model of the user side flexibility resources is constructed, an internal approximation flexibility aggregation method is introduced, and on the basis of power grid topology and operation constraints, in the same node, the user side flexibility resources are aggregated. Homogeneous aggregation is carried out on similar flexible resources of a user side, heterogeneous aggregation is carried out on different types of flexible resources of a cross-node user side, node flexible resource aggregation of multi-type and large-scale distributed flexible resources is realized, and actual distribution of the flexible resources is fitted. The technical problems that an existing power grid flexibility resource aggregation method neglects the network topology constraint of a power grid, all distributed flexibility resources are considered to be centrally accessed to the same node for simplified modeling, the modeling is not consistent with the actual distribution characteristics of the flexibility resources, and the engineering feasibility of the model is limited are solved.

Description

Technical Field

[0001] This invention relates to the field of power grid resource aggregation technology, and in particular to a method and apparatus for aggregating power grid flexibility resources. Background Technology

[0002] Aggregation of grid flexibility resources refers to integrating the power regulation characteristics of multiple devices into a unified regulation capability, thereby enabling it to participate in grid interaction as a single entity. Current research on grid flexibility resources primarily focuses on user-side flexibility resources (Distributed Energy Resources, DERs), which are characterized by small capacity, diverse types, and large quantities. Through aggregation, these scattered resources can be uniformly modeled as virtual generators or virtual energy storage, improving their controllability and market participation.

[0003] Existing methods for aggregating power grid flexibility resources ignore the network topology constraints of the power grid and treat all distributed flexibility resources as being centrally connected to the same node for simplified modeling. This is inconsistent with the actual characteristics of flexibility resources being widely distributed and having diverse access nodes, thus limiting the engineering feasibility of the model. Summary of the Invention

[0004] This invention provides a method and apparatus for aggregating power grid flexibility resources, which addresses the technical problem that existing methods for aggregating power grid flexibility resources ignore the network topology constraints of the power grid and simplify the model by treating all distributed flexibility resources as being centrally connected to the same node, which does not conform to the actual distribution characteristics of flexibility resources and limits the engineering feasibility of the model.

[0005] In view of this, the first aspect of the present invention provides a method for aggregating power grid flexibility resources, comprising:

[0006] Obtain the power grid topology and determine the node information of the power grid topology;

[0007] Based on the node information, the user-side flexibility resources are modeled to obtain the technical constraint model of the user-side flexibility resources.

[0008] Construct a high-dimensional polyhedron composed of the feasible domains of the aforementioned technical constraint model;

[0009] Based on the high-dimensional polyhedron, an internal approximation flexible aggregation method is adopted to homogeneously aggregate similar flexible resources on the user side within the same node.

[0010] Based on the results of the homogeneous aggregation, an internal approximation flexibility aggregation method is adopted to heterogeneously aggregate different types of flexibility resources on the user side across nodes.

[0011] Optionally, based on the high-dimensional polyhedron, an internal approximation flexibility aggregation method is used to homogeneously aggregate similar flexibility resources on the user side within the same node, including:

[0012] A baseline setting is performed on the high-dimensional polyhedron;

[0013] The high-dimensional polyhedron is approximated within the feasible domain by setting displacement and scaling references.

[0014] For the same node, based on the approximate results within the feasible region, similar flexibility resources on the user side are homogeneously aggregated using the Minkowski summation method.

[0015] Optionally, the approximate result within the feasible region is expressed as:

[0016]

[0017] in, This represents an approximate result within the feasible region of the i-th flexibility resource. Let be the displacement factor for the i-th flexibility resource. As an approximate benchmark within the feasible region, The average value of the coefficient matrix of the technical constraint model for flexible resources. The average value of the coefficient vector of flexibility resources. Let i be the decision variable for the i-th flexibility resource. Let be the scaling factor for the i-th flexibility resource.

[0018] Optionally, user-side flexibility resources include energy storage devices, temperature-controlled loads, and electric vehicles.

[0019] Optionally, the technical constraint model for energy storage devices is as follows:

[0020]

[0021]

[0022]

[0023]

[0024] in, Let be the charging power of the i-th energy storage device during time period t. Let be the discharge power of the i-th energy storage device during time period t. Let be the battery dissipation coefficient of the i-th energy storage device. Let be the operating capacity of the i-th energy storage device during time period t. The change in temperature Let be the charging efficiency coefficient of the i-th energy storage device. Let be the discharge efficiency coefficient of the i-th energy storage device. Let i be the maximum charge / discharge power of the i-th energy storage device. Let i be the maximum capacity of the i-th energy storage device. Let be the minimum capacity of the i-th energy storage device.

[0025] Optionally, the technical constraint model for the temperature-controlled load is:

[0026]

[0027]

[0028]

[0029] in, Let be the charging power of the i-th temperature-controlled load during time period t. Let be the discharge power of the i-th temperature-controlled load during time period t. The maximum charging and discharging power of the i-th temperature-controlled load. Let be the indoor temperature of the i-th temperature-controlled load during time period t. Let i be the state variable of the i-th temperature-controlled load. The change in temperature The influence factor of the ambient temperature of the i-th temperature-controlled load is... Let be the temperature dissipation coefficient of the i-th temperature-controlled load. Let be the difference between the ambient temperature and the user-preset temperature for the i-th temperature-controlled load during time period t. Let be the charging efficiency coefficient of the i-th temperature-controlled load. Let be the discharge efficiency coefficient of the i-th temperature-controlled load. The maximum preset temperature value is set for the i-th user with the temperature control load. The minimum preset temperature value for the i-th temperature-controlled load user.

[0030] Optionally, the technical constraint model for electric vehicles is:

[0031]

[0032] in, Let be the charging power of the i-th electric vehicle in time period t. Let be the discharge power of the i-th electric vehicle in time period t. Let i be the grid connection time of the i-th electric vehicle. Let be the time when the i-th electric vehicle leaves the network. Let be the maximum charging power of the i-th electric vehicle. Let be the maximum discharge power of the i-th electric vehicle. Let i be the operating capacity of the i-th electric vehicle in time period t. Let be the consumption coefficient of the i-th electric vehicle. The change in temperature Let be the charging efficiency coefficient of the i-th electric vehicle. Let be the discharge efficiency coefficient of the i-th electric vehicle. Let be the upper limit of the operating capacity of the i-th electric vehicle in time period t. Let be the lower limit of the operating capacity of the i-th electric vehicle in time period t. Let i be the initial capacity before the i-th electric vehicle is connected to the grid. Let be the expected operating capacity when the i-th electric vehicle leaves the grid.

[0033] Optionally, based on the results of the homogeneous aggregation, an internal approximation flexibility aggregation method is used to heterogeneously aggregate different types of flexibility resources across node user sides, including:

[0034] A continuous multi-moment optimal power flow model for the power grid is constructed. The continuous multi-moment optimal power flow model includes an objective function and constraints. The constraints include power constraints at the point of common connection, network constraints, and flexibility resource clustering technology constraints based on homogeneous aggregation results.

[0035] The internal approximation flexibility aggregation method is used to solve the continuous multi-time optimal power flow model, and heterogeneous aggregation of different types of flexibility resources on the user side of cross nodes is performed.

[0036] Optionally, the constraints of the flexible resource clustering technology based on homogeneous aggregation results are:

[0037]

[0038] in, For the cluster decision variables of flexible resources after approximate internal flexible resource aggregation, This is an approximate result within the feasible domain of flexible resource clusters.

[0039] A second aspect of the present invention provides a power grid flexibility resource aggregation device, comprising:

[0040] The power grid topology acquisition module is used to acquire the power grid topology structure and determine the node information of the power grid topology structure.

[0041] The flexibility resource modeling module is used to model the user-side flexibility resources based on the node information to obtain the technical constraint model of the user-side flexibility resources.

[0042] A polyhedron construction module is used to build a high-dimensional polyhedron composed of the feasible domains of the technical constraint model.

[0043] The same type aggregation module is used to perform homogeneous aggregation of similar flexible resources on the user side within the same node, based on the high-dimensional polyhedron and using an internal approximation flexible aggregation method.

[0044] The heterogeneous aggregation module is used to perform heterogeneous aggregation of different types of flexibility resources across node user sides based on the results of the homogeneous aggregation, using an internal approximation flexibility aggregation method.

[0045] As can be seen from the above technical solutions, the power grid flexibility resource aggregation method provided by the present invention has the following advantages:

[0046] The power grid flexibility resource aggregation method provided by this invention constructs a technical constraint model for user-side distributed flexibility resources and introduces an internal approximation flexibility aggregation method. Based on power grid topology and operational constraints, it performs homogeneous aggregation of similar user-side flexibility resources within the same node and heterogeneous aggregation of different types of user-side flexibility resources across nodes. This achieves node flexibility resource aggregation of multiple types and large-scale distributed flexibility resources, closely aligning with the actual distribution of flexibility resources. It solves the technical problem that existing power grid flexibility resource aggregation methods ignore the network topology constraints of the power grid, simplifying the model by treating all distributed flexibility resources as centralized access to the same node, which does not conform to the actual distribution characteristics of flexibility resources and limits the engineering feasibility of the model. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating a method for aggregating power grid flexibility resources provided in an embodiment of the present invention;

[0049] Figure 2 This is a topology diagram of the IEEE 33-node network provided in an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of the flexible aggregation method based on inner approximation provided in an embodiment of the present invention;

[0051] Figure 4 This is a diagram showing the upper boundary result of flexibility resource aggregation provided in this embodiment of the invention;

[0052] Figure 5This is a diagram showing the lower boundary result of flexibility resource aggregation provided in this embodiment of the invention;

[0053] Figure 6 This is a depolymerization decomposition diagram provided in an embodiment of the present invention;

[0054] Figure 7 This is a schematic diagram of a power grid flexibility resource aggregation device provided in an embodiment of the present invention. Detailed Implementation

[0055] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] For easier understanding, please refer to Figure 1 This invention provides an embodiment of a power grid flexibility resource aggregation method, comprising:

[0057] Step 101: Obtain the power grid topology and determine the node information of the power grid topology.

[0058] It should be noted that in this embodiment of the invention, the topology information of the power grid is first obtained, and based on the topology information, the node information of the power grid topology is determined. The node information includes the node number and the user-side flexibility resources connected to the node. Figure 2 Taking the topology of the IEEE 33-node network shown as an example, Figure 2 In this diagram, nodes 1 through 33 represent user-side flexibility resources, with ESS (Energy Storage Device), TCL (Temperature Controlled Load), and EV (Electric Vehicle). This invention uses ESS, TCL, and EV as typical examples of flexibility resources; however, this invention is also applicable to other types of flexibility resource scenarios.

[0059] Step 102: Based on node information, model the user-side flexibility resources to obtain the technical constraint model of the user-side flexibility resources.

[0060] It should be noted that, based on node information, a model is constructed for the user-side flexibility resources, establishing a technical constraint model for these resources. Specifically, for energy storage devices, considering the charging / discharging and state-of-charge boundaries, the constructed technical constraint model for the energy storage device is as follows:

[0061] Power constraints:

[0062]

[0063]

[0064] Energy-power coupling constraints:

[0065]

[0066] Energy constraints:

[0067]

[0068] in, Let be the charging power of the i-th energy storage device during time period t. Let be the discharge power of the i-th energy storage device during time period t. Let be the battery dissipation coefficient of the i-th energy storage device. Let be the operating capacity of the i-th energy storage device during time period t. The change in temperature Let be the charging efficiency coefficient of the i-th energy storage device. Let be the discharge efficiency coefficient of the i-th energy storage device. Let i be the maximum charge / discharge power of the i-th energy storage device. Let i be the maximum capacity of the i-th energy storage device. Let be the minimum capacity of the i-th energy storage device.

[0069] For temperature-controlled loads, the technical constraint model for temperature-controlled loads is as follows:

[0070] Power constraints:

[0071]

[0072] Energy-power coupling constraints:

[0073]

[0074] Energy constraints:

[0075]

[0076] in, Let be the charging power of the i-th temperature-controlled load during time period t. Let be the discharge power of the i-th temperature-controlled load during time period t. The maximum charging and discharging power of the i-th temperature-controlled load. Let be the indoor temperature of the i-th temperature-controlled load during time period t. Let be the state variable of the i-th temperature-controlled load. If the i-th temperature-controlled load is in heating mode, then... If the i-th temperature-controlled load is in cooling mode, then... -1, The change in temperature The influence factor of the ambient temperature of the i-th temperature-controlled load is... Let be the temperature dissipation coefficient of the i-th temperature-controlled load. Let be the difference between the ambient temperature and the user-preset temperature for the i-th temperature-controlled load during time period t. Let be the charging efficiency coefficient of the i-th temperature-controlled load. Let be the discharge efficiency coefficient of the i-th temperature-controlled load. The maximum preset temperature value is set for the i-th user with the temperature control load. The minimum preset temperature value for the i-th temperature-controlled load user.

[0077] For electric vehicles, the controllable time period is limited by their arrival and departure times at the station, and the vehicle owner's expected battery level must be met upon departure. The constructed technical constraint model for electric vehicles is as follows:

[0078]

[0079] in, Let be the charging power of the i-th electric vehicle in time period t. Let be the discharge power of the i-th electric vehicle in time period t. Let i be the grid connection time of the i-th electric vehicle. Let be the time when the i-th electric vehicle leaves the network. Let be the maximum charging power of the i-th electric vehicle. Let be the maximum discharge power of the i-th electric vehicle. Let i be the operating capacity of the i-th electric vehicle in time period t. Let be the consumption coefficient of the i-th electric vehicle. The change in temperature Let be the charging efficiency coefficient of the i-th electric vehicle. Let be the discharge efficiency coefficient of the i-th electric vehicle. Let be the upper limit of the operating capacity of the i-th electric vehicle in time period t. Let be the lower limit of the operating capacity of the i-th electric vehicle in time period t. Let i be the initial capacity before the i-th electric vehicle is connected to the grid. Let be the expected operating capacity when the i-th electric vehicle leaves the grid.

[0080] In practical applications, electric vehicles are typically connected to charging stations in clusters for unified management and control. Cluster management helps transform previously dispersed individual loads into schedulable and controllable flexible resources. In one embodiment of this invention, the technical constraint model for electric vehicle clusters is as follows:

[0081]

[0082] in, Let be the charging power of electric vehicle cluster s during time period t, and EVAs be the charging power of electric vehicle cluster s. The charging power of electric vehicle cluster s during time period t. Let be the discharge power of the electric vehicle cluster s during time period t. Let i be the grid connection status of the i-th electric vehicle in time period t. A value of 1 indicates that the i-th electric vehicle is in a grid-connected state. A value of 0 indicates that the i-th electric vehicle is in an off-grid state.

[0083] Step 103: Establish a high-dimensional polyhedron composed of the feasible domains of the technical constraint model.

[0084] It should be noted that after modeling various flexible resources, the feasible region of the technical constraint model is essentially a high-dimensional polyhedron. Therefore, we construct a high-dimensional polyhedron that is approximately composed of the feasible region of the technical constraint model.

[0085] Step 104: Based on the high-dimensional polyhedron, the flexibility aggregation method of internal approximation is adopted to homogeneously aggregate similar flexibility resources on the user side within the same node.

[0086] It should be noted that homogeneous aggregation refers to the aggregation of flexible resources of the same type under the same node.

[0087] Directly using Minkowski summation for flexibility aggregation is computationally complex and difficult to achieve efficient solutions. Therefore, this invention employs an inner approximation-based flexibility aggregation method. A schematic diagram of the inner approximation-based flexibility aggregation method is shown below. Figure 3 As shown, Figure 3 middle, This represents an approximate result within the feasible region of the i-th flexibility resource. Let be the coefficient matrix of the technical constraint model for the i-th flexibility resource. Let i be the decision variable for the i-th flexibility resource. Let be the coefficient vector of the i-th flexibility resource. As an approximate benchmark within the feasible region, The average value of the coefficient matrix of the technical constraint model for flexible resources. The average value of the coefficient vector of flexibility resources. Let be the displacement factor for the i-th flexibility resource. Let be the scaling factor for the i-th flexibility resource.

[0088] The technical constraint model for flexibility resources can be uniformly described as follows:

[0089]

[0090] The inner approximation aggregation method comprises three steps: benchmark setting, inner approximation, and Minkowski summation. In this embodiment of the invention, an average benchmark is used to set the benchmark for the feasible region corresponding to the high-dimensional polyhedron:

[0091]

[0092]

[0093] Where N represents the number of flexibility resources.

[0094] Setting benchmarks Then, based on displacement and scaling references The following approach is used to achieve a high-dimensional polyhedral feasible region approximation for each flexibility resource:

[0095]

[0096] To make the inner approximate feasible region approximate the original feasible region of the flexibility resources as closely as possible, the objective function should be set to maximize the scaling factor. Therefore, the following linear programming problem exists:

[0097] Objective function:

[0098]

[0099] Constraints:

[0100]

[0101] in, , , Let i be the scaling factor for the i-th flexibility resource. Let be the displacement factor for the i-th flexibility resource. , and Auxiliary variables for optimizing the problem.

[0102] After internal approximation, since the benchmarks are the same within the cluster, the approximate feasible region of each flexible resource individual within the cluster is actually a high-dimensional polyhedron with the same shape, which can be easily aggregated for flexibility using Minkowski summation.

[0103]

[0104] in, This represents the Minkowski summation operation.

[0105] Step 105: Based on the homogeneous aggregation results, use the internal approximation flexibility aggregation method to perform heterogeneous aggregation of different types of flexibility resources on the user side across nodes.

[0106] It should be noted that heterogeneous aggregation refers to the flexible aggregation of different types of flexible resources on different nodes.

[0107] After achieving the first stage of homogeneous aggregation of similar feasible resources within the same node, the second stage of this invention integrates different types of feasible resources across nodes and heterogeneously aggregates different types of flexible resources on the user side across nodes.

[0108] In the second stage, to further improve the system feasibility and adaptability of the aggregation results, network constraints are introduced in this embodiment of the invention. Based on the DistFlow power flow model in the form of phase angle relaxation and second-order cone relaxation, heterogeneous flexibility resources on different nodes are jointly aggregated. A continuous multi-time optimal power flow model of the power grid is constructed, which includes an objective function and constraints.

[0109] Objective function:

[0110] To obtain the upper and lower boundaries of the adjustable power of aggregated flexibility resources, the entire problem can be constructed as a continuous multi-time optimal power flow model. By solving for the maximum and minimum values ​​of energy that flexibility resources can transmit to the outside world, the upper and lower boundaries of their adjustable power can be obtained, i.e.:

[0111]

[0112] Here, E represents the energy that the flexible resource can transmit to the outside world.

[0113] The constraints include common connection point power constraints, network constraints, and flexible resource clustering technology constraints based on homogeneous aggregation results. Network constraints include:

[0114] Common junction point power constraints:

[0115] To quantify the power output boundary of the flexibility resources of different nodes in the network, the output power of the common coupling point should be equal to the load power vector plus the flexibility resource power vector, with power consumption defined as positive and power generation as negative.

[0116]

[0117] in, For the power at the point of common coupling, Power for the flexibility of connecting public connection points. For the load power connected to the point of common coupling, For the number of time periods, For the time change, The power of the flexibility resources connected to the common connection point during time period t.

[0118] Power balance constraints:

[0119]

[0120] in, Let be the active power of node j at time t, k be node k in the system network, and j be node j in the system network. Let be the active power of the branch from node j to node k at time t. Let be the active power of the branch from node i to node j at time t. Let be the branch resistance from node i to node j. Let be the square of the current magnitude of the branch from node i to node j at time t. Let be the reactive power of node j at time t. Let be the reactive power of the branch from node j to node k at time t. Let be the reactive power of the branch from node i to node j at time t. Let be the branch reactance from node i to node j.

[0121] Voltage balance constraint:

[0122]

[0123] in, Let be the square of the voltage magnitude at node j at time t. Let be the square of the voltage magnitude of node i at time t.

[0124] Second-order cone constraint:

[0125]

[0126] Node voltage amplitude constraints:

[0127]

[0128] in, Let be the lower limit of the squared voltage magnitude of node i at time t. Let be the upper limit of the square of the voltage amplitude at node i at time t.

[0129] Generator power constraints:

[0130]

[0131] in, For the root node i of the network structure (i.e. Figure TwoThe active power of node 1) at time t. Let be the lower limit of the active power of the network root node i at time t. Let be the upper limit of the active power of the network root node at time t. Let be the lower limit of reactive power of the root node i of the network structure at time t. Let i be the reactive power of the network root node i at time t. Let be the upper limit of reactive power of the root node i of the network structure at time t.

[0132] Line current amplitude constraint:

[0133]

[0134] in, This is the upper limit of the square of the branch current amplitude from node i to node j.

[0135] Constraints of flexible resource clustering technology based on homogeneous aggregation results:

[0136]

[0137] in, , where represents the charging and discharging power of the resource cluster after the aggregation of flexible resources located at node i. This is a technical constraint on flexible resource clustering after the aggregation of internally approximate flexible resources.

[0138] First, an internal approximation flexibility aggregation method is adopted to homogeneously aggregate similar flexibility resources on the user side within the same node, obtaining the clustering technical constraints of flexibility resources for each node. Next, the upper and lower boundaries of the adjustable power of the homogeneously aggregated flexibility resources are obtained, allowing the entire problem to be constructed as a continuous multi-time optimal power flow model. Heterogeneous aggregation of different types of flexibility resources on the user side across nodes is then achieved by solving for the maximum and minimum energy that can be transmitted to the outside world from the flexibility resources of different nodes.

[0139] The power grid flexibility resource aggregation method provided by this invention constructs a technical constraint model for user-side distributed flexibility resources and introduces an internal approximation flexibility aggregation method. Based on power grid topology and operational constraints, it performs homogeneous aggregation of similar user-side flexibility resources within the same node and heterogeneous aggregation of different types of user-side flexibility resources across nodes. This achieves node flexibility resource aggregation of multiple types and large-scale distributed flexibility resources, closely aligning with the actual distribution of flexibility resources. It solves the technical problem that existing power grid flexibility resource aggregation methods ignore the network topology constraints of the power grid, simplifying the model by treating all distributed flexibility resources as centralized access to the same node, which does not conform to the actual distribution characteristics of flexibility resources and limits the engineering feasibility of the model.

[0140] Meanwhile, the power grid flexibility resource aggregation method provided by this invention fully considers the technical differences of various flexibility resources and establishes a flexibility modeling method that takes into account both power and energy coupling characteristics. Compared with traditional methods, it is more suitable for the operating characteristics of different types of flexibility resources, and while improving aggregation accuracy, it also significantly improves solution efficiency.

[0141] The power grid flexibility resource aggregation method provided by this invention adopts an internal approximate aggregation strategy. After aggregation, the feasible region is always contained within the original feasible region, thus possessing natural de-aggregation feasibility. The aggregation result can be effectively mapped to specific flexibility resource devices, ensuring the individual executability of the response plan, and providing theoretical support and engineering reference for subsequent distributed resource coordination scheduling and flexible control.

[0142] To verify the effectiveness of the power grid flexibility resource aggregation method provided by this invention, a specific calculation example is provided:

[0143] Flexibility resource parameter settings:

[0144] Table 1 Energy Storage Equipment Parameters

[0145]

[0146] Table 2 Temperature control load parameters

[0147]

[0148] Table 3 Electric Vehicle Parameters

[0149]

[0150] Analysis of flexibility aggregation results:

[0151] Based on the parameters in Tables 1-3, the technical constraint model for user-side flexibility resources is solved. 30 energy storage devices, 30 temperature-controlled loads, and 100 electric vehicles are respectively located at... Figure 2 The nodes shown are 23, 21, 10, 8, 3, and 5. The calculations were performed using Matlab software, and the results obtained by calling the Gurobi solver are as follows. Figures 4-6 As shown. Figure 4 and Figure 5 This section illustrates the upper and lower power boundaries of different nodes and different types of flexibility resources after flexibility aggregation. Figure 4 and Figure 5 As can be seen, the flexibility aggregation method proposed in this invention, based on full consideration of network constraints, uniformly represents the flexibility of flexible resource clusters distributed on different nodes through the upper and lower boundaries of the overall power, thereby achieving efficient aggregation of the flexibility of different types of flexible resources.Figure 6 The depolymerization process of the flexible polymerization results is demonstrated. Figure 6 The left-hand graph shows the power decomposition result of a randomly selected energy storage unit after the aggregation result decomposition. The right-hand graph shows the energy decomposition result of 13 temperature-controlled loads within a temperature-controlled load cluster after the aggregation result decomposition. Figure 6 The dashed lines represent the power constraints of the energy storage unit and the indoor temperature limits (i.e., energy constraints) of the temperature-controlled load cluster, respectively. Figure 6 It can be seen that each individual flexible resource operation point after de-aggregation satisfies its own technical constraints, and the aggregation result can be effectively mapped to specific flexible resource devices.

[0152] In summary, the power grid flexibility resource aggregation method provided by this invention can aggregate flexibility resources while simultaneously satisfying network topology constraints and power flow constraints. Furthermore, the aggregation results can be feasiblely allocated to individual flexibility resources through a de-aggregation mechanism, ensuring the feasibility and effectiveness of the method in practical applications.

[0153] For easier understanding, please refer to Figure 7 This invention provides an embodiment of a power grid flexibility resource aggregation device, comprising:

[0154] The power grid topology acquisition module is used to acquire the power grid topology structure and determine the node information of the power grid topology structure.

[0155] The flexibility resource modeling module is used to model the user-side flexibility resources based on the node information to obtain the technical constraint model of the user-side flexibility resources.

[0156] A polyhedron construction module is used to build a high-dimensional polyhedron composed of the feasible domains of the technical constraint model.

[0157] The same type aggregation module is used to perform homogeneous aggregation of similar flexible resources on the user side within the same node, based on the high-dimensional polyhedron and using an internal approximation flexible aggregation method.

[0158] The heterogeneous aggregation module is used to perform heterogeneous aggregation of different types of flexibility resources across node user sides based on the results of the homogeneous aggregation, using an internal approximation flexibility aggregation method.

[0159] In one embodiment, the similar aggregation module is specifically used for:

[0160] A baseline setting is performed on the high-dimensional polyhedron;

[0161] The high-dimensional polyhedron is approximated within the feasible domain by setting displacement and scaling references.

[0162] For the same node, based on the approximate results within the feasible region, similar flexibility resources on the user side are homogeneously aggregated using the Minkowski summation method.

[0163] In one embodiment, the approximate result within the feasible region is expressed as:

[0164]

[0165] in, This represents an approximate result within the feasible region of the i-th flexibility resource. Let be the displacement factor for the i-th flexibility resource. As an approximate benchmark within the feasible region, The average value of the coefficient matrix of the technical constraint model for flexible resources. The average value of the coefficient vector of flexibility resources. Let i be the decision variable for the i-th flexibility resource. Let be the scaling factor for the i-th flexibility resource.

[0166] In one embodiment, user-side flexibility resources include energy storage devices, temperature-controlled loads, and electric vehicles.

[0167] In one embodiment, the technical constraint model for the energy storage device is as follows:

[0168]

[0169]

[0170]

[0171]

[0172] in, Let be the charging power of the i-th energy storage device during time period t. Let be the discharge power of the i-th energy storage device during time period t. Let be the battery dissipation coefficient of the i-th energy storage device. Let be the operating capacity of the i-th energy storage device during time period t. The change in temperature Let be the charging efficiency coefficient of the i-th energy storage device. Let be the discharge efficiency coefficient of the i-th energy storage device. Let i be the maximum charge / discharge power of the i-th energy storage device. Let i be the maximum capacity of the i-th energy storage device. Let be the minimum capacity of the i-th energy storage device.

[0173] In one embodiment, the technical constraint model for the temperature-controlled load is as follows:

[0174]

[0175]

[0176]

[0177] in, Let be the charging power of the i-th temperature-controlled load during time period t. Let be the discharge power of the i-th temperature-controlled load during time period t. The maximum charging and discharging power of the i-th temperature-controlled load. Let be the indoor temperature of the i-th temperature-controlled load during time period t. Let i be the state variable of the i-th temperature-controlled load. The change in temperature The influence factor of the ambient temperature of the i-th temperature-controlled load is... Let be the temperature dissipation coefficient of the i-th temperature-controlled load. Let be the difference between the ambient temperature and the user-preset temperature for the i-th temperature-controlled load during time period t. Let be the charging efficiency coefficient of the i-th temperature-controlled load. Let be the discharge efficiency coefficient of the i-th temperature-controlled load. The maximum preset temperature value is set for the i-th user with the temperature control load. The minimum preset temperature value for the i-th temperature-controlled load user.

[0178] In one embodiment, the technical constraint model for electric vehicles is as follows:

[0179]

[0180] in, Let be the charging power of the i-th electric vehicle in time period t. Let be the discharge power of the i-th electric vehicle in time period t. Let i be the grid connection time of the i-th electric vehicle. Let be the time when the i-th electric vehicle leaves the network. Let be the maximum charging power of the i-th electric vehicle. Let be the maximum discharge power of the i-th electric vehicle. Let i be the operating capacity of the i-th electric vehicle in time period t. Let be the consumption coefficient of the i-th electric vehicle. The change in temperature Let be the charging efficiency coefficient of the i-th electric vehicle. Let be the discharge efficiency coefficient of the i-th electric vehicle. Let be the upper limit of the operating capacity of the i-th electric vehicle in time period t. Let be the lower limit of the operating capacity of the i-th electric vehicle in time period t. Let i be the initial capacity before the i-th electric vehicle is connected to the grid. Let be the expected operating capacity when the i-th electric vehicle leaves the grid.

[0181] In one embodiment, based on the result of the homogeneous aggregation, an internal approximation flexibility aggregation method is used to heterogeneously aggregate different types of flexibility resources across node user sides, including:

[0182] A continuous multi-moment optimal power flow model for the power grid is constructed. The continuous multi-moment optimal power flow model includes an objective function and constraints. The constraints include power constraints at the point of common connection, network constraints, and flexibility resource clustering technology constraints based on homogeneous aggregation results.

[0183] The internal approximation flexibility aggregation method is used to solve the continuous multi-time optimal power flow model, and heterogeneous aggregation of different types of flexibility resources on the user side of cross nodes is performed.

[0184] In one embodiment, the constraints of flexible resource clustering technology based on homogeneous aggregation results are:

[0185]

[0186] in, For the cluster decision variables of flexible resources after approximate internal flexible resource aggregation, This is an approximate result within the feasible domain of flexible resource clusters.

[0187] The power grid flexibility resource aggregation device provided in this invention is used to execute the power grid flexibility resource aggregation method provided in this invention. Its principle and the technical effects achieved are the same as those of the power grid flexibility resource aggregation method provided in this invention, and will not be repeated here.

[0188] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0189] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for aggregating power grid flexibility resources, characterized in that, include: Obtain the power grid topology and determine the node information of the power grid topology; Based on the node information, the user-side flexibility resources are modeled to obtain the technical constraint model of the user-side flexibility resources. Construct a high-dimensional polyhedron composed of the feasible domains of the aforementioned technical constraint model; Based on the high-dimensional polyhedron, an internal approximation flexible aggregation method is adopted to homogeneously aggregate similar flexible resources on the user side within the same node. Based on the results of the homogeneous aggregation, an internal approximation flexibility aggregation method is adopted to heterogeneously aggregate different types of flexibility resources on the user side across nodes.

2. The grid flexibility resource aggregation method according to claim 1, characterized in that, Based on the high-dimensional polyhedron, an internal approximation-based flexible aggregation method is used to homogeneously aggregate similar flexible resources on the user side within the same node, including: A baseline setting is performed on the high-dimensional polyhedron; The high-dimensional polyhedron is approximated within the feasible domain by setting displacement and scaling references. For the same node, based on the approximate results within the feasible region, similar flexibility resources on the user side are homogeneously aggregated using the Minkowski summation method.

3. The grid flexibility resource aggregation method according to claim 2, characterized in that, The approximate result within the feasible region is expressed as follows: in, This represents an approximate result within the feasible region of the i-th flexibility resource. Let be the displacement factor for the i-th flexibility resource. As an approximate benchmark within the feasible region, The average value of the coefficient matrix of the technical constraint model for flexible resources. The average value of the coefficient vector of flexibility resources. Let i be the decision variable for the i-th flexibility resource. Let be the scaling factor for the i-th flexibility resource.

4. The method for aggregating power grid flexibility resources according to claim 1, characterized in that, User-side flexibility resources include energy storage devices, temperature-controlled loads, and electric vehicles.

5. The grid flexibility resource aggregation method according to claim 4, characterized in that, The technical constraint model for energy storage devices is as follows: in, Let be the charging power of the i-th energy storage device during time period t. Let be the discharge power of the i-th energy storage device during time period t. Let be the battery dissipation coefficient of the i-th energy storage device. Let be the operating capacity of the i-th energy storage device during time period t. The change in temperature Let be the charging efficiency coefficient of the i-th energy storage device. Let be the discharge efficiency coefficient of the i-th energy storage device. Let i be the maximum charge / discharge power of the i-th energy storage device. Let i be the maximum capacity of the i-th energy storage device. Let be the minimum capacity of the i-th energy storage device.

6. The grid flexibility resource aggregation method according to claim 4, characterized in that, The technical constraint model for temperature-controlled loads is as follows: in, Let be the charging power of the i-th temperature-controlled load during time period t. Let be the discharge power of the i-th temperature-controlled load during time period t. The maximum charging and discharging power of the i-th temperature-controlled load. Let be the indoor temperature of the i-th temperature-controlled load during time period t. Let i be the state variable of the i-th temperature-controlled load. The change in temperature The influence factor of the ambient temperature of the i-th temperature-controlled load is... Let be the temperature dissipation coefficient of the i-th temperature-controlled load. Let be the difference between the ambient temperature and the user-preset temperature for the i-th temperature-controlled load during time period t. Let be the charging efficiency coefficient of the i-th temperature-controlled load. Let be the discharge efficiency coefficient of the i-th temperature-controlled load. The maximum preset temperature value is set for the i-th user with the temperature control load. The minimum preset temperature value for the i-th temperature-controlled load user.

7. The grid flexibility resource aggregation method according to claim 4, characterized in that, The technical constraint model for electric vehicles is as follows: in, Let be the charging power of the i-th electric vehicle in time period t. Let be the discharge power of the i-th electric vehicle in time period t. Let i be the grid connection time of the i-th electric vehicle. Let be the time when the i-th electric vehicle leaves the network. Let be the maximum charging power of the i-th electric vehicle. Let be the maximum discharge power of the i-th electric vehicle. Let i be the operating capacity of the i-th electric vehicle in time period t. Let be the consumption coefficient of the i-th electric vehicle. The change in temperature Let be the charging efficiency coefficient of the i-th electric vehicle. Let be the discharge efficiency coefficient of the i-th electric vehicle. Let be the upper limit of the operating capacity of the i-th electric vehicle in time period t. Let be the lower limit of the operating capacity of the i-th electric vehicle in time period t. Let i be the initial capacity before the i-th electric vehicle is connected to the grid. Let be the expected operating capacity when the i-th electric vehicle leaves the grid.

8. The method for aggregating power grid flexibility resources according to claim 1, characterized in that, Based on the results of the homogeneous aggregation, an internal approximation flexibility aggregation method is used to heterogeneously aggregate different types of flexibility resources across node user sides, including: A continuous multi-moment optimal power flow model for the power grid is constructed. The continuous multi-moment optimal power flow model includes an objective function and constraints. The constraints include power constraints at the point of common connection, network constraints, and flexibility resource clustering technology constraints based on homogeneous aggregation results. The internal approximation flexibility aggregation method is used to solve the continuous multi-time optimal power flow model, and heterogeneous aggregation of different types of flexibility resources on the user side of cross nodes is performed.

9. The method for aggregating power grid flexibility resources according to claim 8, characterized in that, The constraints of the flexible resource clustering technology based on homogeneous aggregation results are as follows: in, For the cluster decision variables of flexible resources after approximate internal flexible resource aggregation, This is an approximate result within the feasible domain of flexible resource clusters.

10. A power grid flexibility resource aggregation device, characterized in that, include: The power grid topology acquisition module is used to acquire the power grid topology structure and determine the node information of the power grid topology structure. The flexibility resource modeling module is used to model the user-side flexibility resources based on the node information to obtain the technical constraint model of the user-side flexibility resources. A polyhedron construction module is used to build a high-dimensional polyhedron composed of the feasible domains of the technical constraint model. The same type aggregation module is used to perform homogeneous aggregation of similar flexible resources on the user side within the same node, based on the high-dimensional polyhedron and using an internal approximation flexible aggregation method. The heterogeneous aggregation module is used to perform heterogeneous aggregation of different types of flexibility resources across node user sides based on the results of the homogeneous aggregation, using an internal approximation flexibility aggregation method.

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