Adaptive control method of distributed power generation based on measurement-strategy mapping matrix

Abstract

The present invention relates to a distributed power adaptive control method based on a measurement-strategy mapping matrix. Its technical features include: constructing and training a distribution network measurement-strategy mapping matrix generation model, generating a measurement-strategy mapping matrix based on real-time distribution network node voltage measurement data; constructing linearized measurement-strategy mapping constraints based on linear dimensionality-increasing decisions; constructing a distributed power adaptive control model, solving the distributed power adaptive control model, and obtaining the operational control strategy for each distributed power source. The present invention constructs linearized measurement-strategy mapping constraints based on linear dimensionality-increasing decisions, then constructs and solves a distribution network self-distributed power adaptive control model. This method achieves efficient solution of distributed power control strategies in the absence of a distribution network physical parameter model, enables real-time response to distributed power fluctuations, and improves the control accuracy of distributed power operation in complex time-varying scenarios.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed power supply, and in particular to a distributed power supply adaptive control method based on a measurement-strategy mapping matrix. Background Art

[0002] The large-scale and high-proportion integration of distributed generators (DGs) into distribution networks presents numerous challenges to their efficient and economical operation. Due to the intermittent and uncertain nature of DGs, distribution network operation volatility has increased dramatically, with power flows becoming more variable. This has severely impacted the network's economic and secure operation and has led to increased complexity in its optimization and control.

[0003] Physical-based distributed generation (DG) control methods are constrained by precise network parameters, limiting their applicability in weak parameter scenarios. Distribution network sensitivity, a quantitative representation of the relationship between node power and voltage changes, can be analyzed and extracted from historical data to support the development of data-driven DG control strategies. However, sensitivity varies with system operating status and node power, forming a complex nonlinear relationship. If linear sensitivity is derived only at the operating point, it will suffer from reduced accuracy when power changes are large, compromising optimal control. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a distributed power supply adaptive control method based on a measurement-strategy mapping matrix to solve the problem of low control accuracy of distributed power supply operation in complex time-varying scenarios.

[0005] The present invention solves the existing technical problems by adopting the following technical solutions:

[0006] A distributed power adaptive control method based on a measurement-strategy mapping matrix includes the following steps:

[0007] Step 1: Based on the selected active distribution network, input the historical operation data of the distribution network, the connection location and capacity of the distributed power generation in the distribution network, and the real-time measurement data of the voltage at the distribution network nodes;

[0008] Step 2: construct a distribution network measurement-strategy mapping matrix generation model based on the Koopman operator, construct a training set based on the distribution network historical operation data obtained in step 1, and obtain a trained distribution network measurement-strategy mapping matrix generation model after completing model training;

[0009] Step 3: Using the trained distribution network measurement-strategy mapping matrix generated in step 2 to generate a model, a measurement-strategy mapping matrix is ​​generated based on the real-time voltage measurement data of the distribution network nodes;

[0010] Step 4: Based on the measurement-strategy mapping matrix generated in step 3, a linearized measurement-strategy mapping constraint is constructed based on the linear dimensionality increase decision;

[0011] Step 5: Based on the linearized measurement-strategy mapping constraint obtained in step 4, a distributed power adaptive control model is constructed. The distributed power adaptive control model includes: setting the minimum distribution network voltage deviation as the objective function, and considering the linearized measurement-strategy mapping constraint, the distribution network safe operation constraint, and the distributed power operation constraint respectively;

[0012] Step 6: Solve the distributed power adaptive control model constructed in step 5 to obtain the operation control strategy of each distributed power source, and cyclically execute steps 3 to 6 within the range of the total control time T to realize the distributed power adaptive control function.

[0013] Furthermore, the distribution network historical operation data includes distribution network node voltages and reactive power injected into the nodes.

[0014] Furthermore, the distribution network measurement-strategy mapping matrix generation model constructed based on the Koopman operator in step 2 is expressed as:

[0015]

[0016] In the formula, x is the input variable, y is the output variable, and x l represents the input variable after dimensionality increase, χ(x) is the dimensionality increase representation of x, and M is the weight matrix.

[0017] Furthermore, the training set constructed in step 2 is expressed as:

[0018]

[0019]

[0020]

[0021] Where X and Y represent the input matrix and output matrix of the training set respectively. L Represents the training set input matrix after dimensionality increase, x (f) is the input of the fth group of training objects, t is the current moment, T is the total control time, x (f) Including the voltage of each node in the system at time t and the reactive power change of each node at time t+1 y (f) is the output of the fth group of training objects, y (f) Including the voltage changes of each node

[0022] Furthermore, the distribution network measurement-strategy mapping matrix generation model obtained after completing the model training in step 2 is expressed as:

[0023]

[0024] χ(x (f) )=[χ1(x (f) ) χ2(x (f) ) … χ N (x (f) )] T

[0025]

[0026] Where, [·] T is the transpose of the matrix, is the calculation of the Moore-Penrose inverse matrix operation, x (f) is the input of the fth group of training objects, χ(x (f) ) is the input variable expanded by the dimension of the f-th group of training objects, χ i (x (f) ) represents the i-th dimension-raising function of the f-th group of training objects, is the i-th input variable in the f-th group of training objects, c i is the i-th ascending wiki basis vector, c ij is the basis vector c i The jth element in , w represents the total number of elements in the basis vector, and N is the dimension of the increased dimension.

[0027] Furthermore, the measurement-strategy mapping matrix generated in step 3 is expressed as:

[0028]

[0029]

[0030]

[0031]

[0032] Where t is the current time, [·] T is the transpose of the matrix, is the measurement-strategy mapping matrix, is the aspect to which node i belongs in the measurement-strategy mapping matrix, is the reactive power change ΔQ of node i under the system voltage distribution at time t i,s The sensitivity to the voltage change at node j, ΔQ i,s is the s power change values ​​of node i in the measurement-strategy mapping matrix, is the power change ΔQ of node i under the system voltage distribution at time t i,s The input quantity that generates the matrix elements is is the power change ΔQ of node i under the system voltage distribution at time t i,s When generating the input of matrix elements, the dimension-raising variable, is the output obtained by mapping, N n Indicates the total number of nodes, N s Indicates the total change in reactive power.

[0033] Furthermore, the linearized measurement-strategy mapping constraint constructed in step 4 is expressed as:

[0034]

[0035]

[0036]

[0037]

[0038] Where t is the current time, [·] T is the transpose of the matrix, is the reactive power regulation of the distributed generation at node i at time t+1, is the sth decision variable of node i, is the estimated voltage change at node j caused by power regulation at node i at time t+1, ΔQ i,s is the s power change values ​​of node i in the measurement-strategy mapping matrix, is the reactive power change ΔQ of node i under the system voltage distribution at time t i,s The sensitivity to the voltage change of node j is is the decision vector of node i, δ i,s is the sth boundary vector of the domain of the decision variable of node i, N n Indicates the total number of nodes, N s Indicates the total change in reactive power.

[0039] Furthermore, the distributed power adaptive control model constructed in step 5 is expressed as:

[0040]

[0041]

[0042]

[0043]

[0044] Where t is the current time, f V is the distribution network voltage deviation objective function, Ω n is the set of distribution network nodes, is the estimated voltage value of node j at time t+1, is the estimated value of the voltage change at node j at time t+1, is the estimated voltage change of node j caused by the power regulation of node i at time t+1, is the voltage measurement value of node j at time t, are the active and reactive outputs of the distributed generation at node i at time t+1, is the reactive power regulation of the distributed generation at node i at time t+1, S i,DG is the capacity of the distributed power inverter at node i, are the upper and lower limits of node voltage optimization, V min 、V max are the upper and lower limits of node voltage safety operation respectively, are the upper and lower limits of the reactive power adjustment of the distributed power inverter connected to node i, N g is the number of distributed power sources, N n Indicates the total number of nodes.

[0045] The advantages and positive effects of the present invention are:

[0046] The present invention obtains the sensitivity relationship between the power changes of different nodes and the voltage of each node in the current state, and based on the current operating state, uses the Koopman operator to construct the distribution network voltage measurement-strategy mapping matrix in real time, so as to avoid the impact of the perturbation method on the distribution network and realize the accurate characterization of the mapping relationship between the distribution network voltage measurement and the reactive power strategy of each distributed power source in the weak parameter scenario; based on the distribution network voltage measurement-strategy mapping matrix, a linearized measurement-strategy mapping constraint is constructed based on linear dimensionality increase decision, and a distribution network self-distributed power adaptive control model is constructed and solved, so as to realize the efficient solution function of the distributed power control strategy in the absence of the distribution network physical parameter model, respond to the distributed power fluctuation in real time, thereby improving the control accuracy of the distributed power operation in complex time-varying scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of the distributed power adaptive control method based on the measurement-strategy mapping matrix of the present invention;

[0048] Figure 2 This is the improved IEEE 33-node distribution network structure diagram of the present invention;

[0049] Figure 3 yes Figure 2 Distribution network source and load power fluctuation curve;

[0050] Figure 4 1 is a diagram showing the voltage amplitude distribution of the entire network in embodiment scheme 1;

[0051] Figure 5 This is a diagram of the voltage amplitude distribution of the entire network in embodiment II;

[0052] Figure 6 This is a diagram of the voltage amplitude distribution of the entire network in embodiment III. DETAILED DESCRIPTION

[0053] The embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0054] A distributed power adaptive control method based on measurement-strategy mapping matrix, such as Figure 1 As shown, the following steps are included:

[0055] Step 1: Based on the selected active distribution network, input the historical operation data of the distribution network, including: the voltage of the distribution network nodes, the reactive power injected by the nodes; input the access location and capacity of the distributed power generation in the distribution network; input the real-time measurement data of the voltage of the distribution network nodes; set the current time t = 0, and the total control time to T;

[0056] Step 2: Construct a distribution network measurement-strategy mapping matrix generation model based on the Koopman operator, and construct a training set based on the distribution network historical operation data in step 1 to complete model training and obtain the trained distribution network measurement-strategy mapping matrix generation model.

[0057] In this step, the constructed distribution network measurement-strategy mapping matrix generation model, training set, and trained distribution network measurement-strategy mapping matrix generation model are:

[0058] (1) The generation model of the distribution network measurement-strategy mapping matrix is ​​expressed as:

[0059]

[0060] In the formula, x is the input variable, y is the output variable, and x l represents the input variable after dimensionality increase, χ(x) is the dimensionality increase representation of x, and M is the weight matrix.

[0061] (2) The constructed training set is expressed as:

[0062]

[0063] Where X and Y represent the input matrix and output matrix of the training set respectively. L Represents the training set input matrix after dimensionality increase, x (f)is the input quantity of the fth group of training objects, including the voltage of each node in the system at time t and the reactive power change of each node at time t+1 y (f) is the output of the fth group of training objects, including the voltage changes of each node

[0064] (3) The distribution network measurement-strategy mapping matrix generation model after training is expressed as:

[0065]

[0066] Where, [·] T is the transpose of the matrix, is the calculation of the Moore-Penrose inverse matrix operation, x (f) is the input of the fth group of training objects, χ(x (f) ) is the input variable expanded by the dimension of the f-th group of training objects, χ i (x (f) ) represents the i-th dimension-raising function of the f-th group of training objects, is the i-th input variable in the f-th group of training objects, c i is the i-th ascending wiki basis vector, c ij is the basis vector c i The jth element in , w represents the total number of elements in the basis vector, and N is the dimension of the increased dimension.

[0067] Step 3: Using the trained distribution network measurement-strategy mapping matrix generated in step 2, a measurement-strategy mapping matrix is ​​generated based on the real-time measurement data of the distribution network node voltage.

[0068] In this step, the measurement-strategy mapping matrix is ​​generated based on the real-time measurement data of the distribution network node voltage and is expressed as:

[0069]

[0070]

[0071] Where, is the measurement-strategy mapping matrix, is the aspect to which node i belongs in the measurement-strategy mapping matrix, is the reactive power change ΔQ of node i under the system voltage distribution at time t i,s The sensitivity to the voltage change at node j, ΔQ i,s is the s power change values ​​of node i in the measurement-strategy mapping matrix, is the power change ΔQ of node i under the system voltage distribution at time t i,s The input quantity that generates the matrix elements is is the power change ΔQ of node i under the system voltage distribution at time t i,s When generating the input of matrix elements, the dimension-raising variable, is the output obtained by mapping, N n Indicates the total number of nodes, N s Indicates the total change in reactive power.

[0072] Step 4: Based on the measurement-strategy mapping matrix generated in step 3, a linearized measurement-strategy mapping constraint is constructed based on the linear dimensionality increase decision.

[0073] In this step, the linearized measurement-strategy mapping constraint is constructed based on the linear dimensionality increase decision as follows:

[0074]

[0075] Where, is the reactive power regulation of the distributed generation at node i at time t+1, is the sth decision variable of node i, is the estimated voltage change at node j caused by power regulation at node i at time t+1, ΔQ i,s is the s power change values ​​of node i in the measurement-strategy mapping matrix, is the reactive power change ΔQ of node i under the system voltage distribution at time t i,s The sensitivity to the voltage change of node j is is the decision vector of node i, δ i,s is the sth boundary vector of the domain of the decision variable of node i, N n Indicates the total number of nodes, N s Indicates the total change in reactive power.

[0076] Step 5: Based on the linearized measurement-strategy mapping constraints in step 4, a distributed power supply adaptive control model is constructed.

[0077] In this step, the distributed power adaptive control model includes: setting the minimum distribution network voltage deviation as the objective function, and considering the linearized measurement-strategy mapping constraints, distribution network safe operation constraints, and distributed power operation constraints respectively.

[0078] The distributed power adaptive control model constructed in this step is expressed as:

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] Where, f V is the distribution network voltage deviation objective function, Ω n is the set of distribution network nodes, is the estimated voltage value of node j at time t+1, is the estimated value of the voltage change at node j at time t+1, is the estimated voltage change of node j caused by the power regulation of node i at time t+1, is the voltage measurement value of node j at time t, are the active and reactive outputs of the distributed generation at node i at time t+1, is the reactive power regulation of the distributed generation at node i at time t+1, S i,DG is the capacity of the distributed power inverter at node i, are the upper and lower limits of node voltage optimization, V min 、V max are the upper and lower limits of node voltage safety operation respectively, are the upper and lower limits of the reactive power adjustment of the distributed power inverter connected to node i, N g is the number of distributed power sources, N n Indicates the total number of nodes.

[0085] Step 6: Solve the distributed power adaptive control model obtained in step 5 and output the solution, which is the operation control strategy of each distributed power source.

[0086] Finally, steps 3 to 6 are executed cyclically within the range of the total control time to realize the adaptive control function of the distributed power supply.

[0087] In order to verify the advancedness of the method of the present invention, in this embodiment, the following schemes are adopted for comparative analysis:

[0088] Solution I: Do not optimize the reactive power output of distributed generation and obtain the initial operating state of the distribution network;

[0089] Solution II: Based on the method of the present invention, an optimization model is constructed to optimize the output of distributed power sources;

[0090] Scheme III: Use centralized optimization methods to achieve global optimization and obtain theoretically optimal control effects.

[0091] The voltage optimization results of Scheme I, Scheme II and Scheme III are compared in Table 1. The access location and capacity of distributed photovoltaic are shown in Table 2. The access location and size of load are shown in Table 3. The network parameters of distribution network are shown in Table 4. The improved IEEE 33-node distribution network structure is shown in Table 4. Figure 2 As shown, the source load power fluctuation curve is as follows Figure 3 As shown, the voltage amplitude distribution of the entire network in Scheme I is as follows: Figure 4 As shown, the voltage amplitude distribution of the entire network in Scheme II is as follows Figure 5 As shown, the voltage amplitude distribution of the entire network in Scheme III is as follows Figure 6 shown.

[0092] The computer hardware environment for performing optimization calculations is Intel(R) Xeon(R) CPU E5-1620 with a main frequency of 3.70 GHz and a memory of 32 GB; the software environment is Windows 10 operating system.

[0093] The effectiveness of the proposed method was verified by comparing Schemes I and II. Without control measures, the integration of distributed generation (DGs) can cause significant system voltage fluctuations. Based on the proposed adaptive voltage control method for the distribution network, each DG can adjust its reactive power in real time to maintain the system voltage at a safe operating level. A comparison of Schemes II and III demonstrates that the proposed method, driven by data, achieves near-optimal control, eliminating the reliance on precise network parameters inherent in real-time centralized control methods.

[0094] Table 1 Comparison results of power distribution system operation

[0095]

[0096]

[0097] Table 2 Distributed photovoltaic access location and capacity

[0098] Access location Active power capacity / kW type Access location Active power capacity / kW type 12 200 Photovoltaics 32 200 Photovoltaics 13 200 Photovoltaics 16 300 fan 17 200 Photovoltaics 18 300 fan 21 200 Photovoltaics 22 300 fan 25 200 Photovoltaics 31 300 fan 30 200 Photovoltaics 33 300 fan

[0099] Table 3 Load access location and size

[0100]

[0101]

[0102] Table 4 Distribution network line parameters

[0103]

[0104] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific embodiments. Any other embodiments derived by those skilled in the art based on the technical solutions of the present invention also fall within the scope of protection of the present invention.

Claims

1. A distributed power adaptive control method based on a measurement-strategy mapping matrix, characterized by: The following steps are involved: Step 1: Based on the selected active distribution network, input the historical operation data of the distribution network, the connection location and capacity of the distributed power generation in the distribution network, and the real-time measurement data of the voltage at the distribution network nodes; Step 2: construct a distribution network measurement-strategy mapping matrix generation model based on the Koopman operator, construct a training set based on the distribution network historical operation data obtained in step 1, and obtain a trained distribution network measurement-strategy mapping matrix generation model after completing model training; Step 3: Using the trained distribution network measurement-strategy mapping matrix generated in step 2 to generate a model, a measurement-strategy mapping matrix is ​​generated based on the real-time voltage measurement data of the distribution network nodes; Step 4: Based on the measurement-strategy mapping matrix generated in step 3, a linearized measurement-strategy mapping constraint is constructed based on the linear dimensionality increase decision; Step 5: Based on the linearized measurement-strategy mapping constraint obtained in step 4, a distributed power adaptive control model is constructed. The distributed power adaptive control model includes: setting the minimum distribution network voltage deviation as the objective function, and considering the linearized measurement-strategy mapping constraint, the distribution network safe operation constraint, and the distributed power operation constraint respectively; Step 6: Solve the distributed power adaptive control model constructed in step 5 to obtain the operation control strategy of each distributed power source, and cyclically execute steps 3 to 6 within the range of the total control time T to realize the distributed power adaptive control function.

2. The distributed power adaptive control method based on the measurement-strategy mapping matrix according to claim 1, characterized in that: The distribution network historical operation data includes the distribution network node voltage and the reactive power injected by the node.

3. The distributed power adaptive control method based on the measurement-strategy mapping matrix according to claim 1 or 2, characterized in that: The distribution network measurement-strategy mapping matrix generation model constructed based on the Koopman operator in step 2 is expressed as: In the formula, x is the input variable, y is the output variable, and x l represents the input variable after dimensionality increase, χ(x) is the dimensionality increase representation of x, and M is the weight matrix.

4. The distributed power adaptive control method based on the measurement-strategy mapping matrix according to claim 1 or 2, characterized in that: The training set constructed in step 2 is expressed as: Where X and Y represent the input matrix and output matrix of the training set respectively. L Represents the training set input matrix after dimensionality increase, x (f) is the input of the fth group of training objects, t is the current moment, T is the total control time, x (f) Including the voltage of each node in the system at time t and the reactive power change of each node at time t+1 y (f) is the output of the fth group of training objects, y (f) Including the voltage changes of each node 5. The distributed power adaptive control method based on the measurement-strategy mapping matrix according to claim 1 or 2, characterized in that: The distribution network measurement-strategy mapping matrix generation model obtained after completing the model training in step 2 is expressed as: Where, [·] T is the transpose of the matrix, is the calculation of the Moore-Penrose inverse matrix operation, x (f) is the input of the fth group of training objects, χ(x (f) ) is the input variable expanded by the dimension of the f-th group of training objects, χ i (x (f) ) represents the i-th dimension-raising function of the f-th group of training objects, is the i-th input variable in the f-th group of training objects, c i is the i-th ascending wiki basis vector, c ij is the basis vector c i The jth element in , w represents the total number of elements in the basis vector, and N is the dimension of the increased dimension.

6. The distributed power adaptive control method based on the measurement-strategy mapping matrix according to claim 1 or 2, characterized in that: The measurement-strategy mapping matrix generated in step 3 is expressed as: Where t is the current time, [·] T is the transpose of the matrix, is the measurement-strategy mapping matrix, is the aspect to which node i belongs in the measurement-strategy mapping matrix, is the reactive power change ΔQ of node i under the system voltage distribution at time t i,s The sensitivity to the voltage change at node j, ΔQ i,s is the s power change values ​​of node i in the measurement-strategy mapping matrix, is the power change ΔQ of node i under the system voltage distribution at time t i,s The input quantity that generates the matrix elements is is the power change ΔQ of node i under the system voltage distribution at time t i,s When generating the input of matrix elements, the dimension-raising variable, is the output obtained by mapping, N n Indicates the total number of nodes, N s Indicates the total change in reactive power.

7. The distributed power adaptive control method based on the measurement-strategy mapping matrix according to claim 1 or 2, characterized in that: The linearized measurement-strategy mapping constraint constructed in step 4 is expressed as: Where t is the current time, [·] T is the transpose of the matrix, is the reactive power regulation of the distributed generation at node i at time t+1, is the sth decision variable of node i, is the estimated voltage change at node j caused by power regulation at node i at time t+1, ΔQ i,s is the s power change values ​​of node i in the measurement-strategy mapping matrix, is the reactive power change ΔQ of node i under the system voltage distribution at time t i,s The sensitivity to the voltage change of node j is is the decision vector of node i, δ i,s is the sth boundary vector of the domain of the decision variable of node i, N n Indicates the total number of nodes, N s Indicates the total change in reactive power.

8. The distributed power adaptive control method based on the measurement-strategy mapping matrix according to claim 1 or 2, characterized in that: The distributed power adaptive control model constructed in step 5 is expressed as: Where t is the current time, f V is the distribution network voltage deviation objective function, Ω n is the set of distribution network nodes, is the estimated voltage value of node j at time t+1, is the estimated value of the voltage change at node j at time t+1, is the estimated voltage change of node j caused by the power regulation of node i at time t+1, is the voltage measurement value of node j at time t, are the active and reactive outputs of the distributed generation at node i at time t+1, is the reactive power regulation of the distributed generation at node i at time t+1, S i,DG is the capacity of the distributed power inverter at node i, are the upper and lower limits of node voltage optimization, V min 、V max are the upper and lower limits of node voltage safety operation respectively, are the upper and lower limits of the reactive power adjustment of the distributed power inverter connected to node i, N g is the number of distributed power sources, N n Indicates the total number of nodes.

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