A new energy distribution system operation simulation method, system, equipment and medium

Abstract

The present invention relates to the technical field of new energy distribution systems, and discloses a method, system, device, and medium for simulating the operation of a new energy distribution system. The method comprises: adjusting a first node admittance matrix according to a number of operation scenarios to obtain a second node admittance matrix under each operation scenario; fitting historical operation data to obtain a power characteristic curve under the corresponding operation scenario; using Bernstein polynomials to perform segmented fitting on the power characteristic curve under each operation scenario to obtain Bernstein polynomial coefficients for each section of the power characteristic curve under the corresponding operation scenario; performing flow calculation on the second node admittance matrix and the Bernstein polynomial coefficients to obtain voltage phasor Bernstein polynomial coefficients; and restoring the simulated operation of the target new energy distribution system under each operation scenario based on the voltage phasor Bernstein polynomial coefficients of each node. The present invention can effectively improve the accuracy of the operation simulation of the new energy distribution system.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy power distribution systems, and in particular to a new energy power distribution system operation simulation method, system, equipment and medium. Background Art

[0002] With the large-scale integration of renewable energy, the operational characteristics of new energy distribution systems have become increasingly complex. The output of renewable energy sources such as photovoltaic and wind power is subject to significant uncertainty and volatility. These outputs are affected by natural factors such as sunlight intensity and wind speed, making them less stable than traditional energy sources. This poses significant challenges to the reliable operation of power systems. Simulating the operation of new energy distribution systems can comprehensively understand the system's operating status under different operating conditions and identify potential faults in advance, which is crucial for enhancing the reliability of power supply.

[0003] At present, the operation simulation of renewable energy distribution systems mainly relies on numerical analysis and short-term forecasting. Common methods include time series simulation, state estimation, and calculations based on optimization algorithms. However, when dealing with large-scale, multi-time period renewable energy output data, these methods generally have the problem of high computational complexity and are also subject to many limitations in terms of accuracy. For example, existing distribution network power flow calculation methods are mostly based on discrete time sections. They ignore the characteristics of renewable energy output that changes continuously over time, resulting in low computational accuracy and the inability to fully and accurately simulate the dynamic operation status of renewable energy systems. Moreover, traditional simulation methods have always found it difficult to find a balance between computational efficiency and fitting accuracy when processing renewable energy data, and cannot meet the actual needs of high-precision, full-time simulation. Summary of the Invention

[0004] In order to solve the above problems, the present invention provides a new energy power distribution system operation simulation method, system, device and medium.

[0005] In a first aspect, an embodiment of the present invention provides a method for simulating the operation of a new energy power distribution system, comprising:

[0006] Constructing a first node admittance matrix based on network parameters of the target new energy power distribution system, and adjusting the first node admittance matrix according to a plurality of operating scenarios to obtain a second node admittance matrix under each of the operating scenarios;

[0007] Acquiring historical operating data of the target new energy power distribution system under each of the operating scenarios, and fitting the historical operating data to obtain a power characteristic curve corresponding to the operating scenario;

[0008] Using Bernstein polynomials to perform segmented fitting on the power characteristic curve under each of the operating scenarios to obtain Bernstein polynomial coefficients corresponding to each segment of the power characteristic curve under the operating scenario;

[0009] Performing power flow calculation on the second node admittance matrix under each of the operating scenarios and the Bernstein polynomial coefficients of each section of the power characteristic curve to obtain Bernstein polynomial coefficients of the voltage phasor of each node corresponding to the operating scenario;

[0010] Based on the Bernstein polynomial coefficients of the voltage phasor of each node, the simulated operation status of the target new energy power distribution system in each of the operation scenarios is restored.

[0011] Preferably, the constructing of a first node admittance matrix based on the network parameters of the target new energy power distribution system and adjusting the first node admittance matrix according to a plurality of operating scenarios to obtain a second node admittance matrix under each of the operating scenarios includes:

[0012] Based on the topology and component parameters of the target new energy distribution system, a first-node admittance matrix is ​​constructed;

[0013] performing computational processing on the first node admittance matrix to obtain a real part and an imaginary part of the first node admittance matrix;

[0014] Based on the network structure changes of the target new energy power distribution system in each of the operating scenarios, the real part and the imaginary part are adjusted to obtain a second node admittance matrix corresponding to the operating scenario.

[0015] Preferably, acquiring the historical operating data of the target new energy power distribution system in each of the operating scenarios, and fitting the historical operating data to obtain the power characteristic curve corresponding to the operating scenario, includes:

[0016] Obtaining historical photovoltaic output data, historical generator output data, and historical load data for each node of the target new energy distribution system under each of the operating scenarios;

[0017] Fitting the historical photovoltaic output data of each node under each operating scenario using the least squares method to obtain a photovoltaic active output curve and a photovoltaic reactive output curve corresponding to the node under the corresponding operating scenario;

[0018] Fitting the historical generator output data of each node in each operating scenario using the least squares method to obtain a generator active output curve corresponding to the node in the corresponding operating scenario;

[0019] The least squares method is used to fit the historical load data of each node in each operation scenario to obtain an active load curve and a reactive load curve corresponding to the node in the corresponding operation scenario.

[0020] Preferably, the Bernstein polynomial is used to perform segmented fitting on the power characteristic curve in each operation scenario to obtain Bernstein polynomial coefficients corresponding to each segment of the power characteristic curve in the operation scenario, including:

[0021] Segmenting the power characteristic curve under each operation scenario according to a preset duration to obtain a plurality of segments of the power characteristic curve corresponding to the operation scenario;

[0022] A cubic Bernstein polynomial is used to fit each section of the power characteristic curve in each operation scenario to obtain cubic Bernstein polynomial coefficients corresponding to each section of the power characteristic curve in the operation scenario.

[0023] Preferably, performing power flow calculation on the second node admittance matrix under each of the operating scenarios and the Bernstein polynomial coefficients of each section of the power characteristic curve to obtain Bernstein polynomial coefficients of the voltage phasor of each node corresponding to the operating scenario includes:

[0024] The second node admittance matrix under each of the operating scenarios and the Bernstein polynomial coefficients of each section of the power characteristic curve are substituted into a preset linear power flow equation for solution to obtain the voltage amplitude Bernstein polynomial coefficients and voltage phase angle Bernstein polynomial coefficients of each node corresponding to the operating scenario.

[0025] Preferably, the preset linear power flow equation is represented by the following formula:

[0026]

[0027] in, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the active power injected by node i, Indicates the number of nodes, represents the real element of the admittance matrix of the second node in the kth operation scenario, Indicates the time period under the kth operating scenario The coefficients of the cubic Bernstein polynomial for the voltage amplitude at node i, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage amplitude at node j, represents the imaginary element of the admittance matrix of the second node in the kth operation scenario, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage phase angle at node i, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage phase angle at node j are, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the reactive power injected by node i, Represents a collection of nodes, Represents a collection of running scenes.

[0028] Preferably, the voltage phasor Bernstein polynomial coefficients of each node are used to restore the simulated operation status of the target new energy power distribution system in each operation scenario, including:

[0029] The Bernstein polynomial coefficients of the voltage phasor of each node are substituted into the Bernstein polynomial basis function for restoration calculation to obtain the voltage phasor continuous polynomial function of each node of the target new energy distribution system under each of the operating scenarios, and the voltage phasor continuous polynomial function is represented as the simulated operating conditions of the target new energy distribution system under each of the operating scenarios.

[0030] In a second aspect, an embodiment of the present invention provides a new energy distribution system operation simulation system, comprising:

[0031] a matrix construction module, configured to construct a first node admittance matrix based on network parameters of a target new energy power distribution system, and adjust the first node admittance matrix according to a plurality of operating scenarios to obtain a second node admittance matrix under each of the operating scenarios;

[0032] a curve fitting module, configured to obtain historical operating data of the target new energy power distribution system under each of the operating scenarios, and to fit the historical operating data to obtain a power characteristic curve corresponding to the operating scenario;

[0033] a coefficient fitting module, configured to perform segmented fitting of the power characteristic curve under each of the operating scenarios using a Bernstein polynomial to obtain Bernstein polynomial coefficients corresponding to each segment of the power characteristic curve under the operating scenario;

[0034] a power flow solving module, configured to perform power flow calculation on the second node admittance matrix and the Bernstein polynomial coefficients of each section of the power characteristic curve in each operation scenario, to obtain the Bernstein polynomial coefficients of the voltage phasor of each node corresponding to the operation scenario;

[0035] The continuous simulation module is used to restore the simulated operation status of the target new energy distribution system in each operation scenario based on the Bernstein polynomial coefficients of the voltage phasor of each node.

[0036] Preferably, the matrix building module includes:

[0037] A first matrix determination unit is used to construct a first node admittance matrix based on the topological structure and component parameters of the target new energy power distribution system;

[0038] an operation processing unit, configured to perform operation processing on the first node admittance matrix to obtain a real part and an imaginary part of the first node admittance matrix;

[0039] The second matrix determination unit is used to adjust the elements of the real part and the imaginary part based on the network structure changes of the target new energy power distribution system in each of the operating scenarios to obtain a second node admittance matrix corresponding to the operating scenario.

[0040] Preferably, the curve fitting module includes:

[0041] A historical data acquisition unit, configured to acquire historical photovoltaic output data, historical generator output data, and historical load data of each node of the target new energy power distribution system under each of the operating scenarios;

[0042] a first fitting unit, configured to fit the historical photovoltaic output data of each node under each operation scenario using a least squares method to obtain a photovoltaic active output curve and a photovoltaic reactive output curve corresponding to the node under the operation scenario;

[0043] A second fitting unit is configured to fit the historical generator output data of each node in each operation scenario using the least squares method to obtain a generator active output curve corresponding to the node in the operation scenario;

[0044] The third fitting unit is used to fit the historical load data of each node in each operation scenario by using the least squares method to obtain the active load curve and reactive load curve corresponding to the node in the corresponding operation scenario.

[0045] Preferably, the coefficient fitting module includes:

[0046] a curve segmentation unit, configured to segment the power characteristic curve under each of the operating scenarios according to a preset duration, to obtain a plurality of segments of the power characteristic curve corresponding to the operating scenarios;

[0047] The fourth fitting unit is configured to fit each section of the power characteristic curve in each operation scenario using a cubic Bernstein polynomial to obtain cubic Bernstein polynomial coefficients corresponding to each section of the power characteristic curve in the operation scenario.

[0048] Preferably, the power flow solving module includes:

[0049] A polynomial coefficient calculation unit is used to substitute the second node admittance matrix under each of the operating scenarios and the Bernstein polynomial coefficients of each section of the power characteristic curve into a preset linear power flow equation for solution, so as to obtain the Bernstein polynomial coefficients of the voltage amplitude and the Bernstein polynomial coefficients of the voltage phase angle for each node under the corresponding operating scenario.

[0050] Preferably, the preset linear power flow equation is represented by the following formula:

[0051]

[0052] in, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the active power injected by node i, Indicates the number of nodes, represents the real element of the admittance matrix of the second node in the kth operation scenario, Indicates the time period under the kth operating scenario The coefficients of the cubic Bernstein polynomial for the voltage amplitude at node i, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage amplitude at node j, represents the imaginary element of the admittance matrix of the second node in the kth operation scenario, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage phase angle at node i, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage phase angle at node j are, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the reactive power injected by node i, Represents a collection of nodes, Represents a collection of running scenes.

[0053] Preferably, the continuous simulation module includes:

[0054] A calculation and characterization unit is used to substitute the voltage phasor Bernstein polynomial coefficient of each node into the Bernstein polynomial basis function for restoration calculation, obtain the voltage phasor continuous polynomial function of each node of the target new energy distribution system under each of the operating scenarios, and characterize the voltage phasor continuous polynomial function as a simulated operating condition of the target new energy distribution system under each of the operating scenarios.

[0055] In a third aspect, an embodiment of the present invention provides a terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the new energy distribution system operation simulation method as described above is implemented.

[0056] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the new energy distribution system operation simulation method as described above.

[0057] Compared with the prior art, the embodiments of the present invention provide a method, system, device, and medium for simulating the operation of a new energy distribution system. The advantages of the method, system, device, and medium are as follows: by constructing and flexibly adjusting the node admittance matrix according to different operation scenarios, the network characteristics of the new energy distribution system under complex and changeable operation conditions can be accurately reflected; by fitting historical operation data to obtain a power characteristic curve, the system operation rules can be effectively explored, and then Bernstein polynomials are used for piecewise fitting, which greatly improves the accuracy and flexibility of the description of the power characteristic curve and can carefully depict the power changes in different stages; by using the node admittance matrix and Bernstein polynomial coefficients to perform power flow calculations, the Bernstein polynomial coefficients of the voltage phasor of each node can be accurately solved, thereby restoring the simulated operation of the system under various operation scenarios. The present invention fully utilizes the intrinsic connection between the network parameters and historical operation data of the new energy distribution system and the influence of time characteristics on the operation simulation, and uses Bernstein polynomials for continuous operation simulation, which can effectively improve the accuracy of the operation simulation of the new energy distribution system, thereby providing support for power dispatching and smart grid optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flow chart of a method for simulating the operation of a new energy power distribution system according to an embodiment of the present invention;

[0059] Figure 2 This is a schematic diagram of the node topology of a new energy power distribution system according to an embodiment of the present invention;

[0060] Figure 3 This is a schematic diagram of active power output data of each photovoltaic unit in the new energy power distribution system according to an embodiment of the present invention;

[0061] Figure 4 This is a schematic diagram of the reactive power output data of each photovoltaic unit in the new energy power distribution system according to an embodiment of the present invention;

[0062] Figure 5 Schematic diagram of active power of loads at each node in a new energy power distribution system according to an embodiment of the present invention;

[0063] Figure 6 Schematic diagram of reactive power of loads at various nodes in a new energy power distribution system according to an embodiment of the present invention;

[0064] Figure 7 Schematic diagram of voltage amplitude simulation of node 20 in a new energy power distribution system according to an embodiment of the present invention;

[0065] Figure 8 Schematic diagram of voltage phase angle simulation of node 20 in a new energy power distribution system according to an embodiment of the present invention;

[0066] Figure 9 This is a structural diagram of a new energy power distribution system operation simulation system according to an embodiment of the present invention;

[0067] Figure 10 This is a schematic structural diagram of a terminal device according to an embodiment of the present invention;

[0068] Reference numerals:

[0069] 01. Matrix construction module; 02. Curve fitting module; 03. Coefficient fitting module; 04. Power flow solving module; 05. Continuous simulation module; 5000. Terminal device; 5001. Processor; 5002. Bus; 5003. Memory; 5004. Transceiver. DETAILED DESCRIPTION

[0070] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0071] In the description of the present invention, it should be understood that the terms "first" and "second" etc. are used in the present invention to distinguish different objects rather than to describe a specific order.

[0072] In describing the present invention, it should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. Those skilled in the art will understand the specific meanings of the above terms in the present invention in specific circumstances.

[0073] like Figure 1 As shown, it is a flow chart of a method for simulating the operation of a new energy distribution system according to an embodiment of the present invention. An embodiment of the present invention provides a method for simulating the operation of a new energy distribution system, comprising the steps of:

[0074] S1. Constructing a first node admittance matrix based on network parameters of the target new energy distribution system, and adjusting the first node admittance matrix according to a number of operating scenarios to obtain a second node admittance matrix under each operating scenario;

[0075] Specifically, step S1 includes:

[0076] 1) Construct the first node admittance matrix based on the topology and component parameters of the target new energy distribution system;

[0077] For the target new energy distribution system, it is recorded that it includes N nodes, the nodes are numbered from 1 to N, and the node set is Based on the topological structure and component parameters of the target new energy distribution system, the first node admittance matrix of the target new energy distribution system is constructed.

[0078] It is understandable that the node admittance matrix Elements in represents the mutual admittance between node i and node j, Represents the self-admittance of node i. Self-admittance is the sum of the admittances of all branches connected to that node, while mutual admittance is the negative of the branch admittance between nodes i and j. The node admittance matrix reflects the electrical connections and admittance characteristics between nodes in a power system, providing an important mathematical model for power system analysis and calculations.

[0079] 2) performing computational processing on the first node admittance matrix to obtain the real part and the imaginary part of the first node admittance matrix;

[0080] The first node admittance matrix is ​​a complex matrix. The real and imaginary parts of the first node admittance matrix can be extracted using mathematical software such as Python or MATLAB. Specifically, the matrix is the node admittance matrix The real part of the matrix is the node admittance matrix The imaginary part of .

[0081] 3) Based on the network structure changes of the target new energy distribution system in each operation scenario, the real and imaginary parts are adjusted to obtain the second node admittance matrix under the corresponding operation scenario.

[0082] Specifically, the operating scenarios of the target new energy distribution system will continue to change, such as the access or exit of new energy power sources, changes in load, switching between different operating modes, etc. These will cause the network structure of the system to change, thereby causing the admittance relationship between the nodes of the target new energy distribution system to change.

[0083] Furthermore, the matrix is ​​adjusted in real time according to the topology transformation and line disconnection of the target new energy distribution system in various operating scenarios. and matrix The elements in are used to obtain the second node admittance matrix under the corresponding operation scenario.

[0084] S2. Obtain historical operating data of the target new energy distribution system under each operating scenario, and perform fitting on the historical operating data to obtain a power characteristic curve under the corresponding operating scenario;

[0085] For the target new energy distribution system, record its total Each running scenario has Time sections. The running scenes are numbered from 1 to K, and the running scene set is , the time sections are numbered from 1 to T, and the time section set is .

[0086] Specifically, step S2 includes:

[0087] 1) Obtain historical photovoltaic output data, historical generator output data, and historical load data for each node of the target new energy distribution system under each operating scenario;

[0088] 2) The least squares method is used to fit the historical photovoltaic output data of each node under each operating scenario to obtain the photovoltaic active output curve and photovoltaic reactive output curve of the corresponding node under the corresponding operating scenario;

[0089] Specifically, the least squares method is used to fit the historical photovoltaic output data of node i under the kth operation scenario, and the photovoltaic active output curve and photovoltaic reactive output curve are obtained as follows: and .

[0090] 3) Use the least squares method to fit the historical generator output data of each node under each operating scenario to obtain the generator active output curve of the corresponding node under the corresponding operating scenario;

[0091] Specifically, the least squares method is used to fit the historical generator output data of node i under the kth operation scenario, and the generator active output curve is obtained as follows: .

[0092] 4) The least squares method is used to fit the historical load data of each node under each operation scenario to obtain the active load curve and reactive load curve of the corresponding node under the corresponding operation scenario.

[0093] Specifically, the least squares method is used to fit the historical load data of node i under the kth operation scenario, and the obtained active load curve and reactive load curve are respectively and .

[0094] S3. Using Bernstein polynomials to perform segmented fitting on the power characteristic curve under each operating scenario, and obtaining Bernstein polynomial coefficients for each segment of the power characteristic curve under the corresponding operating scenario;

[0095] Specifically, step S3 includes:

[0096] 1) Segment the power characteristic curve under each operating scenario according to the preset duration to obtain several power characteristic curves under the corresponding operating scenario;

[0097] In this embodiment, the power characteristic curve under each operation scenario is segmented according to a preset duration of 1 hour to obtain a plurality of power characteristic curves under the corresponding operation scenario.

[0098] 2) A cubic Bernstein polynomial is used to fit each power characteristic curve under each operating scenario to obtain the cubic Bernstein polynomial coefficients of each power characteristic curve under the corresponding operating scenario.

[0099] Bernstein polynomial fitting is a method that uses Bernstein polynomials to approximate given data points. Generally speaking, higher degrees provide a stronger polynomial fit, but also increase computational complexity. This invention uses cubic Bernstein polynomials for fitting, balancing fitting performance and computational complexity.

[0100] Specifically, in one embodiment, the photovoltaic active power output curve of node i in the kth operating scenario is: , the curve Duan Wei , The basis vectors of the cubic Bernstein polynomial are ,have:

[0101]

[0102] Let the cubic Bernstein polynomial coefficients of this curve be , then:

[0103]

[0104] Furthermore, the cubic Bernstein polynomial coefficients ,have:

[0105]

[0106] in, Indicates the kth running scenario of node i The photovoltaic active output curve at the start time The power change rate, Indicates the kth running scenario of node i The photovoltaic active output curve at the end of the segment The power change rate.

[0107] It is understandable that the cubic Bernstein polynomial coefficients of each section of the power characteristic curve in each operating scenario can be obtained by analogy based on the above formula, and will not be repeated here.

[0108] S4. Perform power flow calculation on the second node admittance matrix and the Bernstein polynomial coefficients of each section of the power characteristic curve in each operating scenario to obtain the Bernstein polynomial coefficients of the voltage phasor of each node in the corresponding operating scenario;

[0109] Specifically, the second node admittance matrix under each operating scenario and the Bernstein polynomial coefficients of each power characteristic curve are substituted into the preset linear power flow equation for solution to obtain the Bernstein polynomial coefficients of the voltage amplitude and the Bernstein polynomial coefficients of the voltage phase angle of each node under the corresponding operating scenario.

[0110] Furthermore, let the second node admittance matrix under the kth operation scenario be , the real and imaginary parts are and After converting the variable values ​​of each time section into the cubic Bernstein polynomial coefficients of each variable in each period, the form of the preset linear power flow equation is as follows:

[0111]

[0112] in, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the active power injected by node i, Indicates the number of nodes, represents the real element of the admittance matrix of the second node in the kth operation scenario, Indicates the time period under the kth operating scenario The coefficients of the cubic Bernstein polynomial for the voltage amplitude at node i, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage amplitude at node j, represents the imaginary element of the admittance matrix of the second node in the kth operation scenario, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage phase angle at node i, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage phase angle at node j are, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the reactive power injected by node i, Represents a collection of nodes, Represents a collection of running scenes.

[0113] For the PQ node (load node) in the target new energy distribution system, and is a known quantity, and is the unknown quantity to be solved; for the PV node (voltage control node) in the target new energy distribution system, and is a known quantity, and Substitute the cubic Bernstein polynomial coefficients of each known quantity into the preset linear power flow equation to solve for the cubic Bernstein polynomial coefficients of each unknown quantity.

[0114] S5. Based on the Bernstein polynomial coefficients of the voltage phasor of each node, the simulated operation status of the target new energy distribution system in each operation scenario is restored.

[0115] Specifically, the Bernstein polynomial coefficients of the voltage phasor of each node are substituted into the Bernstein polynomial basis function for restoration calculation to obtain the voltage phasor continuous polynomial function of each node in each operating scenario of the target new energy distribution system, and the voltage phasor continuous polynomial function is represented as the simulated operation of the target new energy distribution system in each operating scenario. It should be noted that the continuous polynomial function accurately describes the voltage amplitude and phase angle of each node in a mathematical way, and these electrical quantities are key indicators reflecting the operating status of the target new energy distribution system. Therefore, the continuous polynomial function actually represents the operating status of the target new energy distribution system in the form of a mathematical model from the perspective of electrical quantity changes.

[0116] Furthermore, in one embodiment, the time period Coefficients of the cubic Bernstein polynomial of the voltage amplitude at node i ,have:

[0117]

[0118] Furthermore, there are:

[0119]

[0120] in, This formula can be used to recover the continuous polynomial function of the voltage amplitude at node i throughout the entire time period. Similarly, the continuous polynomial function of the voltage phase angle at node i throughout the entire time period can be derived by analogy.

[0121] In order to verify the effectiveness of the above-mentioned new energy distribution system operation simulation method, Figure 2 As shown in FIG, it is a schematic diagram of the node topology of a new energy distribution system according to an embodiment of the present invention. The embodiment of the present invention provides a new energy distribution system that is an IEEE 33-node distribution system. Specifically, the base capacity of the distribution system is 10MVA, and the base voltage is 12.66kV. The distribution system is a radial distribution network, comprising a total of 33 nodes. Among them, nodes 3, 5, 7, 10, 13, 14, 16, 20, 24, 25, 27, 29, 32, and 33 are photovoltaic nodes of type PQ nodes. Node 1 is a balancing node, and the remaining nodes are load nodes.

[0122] In terms of data collection, the distribution system samples the load and photovoltaic output with a sampling period of 15 minutes, and the sampling period covers 48 hours. Based on these sampled data, the distribution system generates a distribution network operation data sample consisting of 192 time sections. The active output data of each photovoltaic in the distribution system is as follows: Figure 3 As shown, the reactive power output data is as follows Figure 4 As shown in Figure 2, the photovoltaic system outputs power during the time sections numbered 28 to 71 and 124 to 167 (during the daytime in 48 hours). The active power of the load at each node of the distribution system is as follows: Figure 5 As shown, the reactive power is Figure 6 shown.

[0123] Furthermore, MATLAB software was used for calculation. After continuous optimization and simulation, the voltage amplitude continuous curve and voltage phase angle continuous curve of each node were obtained. Among them, the continuous simulation of the voltage amplitude of node 20 is as follows: Figure 7 As shown, the continuous simulation of the voltage phase angle is as follows Figure 8 shown. Figure 7 and Figure 8 The red curve in the figure represents a continuous curve obtained from the simulation, while the blue data points represent discrete data at various time intervals. This diagram demonstrates that the method for simulating the operation of a new energy distribution system according to an embodiment of the present invention achieves good continuous fitting of voltage amplitude and voltage phase angle, effectively simulating the actual operation of a new energy distribution system.

[0124] The embodiment of the present invention provides a method for simulating the operation of a new energy distribution system. By constructing and flexibly adjusting the node admittance matrix according to different operation scenarios, the method can accurately reflect the network characteristics of the new energy distribution system under complex and changeable operating conditions; fitting historical operation data to obtain a power characteristic curve, effectively exploring the system operation rules, and then using Bernstein polynomials for piecewise fitting, which greatly improves the accuracy and flexibility of the description of the power characteristic curve and can carefully depict the power changes in different stages; using the node admittance matrix and Bernstein polynomial coefficients to carry out power flow calculations, the Bernstein polynomial coefficients of the voltage phasor of each node can be accurately solved, and then the simulated operation of the system under various operation scenarios can be restored. The present invention makes full use of the inherent connection between the network parameters and historical operation data of the new energy distribution system and the influence of time characteristics on the operation simulation. The Bernstein polynomial is used for continuous operation simulation, which can effectively improve the accuracy of the operation simulation of the new energy distribution system, thereby providing support for power dispatching and smart grid optimization.

[0125] Based on the above-mentioned new energy distribution system operation simulation method, Figure 9 As shown, an embodiment of the present invention provides a new energy distribution system operation simulation system, including:

[0126] Matrix construction module 01 is used to construct a first node admittance matrix based on the network parameters of the target new energy distribution system, and adjust the first node admittance matrix according to a number of operating scenarios to obtain a second node admittance matrix under each operating scenario;

[0127] Curve fitting module 02 is used to obtain historical operating data of the target new energy distribution system under each operating scenario, and fit the historical operating data to obtain the power characteristic curve under the corresponding operating scenario;

[0128] The coefficient fitting module 03 is used to perform segmented fitting of the power characteristic curve under each operation scenario using Bernstein polynomials to obtain Bernstein polynomial coefficients for each segment of the power characteristic curve under the corresponding operation scenario;

[0129] The power flow solving module 04 is used to perform power flow calculation on the second node admittance matrix and the Bernstein polynomial coefficients of each section of the power characteristic curve in each operating scenario, and obtain the Bernstein polynomial coefficients of the voltage phasor of each node in the corresponding operating scenario;

[0130] The continuous simulation module 05 is used to restore the simulated operation status of the target new energy distribution system in each operation scenario based on the Bernstein polynomial coefficients of the voltage phasor of each node.

[0131] Specifically, the matrix building blocks include:

[0132] A first matrix determination unit is used to construct a first node admittance matrix based on the topological structure and component parameters of the target new energy power distribution system;

[0133] an operation processing unit, configured to perform operation processing on the first node admittance matrix to obtain a real part and an imaginary part of the first node admittance matrix;

[0134] The second matrix determination unit is used to adjust the real and imaginary parts of the elements based on the network structure changes of the target new energy distribution system in each operation scenario to obtain the second node admittance matrix in the corresponding operation scenario.

[0135] Specifically, the curve fitting module includes:

[0136] A historical data acquisition unit is used to obtain historical photovoltaic output data, historical generator output data, and historical load data of each node in each operating scenario of the target new energy distribution system;

[0137] The first fitting unit is used to fit the historical photovoltaic output data of each node under each operation scenario using the least squares method to obtain the photovoltaic active output curve and the photovoltaic reactive output curve of the corresponding node under the corresponding operation scenario;

[0138] The second fitting unit is used to fit the historical generator output data of each node under each operation scenario using the least squares method to obtain the generator active output curve of the corresponding node under the corresponding operation scenario;

[0139] The third fitting unit is used to fit the historical load data of each node under each operation scenario using the least square method to obtain the active load curve and reactive load curve of the corresponding node under the corresponding operation scenario.

[0140] Specifically, the coefficient fitting module includes:

[0141] The curve segmentation unit is used to segment the power characteristic curve under each operation scenario according to a preset time length to obtain a plurality of power characteristic curves under the corresponding operation scenario;

[0142] The fourth fitting unit is used to fit each power characteristic curve in each operation scenario using a cubic Bernstein polynomial to obtain cubic Bernstein polynomial coefficients of each power characteristic curve in the corresponding operation scenario.

[0143] Specifically, the power flow solution module includes:

[0144] The polynomial coefficient calculation unit is used to substitute the second node admittance matrix under each operating scenario and the Bernstein polynomial coefficients of each power characteristic curve into the preset linear power flow equation for solution, so as to obtain the Bernstein polynomial coefficients of the voltage amplitude and the Bernstein polynomial coefficients of the voltage phase angle of each node under the corresponding operating scenario.

[0145] Specifically, the following formula is used to represent the preset linear power flow equation:

[0146]

[0147] in, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the active power injected by node i, Indicates the number of nodes, represents the real element of the admittance matrix of the second node in the kth operation scenario, Indicates the time period under the kth operating scenario The coefficients of the cubic Bernstein polynomial for the voltage amplitude at node i, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage amplitude at node j, represents the imaginary element of the admittance matrix of the second node in the kth operation scenario, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage phase angle at node i, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage phase angle at node j are, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the reactive power injected by node i, Represents a collection of nodes, Represents a collection of running scenes.

[0148] Specifically, the continuous simulation module includes:

[0149] The calculation characterization unit is used to substitute the Bernstein polynomial coefficients of the voltage phasor of each node into the Bernstein polynomial basis function for restoration calculation, obtain the voltage phasor continuous polynomial function of each node in each operating scenario of the target new energy distribution system, and characterize the voltage phasor continuous polynomial function as the simulated operating conditions of the target new energy distribution system in each operating scenario.

[0150] It should be noted that each module in the above-mentioned new energy distribution system operation simulation system can be fully or partially implemented by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. For the specific definition of a new energy distribution system operation simulation system, please refer to the definition of a new energy distribution system operation simulation method above. The two have the same functions and effects and will not be repeated here.

[0151] An embodiment of the present invention further provides a terminal device, comprising:

[0152] processor, memory, and bus;

[0153] The bus is used to connect the processor and the memory;

[0154] The memory is used to store operation instructions;

[0155] The processor is used to call the operation instruction, and the executable instruction enables the processor to perform the operation corresponding to the new energy distribution system operation simulation method as described above in the present invention.

[0156] In an optional embodiment, a terminal device is provided, such as Figure 10 As shown, Figure 10 The terminal device 5000 shown includes a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are connected, for example, via a bus 5002. Optionally, the terminal device 5000 may further include a transceiver 5004. It should be noted that in actual applications, the number of transceivers 5004 is not limited to one, and the structure of the terminal device 5000 does not constitute a limitation on the embodiments of the present invention.

[0157] Processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the present disclosure. Processor 5001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0158] The bus 5002 may include a path for transmitting information between the above components. The bus 5002 may be a PCI bus or an EISA bus, etc. The bus 5002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 10 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0159] The memory 5003 can be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an EEPROM, CD-ROM or other optical disk storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0160] The memory 5003 is used to store application code for executing the solution of the present invention, and the execution is controlled by the processor 5001. The processor 5001 is used to execute the application code stored in the memory 5003 to implement the content shown in any of the above method embodiments.

[0161] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for simulating the operation of a new energy power distribution system described above is implemented.

[0162] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk drives, CD-ROMs, optical storage devices, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention may be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0163] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0164] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0166] In summary, the embodiment of the present invention provides a method, system, device and medium for simulating the operation of a new energy distribution system. By constructing and flexibly adjusting the node admittance matrix according to different operation scenarios, it can accurately reflect the network characteristics of the new energy distribution system under complex and changeable operating conditions; fitting historical operation data to obtain a power characteristic curve, effectively exploring the system operation rules, and then using Bernstein polynomials for segmented fitting, which greatly improves the accuracy and flexibility of the description of the power characteristic curve, and can carefully depict the power changes in different stages; using the node admittance matrix and Bernstein polynomial coefficients to carry out power flow calculations, it can accurately solve the Bernstein polynomial coefficients of the voltage phasor of each node, and then restore the simulated operation of the system in various operation scenarios. The present invention makes full use of the intrinsic connection between the network parameters and historical operation data of the new energy distribution system and the influence of time characteristics on the operation simulation, and uses Bernstein polynomials for continuous operation simulation, which can effectively improve the accuracy of the operation simulation of the new energy distribution system, thereby providing support for power dispatching and smart grid optimization.

[0167] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0168] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. A new energy distribution system operation simulation method, characterized in that: include: Constructing a first node admittance matrix based on network parameters of the target new energy power distribution system, and adjusting the first node admittance matrix according to a plurality of operating scenarios to obtain a second node admittance matrix under each of the operating scenarios; Acquiring historical operating data of the target new energy power distribution system under each of the operating scenarios, and fitting the historical operating data to obtain a power characteristic curve corresponding to the operating scenario; Using Bernstein polynomials to perform segmented fitting on the power characteristic curve under each of the operating scenarios to obtain Bernstein polynomial coefficients corresponding to each segment of the power characteristic curve under the operating scenario; Performing power flow calculation on the second node admittance matrix under each of the operating scenarios and the Bernstein polynomial coefficients of each section of the power characteristic curve to obtain Bernstein polynomial coefficients of the voltage phasor of each node corresponding to the operating scenario; Based on the Bernstein polynomial coefficients of the voltage phasor of each node, the simulated operation status of the target new energy power distribution system in each of the operation scenarios is restored.

2. The method for simulating the operation of a new energy power distribution system according to claim 1, characterized in that: The first node admittance matrix is ​​constructed based on the network parameters of the target new energy power distribution system, and the first node admittance matrix is ​​adjusted according to a plurality of operating scenarios to obtain a second node admittance matrix under each of the operating scenarios, including: Based on the topology and component parameters of the target new energy distribution system, a first-node admittance matrix is ​​constructed; performing computational processing on the first node admittance matrix to obtain a real part and an imaginary part of the first node admittance matrix; Based on the network structure changes of the target new energy power distribution system in each of the operating scenarios, the real part and the imaginary part are adjusted to obtain a second node admittance matrix corresponding to the operating scenario.

3. The method for simulating the operation of a new energy power distribution system according to claim 1, characterized in that: The acquiring of historical operating data of the target new energy power distribution system in each operating scenario and fitting the historical operating data to obtain a power characteristic curve corresponding to the operating scenario includes: Obtaining historical photovoltaic output data, historical generator output data, and historical load data for each node of the target new energy distribution system under each of the operating scenarios; Fitting the historical photovoltaic output data of each node under each operating scenario using the least squares method to obtain a photovoltaic active output curve and a photovoltaic reactive output curve corresponding to the node under the corresponding operating scenario; Fitting the historical generator output data of each node in each operating scenario using the least squares method to obtain a generator active output curve corresponding to the node in the corresponding operating scenario; The least squares method is used to fit the historical load data of each node in each operation scenario to obtain an active load curve and a reactive load curve corresponding to the node in the corresponding operation scenario.

4. The method for simulating the operation of a new energy power distribution system according to claim 1, characterized in that: The Bernstein polynomial is used to perform segmented fitting on the power characteristic curve in each operating scenario to obtain Bernstein polynomial coefficients corresponding to each segment of the power characteristic curve in the operating scenario, including: Segmenting the power characteristic curve under each operation scenario according to a preset duration to obtain a plurality of segments of the power characteristic curve corresponding to the operation scenario; A cubic Bernstein polynomial is used to fit each section of the power characteristic curve in each operation scenario to obtain cubic Bernstein polynomial coefficients corresponding to each section of the power characteristic curve in the operation scenario.

5. The method for simulating the operation of a new energy power distribution system according to claim 1, characterized in that: The performing power flow calculation on the second node admittance matrix and the Bernstein polynomial coefficients of each section of the power characteristic curve in each operation scenario to obtain the Bernstein polynomial coefficients of the voltage phasor of each node corresponding to the operation scenario includes: The second node admittance matrix under each of the operating scenarios and the Bernstein polynomial coefficients of each section of the power characteristic curve are substituted into a preset linear power flow equation for solution to obtain the voltage amplitude Bernstein polynomial coefficients and voltage phase angle Bernstein polynomial coefficients of each node corresponding to the operating scenario.

6. The method for simulating the operation of a new energy power distribution system according to claim 5, characterized in that: The preset linear power flow equation is represented by the following formula: in, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the active power injected by node i, Indicates the number of nodes, represents the real element of the admittance matrix of the second node in the kth operation scenario, Indicates the time period under the kth operating scenario The coefficients of the cubic Bernstein polynomial for the voltage amplitude at node i, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage amplitude at node j, represents the imaginary element of the admittance matrix of the second node in the kth operation scenario, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage phase angle at node i, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage phase angle at node j are, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the reactive power injected by node i, Represents a collection of nodes, Represents a collection of running scenes.

7. The method for simulating the operation of a new energy power distribution system according to claim 1, characterized in that: The method of restoring the simulated operation status of the target new energy power distribution system in each operation scenario based on the Bernstein polynomial coefficients of the voltage phasor of each node includes: The Bernstein polynomial coefficients of the voltage phasor of each node are substituted into the Bernstein polynomial basis function for restoration calculation to obtain the voltage phasor continuous polynomial function of each node of the target new energy distribution system under each of the operating scenarios, and the voltage phasor continuous polynomial function is represented as the simulated operating conditions of the target new energy distribution system under each of the operating scenarios.

8. A new energy distribution system operation simulation system, characterized in that: include: a matrix construction module, configured to construct a first node admittance matrix based on network parameters of a target new energy power distribution system, and adjust the first node admittance matrix according to a plurality of operating scenarios to obtain a second node admittance matrix under each of the operating scenarios; a curve fitting module, configured to obtain historical operating data of the target new energy power distribution system under each of the operating scenarios, and to fit the historical operating data to obtain a power characteristic curve corresponding to the operating scenario; a coefficient fitting module, configured to perform segmented fitting of the power characteristic curve under each of the operating scenarios using a Bernstein polynomial to obtain Bernstein polynomial coefficients corresponding to each segment of the power characteristic curve under the operating scenario; a power flow solving module, configured to perform power flow calculation on the second node admittance matrix and the Bernstein polynomial coefficients of each section of the power characteristic curve in each operation scenario, to obtain the Bernstein polynomial coefficients of the voltage phasor of each node corresponding to the operation scenario; The continuous simulation module is used to restore the simulated operation status of the target new energy distribution system in each operation scenario based on the Bernstein polynomial coefficients of the voltage phasor of each node.

9. The new energy distribution system operation simulation system according to claim 8, characterized in that: The matrix building module includes: A first matrix determination unit is used to construct a first node admittance matrix based on the topological structure and component parameters of the target new energy power distribution system; an operation processing unit, configured to perform operation processing on the first node admittance matrix to obtain a real part and an imaginary part of the first node admittance matrix; The second matrix determination unit is used to adjust the elements of the real part and the imaginary part based on the network structure changes of the target new energy power distribution system in each of the operating scenarios to obtain a second node admittance matrix corresponding to the operating scenario.

10. The new energy distribution system operation simulation system according to claim 8, characterized in that: The curve fitting module includes: A historical data acquisition unit, configured to acquire historical photovoltaic output data, historical generator output data, and historical load data of each node of the target new energy power distribution system under each of the operating scenarios; a first fitting unit, configured to fit the historical photovoltaic output data of each node under each operation scenario using a least squares method to obtain a photovoltaic active output curve and a photovoltaic reactive output curve corresponding to the node under the operation scenario; A second fitting unit is configured to fit the historical generator output data of each node in each operation scenario using the least squares method to obtain a generator active output curve corresponding to the node in the operation scenario; The third fitting unit is used to fit the historical load data of each node in each operation scenario by using the least squares method to obtain the active load curve and reactive load curve corresponding to the node in the corresponding operation scenario.

11. The new energy distribution system operation simulation system according to claim 8, characterized in that: The coefficient fitting module includes: a curve segmentation unit, configured to segment the power characteristic curve under each of the operating scenarios according to a preset duration, to obtain a plurality of segments of the power characteristic curve corresponding to the operating scenarios; The fourth fitting unit is configured to fit each section of the power characteristic curve in each operation scenario using a cubic Bernstein polynomial to obtain cubic Bernstein polynomial coefficients corresponding to each section of the power characteristic curve in the operation scenario.

12. The new energy distribution system operation simulation system according to claim 8, characterized in that: The power flow solving module includes: A polynomial coefficient calculation unit is used to substitute the second node admittance matrix under each of the operating scenarios and the Bernstein polynomial coefficients of each section of the power characteristic curve into a preset linear power flow equation for solution, so as to obtain the Bernstein polynomial coefficients of the voltage amplitude and the Bernstein polynomial coefficients of the voltage phase angle for each node under the corresponding operating scenario.

13. The new energy power distribution system operation simulation system according to claim 12, characterized in that: The preset linear power flow equation is represented by the following formula: in, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the active power injected by node i, Indicates the number of nodes, represents the real element of the admittance matrix of the second node in the kth operation scenario, Indicates the time period under the kth operating scenario The coefficients of the cubic Bernstein polynomial for the voltage amplitude at node i, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage amplitude at node j, represents the imaginary element of the admittance matrix of the second node in the kth operation scenario, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage phase angle at node i, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the voltage phase angle at node j are, Indicates the time period under the kth operating scenario The cubic Bernstein polynomial coefficients of the reactive power injected by node i, Represents a collection of nodes, Represents a collection of running scenes.

14. The new energy distribution system operation simulation system according to claim 8, characterized in that: The continuous simulation module includes: A calculation and characterization unit is used to substitute the voltage phasor Bernstein polynomial coefficient of each node into the Bernstein polynomial basis function for restoration calculation, obtain the voltage phasor continuous polynomial function of each node of the target new energy distribution system under each of the operating scenarios, and characterize the voltage phasor continuous polynomial function as a simulated operating condition of the target new energy distribution system under each of the operating scenarios.

15. A terminal device, characterized in that: The system comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for simulating the operation of a new energy distribution system according to any one of claims 1 to 7 is implemented.

16. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the new energy distribution system operation simulation method according to any one of claims 1 to 7.

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