Methods, devices, media and equipment for calculating the operating envelope of distribution network nodes

Abstract

This disclosure relates to the field of power system technology, and provides a method, apparatus, medium, and equipment for calculating the operating envelope of distribution network nodes. The method includes: acquiring basic data including network topology, line parameters, transformer parameters, transmission capacity limits, node voltage limits, and distributed energy output limits, and constructing a feasible region model containing linearized power flow equations and various security constraints based on this data; defining a fully symmetric multicell as its independent operating envelope for each node connected to distributed energy, and ensuring that the Cartesian product of all node envelopes is included in the feasible region model by constraining each node to adjust its power independently within its envelope without violating grid security constraints; and solving for the operating envelope of each node with the objective of maximizing the size of all node envelopes. This embodiment achieves accurate and efficient definition of the safe operating range of distribution network nodes under high-proportion distributed energy access.

Description

Technical Field

[0001] This disclosure relates to the field of power system technology, and more specifically, to a method, apparatus, medium, and equipment for calculating the operating envelope of a distribution network node. Background Technology

[0002] With the advancement of the "carbon peak and carbon neutrality" goals, a new power system based on new energy sources is being rapidly constructed. Distributed and flexible resources such as industrial and commercial photovoltaics and energy storage are being integrated into the distribution network on a large scale. While these resources can improve energy utilization efficiency and assist in grid regulation, their randomness and intermittency also pose serious challenges to the safe operation of the distribution network, easily leading to problems such as node voltage exceeding limits and line overload. Therefore, accurately defining the safe power regulation range of flexible resources at each node has become a key issue in ensuring grid stability and power supply reliability.

[0003] While some related technologies exist that solve problems based on the load capacity set or safe operation envelope of the distribution network, directly calculating the controllable feasible domain has drawbacks such as excessive computational complexity and difficulty in online application when dealing with high-dimensional flexible resource vectors. Summary of the Invention

[0004] This disclosure provides at least one method, apparatus, medium, and device for calculating the operating envelope of a distribution network node. By constructing a feasible domain model of the distribution network operation constraints and introducing fully symmetric polycells to describe the operating envelope of each distributed energy node, the operating range is solved under the objective of maximizing the size of all fully symmetric polycells, thereby achieving an accurate and efficient definition of the safe operating range of distribution network nodes under high-proportion distributed energy access.

[0005] This disclosure provides a method for calculating the operating envelope of a distribution network node, including: Acquire basic data of the power distribution network; wherein, the basic data includes the connection topology of the power distribution network, line parameters, transformer parameters, transmission capacity limits of transmission lines, upper and lower limits of node voltage, and upper and lower limits of output of distributed energy sources; A feasible region model of distribution network operation constraints is constructed based on the aforementioned basic data; wherein, the feasible region model of distribution network operation constraints includes linearized power flow equations, node voltage constraints, line capacity constraints, and distributed energy power constraints, and the feasible region model of distribution network operation constraints is used to describe the set of all distributed energy injection power vectors that meet the conditions for safe operation of the distribution network; For each distribution network node connected to distributed energy, a fully symmetric multicell corresponding to the distribution network node is determined, and an operational envelope model is constructed based on the fully symmetric multicells of all distribution network nodes; wherein, the operational envelope model defines the operational range of each distribution network node connected to distributed energy as the corresponding fully symmetric multicell, and the Cartesian product of the fully symmetric multicells of each distribution network node is constrained to be contained within the set described by the distribution network operational constraint feasible region model; With the goal of maximizing the size of the fully symmetric multicell of all distribution network nodes, and using the node voltage constraints, line capacity constraints, and distributed energy power constraints as constraints, the operating envelope model is calculated to determine the operating envelope of each distribution network node connected to distributed energy.

[0006] This disclosure provides a device for calculating the operating envelope of a distribution network node, comprising: The data acquisition module is used to acquire basic data of the power distribution network; wherein, the basic data includes the connection topology of the power distribution network, line parameters, transformer parameters, transmission capacity limits of transmission lines, upper and lower limits of node voltage, and upper and lower limits of output of distributed energy sources. The constraint construction module is used to construct a feasible region model of distribution network operation constraints based on the basic data; wherein, the feasible region model of distribution network operation constraints includes linearized power flow equations, node voltage constraints, line capacity constraints, and distributed energy power constraints, and the feasible region model of distribution network operation constraints is used to describe the set of all distributed energy injected power vectors that meet the conditions for safe operation of the distribution network; The model building module is used to determine a fully symmetric polycell corresponding to each distribution network node connected to distributed energy, and to build an operating envelope model based on the fully symmetric polycells of all distribution network nodes; wherein, the operating envelope model defines the operating range of each distribution network node connected to distributed energy as the corresponding fully symmetric polycell, and the Cartesian product of the fully symmetric polycells of each distribution network node is constrained to be contained within the set described by the distribution network operating constraint feasible region model; The model solving module is used to calculate the operating envelope model with the objective of maximizing the size of the fully symmetric multicell of all distribution network nodes, and with the node voltage constraint, the line capacity constraint, and the distributed energy power constraint as constraints, to determine the operating envelope of each distribution network node connected to distributed energy.

[0007] This disclosure provides a computer device, including a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the distribution network node operating envelope calculation method as described in any of the above possible embodiments is executed.

[0008] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the distribution network node operating envelope calculation method as described in any of the above possible embodiments.

[0009] The distribution network node operation envelope calculation method, apparatus, medium, and equipment provided in this disclosure decompose the high-dimensional joint feasible domain into multiple low-dimensional independent envelopes, effectively reducing the problem scale; at the same time, the Cartesian product inclusion constraint of each node envelope ensures the safety of distributed regulation; with maximizing the envelope size as the optimization objective, it provides the largest possible flexible adjustment space for distributed energy. Thus, it achieves accurate and efficient definition of the safe operating range of distribution network nodes under high-proportion distributed energy access.

[0010] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings referenced in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0012] Figure 1 A flowchart of a method for calculating the operating envelope of a distribution network node provided in an embodiment of this disclosure is shown; Figure 2 A flowchart of a method for constructing an envelope model provided by an embodiment of this disclosure is shown; Figure 3 A flowchart of a method for solving an envelope model provided in an embodiment of this disclosure is shown; Figure 4 A schematic diagram of the structure of a power distribution network node operation envelope calculation device provided in an embodiment of this disclosure is shown. Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of this disclosure is shown. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0014] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0015] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0016] With the increasing global demand for clean energy and growing environmental awareness, the "carbon peaking and carbon neutrality" goal has provided a clear direction for the transformation of the power system. To adapt to this trend, building a new power system based on renewable energy sources has become an inevitable choice. Against this backdrop, distributed flexible resources such as industrial and commercial photovoltaics and user-side energy storage are being used more and more widely in distribution networks, with their quantity and types continuously increasing. These resources not only help improve energy efficiency and reduce greenhouse gas emissions, but also optimize the overall operating efficiency of the power system by participating in grid regulation.

[0017] Nevertheless, the high proportion of flexible resources integrated also brings new challenges to the safe and stable operation of the distribution network. On the one hand, these resources can effectively reduce the peak-to-valley load difference and promote the absorption of fluctuating renewable energy sources; on the other hand, their randomness and intermittency may lead to problems such as voltage deviations at distribution network nodes and line overloads, increasing safety risks. Therefore, how to accurately assess the permissible power range for flexible resources at each node to participate in distribution network interaction, while ensuring that the safety constraints of the distribution network are not violated, has become an important research topic. This not only relates to the stability of the power system but also directly affects the quality and reliability of power supply.

[0018] Research has revealed that existing studies often employ methods based on distribution network load capacity sets or safe operation envelopes to address these issues. However, these methods face the challenge of high computational complexity when dealing with a large number of flexible resources. Because the active power vector dimension of flexible resources is high, directly calculating the feasible region for regulation is both time-consuming and difficult.

[0019] Based on the above research, this disclosure provides a method, apparatus, medium, and device for calculating the operating envelope of a distribution network node. First, basic data including network topology, line parameters, transformer parameters, transmission capacity limits, node voltage limits, and distributed energy output limits are obtained. Based on this, a feasible region model containing linearized power flow equations and various security constraints is constructed. Then, a fully symmetric multicell is defined as the independent operating envelope for each node connected to distributed energy. By constraining the Cartesian product of all node envelopes to be included within the feasible region model, it is ensured that each node does not violate grid security constraints when arbitrarily adjusting its power within its envelope. The solution aims to maximize the size of all node envelopes, ultimately obtaining the operating envelope of each node.

[0020] In this embodiment, the high-dimensional joint feasible region is decomposed into multiple low-dimensional independent envelopes, effectively reducing the problem size. Simultaneously, the Cartesian product inclusion constraint of each node's envelope ensures the safety of distributed regulation. With maximizing the envelope size as the optimization objective, the system provides the largest possible flexible adjustment space for distributed energy resources. Thus, an accurate and efficient definition of the safe operating range of distribution network nodes under high-proportion distributed energy access is achieved.

[0021] To facilitate understanding of this embodiment, the executing entity of the distribution network node operation envelope calculation method provided in this disclosure will first be described in detail. The executing entity of the distribution network node operation envelope calculation method provided in this disclosure is a computer device. This computer device can be a terminal device or a server. The terminal device can also be a mobile device, user terminal, terminal, handheld device, computing device, vehicle-mounted device, wearable device, etc. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data, and artificial intelligence platforms. Optionally, this method can also be applied to an implementation environment composed of computer devices and servers.

[0022] The method for calculating the operating envelope of a distribution network node provided in this application will be described in detail below with reference to the accompanying drawings. See also Figure 1 The diagram shows a flowchart of a method for calculating the operating envelope of a distribution network node according to an embodiment of this disclosure. The method includes the following steps S101 to S104: S101, Obtain basic data of the power distribution network.

[0023] As can be understood, a power distribution network refers to a power distribution system composed of equipment such as distribution lines, transformers, circuit breakers, busbars, and distributed energy sources. Its function is to distribute electrical energy from upstream substations to various user terminals, while simultaneously accepting electrical energy generated by distributed energy sources, thereby achieving balanced distribution and secure transmission of electrical energy among multiple sources and loads. The basic data corresponding to the power distribution network is a set of numerical values ​​describing the network structure, equipment parameters, and operating boundary conditions. These are used as input parameters for subsequently constructing the feasible region model and operating envelope model of the power distribution network operation constraints. This basic data includes data items such as the connection topology of the power distribution network, line parameters, transformer parameters, transmission line transmission capacity limits, node voltage upper and lower limits, and distributed energy output upper and lower limits.

[0024] Specifically, connection topology refers to the adjacency relationships between nodes in a power distribution network connected by lines, which can be used to determine the power flow path in power flow calculations. Line parameters can include line resistance and line reactance, with resistance in ohms and reactance in ohms, used to calculate voltage drop and power loss during power transmission. Transformer parameters mainly include transformer resistance, transformer reactance, and turns ratio, which is the ratio of the primary side voltage to the secondary side voltage, used to convert the voltage and power on both sides of the transformer. The transmission capacity limit of a transmission line refers to the maximum apparent power allowed to pass through each line, in kilovolt-amperes (kVA). Exceeding this limit will cause the line to overheat or trigger protection mechanisms.

[0025] Specifically, the node voltage upper and lower limits represent the minimum and maximum allowable squares of the effective voltage value at each distribution network node. The square of the effective voltage value is simply referred to as the voltage amplitude square. In the linearized power flow equations of the distribution network, the node voltage state directly uses the voltage amplitude square as a variable; therefore, the constraints can also be directly expressed in the form of voltage amplitude square. Voltage values ​​are represented using a per-unit system, with the per-unit value of the rated operating voltage being 1.0, and the corresponding per-unit value of the voltage amplitude square being 1.0. According to the distribution network safety operation standards, the effective value of the node voltage is typically allowed to fluctuate between 0.95 and 1.05 times the rated value. Therefore, the lower limit of the voltage amplitude square is 0.95 squared, i.e., 0.9025, and the upper limit is 1.05 squared, i.e., 1.1025. For a distribution network with a rated voltage of 380 volts, the actual voltage amplitude square corresponding to 0.9025 is approximately 0.9025 multiplied by the square of 380 volts. The output limits of distributed energy refer to the minimum and maximum values ​​of active and reactive power that each distributed energy device connected to the distribution network node can output at the grid connection point. Active power is measured in kilowatts (kW), and reactive power is measured in kilovars (kvars). Distributed energy refers to small-scale power generation or storage devices connected to the distribution network and located on the user side or near the load center. These are power supply units capable of independently generating or storing electrical energy, and can include photovoltaic power generation systems, wind power generation systems, energy storage systems, fuel cells, micro gas turbines, and diesel generators, among other power generation or energy storage devices.

[0026] Here, we use an example system containing three distribution network nodes and two transmission lines to illustrate the basic data of the distribution network in this example system. Node 1 is the balancing node, connected to the upstream grid; Node 2 is connected to a photovoltaic power generation system with a rated capacity of 100 kVA. The active power output of this photovoltaic system has a lower limit of 0 kW and an upper limit of 80 kW, and a reactive power output lower limit of -40 kV and an upper limit of +40 kV; Node 3 is connected to an energy storage system with a rated capacity of 120 kVA. The active power output of this energy storage system has a lower limit of -60 kW (representing charging) and an upper limit of 60 kW (representing discharging), and a reactive power output lower limit of -50 kV and an upper limit of +50 kV. Lines 1-2 have a resistance of 0.1 ohms and a reactance of 0.2 ohms, while lines 2-3 have a resistance of 0.15 ohms and a reactance of 0.25 ohms. The transmission capacity limit of both transmission lines is 200 kVA. The lower limit of the squared voltage amplitude of all nodes is uniformly set at 0.9025, and the upper limit is uniformly set at 1.1025.

[0027] In some other embodiments, the basic data of the distribution network may also include the reference values ​​of active and reactive power of each node, node type identification (such as balancing node, voltage control node, load node), long-term allowable current carrying capacity of the line, rated capacity and tap adjustment range of the transformer, rated capacity and power factor adjustment range of distributed energy, and other data such as whether each node is connected to energy storage devices or reactive power compensation devices. These data can be supplemented according to the actual configuration and scheduling needs of the distribution network, and are not specifically limited here.

[0028] S102, Construct a feasible domain model of power distribution network operation constraints based on the aforementioned basic data.

[0029] Here, after obtaining the connection topology, line parameters, transformer parameters, transmission line capacity limits, node voltage limits, and distributed energy output limits of the distribution network, a feasible region model of the distribution network operation constraints can be constructed using linearized power flow equations and various security constraints. This model can be represented as the set of all distributed energy injection power vectors that satisfy the linearized power flow equations, node voltage constraints, line capacity constraints, and distributed energy power constraints. It can be used to describe the set of all distributed energy injection power vectors that meet the safe operation conditions of the distribution network, that is, to mathematically characterize the allowable range of active and reactive power of distributed energy, ensuring that any power combination within this range does not violate the physical limits of the distribution network.

[0030] Specifically, the feasible region model of distribution network operation constraints mainly includes linearized power flow equations, node voltage constraints, line capacity constraints, and distributed energy power constraints. Linearized power flow equations refer to linear algebraic equations obtained by performing a first-order Taylor expansion or approximation on the nonlinear AC power flow equations near the rated operating point. These equations can be constructed based on the LinDistFlow model or linearization methods based on the impedance matrix, taking into account the distribution network's connection topology, line resistance, line reactance, transformer resistance, transformer reactance, and turns ratio. This equation ignores higher-order terms related to line resistance and reactance, expressing the square of the node voltage amplitude as a linear function of the injected active and reactive power of distributed energy sources. It also expresses the line power flow as a linear function of the injected active and reactive power, thus describing the linear relationship between the injected active and reactive power of distributed energy sources and the square of the node voltage amplitude and the line power flow. The node voltage constraint states that the square of the voltage amplitude of each distribution network node is not less than the lower voltage limit and not greater than the upper voltage limit corresponding to that node, providing a mathematical description of the node voltage safety boundary. The line capacity constraint means that the absolute value of the power flow of each line is not greater than the transmission capacity limit of that line, preventing line overload. The distributed energy power constraint is a boundary condition where the active and reactive power of each distributed energy device are not less than their respective lower limits and not greater than their respective upper limits, restricting the actual output range of distributed energy sources.

[0031] For example, when constructing a feasible region model of distribution network operation constraints based on basic data, the following steps (1) to (5) may be included: (1) Establish linearized power flow equations based on the connection topology, line parameters, and transformer parameters of the power distribution network; (2) Establish node voltage constraints based on the upper and lower limits of node voltage; (3) Establish line capacity constraints based on the transmission capacity limit of the transmission line; (4) Establish power constraints for distributed energy sources based on the upper and lower limits of their output; (5) The set of all distributed energy injection power vectors that satisfy the linearized power flow equation, node voltage constraints, line capacity constraints and distributed energy power constraints is used as the feasible domain model of the distribution network operation constraints.

[0032] Understandably, when establishing linearized power flow equations, the radial topology of the distribution network and line impedance parameters can be used as an example. Taking the LinDistFlow model as an example, when deriving the linear algebraic relationship between the square of the node voltage magnitude and the injected power at the node, it can be assumed that the per-unit voltage values ​​of each node are close to 1.0 and the ratio of line resistance to reactance is within a certain range. This simplifies the nonlinear power flow equations into linear equations, determining the constant vector, voltage sensitivity matrix, and power flow sensitivity matrix. Specifically, the LinDistFlow model, by neglecting the second-order effect of power loss on voltage drop on the line, expresses the square of the node voltage magnitude as a linear combination of all injected power along the path from the root node to that node, thus obtaining a set of linear algebraic equations. These equations include a constant vector, a voltage sensitivity matrix, and a power flow sensitivity matrix, whose coefficients are uniquely determined by the network topology, line impedance, and transformer turns ratio. The upper and lower limits of node voltage are the minimum and maximum allowable squared voltage amplitudes for each distribution network node. Using these two values ​​as the lower and upper bounds of an inequality, respectively, and combining them with the linear expression for the squared voltage amplitude of the node, we can form two inequalities for each node: the squared voltage amplitude is no lower than the lower limit and no higher than the upper limit. The transmission line capacity limit is the maximum apparent power allowed through each line. Limiting the absolute value of the power flow of the line to no greater than this limit forms an absolute value inequality for each line. The upper and lower limits of distributed energy output include the minimum and maximum injected active power and the minimum and maximum injected reactive power. Using these values ​​as inequality boundaries, we can form four inequalities for each distributed energy source: active power is no lower than the lower active power limit and no higher than the upper active power limit; reactive power is no lower than the lower reactive power limit and no higher than the upper reactive power limit.

[0033] Here, when constructing the feasible region model of distribution network operation constraints, the constant vector and sensitivity matrix in the linearized power flow equations can be pre-calculated based on the basic data, and these coefficients can be stored in the form of matrices and vectors. Then, the node voltage constraints, line capacity constraints, and distributed energy power constraints can be transformed into linear inequalities respectively. Finally, these linear inequalities are combined with the linearized power flow equations to form a complete linear constraint system, namely the feasible region model of distribution network operation constraints.

[0034] Furthermore, after obtaining the linearized power flow equations, node voltage constraints, line capacity constraints, and distributed energy power constraints, the set of all distributed energy injected power vectors that simultaneously satisfy the linearized power flow equations and all inequality constraints can be used as the feasible region model of the distribution network operation constraints. Here, each distributed energy node has two independent variables: injected active power and injected reactive power. Therefore, for a distribution network with N distributed energy nodes, the dimension of the injected power vector is 2N, and each element in this set is a 2N-dimensional column vector. This set is jointly defined by linear equations and linear inequalities, and geometrically possesses a convex polyhedral structure, which can serve as a reference benchmark for Cartesian product inclusion verification in the subsequent operational envelope model.

[0035] For example, following the specific construction steps of the above-described feasible region model for distribution network operation constraints, it can be recorded that... This represents the injected power of distributed energy resources (including injected active power p and injected reactive power q), where Given the number of nodes connected to distributed energy resources, the mathematical expression for the feasible region model of the distribution network operation constraints can be represented as follows: ; in, To enable distributed energy to inject power The true feasible region satisfying the distribution network operation constraints, where T represents the constraint conditions, can include: ; ; ; ; ; ; In the formula, Represented as the square of the node voltage magnitude; The voltage is represented by constant coefficients; and These are respectively represented as the active power injected into distributed energy resources and the reactive power injected into them; and This is represented as the voltage sensitivity matrix corresponding to the injected active power and injected reactive power of distributed energy sources. Expressed as line power flow; and These are respectively represented as the power flow sensitivity matrices corresponding to the injected active power and injected reactive power of distributed energy sources; , , , and All of these can be pre-calculated based on network parameters such as line impedance and node load; and These are respectively expressed as the square of the node voltage magnitude. The upper and lower limits; This represents the transmission capacity limit of the power transmission line; and These represent the upper and lower limits of the injected active power of distributed energy resources, respectively. and These represent the upper and lower limits of the injected reactive power of distributed energy sources, respectively.

[0036] Here, the above , , , and The specific process of pre-calculating based on network parameters such as line impedance and node load can be summarized as follows: First, determine the number of nodes and lines based on the connection topology of the distribution network, and set the reference value for the voltage of the slack node. Using the square of the voltage amplitude of the slack node as a reference value, and combining the resistance and reactance parameters of each line, the forward-backward substitution method is used to calculate the voltage in the absence of distributed energy injection (i.e., all...). and The squared value of the no-load voltage at each node when the voltage is zero is used as the constant term in the resulting column vector. Furthermore, based on the line impedance parameters and the linearization coefficients of the node loads, by solving for the partial derivatives of the node voltage with respect to the injected active and reactive power of the distributed energy source, the voltage sensitivity matrices corresponding to the injected active and reactive power can be obtained. and The specific method can be expressed as follows: At the rated operating point, perform a first-order Taylor expansion of the power flow equation, ignoring higher-order terms, and calculate the sensitivity coefficients of the square of the voltage magnitude to active power injection and reactive power injection respectively, forming two N×N matrices. Simultaneously, through the linear relationship between line power and the active or reactive power injected by distributed energy sources, and using the correlation matrix and line impedance parameters, the power flow sensitivity matrices corresponding to the active and reactive power injected by distributed energy sources can be calculated. and For each line, the partial derivatives of its power flow with respect to the active and reactive power injections at each node can be calculated, forming two L×N matrices, where L is the number of lines. Furthermore, in practical calculations, the process of solving the sensitivity matrix can be encapsulated as an offline calculation module and re-run after network parameter updates.

[0037] In some other embodiments, the above parameters can also be determined by the injected power perturbation method or by a linearization method based on the impedance matrix. , , , and For example, small disturbances are applied sequentially to the active and reactive power of each node at the rated operating point, and the changes in voltage and power flow are recorded. The sensitivity matrix is ​​then approximated by differential calculation. Alternatively, the node impedance matrix and line impedance matrix of the distribution network can be directly used to derive the analytical expressions of each sensitivity matrix through matrix operations. No specific limitations are imposed here.

[0038] S103, determine a fully symmetric multicell corresponding to each distribution network node connected to distributed energy, and construct an operating envelope model based on the fully symmetric multicells of all distribution network nodes.

[0039] Understandably, a fully symmetric polytope is a centrally symmetric convex polyhedron, completely determined by a central point vector and a set of generating vectors. Mathematically, it is the set of all points of the form a linear combination of the central point and generating vectors, where each coefficient of the linear combination lies within a closed interval from negative one to positive one. Since only distribution network nodes connected to distributed energy sources have adjustable injected active and reactive power, it is necessary to independently define the safe operating range of their injected power for each such node. A fully symmetric polytope can flexibly describe any convex symmetric region in the two-dimensional active-reactive space using concise parametric forms. Therefore, distribution network nodes connected to distributed energy sources can be chosen as the carrier of the fully symmetric polytope, allowing for the independent definition of the safe operating range of their injected power for each node.

[0040] Specifically, by defining a fully symmetric multicell for each distribution network node connected to distributed energy, the high-dimensional joint feasible domain can be decomposed into multiple low-dimensional independent envelopes. Each envelope only describes the adjustable range of active and reactive power of the node, thereby significantly reducing the variable scale of the subsequent optimization problem.

[0041] Furthermore, an operational envelope model can be constructed based on the fully symmetric polytopes of all distribution network nodes connected to distributed energy sources. This model defines the operational range of each distribution network node connected to distributed energy sources as its corresponding fully symmetric polytope. To ensure that any combination of nodes independently adjusting power does not violate the safety constraints of the distribution network, the Cartesian product of the fully symmetric polytopes of each distribution network node can be constrained to be contained within the set described by the feasible region model of the distribution network operational constraints. Here, the Cartesian product refers to the set of all possible combinations formed by selecting one element from the fully symmetric polytope of each node; it is the result of a direct product operation of multiple sets.

[0042] For example, since the center point variables and generating vector variables of a fully symmetric multicell are unknown at the initial moment and their specific values ​​need to be determined through subsequent optimization calculations, and these optimization calculations need to be reflected in the model as variables, these variables to be solved can be explicitly defined for each node when constructing the running envelope model, referring to... Figure 2 As shown, the specific steps may include the following steps S201~S204: S201, for each distribution network node connected to distributed energy, define a central point variable to be solved and a set of generation vector variables to be solved for the distribution network node.

[0043] Specifically, the center point variable is a two-dimensional vector, whose first component represents the baseline value of the active power injected by the distributed energy source at the node, and the second component represents the baseline value of the injected reactive power. It can be used to represent the baseline position of the power injected by the distributed energy source at the distribution network node. The generated vector variable is a set of two-dimensional column vectors. The two components of each generated vector correspond to the adjustment direction and magnitude of the active power and reactive power, respectively. It can be used to represent the adjustable direction and magnitude of the power injected by the distributed energy source at the distribution network node around the center point variable, so as to determine the extension range of the fully symmetric multicell in various directions.

[0044] Here, by defining a central point variable to be solved and a set of generation vector variables to be solved for each distribution network node connected to distributed energy, the shape, position and size of the node's operating envelope can be used as optimization variables, thereby obtaining the most flexible adjustment space by maximizing the envelope size in subsequent steps.

[0045] S202, for each distribution network node connected to distributed energy, the spatial range spanned by the linear combination of the center point variable and the generated vector variable of the distribution network node is determined as the fully symmetric multicell of the distribution network node.

[0046] It is understandable that, based on the set of center point variables and generating vector variables determined in step S201 above, the set of points obtained by adding all possible linear combinations (combination coefficients between negative one and positive one) to the center point is the spatial range spanned by the linear combination of the center point variables and the generating vector variables. This spatial range is determined as the fully symmetric polytope of this node, which is used to describe the independent safety regulation boundary of the active and reactive power injected by the distributed energy of this node.

[0047] S203, the central point variables of all distribution network nodes are concatenated into a joint central point variable according to the order of the distribution network nodes, and the generated vector variable groups of all distribution network nodes are combined into a joint generated matrix variable according to the order of the distribution network nodes.

[0048] Furthermore, after obtaining the central point variables and generated vector variable groups for each node, since subsequent optimization requires coupling the variables of all nodes into the same set of constraints, the central point variables of each node can be vertically concatenated according to the ascending order of the distribution network node numbers to obtain a column vector with a dimension of twice the number of nodes as a joint central point variable; the generated vector variable groups of each node are combined into a joint generated matrix variable as row blocks or column blocks in the same order, with the number of rows of the matrix being twice the number of nodes and the number of columns being the sum of the number of generated vectors of all nodes.

[0049] Among them, the distribution network node sequence refers to the pre-assigned numbering order of each node connected to distributed energy, which can be determined based on the connection topology of the distribution network or the node access order.

[0050] S204. Based on the joint central point variables and the joint generation matrix variables, a joint fully symmetric polycell is constructed, and the spatial range represented by the joint fully symmetric polycell is defined to be the same as the Cartesian product of the fully symmetric polycells of each distribution network node. The Cartesian product is defined to be included in the set described by the feasible region model of the distribution network operation constraints. Based on the joint fully symmetric polycell and the definition results, the operation envelope model is obtained.

[0051] Specifically, a joint fully symmetric multicell can be defined as the set of points obtained by multiplying the joint centroid variable by the joint generator matrix variable and the coefficient vector, where each component of the coefficient vector is between -1 and +1. Since this definition of the joint fully symmetric multicell is exactly equivalent to the Cartesian product of the fully symmetric multicells of each node, this equivalence relation allows the combination of multi-node envelopes to be expressed using a single joint multicell. That is, the spatial range represented by the joint fully symmetric multicell is defined to be the same as the Cartesian product of the fully symmetric multicells of each distribution network node. Simultaneously, defining this joint fully symmetric multicell as included within the feasible region model set constructed in step S102 ensures that each power combination within the joint multicell satisfies all security constraints. The resulting operating envelope model is a complete convex optimization problem framework, with decision variables being the joint centroid variable and the joint generator matrix variable. Constraints include the aforementioned inclusion relation and linearized forms of the original security constraints such as node voltage, line capacity, and distributed energy power. The optimization objective will be set as maximizing the envelope size in subsequent steps.

[0052] In this embodiment, by defining a fully symmetric multicell as an independent operating envelope for each distribution network node connected to distributed energy, and constraining the Cartesian product of all node envelopes to a feasible region model containing the distribution network operating constraints, it can be ensured that any independent power adjustment by each node does not violate safety constraints. At the same time, convex optimization is performed with the goal of maximizing the envelope size, obtaining the maximum adjustment space within the safety range, and decomposing the high-dimensional joint feasible region into multiple low-dimensional envelopes, significantly reducing computational complexity, meeting the real-time requirements of online scheduling, and improving the distribution network's ability to absorb a high proportion of distributed energy and its operational economy.

[0053] For example, a set of distribution network nodes containing distributed energy resources can be represented as The injected power of distributed energy sources needs to meet the safety operation constraints of the distribution network. Therefore, for each distribution network node connected to distributed energy sources... Define a fully symmetric multicell as its operational envelope to delineate the operational envelope corresponding to this node. Here, the operating envelope of each distribution network node connected to distributed energy resources must satisfy the following equation: ; in, The Cartesian product operation of a set refers to the true feasible region where any combination of elements within the operating envelope of each node must satisfy the distribution network operation constraints. ; In this way, distribution network nodes connected to distributed energy resources can be... A fully symmetrical multicellular body is defined as: ; In this context, Y, like T, represents a constraint condition. ; This represents the injected active power of the distributed energy source at node i of the distribution network connected to the distributed energy source. This represents the injected reactive power of the distributed energy source at node i of the distribution network connected to the distributed energy source.

[0054] Thus, the expression for running the envelope model can be represented as: ; in, This is represented as the running envelope model; Represented as joint central point variables, To jointly generate vector variables, It is represented as a coefficient vector, where each component lies within a closed interval from negative one to positive one, and m represents the number of generating vector variables.

[0055] here, This represents the number of distribution network nodes connected to distributed energy resources. Each node corresponds to two-dimensional active and reactive power variables, therefore the dimension of the center point vector is... Each column of the generated vector variable matrix represents a generated vector variable, and each generated vector variable corresponds to a degree of freedom. This is achieved by adjusting the coefficient vector. It can be continuously varied in this degree of freedom, thus spanning the entire fully symmetrical multicellular structure.

[0056] S104, with the goal of maximizing the size of the fully symmetric multicell of all distribution network nodes, and with the node voltage constraint, the line capacity constraint, and the distributed energy power constraint as constraints, the operating envelope model is calculated to determine the operating envelope of each distribution network node connected to distributed energy.

[0057] Understandably, in the calculation process of solving the operating envelope model, in order to provide the largest possible adjustment range for distributed energy sources while ensuring the safe operation of the distribution network, maximizing the size of the fully symmetric polycell of all nodes can be used as the calculation objective. Node voltage constraints, line capacity constraints, and distributed energy power constraints can be used as constraints. The decision variables in the operating envelope model (i.e., the centroid variables and generator vector variables of each node) are optimized to determine the optimal operating envelope for each node, thus obtaining the operating envelope of each distribution network node connected to distributed energy sources. The size of the fully symmetric polycell of all nodes can be expressed as the sum of the Euclidean lengths of all generator vector variables of the fully symmetric polycell of each distribution network node, or as the L2,1 norm of the joint generator matrix formed by all generator vector variables (i.e., calculating the Euclidean length of each row of the matrix and then summing them). This is the scalar objective function of the optimization problem, which can be maximized by calculating the L2 norm of each generator vector and summing them, or by directly calculating the L2,1 norm of the joint generator matrix.

[0058] Here, the operating envelope refers to the safe operating area allowed for each distribution network node connected to distributed energy sources on the plane of injected active power and injected reactive power. It is the set of power that the distributed energy sources of the node can independently adjust without causing the grid to exceed its limits. It can be used by the distribution network dispatching system for online control of the distributed energy sources of the node.

[0059] For example, the specific mathematical expression for maximizing the size of the fully symmetric multicell of all distribution network nodes can be represented as: ; ; in, The k-th generating vector variable, represented as a fully symmetric multicell, is a two-dimensional column vector whose two components correspond to the adjustment directions of injected active power and injected reactive power, respectively. This is expressed as the Euclidean length of the generated vector, which is the square root of the sum of the squares of its components. By maximizing the sum of the lengths of all generated vectors, the largest possible node running envelope can be obtained while satisfying safety constraints.

[0060] In some possible embodiments, after obtaining the joint fully symmetric multicellular object based on steps S201-S204 above, it can be used as the basic geometric object for subsequent optimization. For ease of practical solution, refer to... Figure 3 As shown, the calculation of the running envelope model may include the following steps S301~S305: S301, based on the joint fully symmetric multicell, the node voltage constraint, the line capacity constraint, and the distributed energy power constraint are transformed into a set of linear inequalities with respect to the joint central point variable and the joint generating matrix variable.

[0061] Here, the system of linear inequalities concerning the joint centroid variables and the joint generator matrix variables is represented as a series of algebraic inequalities of the form of linear expressions less than or equal to constants. This system forms the core component of the constraints in subsequent convex optimization problems. By leveraging the joint fully symmetric multicell and linearized power flow equations, the original nonlinear or absolute value constraints can be transformed into linear inequalities concerning the joint centroid variables and the joint generator matrix variables based on the linear mapping relationship between voltage, power flow, and injected power. This yields constraint expressions that can be directly input into the convex optimization solver.

[0062] For example, the system of linear inequalities may include multiple linear inequalities, corresponding to node voltage constraints, line capacity constraints, and distributed energy power constraints, respectively. Specifically, the system of linear inequalities can be constructed through the following steps (a) to (d): (a) Based on the joint fully symmetric multiple cell and the linearized power flow equation, the square of the voltage magnitude of each distribution network node is expressed as a linear function of the joint centroid variable and the joint generator matrix variable, thus obtaining the voltage fully symmetric multiple cell. Using the property of the range of values ​​determined by the sum of the absolute values ​​of all elements in the corresponding row of the generator matrix variable of the voltage fully symmetric multiple cell plus or minus the components of the centroid variable of the voltage fully symmetric multiple cell, the lower limit constraint and upper limit constraint of the square of the voltage magnitude of each distribution network node are transformed into two voltage linear inequalities concerning the joint centroid variable and the joint generator matrix variable, respectively.

[0063] Here, the voltage-symmetric polycell is a derived voltage-symmetric polycell obtained by mapping the joint voltage-symmetric polycell to the voltage sensitivity matrix. It can be used to describe all possible values ​​of the square of the node voltage magnitude as a function of injected power. By transforming the lower and upper voltage constraints into two linear inequalities, we can obtain the voltage safety boundary expression for the joint centroid variables and the joint generation matrix variables.

[0064] For example, when describing the linear mapping relationship between injected power and voltage, it can be set as follows: Then the voltage is a fully symmetric multicell It can be represented as: ; in, , ; The voltage linear inequality can be expressed as: ; .

[0065] (b) Based on the joint fully symmetric multicell and the linearized power flow equation, the power flow of each line is expressed as a linear function of the joint centroid variable and the joint generator matrix variable, thus obtaining the line power flow fully symmetric multicell. Utilizing the property of the range of values ​​determined by the sum of the absolute values ​​of all elements in the corresponding row of the generator matrix variable of the line power flow fully symmetric multicell plus or minus the components of the centroid variable, the line capacity constraint of each line is transformed into a capacity linear inequality with respect to the joint centroid variable and the joint generator matrix variable. The capacity linear inequality requires that the sum of the absolute values ​​of the components of the centroid variable and the absolute values ​​of all elements in the corresponding row of the generator matrix variable of the line power flow fully symmetric multicell does not exceed the transmission capacity limit of the transmission line.

[0066] It is understandable that a fully symmetric power flow cell can reflect the entire possible range of power flow across each line when the injected power changes. By transforming its absolute value constraint into a linear inequality, the capacity safety boundary expression can be obtained. By adding the absolute values ​​of the variable components at the center point of the fully symmetric power flow cell to the L1 norm of the corresponding row of the generating matrix and limiting them within the transmission capacity limit, line overload can be prevented.

[0067] For example, when describing the linear mapping relationship between injected power and line power flow, the line power flow fully symmetric multicell... It can be represented as: ; in, , ; The capacity linear inequality can be expressed as: ; .

[0068] (c) Based on the joint fully symmetric polycell, the injected active power of each distribution network node is expressed as a linear combination of the active power component of the joint central point variable corresponding to the distribution network node and the row vector of the active power of the joint generator matrix variable corresponding to the distribution network node. The injected reactive power of each distribution network node is expressed as a linear combination of the injected reactive power component of the joint central point variable corresponding to the distribution network node and the row vector of the injected reactive power of the joint generator matrix variable corresponding to the distribution network node, thus obtaining the fully symmetric polycell of injected power. Using the property of the value range determined by the sum of the absolute values ​​of all elements in the corresponding row of the generator matrix variable of the fully symmetric polycell of injected power, the lower limit constraint of injected active power, the upper limit constraint of injected active power, the lower limit constraint of injected reactive power, and the upper limit constraint of injected reactive power of each distribution network node are respectively transformed into four power linear inequalities with respect to the joint central point variable and the joint generator matrix variable.

[0069] Here, since the injected power fully symmetric polycell is itself a geometric object directly defined by the joint central point variables and the joint generating matrix variables, the range of values ​​for each dimension has a clear analytical expression. Therefore, the active power lower limit, active power upper limit, reactive power lower limit, and reactive power upper limit for each node can be treated as four independent inequalities. By utilizing the range property of the injected power fully symmetric polycell, the power boundary expression for each distributed energy source can be obtained. Finally, by summing the four inequalities for each node and combining them with all nodes, the output constraint of the distributed energy source itself can be transformed into a system of linear inequalities, thereby describing the physical limits of the device.

[0070] For example, for any fully symmetric multicellular organism , its first The extreme value of dimension can be expressed as: ; Where H represents the joint generation matrix variable of the fully symmetric multicell, and each column of the matrix corresponds to a generation vector variable; Indicated as The A row vector, that is, a row vector consisting of all elements in the i-th row of the generating matrix; Represented as a pair of vectors The L1 norm, which is the sum of the absolute values ​​of all elements in the vector, is used to describe the length of the semi-axis of a fully symmetric polycell in this dimension. The power linear inequality can be expressed as: ; ; ; .

[0071] (d) Based on the voltage linear inequality set, the capacity linear inequality set, and the power linear inequality set, construct a set of linear inequalities for the joint central point variable and the joint generating matrix variable.

[0072] Furthermore, after obtaining the voltage linear inequalities for all nodes, the capacity linear inequalities for all lines, and the power linear inequalities for all distributed energy sources, these inequalities can be combined to construct a complete set of linear inequalities concerning the joint central point variables and the joint generating matrix variables. This set of inequalities can serve as constraints for subsequent convex optimization problems.

[0073] In this way, a computational model for the operating envelope of distribution network nodes based on fully symmetric multicells can be established, as follows: ; .

[0074] S302, Introduce auxiliary variables into the system of linear inequalities to linearize the nonlinear terms containing the L1 norm, thereby obtaining the constraints of the convex optimization problem.

[0075] Understandably, since the above set of linear inequalities includes an expression for the sum of the absolute values ​​of all elements in the corresponding row of the generating matrix (i.e., the L1 norm), which is mathematically nonlinear, in order to solve it efficiently using linear programming or convex optimization solvers, a set of auxiliary nonnegative variables can be introduced. Each auxiliary variable is used to replace an absolute value term, and additional linear equality or inequality constraints are added to ensure that the auxiliary variables are equivalent to the original absolute value terms, thereby linearizing the nonlinear terms and obtaining completely linear constraints.

[0076] For example, to eliminate For example: it can be made ,but The Behavior .

[0077] S303, with the goal of maximizing the sum of the lengths of the generating vector variables of the fully symmetric multicells of each distribution network node, constructs the objective function of the convex optimization problem.

[0078] Here, the sum of the Euclidean lengths of all generated vector variables of the fully symmetric multicell of each distribution network node is used as the optimization objective. This maximizes the extension of the envelope of each node in all directions, thereby obtaining the maximum flexible adjustment space under safety constraints. The objective function is convex and can be equivalent to finding the matrix... The L2,1 norm, in Within the range, the linear constraints together constitute a convex optimization problem.

[0079] S304, Solve the convex optimization problem consisting of the objective function and the constraints to obtain the optimal solution for the joint central point variable and the joint generating matrix variable.

[0080] Specifically, after obtaining the constructed objective function and linear constraints, a convex optimization solver (such as an interior-point solver) can be called to solve the convex optimization problem composed of the objective function and constraints, in order to determine the values ​​of the joint central point variables and the joint generating matrix variables that maximize the objective function. The optimal solution includes the optimal values ​​of the joint central point variables and the optimal values ​​of the joint generating matrix variables, which can maximize the running envelope of all nodes under safe constraints.

[0081] In some possible implementations, the solution can be obtained using commercial solvers such as Gurobi or CPLEX, or by using distributed optimization methods such as the alternating direction multiplier method.

[0082] S305, based on the optimal solutions of the joint central point variables and the joint generation matrix variables, the optimal solutions of the central point variables and the generation vector variables of each distribution network node are parsed, and the operating envelope of each distribution network node connected to distributed energy is output.

[0083] Furthermore, after obtaining the optimal solution of the joint central point variable and the joint generating matrix variable, the system can be split according to the reverse process of splicing in step S203, and the optimal value of the central point variable and the optimal value of the generating vector variable corresponding to each node can be obtained analytically. The spatial range spanned by the linear combination of the optimal value of the central point variable and the optimal value of the generating vector variable of each node is taken as the running envelope of that node.

[0084] Here, after finding the optimal solutions for c and G, the optimal solution for the joint central point variable and the joint generating matrix variable can be expressed as: ; Distribution network nodes connected to distributed energy resources The node running envelope can be represented as: ; in, express The One element, express The The row vectors provide the operating envelope of each distribution network node connected to distributed energy resources.

[0085] In some possible embodiments, after obtaining the operating envelope of each distribution network node connected to distributed energy sources, the calculated operating envelope of each node can be sent to the distribution network dispatching system as a safety boundary for the dispatching system to issue power commands to the distributed energy sources. During real-time operation, the dispatching system can utilize the operating envelope to regulate the power of each distributed energy source. For example, based on the current grid status and operating envelope, it can set target values ​​for active and reactive power for each distributed energy source, ensuring that the injected active and reactive power of each distributed energy source always remain within its corresponding operating envelope, thus achieving safe and economical dispatching under a high proportion of distributed energy source integration.

[0086] The distribution network node operation envelope calculation method, apparatus, medium, and equipment provided in this disclosure decompose the high-dimensional joint feasible domain into multiple low-dimensional independent envelopes, effectively reducing the problem scale; at the same time, the Cartesian product inclusion constraint of each node envelope ensures the safety of distributed regulation; with maximizing the envelope size as the optimization objective, it provides the largest possible flexible adjustment space for distributed energy. Thus, it achieves accurate and efficient definition of the safe operating range of distribution network nodes under high-proportion distributed energy access.

[0087] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0088] Based on the same inventive concept, this disclosure also provides a distribution network node operating envelope calculation device corresponding to the distribution network node operating envelope calculation method. Since the principle of the device in this disclosure for solving the problem is similar to the distribution network node operating envelope calculation method described above in this disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0089] Reference Figure 4 The diagram shown is a schematic of a distribution network node operation envelope calculation device 400 provided in an embodiment of this disclosure. The device includes: The data acquisition module 401 is used to acquire basic data of the power distribution network; wherein, the basic data includes the connection topology of the power distribution network, line parameters, transformer parameters, transmission capacity limits of transmission lines, upper and lower limits of node voltage, and upper and lower limits of output of distributed energy sources. The constraint construction module 402 is used to construct a feasible region model of distribution network operation constraints based on the basic data; wherein, the feasible region model of distribution network operation constraints includes linearized power flow equations, node voltage constraints, line capacity constraints, and distributed energy power constraints, and the feasible region model of distribution network operation constraints is used to describe the set of all distributed energy injected power vectors that meet the conditions for safe operation of the distribution network; The model building module 403 is used to determine a fully symmetric polycell corresponding to each distribution network node connected to distributed energy, and to build an operating envelope model based on the fully symmetric polycells of all distribution network nodes; wherein, the operating envelope model defines the operating range of each distribution network node connected to distributed energy as the corresponding fully symmetric polycell, and the Cartesian product of the fully symmetric polycells of each distribution network node is constrained to be contained within the set described by the distribution network operating constraint feasible region model; The model solving module 404 is used to solve the operating envelope model with the objective of maximizing the size of the fully symmetric multicell of all distribution network nodes, and with the node voltage constraint, the line capacity constraint, and the distributed energy power constraint as constraints, to determine the operating envelope of each distribution network node connected to distributed energy.

[0090] In some possible embodiments, the line parameters of the power distribution network include line conductance and susceptance parameters, the transformer parameters include transformer conductance and susceptance parameters, and the upper and lower limits of the output of the distributed energy source include upper and lower limits of injected active power and upper and lower limits of injected reactive power.

[0091] In some possible embodiments, the constraint construction module 402 is specifically used for: Based on the connection topology of the distribution network, the line parameters, and the transformer parameters, the linearized power flow equations are established; wherein, the linearized power flow equations are used to describe the linear relationship between the injected active power and injected reactive power of the distributed energy source and the square of the node voltage amplitude and the line power flow. The node voltage constraint is established based on the node voltage upper and lower limits; The line capacity constraint is established based on the transmission capacity limit of the transmission line. The power constraints of the distributed energy source are established based on the upper and lower limits of its output. The set of all distributed energy injection power vectors that satisfy the linearized power flow equations, the node voltage constraints, the line capacity constraints, and the distributed energy power constraints is used as the feasible region model of the distribution network operation constraints.

[0092] In some possible embodiments, the model building module 403 is specifically used for: For each distribution network node connected to distributed energy, a central point variable to be solved and a set of generation vector variables to be solved are defined for the distribution network node; wherein, the central point variable is used to represent the reference position of the power injected by the distributed energy on the distribution network node, and the generation vector variables are used to represent the adjustable direction and amplitude of the power injected by the distributed energy on the distribution network node around the central point variable; For each distribution network node connected to distributed energy, the spatial range spanned by the linear combination of the center point variable and the generated vector variable of the distribution network node is determined as the fully symmetric multicell of the distribution network node. The central point variables of all distribution network nodes are concatenated into a joint central point variable according to the order of the distribution network nodes, and the generated vector variable groups of all distribution network nodes are combined into a joint generated matrix variable according to the order of the distribution network nodes. Based on the joint central point variables and the joint generation matrix variables, a joint fully symmetric polycell is constructed, and the spatial range represented by the joint fully symmetric polycell is defined to be the same as the Cartesian product of the fully symmetric polycells of each distribution network node. The Cartesian product is defined to be included in the set described by the feasible region model of the distribution network operation constraints. Based on the joint fully symmetric polycell and the definition results, the operation envelope model is obtained.

[0093] In some possible embodiments, the model solving module 404 is specifically used for: Based on the joint fully symmetric multicell, the node voltage constraint, the line capacity constraint, and the distributed energy power constraint are transformed into a set of linear inequalities with respect to the joint centroid variables and the joint generating matrix variables; By introducing auxiliary variables into the system of linear inequalities, the nonlinear terms containing the L1 norm are linearized, thus obtaining the constraints of the convex optimization problem. The objective function of the convex optimization problem is constructed with the goal of maximizing the sum of the lengths of the generating vector variables of the fully symmetric multicells of each distribution network node. Solve the convex optimization problem consisting of the objective function and the constraints to obtain the optimal solution for the joint central point variables and the joint generating matrix variables; Based on the optimal solutions of the joint centroid variables and the joint generator matrix variables, the optimal solutions of the centroid variables and generator vector variables of each distribution network node are parsed, and the operating envelope of each distribution network node connected to distributed energy is output.

[0094] In some possible embodiments, the model solving module 404 is specifically used for: Based on the joint fully symmetric multiple cell and the linearized power flow equations, the squared voltage magnitude of each distribution network node is expressed as a linear function of the joint centroid variable and the joint generator matrix variable, thus obtaining a voltage fully symmetric multiple cell. Utilizing the range of values ​​determined by adding or subtracting the sum of the absolute values ​​of all elements in the corresponding row of the generator matrix variable of the voltage fully symmetric multiple cell, the lower and upper bound constraints of the squared voltage magnitude of each distribution network node are transformed into two voltage linear inequalities concerning the joint centroid variable and the joint generator matrix variable, respectively. Based on the joint fully symmetric multicell and the linearized power flow equation, the power flow of each line is expressed as a linear function of the joint centroid variable and the joint generator matrix variable, thus obtaining the line power flow fully symmetric multicell. Utilizing the range of values ​​determined by adding or subtracting the sum of the absolute values ​​of all elements in the corresponding row of the generator matrix variable of the line power flow fully symmetric multicell, the line capacity constraint of each line is transformed into a capacity linear inequality regarding the joint centroid variable and the joint generator matrix variable. This capacity linear inequality requires that the sum of the absolute values ​​of the components of the centroid variable and the absolute values ​​of all elements in the corresponding row of the generator matrix variable of the line power flow fully symmetric multicell does not exceed the transmission line's transmission capacity limit. Based on the aforementioned joint fully symmetric polycell, the injected active power of each distribution network node is expressed as a linear combination of the active power component of the joint central point variable corresponding to the distribution network node and the row vector of the active power component of the joint generator matrix variable corresponding to the distribution network node. Similarly, the injected reactive power of each distribution network node is expressed as a linear combination of the injected reactive power component of the joint central point variable and the row vector of the injected reactive power component of the joint generator matrix variable, thus obtaining the injected power fully symmetric polycell. Utilizing the value range property determined by adding and subtracting the sum of the absolute values ​​of all elements in the corresponding row of the generator matrix variable of the injected power fully symmetric polycell, the lower limit constraint, upper limit constraint, lower limit constraint, and upper limit constraint of the injected active power of each distribution network node are transformed into four linear power inequalities concerning the joint central point variable and the joint generator matrix variable, respectively. Based on the voltage linear inequality set, the capacity linear inequality set, and the power linear inequality set, construct a set of linear inequalities concerning the joint central point variable and the joint generating matrix variable.

[0095] In some possible embodiments, the model solving module 404 is further configured to: The calculated operating envelope of each distribution network node connected to distributed energy is sent to the distribution network dispatching system. The distribution network dispatching system uses the operating envelope to regulate the power of each distributed energy source, so that the injected active power and injected reactive power of each distributed energy source are always within the corresponding operating envelope.

[0096] Based on the same technical concept, this disclosure also provides a computer device. (See also...) Figure 5 The diagram shows the structure of a computer device 500 provided in this embodiment of the present disclosure, including a processor 501, a memory 502, and a bus 503. The memory 502 is used to store execution instructions and includes a main memory 5021 and an external memory 5022. The main memory 5021, also called internal memory, is used to temporarily store computational data in the processor 501, as well as data exchanged with external memory 5022 such as a hard disk. The processor 501 exchanges data with the external memory 5022 through the main memory 5021.

[0097] In this embodiment, the memory 502 is specifically used to store application code that executes the solution of this application, and its execution is controlled by the processor 501. That is, when the computer device 500 is running, the processor 501 communicates with the memory 502 through the bus 503, so that the processor 501 executes the application code stored in the memory 502, and then executes the method described in any of the foregoing embodiments.

[0098] The memory 502 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0099] Processor 501 may be an integrated circuit chip with signal processing capabilities. The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0100] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the computer device 500. In other embodiments of this application, the computer device 500 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0101] This disclosure also provides a computer-readable storage medium storing a computer program. When a processor runs the computer program, it executes the steps of the distribution network node envelope calculation method described in the above-described method embodiments. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0102] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the distribution network node operation envelope calculation method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0103] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0104] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0105] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0106] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0107] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0108] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A method for computing power distribution network node operating envelope, characterized in that, include: Acquire basic data of the power distribution network; wherein, the basic data includes the connection topology of the power distribution network, line parameters, transformer parameters, transmission capacity limits of transmission lines, upper and lower limits of node voltage, and upper and lower limits of output of distributed energy sources; A feasible region model of distribution network operation constraints is constructed based on the aforementioned basic data; wherein, the feasible region model of distribution network operation constraints includes linearized power flow equations, node voltage constraints, line capacity constraints, and distributed energy power constraints, and the feasible region model of distribution network operation constraints is used to describe the set of all distributed energy injection power vectors that meet the conditions for safe operation of the distribution network; For each distribution network node connected to distributed energy, a fully symmetric multicell corresponding to the distribution network node is determined, and an operational envelope model is constructed based on the fully symmetric multicells of all distribution network nodes; wherein, the operational envelope model defines the operational range of each distribution network node connected to distributed energy as the corresponding fully symmetric multicell, and the Cartesian product of the fully symmetric multicells of each distribution network node is constrained to be contained within the set described by the distribution network operational constraint feasible region model; With the goal of maximizing the size of the fully symmetric multicell of all distribution network nodes, and using the node voltage constraints, line capacity constraints, and distributed energy power constraints as constraints, the operating envelope model is solved to determine the operating envelope of each distribution network node connected to distributed energy.

2. The method of claim 1, wherein, The line parameters of the power distribution network include line conductance and susceptance parameters, the transformer parameters include transformer conductance and susceptance parameters, and the upper and lower limits of the output of the distributed energy source include upper and lower limits of injected active power and upper and lower limits of injected reactive power.

3. The method of claim 2, wherein, The construction of the feasible region model of distribution network operation constraints based on the basic data includes: Based on the connection topology of the distribution network, the line parameters, and the transformer parameters, the linearized power flow equations are established; wherein, the linearized power flow equations are used to describe the linear relationship between the injected active power and injected reactive power of the distributed energy source and the square of the node voltage amplitude and the line power flow. The node voltage constraint is established based on the node voltage upper and lower limits; The line capacity constraint is established based on the transmission capacity limit of the transmission line. The power constraints of the distributed energy source are established based on the upper and lower limits of its output. The set of all distributed energy injection power vectors that satisfy the linearized power flow equations, the node voltage constraints, the line capacity constraints, and the distributed energy power constraints is used as the feasible region model of the distribution network operation constraints.

4. The method of claim 1, wherein, The step of determining a fully symmetric multicell corresponding to each distribution network node connected to distributed energy resources, and constructing an operational envelope model based on the fully symmetric multicells of all distribution network nodes, includes: For each distribution network node connected to distributed energy, a central point variable to be solved and a set of generation vector variables to be solved are defined for the distribution network node; wherein, the central point variable is used to represent the reference position of the power injected by the distributed energy on the distribution network node, and the generation vector variables are used to represent the adjustable direction and amplitude of the power injected by the distributed energy on the distribution network node around the central point variable; For each distribution network node connected to distributed energy, the spatial range spanned by the linear combination of the center point variable and the generated vector variable of the distribution network node is determined as the fully symmetric multicell of the distribution network node. The central point variables of all distribution network nodes are concatenated into a joint central point variable according to the order of the distribution network nodes, and the generated vector variable groups of all distribution network nodes are combined into a joint generated matrix variable according to the order of the distribution network nodes. Based on the joint central point variables and the joint generation matrix variables, a joint fully symmetric polycell is constructed, and the spatial range represented by the joint fully symmetric polycell is defined to be the same as the Cartesian product of the fully symmetric polycells of each distribution network node. The Cartesian product is defined to be included in the set described by the feasible region model of the distribution network operation constraints. Based on the joint fully symmetric polycell and the definition results, the operation envelope model is obtained.

5. The method of claim 4, wherein, Solving the runtime envelope model includes: Based on the joint fully symmetric multicell, the node voltage constraint, the line capacity constraint, and the distributed energy power constraint are transformed into a set of linear inequalities with respect to the joint centroid variables and the joint generating matrix variables; By introducing auxiliary variables into the system of linear inequalities, the nonlinear terms containing the L1 norm are linearized, thus obtaining the constraints of the convex optimization problem. The objective function of the convex optimization problem is constructed with the goal of maximizing the sum of the lengths of the generating vector variables of the fully symmetric multicells of each distribution network node. Solve the convex optimization problem consisting of the objective function and the constraints to obtain the optimal solution for the joint central point variables and the joint generating matrix variables; Based on the optimal solutions of the joint centroid variables and the joint generator matrix variables, the optimal solutions of the centroid variables and generator vector variables of each distribution network node are parsed, and the operating envelope of each distribution network node connected to distributed energy is output.

6. The method of claim 5, wherein, The process of transforming the node voltage constraints, line capacity constraints, and distributed energy power constraints based on the joint fully symmetric multicell into a set of linear inequalities concerning the joint centroid variables and the joint generating matrix variables includes: Based on the joint fully symmetric multiple cell and the linearized power flow equations, the squared voltage magnitude of each distribution network node is expressed as a linear function of the joint centroid variable and the joint generator matrix variable, thus obtaining a voltage fully symmetric multiple cell. Utilizing the range of values ​​determined by adding or subtracting the sum of the absolute values ​​of all elements in the corresponding row of the generator matrix variable of the voltage fully symmetric multiple cell, the lower and upper bound constraints of the squared voltage magnitude of each distribution network node are transformed into two voltage linear inequalities concerning the joint centroid variable and the joint generator matrix variable, respectively. Based on the joint fully symmetric multicell and the linearized power flow equation, the power flow of each line is expressed as a linear function of the joint centroid variable and the joint generator matrix variable, thus obtaining the line power flow fully symmetric multicell. Utilizing the range of values ​​determined by adding or subtracting the sum of the absolute values ​​of all elements in the corresponding row of the generator matrix variable of the line power flow fully symmetric multicell, the line capacity constraint of each line is transformed into a capacity linear inequality regarding the joint centroid variable and the joint generator matrix variable. This capacity linear inequality requires that the sum of the absolute values ​​of the components of the centroid variable and the absolute values ​​of all elements in the corresponding row of the generator matrix variable of the line power flow fully symmetric multicell does not exceed the transmission line's transmission capacity limit. Based on the aforementioned joint fully symmetric polycell, the injected active power of each distribution network node is expressed as a linear combination of the active power component of the joint central point variable corresponding to the distribution network node and the row vector of the active power component of the joint generator matrix variable corresponding to the distribution network node. Similarly, the injected reactive power of each distribution network node is expressed as a linear combination of the injected reactive power component of the joint central point variable and the row vector of the injected reactive power component of the joint generator matrix variable, thus obtaining the injected power fully symmetric polycell. Utilizing the value range property determined by adding and subtracting the sum of the absolute values ​​of all elements in the corresponding row of the generator matrix variable of the injected power fully symmetric polycell, the lower limit constraint, upper limit constraint, lower limit constraint, and upper limit constraint of the injected active power of each distribution network node are transformed into four linear power inequalities concerning the joint central point variable and the joint generator matrix variable, respectively. Based on the voltage linear inequality set, the capacity linear inequality set, and the power linear inequality set, construct a set of linear inequalities concerning the joint central point variable and the joint generating matrix variable.

7. The method of claim 1, wherein, After determining the operating envelope of each distribution network node connected to distributed energy resources, the process includes: The calculated operating envelope of each distribution network node connected to distributed energy is sent to the distribution network dispatching system. The distribution network dispatching system uses the operating envelope to regulate the power of each distributed energy source, so that the injected active power and injected reactive power of each distributed energy source are always within the corresponding operating envelope.

8. A power distribution grid node operating envelope computation apparatus, characterized by, include: The data acquisition module is used to acquire basic data of the power distribution network; wherein, the basic data includes the connection topology of the power distribution network, line parameters, transformer parameters, transmission capacity limits of transmission lines, upper and lower limits of node voltage, and upper and lower limits of output of distributed energy sources. The constraint construction module is used to construct a feasible region model of distribution network operation constraints based on the basic data; wherein, the feasible region model of distribution network operation constraints includes linearized power flow equations, node voltage constraints, line capacity constraints, and distributed energy power constraints, and the feasible region model of distribution network operation constraints is used to describe the set of all distributed energy injected power vectors that meet the conditions for safe operation of the distribution network; The model building module is used to determine a fully symmetric polycell corresponding to each distribution network node connected to distributed energy, and to build an operating envelope model based on the fully symmetric polycells of all distribution network nodes; wherein, the operating envelope model defines the operating range of each distribution network node connected to distributed energy as the corresponding fully symmetric polycell, and the Cartesian product of the fully symmetric polycells of each distribution network node is constrained to be contained within the set described by the distribution network operating constraint feasible region model; The model solving module is used to solve the operating envelope model with the objective of maximizing the size of the fully symmetric multicell of all distribution network nodes, and with the node voltage constraints, the line capacity constraints, and the distributed energy power constraints as constraints, to determine the operating envelope of each distribution network node connected to distributed energy.

9. A storage medium having stored thereon a computer program, characterized in that When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

10. A computer device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

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