Power distribution area flexible interconnection group planning method oriented to energy mutual aid

Abstract

The invention discloses a power distribution area flexible interconnection group planning method for mutual energy assistance, and relates to the technical field of power distribution network planning, and the method comprises the steps: screening out area pairs meeting conditions based on the topological parameters of a power distribution network, area loads and new energy output data, and constructing an initial interconnection set; constructing an initial two-stage opportunity constraint optimization model comprising a planning stage and an operation stage based on the set; in the planning stage, an interconnection strategy and a new energy consumption rate are determined by taking minimization of investment cost and punishment cost as a target; the strategy feasibility is verified through a random scene in the operation stage; carrying out polyhedral approximate linearization processing on nonlinear constraints in the model, reconstructing a power flow model into a compact form, converting probabilistic opportunity constraints into deterministic bilinear constraints by using a sampling average approximation method, forming a deterministic approximation model, and carrying out iterative solution through a column constraint generation decomposition algorithm to obtain a target interconnection grouping scheme; and high-efficiency energy mutual aid and load balancing between power distribution stations are realized.

Description

Technical Field

[0001] This application relates to the technical field of distribution network planning, and relates to a flexible interconnection group planning method for distribution substations oriented to energy mutual assistance. Background Technique

[0002] With the diversification of household loads and the widespread access of distributed energy at the low-voltage side of the distribution network, the low-voltage distribution substation, as a distribution unit directly serving end users, its reliability and economy have a more significant impact on users and grid operators. Under this background, higher requirements are put forward for the planning and optimization of distribution substations to adapt to the transformation of the energy structure and meet the diverse electricity consumption needs of users.

[0003] Currently, for distribution network planning, especially at the medium and high voltage distribution network levels, a large number of studies have introduced uncertainty modeling and optimization solution methods. These studies mainly focus on how to improve the stability and economy of the distribution network in the face of uncertain factors through advanced mathematical models and algorithms. At the same time, traditional distribution network planning methods are also constantly improving, aiming to reduce the conservatism of the model and improve the risk aversion ability. However, existing research and methods have exposed some problems when facing the planning of low-voltage distribution substations. The continuous growth of the access scale of distributed energy has posed severe challenges to the distribution substation in terms of dynamic capacity expansion, power balance, and operation reliability. The traditional single-substation isolated operation mode can no longer meet the needs of efficiently absorbing distributed energy and flexibly adjusting power, especially when dealing with the random fluctuations of power sources and loads, it is difficult to achieve power mutual assistance between substations and load balance. In addition, when existing planning methods handle the complexity and uncertainty of low-voltage distribution substations, the model solution efficiency is low, and it is difficult to quickly respond to and adapt to changes in actual operation. Therefore, how to achieve efficient energy mutual assistance and load balance between distribution substations has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of this application is to provide a flexible interconnection group planning method for distribution substations oriented to energy mutual assistance, aiming to solve the technical problem of how to achieve efficient energy mutual assistance and load balance between distribution substations.

[0005] To achieve the above object, the present application proposes a flexible interconnection group planning method for distribution transformer areas oriented to energy mutual assistance. The method includes: based on the distribution network topology parameters, transformer area loads, and new energy output data, screening transformer area pairs that meet the preset load rate condition or the output surplus condition, and constructing an initial interconnection set; according to the initial interconnection set, constructing an initial two-stage chance-constrained optimization model including a planning stage and an operation stage. In the planning stage, the interconnection strategy and the new energy consumption rate are determined with the goal of minimizing the investment cost and the penalty cost. In the operation stage, the feasibility of the interconnection strategy is verified based on random scenarios; performing polyhedral approximation linearization on the non-linear constraints in the initial two-stage chance-constrained optimization model, and reconstructing the power flow model in the initial two-stage chance-constrained optimization model into a compact form to obtain a target two-stage chance-constrained optimization model; converting the probabilistic chance constraints in the target two-stage chance-constrained optimization model into deterministic bilinear constraints through the sample average approximation method to obtain a deterministic approximation model; and iteratively solving the deterministic approximation model through the column constraint generation decomposition algorithm to obtain a target interconnection group plan.

[0006] In addition, to achieve the above object, the present application also proposes a flexible interconnection group planning device for distribution transformer areas oriented to energy mutual assistance. The device includes: an initial set construction module for screening transformer area pairs that meet the preset load rate condition or the output surplus condition based on the distribution network topology parameters, transformer area loads, and new energy output data, and constructing an initial interconnection set; a model construction module for constructing an initial two-stage chance-constrained optimization model including a planning stage and an operation stage according to the initial interconnection set. In the planning stage, the interconnection strategy and the new energy consumption rate are determined with the goal of minimizing the investment cost and the penalty cost. In the operation stage, the feasibility of the interconnection strategy is verified based on random scenarios; a linearization processing module for performing polyhedral approximation linearization on the non-linear constraints in the initial two-stage chance-constrained optimization model, and reconstructing the power flow model in the initial two-stage chance-constrained optimization model into a compact form to obtain a target two-stage chance-constrained optimization model; an equivalent conversion module for converting the probabilistic chance constraints in the target two-stage chance-constrained optimization model into deterministic bilinear constraints through the sample average approximation method to obtain a deterministic approximation model; and a solution module for iteratively solving the deterministic approximation model through the column constraint generation decomposition algorithm to obtain a target interconnection group plan.

[0007] In addition, to achieve the above object, the present application also proposes a flexible interconnection group planning device for distribution transformer areas oriented to energy mutual assistance. The device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. The computer program is configured to implement the steps of the flexible interconnection group planning method for distribution transformer areas oriented to energy mutual assistance as described above.

[0008] In addition, to achieve the above object, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the flexible interconnection group planning method for a distribution transformer area oriented to energy mutual assistance as described above are implemented.

[0009] One or more technical solutions proposed by the present application have at least the following technical effects: First, based on the distribution network topology parameters, transformer area load, and new energy output data, the planning system screens transformer area pairs that meet the preset load rate condition or over-generation condition, and constructs an initial interconnection set. This step initially determines the transformer areas that may need to be interconnected by screening eligible transformer area pairs, effectively narrowing the optimization scope and improving the efficiency of the subsequent optimization process. Next, according to the initial interconnection set, an initial two-stage chance-constrained optimization model including the planning stage and the operation stage is constructed. In the planning stage, the interconnection strategy and new energy consumption rate are determined with the goal of minimizing the investment cost and penalty cost. In the operation stage, the feasibility of the interconnection strategy is verified based on random scenarios. This step can consider the requirements of both the planning and operation stages simultaneously, ensuring the balance between economy and reliability of the optimization plan. Then, the non-linear constraints in the initial two-stage chance-constrained optimization model are approximated and linearized by polyhedra, and the power flow model is reconstructed into a compact form to obtain the target two-stage chance-constrained optimization model. This step improves the solution efficiency by simplifying the complexity of the model. After that, the probabilistic chance constraints in the target two-stage chance-constrained optimization model are transformed into deterministic bilinear constraints by the sample average approximation method to obtain a deterministic approximation model. This step enables the optimization model to be solved on a finite sample set, significantly improving the solvability and computational efficiency of the model. Finally, the target interconnection group plan is obtained by iteratively solving the deterministic approximation model through the column constraint generation decomposition algorithm. This algorithm gradually approaches the optimal solution by dynamically adding cut constraints, improving the solution efficiency and accuracy. The finally obtained target interconnection group plan realizes efficient energy mutual assistance and load balancing between distribution transformer areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0011] Figure 1 It is a schematic flow chart provided for Embodiment 1 of the flexible interconnection group planning method for a distribution transformer area oriented to energy mutual assistance according to the present application; Figure 2 It is a schematic diagram of the normalized wind power and photovoltaic output prediction curves and daily load change curves provided for Embodiment 1 of the flexible interconnection group planning method for a distribution transformer area oriented to energy mutual assistance according to the present application; Figure 3 It is a schematic flowchart provided for the second embodiment of the flexible interconnection group planning method for distribution transformer areas facing energy mutual assistance in this application; Figure 4 It is a schematic diagram showing the change of the total system cost under different violation probabilities provided for the second embodiment of the flexible interconnection group planning method for distribution transformer areas facing energy mutual assistance in this application; Figure 5 It is a schematic diagram showing the change of infeasible scenarios, actual violation probability and iterative convergence of the master and sub-problems in the CCG algorithm provided for the second embodiment of the flexible interconnection group planning method for distribution transformer areas facing energy mutual assistance in this application; Figure 6 It is a schematic diagram showing the flexible interconnection planning result and the transformer area group situation provided for the second embodiment of the flexible interconnection group planning method for distribution transformer areas facing energy mutual assistance in this application; Figure 7 It is a schematic diagram of the module structure of the flexible interconnection group planning device for distribution transformer areas facing energy mutual assistance according to the embodiment of this application; The realization of the purpose, functional features and advantages of this application will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0012] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not used to limit this application.

[0013] In order to better understand the technical solutions of this application, the following will be described in detail in combination with the accompanying drawings of the specification and the specific implementation manners.

[0014] It should be noted that the execution subject of the embodiments of this application can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a planning system, a computer system, etc. that can implement the above functions. The following takes the planning system as an example to illustrate this embodiment and the following embodiments.

[0015] Based on this, the embodiments of this application provide a flexible interconnection group planning method for distribution transformer areas facing energy mutual assistance. Refer to Figure 1 , Figure 1 It is a schematic flowchart of the first embodiment of the flexible interconnection group planning method for distribution transformer areas facing energy mutual assistance in this application.

[0016] In this embodiment, the flexible interconnection group planning method for distribution transformer areas facing energy mutual assistance includes steps S10 to S50: Step S10, based on the distribution network topology parameters, transformer area load and new energy output data, screen the transformer area pairs that meet the preset load rate condition or output surplus condition, and construct an initial interconnection set.

[0017] It should be noted that the distribution network topology parameters refer to various data describing the structure and connection relationships of the distribution network, including node information of the distribution network, line connection relationships, parameters such as the resistance and reactance of the lines, etc. These parameters are used to determine the connection methods and electrical distances between distribution substations and are the basic data for constructing the interconnection model of distribution substations. For example, the node numbers in the IEEE 33-node distribution network system, the connection order between nodes, and the resistance and reactance values of each line segment all belong to the distribution network topology parameters.

[0018] The substation load refers to the total load power of all electrical equipment in the distribution substation, usually expressed in active power and reactive power, which reflects the power consumption demand of the distribution substation at a certain moment. In this embodiment, the substation load is determined by obtaining the load power data of the power distribution system, used to evaluate the load situation of the substation, judge whether there is an overload risk, and thus provide a basis for the interconnection decision of the substation.

[0019] The new energy output data refers to the actual power generation of distributed new energy (such as wind power, photovoltaic power, etc.) connected to the distribution substation at a certain moment, including active power and reactive power, which reflects the power generation capacity of the new energy equipment and is an important basis for evaluating the new energy consumption capacity and power balance of the substation. In this embodiment, the new energy output data is used to judge whether there is a situation of excessive new energy output in the substation, and then decide whether to include this substation in the interconnection set.

[0020] Please refer to Figure 2 , Figure 2 which is a schematic diagram of the normalized wind power and photovoltaic output prediction curves and the daily load change curve provided for Embodiment 1 of the flexible interconnection group planning method for distribution substations facing energy mutual assistance in this application. In the figure, the blue curve represents the daily load change on the load demand side, the orange curve represents the daily output change of photovoltaic power generation, and the green curve represents the daily output change of wind power. It can be seen that the load curve shows two obvious peaks in a day, which appear at noon and evening respectively, because these two periods are the peak periods of residential electricity consumption. The photovoltaic output reaches the peak when the solar radiation is the strongest during the day, while the wind power output is relatively stable throughout the day, but there is a certain volatility affected by the wind speed change. The dotted lines in the figure represent the prediction error intervals. The actual output error intervals of photovoltaic and wind power are ±5% of the prediction curve, and the error interval of the actual load power is set to ±10% of the prediction curve. These curves are used to simulate and analyze the power balance of new energy in the distribution network, help the planning system formulate operation strategies in the face of uncertainties, ensure that while meeting the load demand, the consumption rate of new energy is improved, and the phenomena of wind and light abandonment are reduced.

[0021] The preset load rate condition refers to the load rate threshold set for screening the distribution transformer areas that need to be interconnected when planning the interconnection of distribution transformer areas; in this embodiment, when the load rate of the distribution transformer in a transformer area exceeds 80%, this transformer area is considered to need to be interconnected with other transformer areas to achieve power mutual assistance and avoid overloading operation. The over-generation condition means that when the power generation power of the distributed new energy connected to a transformer area exceeds the load demand of this transformer area itself, this transformer area is considered to have an over-generation situation; in this embodiment, this condition is used to screen out the transformer areas that can perform power mutual assistance with other transformer areas to achieve effective consumption of new energy and energy balance between transformer areas. A transformer area pair refers to a combination of two distribution transformer areas that are considered for interconnection under certain conditions (such as too high load rate or over-generation) in the distribution transformer area interconnection planning.

[0022] The initial interconnection set refers to the set of transformer area pairs screened according to the preset load rate condition and over-generation condition in the initial stage of the distribution transformer area interconnection planning. This set contains all the transformer area pairs that may need to be interconnected and is the input basis for the subsequent two-stage chance-constrained distribution transformer area interconnection planning model. The initial interconnection set L is expressed as follows: Among them, represents the p-th transformer area pair in the set L; and : respectively represent the identifiers of the two interconnected transformer areas in the p-th transformer area pair; at least one of the transformer areas in each interconnected transformer area pair in the set satisfies one of the following conditions: ① The load rate of the distribution transformer in the transformer area exceeds 80%; ② The wind and light generation in the transformer area is over-generated.

[0023] As an example, the steps of screening the transformer area pairs that meet the preset load rate condition or over-generation condition based on the distribution network topology parameters, transformer area load, and new energy output data, and constructing the initial interconnection set include: obtaining the distribution network topology parameters, transformer area load, and new energy output data, where the distribution network topology parameters include the connection relationship of distribution network nodes and line impedance parameters, and the transformer area load includes the load power of the transformer area; calculating the load rate of the distribution transformer in each transformer area according to the load power of the transformer area, and identifying the first type of transformer areas whose load rate of the distribution transformer exceeds the preset load rate threshold; according to the new energy output data, identifying the second type of transformer areas whose wind and light output exceeds the preset rated capacity; screening the target transformer area pairs that contain at least one of the first type of transformer areas or the second type of transformer areas within the full range of distribution network nodes according to the connection relationship of distribution network nodes; calculating the effective electrical distance of the target transformer area pairs according to the line impedance parameters; removing the to-be-removed transformer area pairs in the target transformer area pairs to generate the initial interconnection set, and the effective electrical distance of the to-be-removed transformer area pairs is greater than the preset distance threshold.

[0024] The connection relationship of distribution network nodes refers to the connection sequence and topological structure among various nodes in the distribution network, which reflects the electrical connection paths between nodes. It is used to clarify the physical connection situation between distribution transformer areas and is the basis for constructing the interconnection model of distribution transformer areas. The line impedance parameter refers to the resistance and reactance values of the lines in the distribution network and is used to describe the electrical characteristics of the lines. In this embodiment, it is used to calculate the effective electrical distance between target transformer areas, so as to evaluate the engineering feasibility and economy of transformer area interconnection. The load power of a transformer area refers to the total power demand of all electrical equipment in the distribution transformer area at a certain moment, usually including active power and reactive power. In this embodiment, it is a part of obtaining the operation data of the distribution network and is used to calculate the load rate of the distribution transformer in the transformer area. The load rate of the distribution transformer refers to the ratio of the actual load power of the transformer in the distribution transformer area to its rated capacity and is used to measure the load level of the transformer. The preset load rate threshold refers to the upper limit value of the load rate set when planning the interconnection of distribution transformer areas to judge whether a transformer area is overloaded. In this embodiment, this threshold is set to 80%. When the load rate of the distribution transformer in a transformer area exceeds this value, this transformer area is considered to need to be interconnected with other transformer areas to achieve power mutual assistance.

[0025] The first type of transformer area refers to the distribution transformer area whose load rate of the distribution transformer exceeds the preset load rate threshold. The wind-solar output refers to the actual power generation of distributed new energy devices such as wind power and photovoltaic power connected to the distribution transformer area at a certain moment. It is a part of the new energy output data and is used to evaluate the new energy consumption capacity and power balance situation of the transformer area. The preset rated capacity refers to the designed maximum power generation capacity of the distributed new energy device. It is the standard for judging whether the new energy device has excessive output. When the actual output exceeds this value, the transformer area is considered to have a situation of excessive output. The second type of transformer area refers to the distribution transformer area whose wind-solar output exceeds the preset rated capacity. Such transformer areas have the potential to be interconnected with other transformer areas due to excessive new energy output to achieve effective consumption of new energy. The full-node range of the distribution network refers to the set of all nodes in the distribution network, covering all the topological structures of the distribution network, ensuring to find transformer area pairs that meet the conditions within the entire network.

[0026] The target transformer area pair refers to the transformer area pair that contains at least one first type of transformer area or second type of transformer area screened according to the connection relationship of distribution network nodes within the full-node range of the distribution network. These transformer area pairs are the basis for constructing the initial interconnection set, aiming to identify the transformer area combinations that may need to optimize load distribution or new energy consumption through interconnection. The screening of target transformer area pairs takes into account the electrical connection relationship between transformer areas to ensure that the selected transformer area pairs are electrically feasible for interconnection and have potential mutual assistance benefits.

[0027] The effective electrical distance refers to the electrical distance calculated based on the line impedance parameters between target substation areas, which reflects the electrical characteristics and engineering feasibility of the interconnection between substation areas and is used to evaluate whether the target substation area pair is suitable for interconnection. The preset distance threshold refers to the maximum electrical distance set for determining whether a substation area pair is suitable for interconnection when screening the initial interconnection set. In this embodiment, this threshold is set to 1 km. The substation area pairs to be excluded refer to the target substation area pairs that are not suitable for interconnection due to the effective electrical distance being greater than the preset distance threshold when screening the initial interconnection set.

[0028] First, the planning system collects detailed data of the distribution network, including topological parameters (node connection relationships and line impedance parameters), the load power of each substation area, and the output data of the new energy equipment connected, which are the basis for constructing the initial interconnection set and are used to comprehensively understand the operating status of the distribution network and potential interconnection requirements. Second, the system calculates the distribution transformer load rate of each substation area using the substation area load power and identifies the substation areas with a load rate exceeding the preset threshold as the first type of substation areas. These substation areas may face overload risks and need to be alleviated through interconnection. At the same time, the system identifies the substation areas with wind and light output exceeding the preset rated capacity as the second type of substation areas according to the new energy output data. These substation areas have the potential to output excess energy to other substation areas.

[0029] Then, the planning system screens out the target substation area pairs that contain at least one first type of substation area or second type of substation area within the full nodes of the distribution network according to the node connection relationships. This is to ensure that the interconnected substation area pairs can effectively solve the overload problem or achieve the effective consumption of new energy. After that, the system calculates the effective electrical distance of these target substation area pairs using the line impedance parameters to evaluate the engineering feasibility and economy of the interconnection. Finally, the planning system excludes the substation area pairs with an effective electrical distance greater than the preset distance threshold because too long an electrical distance may lead to too high an interconnection cost or be technically infeasible. After this series of screening and calculations, an initial interconnection set is generated, providing an optimized basis for the subsequent flexible interconnection planning of distribution substations to ensure that the interconnection plan is both economical and efficient.

[0030] Step S20: According to the initial interconnection set, construct an initial two-stage chance-constrained optimization model including the planning stage and the operation stage. The planning stage determines the interconnection strategy and the new energy consumption rate with the goal of minimizing the investment cost and the penalty cost, and the operation stage verifies the feasibility of the interconnection strategy based on random scenarios.

[0031] It should be noted that the planning stage refers to the stage in the flexible interconnection group planning of the distribution transformer area. Based on the predicted load power and distributed new energy output data, with the goal of minimizing the system's comprehensive investment cost and penalty cost, the optimal interconnection strategy and new energy consumption rate are determined. The operation stage refers to the stage in the flexible interconnection group planning of the distribution transformer area, where the feasibility of the interconnection strategy determined in the planning stage is tested and corrected based on the actual operation scenario. The initial two-stage chance-constrained optimization model is a mathematical model for the flexible interconnection group planning of the distribution transformer area, including the planning stage and the operation stage. By introducing chance constraints, this model allows certain constraint conditions to be violated within a certain probability range, thus achieving a balance between optimization accuracy and computational cost and improving the adaptability and solution efficiency of the model. The investment cost refers to the capital expenditure related to the construction of interconnection lines, equipment installation, etc. in the flexible interconnection group planning of the distribution transformer area. It includes the annualized investment cost of interconnection lines, equipment purchase costs, etc. In the planning stage, the investment cost is part of the optimization goal, aiming to reduce the overall investment burden of the system through reasonable planning of the interconnection strategy and ensure the economic feasibility of the planning scheme.

[0032] The penalty cost refers to the additional cost incurred in the flexible interconnection group planning of the distribution transformer area due to violating certain operation constraints (such as distribution transformer overload, new energy abandonment, etc.), including the penalty cost for wind and light abandonment, the penalty cost for distribution transformer overload, etc. The penalty cost reflects the risks and uneconomical nature in system operation and is part of the optimization goal, aiming to reduce the risks and additional costs in system operation by optimizing the interconnection strategy and new energy consumption rate. The interconnection strategy refers to the decision-making scheme in the flexible interconnection group planning of the distribution transformer area to determine which transformer areas are interconnected and how to interconnect, including the deployment location of interconnection lines, interconnection priorities, etc. The optimization goal of the interconnection strategy is to achieve power mutual assistance and load balance between transformer areas while meeting the system operation constraints, and improve the reliability and economy of the system.

[0033] The new energy consumption rate refers to the ratio of the actually consumed new energy electricity to the maximum available capacity of new energy equipment in the flexible interconnection group of the distribution transformer area, reflecting the effective utilization degree of the system for new energy. In the planning stage, the new energy consumption rate is part of the optimization goal, aiming to improve the new energy consumption level, reduce the phenomenon of wind and light abandonment, and enhance the energy utilization efficiency of the system through reasonable planning of the interconnection strategy. The stochastic scenario refers to the various possible operation situations generated by random sampling in the operation stage based on the uncertainty of distributed new energy output and load power. These scenarios reflect the uncertainty in system operation and are used to verify the feasibility and reliability of the interconnection strategy under different operation conditions.

[0034] It can be understood that, first, the planning system uses the data in the initial interconnection set to construct an initial two-stage chance-constrained optimization model that includes a planning stage and an operation stage. In the planning stage, the system uses mathematical optimization methods to calculate the optimal interconnection strategy and new energy consumption rate with the goal of minimizing investment costs and penalty costs. The purpose of this stage is to find an economically efficient interconnection solution at the planning level to achieve the effective consumption of new energy and the overall economy of the system. Secondly, in the operation stage, the system verifies the feasibility of the interconnection strategy determined in the planning stage in actual operation based on stochastic scenarios. The system simulates a variety of possible operation scenarios to test the performance of the interconnection strategy under different conditions, ensuring its effective operation in the face of uncertainties, thereby improving the reliability and adaptability of the system. The purpose of this stage is to verify the feasibility and robustness of the interconnection strategy determined in the planning stage in actual operation. Finally, through the optimization and verification of these two stages, the planning system generates an economically and reliable interconnection planning scheme to provide decision-making support for the flexible interconnection of distribution substations, ensuring power mutual assistance and load balancing between substations while meeting the system operation constraints, and at the same time improving the new energy consumption level.

[0035] Step S30: Perform polyhedral approximation linearization on the non-linear constraints in the initial two-stage chance-constrained optimization model, and reconstruct the power flow model in the initial two-stage chance-constrained optimization model into a compact form to obtain a target two-stage chance-constrained optimization model.

[0036] It should be noted that non-linear constraints refer to those in which the mathematical expressions of the constraint conditions in the optimization model contain non-linear terms, such as product terms, square terms, etc. In this embodiment, non-linear constraints mainly appear in parts such as VSC transmission power constraints and security constraints, which describe the non-linear relationships in system operation, such as the relationship between the transmission power of VSC and the substation load. Non-linear constraints make the model solution more complex, so linearization is required to simplify the solution process.

[0037] Polyhedral approximation linearization means approximating the feasible region of non-linear constraints by constructing a polyhedron (i.e., a geometric body surrounded by multiple planes), thereby transforming non-linear constraints into a set of linear constraints. By constructing a compact polyhedral approximation envelope, the complex non-linear relationship is simplified into a linear form, making the optimization model easier to solve while maintaining the physical meaning of the original model and the basic characteristics of the constraint conditions.

[0038] The power flow model refers to a mathematical model that describes the power flow and voltage distribution in a distribution network. In this embodiment, the power flow model includes the linear power flow equations of the distribution network, such as node power balance equations, line power transmission equations, etc. It is the basis for evaluating the operation state of the distribution network and making optimization decisions, reflecting the physical characteristics of the distribution network under given operating conditions.

[0039] The compact form refers to simplifying a complex mathematical model or system of equations into a more concise and manageable form. In this embodiment, reconstructing the power flow model into a compact form means transforming the linear power flow equations of the distribution network into a more concise matrix form for use in the optimization model.

[0040] The target two-stage chance-constrained optimization model refers to the final optimization model obtained after linearizing the initial two-stage chance-constrained optimization model and reconstructing the power flow model. It is simplified and optimized, making it easier to solve while maintaining the main features and optimization objectives of the original model.

[0041] As an example, the non-linear constraints include the transmission power constraint of the voltage source converter and the load rate constraint of the distribution transformer. The power flow model includes the active power flow equation, reactive power flow equation, and voltage magnitude equation of the distribution network. The steps of performing polyhedral approximation linearization on the non-linear constraints in the initial two-stage chance-constrained optimization model and reconstructing the power flow model in the initial two-stage chance-constrained optimization model into a compact form to obtain the target two-stage chance-constrained optimization model include: using the polyhedral envelope method to perform linear approximation on the transmission power constraint of the voltage source converter and the load rate constraint of the distribution transformer to generate linearized constraint conditions; constructing an impedance matrix based on the line impedance parameters in the distribution network topology parameters; reconstructing the active power flow equation, reactive power flow equation, and voltage magnitude equation of the distribution network into a node power-voltage matrix relationship according to the impedance matrix; integrating the linearized constraint conditions with the node power-voltage matrix relationship and replacing the non-linear constraints and power flow equations in the initial two-stage chance-constrained optimization model to obtain the target two-stage chance-constrained optimization model.

[0042] The transmission power constraint of the voltage source converter (VSC) refers to the constraint condition that limits the active power and reactive power transmitted by the VSC at a certain moment not to exceed its rated capacity. In this embodiment, this constraint ensures that the VSC will not be damaged due to power exceeding the design range during operation. The formula for the VSC transmission power constraint is as follows: where and respectively represent the active power and reactive power of the VSC in the i-th substation area at time t; represents the rated capacity of the VSC.

[0043] The load rate constraint of the distribution transformer refers to the constraint condition that limits the load rate of the distribution transformer in the substation area not to exceed a preset threshold (such as 80%) to prevent the transformer from overloading and ensure the safety and reliability of the substation area.

[0044] The active power flow equation of the distribution network refers to the mathematical equation that describes the active power flow among the nodes in the distribution network, reflects the active power balance relationship between the power sources and loads in the distribution network, and is the basis for evaluating the operating state of the distribution network and making optimization decisions. In this embodiment, the active power flow equation is used to calculate the active power injection and outflow of each node to ensure the power balance of the system.

[0045] The reactive power flow equation refers to the mathematical equation that describes the reactive power flow among the nodes in the distribution network, reflects the distribution of reactive power in the distribution network, and is of great significance for maintaining the system voltage stability and improving the power quality. In this embodiment, the reactive power flow equation is used to calculate the reactive power injection and outflow of each node to ensure the reactive power balance of the system.

[0046] The voltage magnitude equation refers to the mathematical equation that describes the voltage magnitudes of the nodes in the distribution network, reflects the voltage levels of the nodes in the distribution network, and is an important indicator for evaluating the system voltage stability and power quality. In this embodiment, the voltage magnitude equation is used to calculate the voltage values of each node to ensure that the system operates within a safe voltage range. The linearized constraint conditions refer to the constraint conditions obtained by approximating the nonlinear constraints into a linear form through methods such as the polyhedral envelope method. The impedance matrix refers to the matrix constructed based on the line impedance parameters in the distribution network topology parameters, which is used to describe the electrical connection relationship among the nodes in the distribution network. The node power-voltage matrix relationship refers to the matrix form relationship obtained by reconstructing the active power flow equation, reactive power flow equation, and voltage magnitude equation of the distribution network through the impedance matrix. This relationship is used to simplify the expression of the power flow model, making the optimization model more compact and facilitating the solution.

[0047] First, the planning system linearly approximates the power transmission constraints of the voltage source converter and the distribution transformer load rate constraints through the polyhedral envelope method. Specifically, the system approximately represents the feasible region of these nonlinear constraints with a polyhedron, and by defining the boundaries of the polyhedron, it transforms the complex nonlinear relationship into a set of linear inequality constraints, thereby generating the linearized constraint conditions. The purpose of this process is to simplify the difficult-to-directly-solve nonlinear constraints into a linear form so that the linear programming method can be used for solution later. The formula for the linearized constraint conditions is as follows: ; ; where represents the power through the voltage source converter (VSC) in the distribution substation area i during the time period t, and represent the active power and reactive power of the VSC in the substation area i at time t respectively, represents the rated capacity of the voltage source converter; Denote the power vector of node \(i\) at time period \(t\), including the active power and the reactive power ; Denote the maximum load rate allowed for node \(i\); Denote the rated capacity of the transformer of node \(i\); Denote the maximum power vector of node \(i\) at time period \(t\), including the maximum active power and the maximum reactive power ; \(\varepsilon\) represents a small positive number, which is used to ensure the feasibility of the constraints and allow a certain probability of violation; and respectively denote the coefficient matrix and the constant term vector that define the boundary of the polyhedron.

[0048] Secondly, the system constructs an impedance matrix based on the line impedance parameters in the distribution network topology parameters. The specific operation is that the system extracts the resistance and reactance values of each line and organizes these parameters into a matrix form, where the elements of the matrix represent the electrical connection relationships between nodes. The purpose of constructing the impedance matrix is to transform the power flow equations of the distribution network from the form of scattered node equations into matrix form, which is convenient for subsequent matrix operations and model simplification. Then, the system reconstructs the active power flow equation, reactive power flow equation, and voltage magnitude equation of the distribution network into a node power-voltage matrix relationship according to the impedance matrix. The specific method is to use the impedance matrix to represent the relationship between the power injection of each node and the voltage magnitude in matrix form, and simplify the power flow equation into a compact form through matrix operations. The purpose of doing this is to further simplify the power flow model and make it easier to use in the optimization model. The compact form formula is as follows: ; ; where, 、 、 、 、 respectively denote the vectors of all nodes \(i\) at time \(t\) of 、 、 、 、 , and are respectively calculated from and , and are diagonal matrices containing the resistance and reactance of all distribution network lines; \(P\) t Denote the vector of node active power injection at time step \(t\); \(A\) -TDenotes the transpose inverse matrix of the node-branch incidence matrix, which is used to convert branch power into node power; Denotes the active power flow vector of the branch at time step t; Denotes the reactive power injection vector of the node at time step t; Denotes the reactive power flow vector of the branch at time step t; Denotes the node voltage magnitude vector at time step t; Denotes the resistance matrix of the distribution network; Denotes the reactance matrix of the distribution network; V0 denotes the voltage magnitude of the reference node (usually the root node); Denotes an N-dimensional vector with all elements equal to 1.

[0049] Finally, the system integrates the linearized constraint conditions with the node power-voltage matrix relationship, and replaces the non-linear constraints and power flow equations in the initial two-stage chance-constrained optimization model. Specifically, the linearized constraint conditions and the compact form of the power flow model are substituted into the initial model to replace the original complex non-linear part. The purpose of this process is to transform the initial model into the target two-stage chance-constrained optimization model, making it easier to solve while maintaining the main characteristics and optimization objectives of the original model. Through this series of steps, the planning system finally obtains a simplified and easily solvable target optimization model, providing efficient support for the flexible interconnection planning of the distribution substation area.

[0050] Step S40, convert the probabilistic chance constraints in the target two-stage chance-constrained optimization model into deterministic bilinear constraints through the sample average approximation method to obtain a deterministic approximation model.

[0051] It should be noted that the sample average approximation method (SAA) is a method for dealing with uncertainty optimization problems. By using Monte Carlo sampling technology to generate a sample set of random variables, the true expected value of the random variable is approximated by the average value of the samples. This method simulates uncertainty by introducing sample scenarios, enabling the optimization model to be solved on a finite sample set, thereby improving the solvability and computational efficiency of the model.

[0052] Probabilistic chance constraints refer to the situation in the optimization model where some constraint conditions are allowed to be violated with a certain probability rather than being strictly satisfied. In this embodiment, these constraint conditions are usually related to the uncertainty of distributed new energy output and load power. For example, the constraint conditions may require that the system operating state meet specific requirements (such as the transformer not being overloaded or the new energy consumption rate not being lower than a certain threshold) with a certain probability (such as 95%). This constraint allows considering uncertainty in the optimization process while controlling the risk level.

[0053] A deterministic bilinear constraint refers to a constraint condition obtained by transforming a probabilistic chance constraint into a deterministic form through the sampling average approximation method. In this embodiment, these constraint conditions are represented by introducing binary variables and sample scenarios, allowing the probability of constraint violation to be controlled within a finite sample set. This constraint form facilitates solution in an optimization model while maintaining the original model's ability to handle uncertainty.

[0054] A deterministic approximation model refers to an optimization model obtained by transforming probabilistic chance constraints into deterministic bilinear constraints through the sampling average approximation method. It is an improved two-stage chance-constrained optimization model. By introducing sample scenarios and binary variables, it makes the solution of the optimization problem more efficient and can effectively control the risk level.

[0055] It can be understood that, first, the planning system generates a large number of random sample scenarios through the Monte Carlo sampling technique. These scenarios simulate the uncertainties of distributed new energy output and load power. Each sample scenario represents a possible operating condition, and its quantity and distribution are determined according to the statistical characteristics of the prediction error (such as a Gaussian distribution with a mean of zero and a standard deviation of 3.33% of the predicted value) to ensure that the samples can fully reflect the uncertainties in actual operation. Second, for each sample scenario, the planning system calculates the satisfaction of the probabilistic chance constraint and introduces binary variables to identify whether each scenario satisfies the constraint condition. Specifically, it is expressed as: Among them, represents the probability of event A occurring; is an indicator variable representing whether event A occurs or not. means the event occurs, means the event does not occur; is a binary variable representing whether event A occurs or not. means the event occurs, means the event does not occur.

[0056] Specifically, if the constraint condition in a certain scenario is satisfied, the corresponding binary variable takes the value of 1; otherwise, it takes the value of 0. In this way, the probabilistic constraint is transformed into a deterministic bilinear constraint, that is, the probability-based constraint condition in the original model is transformed into a sample-scenario-based linear constraint condition. The deterministic bilinear constraint formula is as follows: Among them, b represents the coefficient matrix and the constant term vector defining the polyhedron boundary; z mrepresents the binary variable (identification variable) associated with node i, used to indicate whether the node is considered in the model; m refers to a sample scenario; M refers to the set of equiprobable random scenarios; α represents the minimum absorption ratio of new energy; and respectively represent the maximum active power of the photovoltaic and wind power equipment w in time period t; the explanations of other variables are the same as those in the linearized constraint condition formula and the curtailment penalty cost formula of wind and light.

[0057] In the deterministic bilinear constraint, represents excluding scenario m to allow it to violate the constraint, represents considering scenario m and thus must strictly satisfy the chance constraint. To maintain consistency with the violation probability setting in the original chance constraint and to meet the requirements of the original chance constraint, it is required that the number of scenarios satisfying the constraint is not less than : ; Simplified to: where ε represents a small positive number, used to ensure the feasibility of the constraint and allow a certain violation probability; the explanations of other variables are the same as those in the deterministic bilinear constraint formula.

[0058] Therefore, the objective function in the operation stage is reconstructed as: where E represents the expectation; the explanations of other variables are the same as those in the deterministic bilinear constraint formula and the operation stage objective function formula.

[0059] Let the decision variable in the first stage be , and the decision variable in scenario m in the second stage is represented as , then the two-stage chance-constrained optimization model after reconstruction can be further transformed into the following compact form: ; ; ; ; where, and respectively represent the cost coefficient vector in the first stage and the cost coefficient vector in scenario m in the second stage; , and , respectively represent the constraint coefficient matrices and constraint lower bound vectors in the first stage and the second stage; and represent the coupling constraint matrices in the first stage and the second stage, represents the combined constraint lower bound vector; , and respectively represent the coefficient matrices and the corresponding right - hand side constant terms of the first - and second - stage variables in the chance constraint. The explanations of other variables are the same as above.

[0060] Finally, the planning system integrates these deterministic bilinear constraints into the optimization model, replacing the original probabilistic chance constraints, thus obtaining a deterministic approximation model. This improved model transforms the uncertainty problem into a deterministic problem, facilitating the use of traditional optimization methods for solution. At the same time, by controlling the number and distribution of sample scenarios, it ensures the feasibility and reliability of the model in practical applications, significantly improving the solvability and computational efficiency of the model.

[0061] Step S50, iteratively solve the deterministic approximation model through the column - and - constraint generation decomposition algorithm to obtain the target interconnected group scheme.

[0062] It should be noted that the column - and - constraint generation (CCG) decomposition algorithm is an iterative algorithm for solving large - scale optimization problems, especially suitable for dealing with two - stage optimization models containing chance constraints. In this embodiment, the algorithm decomposes the original problem into a master problem and multiple sub - problems, and alternately solves the master problem and the sub - problems. The master problem is responsible for optimizing the decision variables (such as interconnected lines and new - energy consumption rates), while the sub - problems are used to verify the feasibility of these decisions under different scenarios. By dynamically adding cutting constraints during the iterative process (cutting constraints refer to the constraint conditions that are dynamically added to the master problem in the optimization algorithm to gradually approximate the optimal solution, used to exclude infeasible solutions or push the solution towards a better direction, thereby gradually narrowing the feasible solution space), it gradually approaches the global optimal solution. It can significantly reduce the computational complexity of the model and improve the solution efficiency, and is suitable for dealing with uncertainty optimization problems.

[0063] The target interconnected group scheme refers to the final flexible interconnection planning scheme of the distribution sub - area obtained by solving through the optimization model. In this embodiment, this scheme includes the specific deployment of interconnected lines (such as which sub - areas are interconnected and the specific parameters of the interconnected lines), new - energy consumption rate parameters, and other relevant decision variables. These decision variables are gradually determined during the iterative process of the column - and - constraint generation decomposition algorithm, and finally form an interconnected scheme that is both economical and reliable. The target interconnected group scheme aims to achieve power mutual assistance and load balancing among distribution sub - areas, while improving the new - energy consumption level and ensuring the stable operation of the system under uncertain conditions.

[0064] It can be understood that, first, the planning system initializes the solution process of the deterministic approximation model through the column constraint generation decomposition algorithm. Then, the system alternately solves the master problem (optimizing decision variables such as interconnected lines and new energy consumption rates) and the sub-problem (verifying the feasibility of decision variables under different scenarios). If the sub-problem is infeasible or not optimal, cut constraints are added to the master problem to update the decision variables. Finally, when the master problem no longer requires new cut constraints, the current solution (the solution of the master problem) is the target interconnected group solution, which includes decision variables such as specific interconnected lines and consumption rate parameters, and is output as the optimal solution.

[0065] This embodiment provides a flexible interconnected group planning method for distribution substations oriented to energy mutual assistance. First, based on the distribution network topology parameters, substation load, and new energy output data, the planning system screens substation pairs that meet the preset load rate conditions or over-generation conditions to construct an initial interconnection set. This step preliminarily determines the substations that may need to be interconnected by screening eligible substation pairs, effectively narrowing the optimization scope and improving the efficiency of the subsequent optimization process. Then, according to the initial interconnection set, an initial two-stage chance-constrained optimization model including the planning stage and the operation stage is constructed. In the planning stage, the interconnection strategy and new energy consumption rate are determined with the goal of minimizing the investment cost and penalty cost. In the operation stage, the feasibility of the interconnection strategy is verified based on random scenarios. This step can consider the requirements of both the planning and operation stages simultaneously to ensure the balance between economy and reliability of the optimization solution. Then, the non-linear constraints in the initial two-stage chance-constrained optimization model are approximated and linearized by polyhedra, and the power flow model is reconstructed into a compact form to obtain the target two-stage chance-constrained optimization model. This step simplifies the complexity of the model and improves the solution efficiency. After that, the probabilistic chance constraints in the target two-stage chance-constrained optimization model are transformed into deterministic bilinear constraints by the sample average approximation method to obtain a deterministic approximation model. This step enables the optimization model to be solved on a finite sample set, significantly improving the solvability and computational efficiency of the model. Finally, the target interconnected group solution is obtained by iteratively solving the deterministic approximation model through the column constraint generation decomposition algorithm. This algorithm gradually approaches the optimal solution by dynamically adding cut constraints, improving the solution efficiency and accuracy. The resulting target interconnected group solution realizes efficient energy mutual assistance and load balance among distribution substations.

[0066] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar content as in the above-mentioned first embodiment can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 3 , Figure 3 is a schematic flow chart of the second embodiment of the flexible interconnected group planning method for distribution substations oriented to energy mutual assistance of the present application. The step S20 of the flexible interconnected group planning method for distribution substations oriented to energy mutual assistance includes steps S21 to S24: Step S21, defining a planning stage objective function according to the initial interconnection set, wherein the planning stage objective function represents minimization of the sum of interconnection line investment cost, wind and solar power abandonment penalty cost, and distribution transformer overload penalty cost.

[0067] It should be noted that the planning stage objective function refers to the mathematical expression used to measure and optimize the economic and feasibility of the plan during the planning stage of the flexible interconnection group planning of the distribution substation area. It comprehensively considers the economic investment in building interconnection lines, the penalty for wind and solar abandonment caused by insufficient new energy consumption, and the additional costs caused by transformer overload operation. The planning stage objective function formula is as follows: Where C is the total cost of flexible interconnection group planning in the distribution area; It refers to the investment cost of interconnection lines; Refers to the penalty cost for curtailing wind and solar power; It refers to the penalty cost of distribution transformer overload.

[0068] Please refer to Figure 4 , Figure 4 A schematic diagram of the change in total system cost under different violation probabilities is provided for Example 2 of the planning method for flexible interconnection of distribution station areas for energy mutual assistance in this application. In the figure, the horizontal axis represents the violation probability ε, and the vertical axis represents the total cost of the system. As the violation probability decreases, that is, the system's tolerance for uncertainty decreases, the total system cost also increases. This is because a lower violation probability requires that the system design must be more conservative to ensure that the predetermined goals and constraints can still be met under a wider range of uncertainty conditions, resulting in increased planning costs. The curve in the figure shows that when the violation probability increases from 0 to 0.1, the total system cost drops from approximately 11,100 to approximately 10,700. This shows that by appropriately adjusting the violation probability, while ensuring system reliability, costs can be effectively controlled to achieve a balance between cost and reliability. This trend is consistent with the expected results of the two-stage opportunity constraint model proposed in the briefing book, and verifies the cost-benefit analysis of the model under different violation probability settings. Interconnection line investment cost The formula is as follows: in, It refers to the annualized investment cost per unit length of interconnection line, which is a constant; Representing interconnected sets The interconnection priority of the p-th area pair in; is a binary variable representing whether the p-th substation pair is interconnected (1 indicates interconnection, 0 indicates no interconnection); L p represents the actual electrical distance between the p-th substation pair, which is used to calculate the physical length of the interconnection line; r represents the discount rate; Indicates the economic service life of the interconnection line. The penalty cost for curtailment of wind and solar power The formula is as follows: Among them, represents the unit penalty cost for curtailment of wind and solar power, that is, the penalty amount required for each unit of unconsumed new energy power generation; represents the optimal new energy consumption rate, and respectively represent the maximum available capacities of photovoltaic and wind power of the new energy unit w at time t; T is the set of total time periods considered; W is the set of all wind power and photovoltaic devices. Refers to the penalty cost for distribution transformer overload The formula is as follows: Among them, represents the unit penalty cost for distribution transformer overload, that is, the penalty amount required for each unit of overloaded transformer capacity; T is the set of total time periods considered; N is the set of all substations; represents the load rate of substation i at time t, represents the rated capacity of the transformer of substation i.

[0069] The interconnection line investment cost refers to the economic investment required for building the interconnection line in the flexible interconnection planning of distribution substations, including the material costs, construction costs, and related maintenance costs of line construction, etc. In this embodiment, its calculation depends on the initial interconnection set, that is, which substations need to be interconnected. The penalty cost for curtailment of wind and solar power refers to the additional cost generated in the flexible interconnection planning of distribution substations due to the inability to fully consume distributed new energy. When the power generation of new energy equipment exceeds the consumption capacity of the substation, it will cause some new energy to be curtailed (curtailment of wind or solar power), thus generating penalty costs. The penalty cost for distribution transformer overload refers to the additional cost generated in the flexible interconnection planning of distribution substations due to the transformer load rate exceeding the preset threshold (such as 80%). When the load rate of the distribution transformer of the substation exceeds this threshold, it will cause the transformer to operate overloaded, thus increasing the risk of equipment damage and operating costs.

[0070] It is understandable that, first, based on the substation pair information in the initial interconnection set, the planning system determines the potential construction locations and scales of the interconnection lines, thereby defining the specific calculation method for the investment cost of the interconnection lines, which is estimated based on the electrical distances and line parameters between the substation pairs in the initial interconnection set. Second, the system combines the output data of new energy equipment and the consumption capacity of the substations to calculate the possible curtailment of wind and solar power, and estimates the curtailment penalty cost accordingly. This step is to evaluate the economic impact of insufficient new energy consumption. Finally, the system identifies the transformers that may be overloaded based on the load rate data of the distribution transformers in the substations and calculates the corresponding distribution transformer overload penalty cost. This step is to evaluate the risks and additional costs brought by the overloaded operation of the transformers. By adding and minimizing these three parts of costs, the planning system defines the objective function in the planning stage, aiming to reduce the overall planning cost by optimizing the interconnection strategy and the new energy consumption rate, and ensuring the balance between economy and reliability of the plan.

[0071] Step S22, set the constraints in the planning stage based on the physical laws and safety standards of the distribution network.

[0072] It should be noted that the physical laws of the distribution network refer to the basic physical laws that describe the power transmission and conversion in the distribution network, including Ohm's law, Kirchhoff's law, etc. They determine the relationships between current, voltage, and power in the distribution network and are the basis for constructing the linear power flow constraints and AC-DC power balance constraints of the distribution network, ensuring that the model can accurately reflect the operating characteristics of the distribution network. The safety standards refer to the technical specifications and safety requirements that must be complied with in the operation and planning of the distribution network, including the rated capacity limits of equipment, the safe ranges of voltage and current, and the overload protection requirements of equipment. The constraints in the planning stage refer to a series of mathematical constraint conditions set in the planning stage of the flexible interconnection group planning of the distribution substations to ensure the feasibility and safety of the optimization plan, including the voltage source converter capacity constraint, the AC-DC power balance constraint, the linear power flow constraint of the distribution network, the safe operation constraint, and the new energy consumption rate constraint, etc. In this embodiment, the constraints in the planning stage are used to limit the value ranges of the optimization variables. The voltage source converter capacity constraint refers to the constraint condition that limits the active power and reactive power transmitted by the voltage source converter at a certain moment not to exceed its rated capacity. The AC-DC power balance constraint refers to the power balance relationship between the AC system and the DC system in the distribution substation, ensuring that at each node, the input active power and reactive power are equal to the output power, thereby maintaining the power balance of the system. The AC-DC power balance constraint requires the active power and reactive power balance of the distribution network node i and the substation i. The formula is as follows: Among them, and respectively represent the active power and reactive power flowing out of the distribution network node i at time t, and respectively represent the active power and reactive power of the AC load of the distribution area i at time t, and respectively represent the photovoltaic output and wind power output of the new energy unit w at time t, represents the total power demand of the DC load of the distribution area i during the time period t, represents the transmission power of the interconnection line between the distribution area and p at time t, and respectively represent the set of distributed new energy connected to the distribution area i and the set of all distribution areas interconnected with the distribution area i.

[0073] The linear power flow constraint of the distribution network refers to the linearized equation that describes the power flow and voltage distribution in the distribution network, ensuring that the power flow in the distribution network conforms to the physical laws. Specifically, it describes the power transmission relationship between each node through the power flow equation in matrix form, ensuring that the power flow of the system is within a safe range. The formula is as follows: Among them, represents the reactive power transmitted by the distribution network line ij at time t; represents the sum of all reactive powers flowing out from node j to its directly connected node k during the time period t; represents the reactive power demand or power generation power of node j itself during the time period t; and respectively represent the set of the parent node and the child node of node j; represents all network nodes except the source node. Since node 0 is the starting point of the feeder and serves as the system power injection point, it only undertakes the power supply function and does not need to meet the power flow balance constraint, so it is excluded in the constraint modeling; represents the active power demand or power generation power of node j itself during the time period t; represents the sum of all active powers flowing out from node j to its directly connected node k during the time period t; refers to the active power transmitted by the distribution network line ij at time t; represents the voltage amplitude of node i at time t, and respectively represent the resistance and reactance of the distribution network line ij.

[0074] Safety operation constraints refer to a series of constraints set to ensure the safe and stable operation of the distribution network during operation, including that the transformer load rate does not exceed the preset threshold, the transmission power of the interconnected line does not exceed the allowed maximum value, and the node voltage does not exceed the limit, etc. The formula is as follows: ; Among them, represents the load rate of node i during time period t; represents the active power of node i during time period t; represents the reactive power of node i during time period t; represents the rated capacity of the transformer of node i (high-voltage side of apparent power); represents the maximum allowable load rate of the distribution transformer. represents the reactance value of line p, which is used to calculate the transmission capacity of the line; represents the maximum allowable transmission power of the interconnected line in the substation area, represents the transmission power of line p during time period t; and respectively represent the upper and lower voltage limits allowed for node i at time t; represents the voltage amplitude of node i during time period t.

[0075] The new energy consumption rate constraint means that in the distribution substation area, the generated electricity of new energy must be effectively consumed within a certain proportion, ensuring that the generated electricity of new energy equipment will not be largely abandoned due to insufficient consumption, thereby improving the utilization efficiency of new energy. The new energy consumption level should not be lower than the optimal consumption rate , and the formula is as follows: ; Among them, refers to the optimal consumption rate; represents the generated electricity of photovoltaic equipment w during time period t; represents the generated electricity of wind power equipment w during time period t; represents the abandoned electricity consumption of photovoltaic equipment w during time period t; represents the abandoned electricity consumption of wind power equipment w during time period t.

[0076] It is understandable that, first, according to the physical laws of the distribution network, such as Ohm's law and Kirchhoff's law, the voltage drop in the line and the node power balance relationship are clarified, so as to set the AC-DC power balance constraint and the distribution network linear power flow constraint. This step is to ensure that the optimization model can accurately reflect the operating characteristics of the distribution network and ensure that the relationships among current, voltage, and power conform to physical laws. Secondly, according to safety standards such as the rated capacity and safe operating range of equipment, the voltage source converter capacity constraint and safe operation constraint are set to ensure that the equipment operates within a safe range and avoid equipment damage or system instability caused by problems such as overload or voltage over-limit. Finally, combined with the new energy consumption target, the new energy consumption rate constraint is set to ensure the effective utilization of new energy, avoid the phenomenon of wind and light abandonment, and improve the economy and environmental protection of the system. Through these steps, based on the physical laws and safety standards of the distribution network, the constraint conditions in the planning stage are comprehensively set to ensure that the optimization scheme meets both physical characteristics and safety requirements, and at the same time realizes the effective consumption of new energy.

[0077] Step S23: Generate decision variables for the planning stage based on the objective function and constraints of the planning stage.

[0078] It should be noted that the decision variables for the planning stage refer to the set of variables used for optimization decisions in the planning stage of the flexible interconnection group planning of the distribution substation area. In this embodiment, these decision variables include the interconnection strategy binary variable and the new energy consumption rate. These variables are the core components of the optimization model. By adjusting their values, an optimal planning scheme can be found on the premise of meeting the objective function and constraint conditions of the planning stage, that is, by optimizing these decision variables, the system investment cost and penalty cost can be minimized, while ensuring the safe operation of the system and the effective consumption of new energy.

[0079] It is understandable that, first, according to the definition of the objective function of the planning stage, the planning system clarifies the key indicators to be optimized, that is, to minimize the sum of the investment cost of the interconnection line, the penalty cost of wind and light abandonment, and the penalty cost of distribution transformer overload. Secondly, the planning system combines the constraint conditions of the planning stage to determine the feasible value range of the decision variables. Finally, the planning system solves the objective function of the planning stage through mathematical optimization methods (such as linear programming or mixed integer programming), and generates the decision variables for the planning stage on the premise of meeting all constraint conditions, including the interconnection strategy binary variable (determining which substations are interconnected) and the new energy consumption rate (reflecting the utilization efficiency of the power generation of new energy equipment).

[0080] Step S24: Construct an initial two-stage chance-constrained optimization model including the planning stage and the operation stage based on the decision variables of the planning stage, the load of the substation area, and the new energy output data.

[0081] As an example, the steps of constructing an initial two-stage chance-constrained optimization model including the planning stage and the operation stage based on the decision variables in the planning stage, the load of the distribution transformer area, and the new energy output data include: defining an operation-stage objective function according to the decision variables in the planning stage, where the operation-stage objective function represents the minimization of the sum of the interconnected line loss cost and the expected value of the load shedding cost; setting an operation-stage probability constraint based on the operation-stage objective function; applying a preset proportion of Gaussian perturbation to the load of the distribution transformer area and the new energy output data to generate an equiprobable random scenario set, and defining an operation-stage random scenario variable based on the random scenario set; coupling the decision variables in the planning stage, the operation-stage probability constraint, and the operation-stage random scenario variable to form an initial two-stage chance-constrained optimization model.

[0082] The operation-stage objective function refers to a mathematical expression used to measure and optimize the operation cost during the operation stage of the flexible interconnection group planning of the distribution transformer area. In this embodiment, this objective function is expressed as the minimization of the sum of the interconnected line loss cost and the expected value of the load shedding cost, reflecting the economic impact caused by line transmission losses and possible load curtailment during actual operation, aiming to reduce the operation cost of the system by optimizing the operation strategy. The formula of the operation-stage objective function is as follows: ; Among them, represents the expected value, refers to the comprehensive operation cost, refers to the interconnected line loss cost, refers to the load shedding cost; The cost of interconnected line losses refers to the economic cost incurred during the flexible interconnection operation of a distribution substation area due to the power losses generated when the interconnected lines transmit electric energy. This part of the cost is related to the transmission power of the interconnected lines and the line parameters, reflecting the impact of line losses on the system economy. The expected value of the load shedding cost refers to the expected value of the economic compensation cost incurred during the flexible interconnection operation of a distribution substation area due to reducing the load of some users as required by the system operation. This part of the cost is related to the amount of load shedding and the unit load shedding cost, reflecting the impact of load shedding on the system economy. The probability constraints in the operation stage refer to a series of probabilistic constraint conditions set in the operation stage of the flexible interconnection group planning of a distribution substation area to ensure the reliability and security of the system operation (in addition, the operation stage may comply with the above-mentioned constraint conditions in the planning stage, which will not be repeated here). In this embodiment, these constraints include the load shedding power constraint, the probability constraint of the distribution transformer load rate, and the probability constraint of the new energy consumption rate, ensuring that under a certain probability level, the load rate of the transformer does not exceed the safety threshold and the new energy consumption rate reaches the preset standard. The load shedding power constraint refers to the limitation imposed on the amount of load to be shed in an emergency during the operation of the power system to ensure the stability and security of the system. It ensures that the shed load power does not exceed the total available active power in the system, that is, the sum of the maximum active powers on the AC side and the DC side. The purpose is to maintain the power balance and voltage stability of the system by shedding some loads in case of system faults or emergencies, preventing system collapse or equipment damage. The formula is as follows: Where, and respectively represent the active power magnitudes of the AC and DC loads of substation area i at time t under scenario m, represents the maximum shed active power when load shedding is performed on distribution substation area i at time t under scenario m.

[0083] The probability constraint of the distribution transformer load rate refers to the probabilistic constraint condition set in the operation stage of the flexible interconnection group planning of a distribution substation area to ensure that the load rate of the transformer does not exceed the safety threshold. By introducing probability constraints, it allows the load rate of the transformer to exceed the safety threshold in a small number of scenarios (such as no more than 5%). The formula for the probability constraint of the distribution transformer load rate is as follows: ; Where, represents the probability, and the maximum allowable violation probability is set to , represents the power vector of distribution substation area i in time period t, which is used to describe the comprehensive state of the power of this substation area in a specific time period, and respectively represent the active power and reactive power flowing out of distribution network node i at time t under scenario m. represents the rated capacity of the transformer in distribution substation area i. represents the maximum allowable load rate.

[0084] The probabilistic constraint on the new energy consumption rate means that in the operation stage of the flexible interconnection group planning of the distribution substation area, a probabilistic constraint condition is set to ensure the effective consumption of new energy. It requires that under a certain probability level (such as 95%), the new energy consumption rate is not lower than the preset optimal value (such as 90%). The formula is as follows: where, and respectively represent the photovoltaic and wind power output of new energy unit w at time t under scenario m. refers to the maximum allowable violation probability. represents the minimum consumption ratio of new energy. and respectively represent the curtailment electricity of photovoltaic and wind power equipment w in time period t. T is the set of total time periods considered, and W is the set of all wind power and photovoltaic equipment.

[0085] The preset ratio refers to the perturbation ratio imposed on the substation area load and new energy output data when generating random scenarios. In this embodiment, the preset ratio is 5%, that is, a Gaussian perturbation of ±5% is imposed on the original data to simulate the uncertainty of actual operation.

[0086] Gaussian perturbation refers to the random perturbation that conforms to the Gaussian distribution (normal distribution) imposed on the substation area load and new energy output data. The mean of the Gaussian perturbation is zero, and the standard deviation is the preset ratio of the original data, which can effectively reflect the random fluctuations in actual operation.

[0087] The equiprobable random scenario set refers to a set of random scenarios generated by imposing Gaussian perturbations, and each scenario has an equal probability of occurrence. This random scenario is used to simulate the uncertainty of distributed new energy output and load power, providing data support for the probabilistic constraints in the operation stage.

[0088] The random scenario variables in the operation stage refer to the variables defined based on the equiprobable random scenario set in the operation stage, which are used to represent the system operation state under each random scenario. In this embodiment, these variables include the substation area load, new energy output, and interconnection line transmission power under each scenario, reflecting the operation of the system under different random scenarios. By introducing these random scenario variables, the optimization model can effectively optimize and control risks under uncertain conditions.

[0089] First, according to the interconnection strategy and new energy consumption rate determined in the planning stage, the planning system defines the objective function in the operation stage as the minimization of the sum of the interconnection line loss cost and the expected value of the load shedding cost, quantifies these costs through mathematical expressions to optimize the economy in the operation stage. Then, based on this objective function, probabilistic constraints in the operation stage are set. Specifically, according to historical data and statistical analysis, the probability distributions of the distribution transformer load rate and the new energy consumption rate are determined, and then the maximum probability of allowing violations of these constraints is set to balance risks and costs. Next, a Gaussian perturbation with a preset ratio is applied to the data of the substation area load and new energy output, and a large number of random samples are generated through Monte Carlo simulation. These samples form an equiprobable random scenario set, and each scenario reflects a possible operation state. Finally, the decision variables in the planning stage (such as the binary variables of the interconnection strategy and the new energy consumption rate), the probabilistic constraints in the operation stage, and the random scenario variables in the operation stage are integrated into a unified mathematical model. By introducing binary variables and constraint conditions, it is ensured that the model simultaneously considers the requirements of the planning and operation stages during the optimization process, thus forming an initial two-stage chance-constrained optimization model.

[0090] As an example, the steps of converting the probabilistic chance constraint in the target two-stage chance-constrained optimization model into a deterministic bilinear constraint through the sample average approximation method to obtain a deterministic approximation model include: extracting the probabilistic chance constraint in the target two-stage chance-constrained optimization model; defining binary identification variables for each scenario in the equiprobable random scenario set; converting the probabilistic chance constraint into a bilinear inequality containing the identification variables; constructing an identification variable summation constraint based on the probability estimation principle of the sample average approximation method; integrating the bilinear inequality and the identification variable summation constraint to replace the probabilistic chance constraint to obtain a deterministic approximation model.

[0091] An identification variable refers to a binary variable used in the sampling average approximation method to indicate whether each random scenario satisfies a probabilistic chance constraint. By introducing the identification variable, the probabilistic constraint can be transformed into a linear constraint, facilitating the solution of the optimization model. A bilinear inequality refers to a linear inequality containing decision variables and identification variables formed after introducing the identification variable when transforming the probabilistic chance constraint into a deterministic constraint. In this embodiment, the bilinear inequality is used to represent the constraint conditions for each scenario, ensuring that the optimization model can operate effectively while satisfying the probabilistic constraint. The probability estimation principle refers to a method of estimating the probability distribution or expected value of a random variable through sample data. Specifically, by calculating the proportion of the number of scenarios that satisfy the constraint to the total number of scenarios, the satisfaction probability of the constraint condition is approximated, thereby realizing the deterministic approximation of the probabilistic constraint. The identification variable summation constraint refers to a constraint condition constructed by summing up the identification variables of all scenarios in the sampling average approximation method. In this embodiment, the identification variable summation constraint is used to ensure that in all sample scenarios, the number of scenarios that satisfy the probabilistic chance constraint reaches a preset probability level (such as 95%).

[0092] First, extract the probabilistic chance constraints from the target two-stage chance-constrained optimization model, including the probability constraints of the distribution transformer load rate and the probability constraints of the new energy consumption rate. These constraints allow violations of the constraints at a certain probability level. The specific operation is to identify all the constraint terms involving probability in the model and extract them separately for subsequent processing. Then, define a binary identification variable for each scenario in the equiprobable random scenario set. The value of the identification variable being 1 indicates that the scenario satisfies the constraint, and the value being 0 indicates that it does not satisfy the constraint, so as to be able to track whether each scenario meets the constraint conditions. Next, transform the probabilistic chance constraints into bilinear inequalities containing the identification variables. By introducing the multiplication of the identification variables and the variables in the original constraint conditions to form bilinear inequalities, this step transforms the probabilistic constraints into a linear form, making the optimization model easier to solve. In this way, when the identification variable is 0, the constraint conditions are relaxed, and when the identification variable is 1, the constraint conditions must be satisfied. Then, construct the identification variable summation constraint based on the probability estimation principle of the sampling average approximation method. The specific operation is to calculate the sum of the identification variables in all scenarios and compare it with the total number of scenarios multiplied by (1 - the allowable violation probability), that is, Σ identification variable ≥ total number of scenarios × (1 - the allowable violation probability). This step ensures that in all scenarios, the number of scenarios that satisfy the constraints reaches the preset probability level, thus approximately realizing the probabilistic constraints in the optimization model. Finally, integrate the bilinear inequalities and the identification variable summation constraint into the original model to replace the original probabilistic chance constraints, obtaining a deterministic approximation model. This step makes the model solvable on a finite sample set by adding the new constraint conditions to the model and removing the original probabilistic constraints, significantly improving the solvability and computational efficiency of the model while maintaining the original model's ability to handle uncertainties.

[0093] As an example, the steps of iteratively solving the deterministic approximation model by the column constraint generation decomposition algorithm to obtain the target interconnection group scheme include: initializing the parameters of the column constraint generation decomposition algorithm, where the parameters include a convergence threshold, an upper bound of the objective function, and a lower bound of the objective function; constructing a master problem based on the deterministic approximation model, where the master problem includes an interconnection strategy variable, a new energy consumption rate variable, and a scenario cost upper bound variable; solving the master problem to obtain an optimal solution of the master problem, where the optimal solution of the master problem includes an interconnection strategy, a new energy consumption rate, and a scenario identifier; screening target random scenarios from the equiprobable random scenario set according to the scenario identifier; constructing a subproblem for each of the target random scenarios, where the subproblem includes a line transmission power variable and a load shedding variable; solving the subproblem to obtain a subproblem objective value and a feasibility status; adding a cut constraint to the master problem according to the subproblem feasibility status, and updating the upper bound of the objective function and the lower bound of the objective function according to the optimal solution of the master problem; when the relative error between the upper bound of the objective function and the lower bound of the objective function is greater than or equal to the convergence threshold, return to the step of constructing the master problem based on the deterministic approximation model; when the relative error is less than the convergence threshold, obtain the target interconnection group scheme according to the interconnection strategy and the new energy consumption rate in the optimal solution of the master problem.

[0094] The convergence threshold refers to a preset value used to determine whether the algorithm converges in the column-constrained decomposition algorithm. In this embodiment, the convergence threshold is set to 0.01, indicating that when the relative error between the upper bound and the lower bound of the objective function is less than or equal to 0.01, the algorithm is considered to have converged and the iteration can be stopped. The upper bound of the objective function refers to an estimate of the maximum value that the objective function may reach in the column-constrained decomposition algorithm. In this embodiment, the upper bound of the objective function is initialized to positive infinity, indicating that at the beginning of the algorithm, there is no limit on the maximum value of the objective function. As the algorithm iterates, the upper bound of the objective function is updated according to the currently found optimal solution, gradually approaching the true maximum value of the objective function. The lower bound of the objective function refers to an estimate of the minimum value that the objective function may reach in the column-constrained decomposition algorithm. In this embodiment, the lower bound of the objective function is initialized to negative infinity, indicating that at the beginning of the algorithm, there is no limit on the minimum value of the objective function. As the algorithm iterates, the lower bound of the objective function is updated according to the currently found optimal solution, gradually approaching the true minimum value of the objective function. The master problem is the core optimization problem in the column-constrained decomposition algorithm, which includes decision variables (such as interconnection strategy variables and new energy consumption rate variables) and the objective function. In this embodiment, the objective of the master problem is to optimize the interconnection strategy and the new energy consumption rate to minimize the comprehensive cost of the system. The solution of the master problem provides a basis for the sub-problems and is iteratively updated through the feedback of the sub-problems. The sub-problem is an optimization problem constructed for each target random scenario in the column-constrained decomposition algorithm. In this embodiment, the sub-problem includes line transmission power variables and load shedding variables, which are used to verify the feasibility of the master problem solution in a specific scenario and optimize its cost. For the convenience of algorithm implementation and structural analysis, and to simplify the expression and improve the operability of the model, the master problem and the sub-problem are written in a compact form. The compact form of the master problem is as follows: ; ; ; where, represents the average upper bound estimate of the second-stage scenario cost, which approaches the true optimal expected value through continuous updating in each master-subproblem iterative solution; is the feasibility index of the sub-problem, indicates that all sub-problems in the considered scenario are feasible, otherwise, indicates that there are infeasible sub-problems; represents the optimal solution obtained by solving the second-stage model in scenario m; represents the set of scenarios of all infeasible sub-problems, and the interpretations of other variables are the same as those in the compact form formula of the two-stage chance-constrained optimization model.

[0095] Since the scenarios are regarded as being allowed to violate the chance constraints and are thus excluded from the optimization and do not need to be solved as sub-problems; for the scenarios m that satisfy , the compact form of the corresponding sub-problems is as follows: ; ; ; wherein represents the optimal solution obtained by solving the first-stage model, and the explanations of other variables are the same as those in the compact form of the master problem.

[0096] The interconnection strategy variables refer to the decision variables used in the master problem to represent which substations are interconnected. In this embodiment, these variables are usually binary variables, with a value of 1 indicating that an interconnection line is established between two substations and a value of 0 indicating that no interconnection line is established. By optimizing these variables, the optimal interconnection strategy can be determined. The new energy consumption rate variables refer to the decision variables used in the master problem to represent the utilization efficiency of the power generation of new energy equipment, reflecting the effective utilization degree of the system for new energy. By optimizing these variables, the new energy consumption level can be improved and the phenomena of wind curtailment and light curtailment can be reduced. The scenario cost upper bound variables refer to the variables used in the master problem to represent the upper limit of the system operation cost under each random scenario. These variables are used to ensure that under each scenario, the system operation cost does not exceed the preset upper limit, thus ensuring the economy of the system. The target random scenarios refer to the specific scenarios screened from the set of equiprobable random scenarios according to the scenario identifiers in the column constraint generation decomposition algorithm. In this embodiment, the scenario identifier variables of the target random scenarios are zero, indicating that these scenarios need to be further analyzed to verify their feasibility or optimize their costs. The line transmission power variables refer to the decision variables used in the sub-problems to represent the transmission power of the interconnection lines, reflecting the actual power transmission situation of the interconnection lines under specific scenarios. By optimizing these variables, it can be ensured that the line transmission power does not exceed its capacity limit. The load shedding variables refer to the decision variables used in the sub-problems to represent the amount of load to be shed under specific scenarios, reflecting the amount of load that may need to be shed during the operation of the system to ensure the safe operation of the system. The sub-problem objective value refers to the numerical value of the system operation cost or optimization objective obtained during the solution process of the sub-problems. In this embodiment, the sub-problem objective value reflects the actual value of the system operation cost or optimization objective under specific scenarios and is used to evaluate the feasibility and optimization degree of the solution of the master problem. The feasibility status refers to the status of judging whether the current solution satisfies all the constraint conditions during the solution process of the sub-problems. If the solution of the sub-problem satisfies all the constraint conditions, the status is feasible; otherwise, it is infeasible. The feasibility status is used to decide whether to add cut constraints to the master problem.

[0097] First, initialize the parameters of the column constraint generation decomposition algorithm. Set the convergence threshold to 0.01, the upper bound of the objective function to positive infinity, and the lower bound to negative infinity. These parameters are used to control the iterative process of the algorithm and determine the convergence conditions. Next, construct the master problem based on the deterministic approximation model. The master problem includes the interconnection strategy variables, the new energy consumption rate variables, and the upper bound variables of the scenario cost, aiming to optimize the interconnection strategy and the new energy consumption rate while controlling the cost upper limit of each scenario. Then, solve the master problem to obtain the optimal solution of the master problem, including the interconnection strategy, the new energy consumption rate, and the scenario identifier. These results provide the basis for the construction of subsequent sub-problems. According to the scenario identifier of the master problem, screen the target random scenarios from the equiprobable random scenario set. Construct sub-problems for each target random scenario. The sub-problems are used to verify the feasibility of the master problem solution in specific scenarios and optimize the cost. Solve the sub-problems to obtain the objective function values and feasibility status of the sub-problems. Add cut constraints to the master problem according to the feasibility status of the sub-problems. When the sub-problem is infeasible, add a feasible cut constraint (this constraint condition is used to exclude the solutions that cause the sub-problem to be infeasible, thus guiding the solution process of the master problem to avoid these infeasible regions); when the sub-problem is feasible, add an optimal cut constraint (usually constructed based on the objective function value of the sub-problem. It introduces new constraint conditions and uses the optimal objective function value of the sub-problem as a new lower bound of the objective function value of the master problem, thus guiding the solution process of the master problem to approach a better solution), to promote the solution to develop in a better direction. At the same time, update the upper bound of the objective function according to the optimal solution of the master problem (update it to the actual comprehensive cost value of the current optimal solution of the master problem, which is calculated by substituting the optimal solution of the master problem into the objective function of the deterministic approximation model) and the lower bound (update it to the current objective function value of the master problem, which refers to the objective function value corresponding to the current optimal solution of the master problem in the column constraint generation decomposition algorithm). When the relative error between the upper bound and the lower bound of the objective function is greater than or equal to the convergence threshold, return to the step of constructing the master problem and continue the iteration; when the relative error is less than the convergence threshold, determine the specific interconnection line deployment plan according to the interconnection strategy in the optimal solution of the master problem, that is, clarify which substations need to establish interconnection lines and the specific parameters of the interconnection lines. At the same time, determine the new energy consumption level of each substation according to the new energy consumption rate to ensure the effective utilization of new energy. Integrate this information to form the target interconnection group plan. This plan not only includes the specific interconnection line deployment but also covers the optimized new energy consumption strategy, which can achieve efficient energy mutual assistance and load balancing between distribution substations and meet the requirements of the system in terms of economy and reliability.

[0098] Please refer to Figure 5 , Figure 5This is a schematic diagram showing the changes in infeasible scenarios, the actual violation probability, and the iterative convergence of the master and sub-problems in the CCG algorithm provided in the second embodiment of the flexible interconnection group planning method for distribution transformer areas facing energy mutual assistance in this application. During the iterative process of the CCG algorithm, the changes in the upper bound (UB) and lower bound (LB) of the objective function, the number of infeasible scenarios, and the actual violation probability are shown. In the figure, the horizontal axis represents the iteration number it, the left vertical axis represents the scenario number, and the right vertical axis represents the actual violation probability (%). The blue triangles represent the violation probability, the green dots represent the eliminated scenarios, and the red dots represent the infeasible scenarios. It can be seen from the figure that as the number of iterations increases, the number of infeasible scenarios gradually decreases, the violation probability rapidly decreases and tends to be stable, and finally stabilizes at about 4%. This indicates that the algorithm effectively reduces the infeasible scenarios and lowers the actual violation probability. The UB and LB in the figure are represented by red and yellow lines respectively. As the number of iterations increases, the two gradually approach, indicating that the algorithm gradually converges. The inset further shows the changes in the total cost in the first few iterations, showing that the cost rapidly decreases at the beginning and then tends to be stable. These results verify the effectiveness of the CCG algorithm in dealing with uncertainty optimization problems, which can gradually approach the optimal solution during the iterative process, improving the computational efficiency and solution performance of the model.

[0099] The formulaic solution process of the CCG decomposition algorithm is as follows: (1) Initialization: Set the convergence threshold , the iteration index , the lower bound of the objective function , the upper bound of the objective function , the feasibility index of the sub-problem , the set of scenarios of all infeasible sub-problems .

[0100] (2) Solve the master problem: Obtain the objective function value of the master problem, the interconnection strategy , the optimal accommodation rate , and the scenario identification variable , and update the lower bound of the model to .

[0101] (3) Verify the feasibility of and for all scenarios that satisfy : ① If all sub-problems are feasible, then , and obtain the objective function values (the cost under scenario m) of all sub-problems, and add the following optimal cut constraint to the master problem: where the explanations of the variables are the same as above.

[0102] ② If there are infeasible sub - problems, then , set the objective function of the second stage (operation stage) , obtain the scenarios of all infeasible sub - problems and add them to the set , add the following feasible cut constraints to the master problem: Among them, the explanations of the variables are the same as above (refer to formulas such as the compact form of the two - stage chance - constrained optimization model, etc.).

[0103] (4) Update the upper bound of the model: Among them, is the probability that scenario m is selected, refers to the expected cost.

[0104] (5) Convergence judgment: If , then the problem converges and returns the optimal solution to obtain the target interconnected group scheme. Otherwise, update and return to (2).

[0105] Please refer to Figure 6 , Figure 6 is the schematic diagram of the flexible interconnection planning result and the situation of the distribution transformer area group for the second embodiment of the flexible interconnection group planning method for the energy - mutual - aid - oriented distribution transformer area in this application. In the figure, the substation is represented by a black square, and the low - voltage DC connection line is represented by a blue line, connecting different distribution transformer areas to form an interconnected group. Each distribution transformer area is identified by a number, such as Group 1, Group 2, Group 3, and Group 4, etc., and is represented by background areas of different colors. The orange dots in the figure represent the distribution transformer areas with excess distributed resources, and the red triangles represent the distribution transformers with overload. Through flexible interconnection, these areas can share resources, optimize power distribution, improve energy utilization efficiency, and reduce the phenomenon of distribution transformer overload. For example, when the distributed energy generation of a certain area exceeds its load demand, the excess power can be transmitted to other areas through the low - voltage DC connection line, thus realizing energy mutual aid. This kind of interconnected group planning method helps to improve the reliability and economy of the distribution network. Especially in the case of the continuous growth of the distributed energy access scale, it can effectively cope with the uncertainty problems brought by the random fluctuations of the power source and load, and ensure the safe and stable operation of the distribution network.

[0106] In this embodiment, the objective function of the planning stage is first defined according to the initial interconnection set. This objective function comprehensively considers the investment cost of interconnection lines, the penalty cost of wind and light curtailment, and the penalty cost of distribution transformer overload. By minimizing the sum of these costs, it aims to find the interconnection strategy that is optimal in terms of economy and reliability. This step ensures that the planning scheme meets the system operation requirements while minimizing the overall cost. Next, the constraints of the planning stage are set based on the physical laws and safety standards of the distribution network, including the capacity constraints of voltage source converters, the AC-DC power balance constraints, etc. These constraints ensure that the optimization scheme is physically feasible and meets the safety requirements, guaranteeing the security and reliability of the system. Then, using the objective function and constraints of the planning stage, the decision variables of the planning stage are generated through mathematical optimization methods, including the binary variables of the interconnection strategy and the new energy consumption rate. This step finds the interconnection strategy with the lowest cost and the new energy consumption scheme that meet the constraints by optimizing the decision variables. Finally, based on the decision variables of the planning stage, the load of the distribution area, and the new energy output data, an initial two-stage chance-constrained optimization model including the planning stage and the operation stage is constructed. This model optimizes the interconnection strategy and the new energy consumption rate in the planning stage, and verifies the feasibility of the strategy through random scenarios in the operation stage. This method of staged optimization can effectively cope with uncertainties and improve the adaptability and robustness of the system.

[0107] This application also provides a flexible interconnection group planning device for distribution transformers oriented to energy mutual assistance. Please refer to Figure 7 , and the flexible interconnection group planning device for distribution transformers oriented to energy mutual assistance includes: An initial set construction module 10, configured to screen pairs of distribution transformers that meet the preset load rate condition or the over-generation condition based on the topology parameters of the distribution network, the load of the distribution area, and the new energy output data, and construct an initial interconnection set; A model construction module 20, configured to construct an initial two-stage chance-constrained optimization model including the planning stage and the operation stage according to the initial interconnection set. In the planning stage, the interconnection strategy and the new energy consumption rate are determined with the goal of minimizing the investment cost and the penalty cost. In the operation stage, the feasibility of the interconnection strategy is verified based on random scenarios; A linearization processing module 30, configured to perform polyhedral approximation linearization processing on the non-linear constraints in the initial two-stage chance-constrained optimization model, and reconstruct the power flow model in the initial two-stage chance-constrained optimization model into a compact form to obtain a target two-stage chance-constrained optimization model; An equivalent transformation module 40, configured to transform the probabilistic chance constraints in the target two-stage chance-constrained optimization model into deterministic bilinear constraints through the sample average approximation method to obtain a deterministic approximation model; A solution module 50, configured to iteratively solve the deterministic approximation model through the column constraint generation decomposition algorithm to obtain a target interconnection group scheme.

[0108] The present application provides a flexible interconnection group planning device for a distribution substation area oriented to energy mutual assistance. The flexible interconnection group planning device for a distribution substation area oriented to energy mutual assistance includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the flexible interconnection group planning method for a distribution substation area oriented to energy mutual assistance in the first embodiment above.

[0109] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the flexible interconnection group planning method for a distribution substation area oriented to energy mutual assistance in the above embodiment.

[0110] The flexible interconnection group planning device, equipment and storage medium for a distribution substation area oriented to energy mutual assistance provided by the present application adopt the flexible interconnection group planning method for a distribution substation area oriented to energy mutual assistance in the above embodiment, and can solve the technical problem of how to achieve efficient energy mutual assistance and load balance between distribution substations. Compared with the prior art, the beneficial effects of this device are the same as those of the flexible interconnection group planning method for a distribution substation area oriented to energy mutual assistance provided in the above embodiment, and other technical features in the flexible interconnection group planning device for a distribution substation area oriented to energy mutual assistance are the same as the features disclosed in the above embodiment method, and will not be elaborated here.

[0111] The above are only partial embodiments of the present application, and thus do not limit the patent scope of the present application. All those directly / indirectly applied in other related technical fields under the technical concept of the present application are included in the patent protection scope of the present application.

Claims

1. A flexible interconnection group planning method for distribution transformer areas oriented to energy mutual assistance, characterized in that, The method includes: Based on the distribution network topology parameters, substation area load, and new energy output data, select substation area pairs that meet the preset load rate conditions or output surplus conditions, and construct an initial interconnection set; According to the initial interconnection set, construct an initial two-stage chance-constrained optimization model including a planning stage and an operation stage. In the planning stage, determine the interconnection strategy and new energy consumption rate with the goal of minimizing the investment cost and penalty cost. In the operation stage, verify the feasibility of the interconnection strategy based on stochastic scenarios; Perform polyhedral approximation linearization on the non-linear constraints in the initial two-stage chance-constrained optimization model, and reconstruct the power flow model in the initial two-stage chance-constrained optimization model into a compact form to obtain the target two-stage chance-constrained optimization model; Convert the probabilistic chance constraints in the target two-stage chance-constrained optimization model into deterministic bilinear constraints through the sample average approximation method to obtain a deterministic approximation model; Iteratively solve the deterministic approximation model through the column constraint generation decomposition algorithm to obtain the target interconnection group scheme.

2. The method according to claim 1, wherein The step of constructing an initial two-stage chance-constrained optimization model including a planning stage and an operation stage according to the initial interconnection set includes: Define the planning stage objective function according to the initial interconnection set. The planning stage objective function represents the minimization of the sum of the investment cost of the interconnection line, the penalty cost of wind and light curtailment, and the penalty cost of distribution transformer overload; Set the planning stage constraints based on the physical laws and safety standards of the distribution network; Generate the planning stage decision variables based on the planning stage objective function and the planning stage constraints; Construct an initial two-stage chance-constrained optimization model including a planning stage and an operation stage according to the planning stage decision variables, substation area load, and new energy output data.

3. The method according to claim 2, characterized in that, The step of constructing an initial two-stage chance-constrained optimization model including a planning stage and an operation stage according to the planning stage decision variables, substation area load, and new energy output data includes: Define the operation stage objective function according to the planning stage decision variables. The operation stage objective function represents the minimization of the sum of the interconnection line loss cost and the expected value of the load shedding cost; Set the operation stage probabilistic constraints based on the operation stage objective function; Apply a preset proportion of Gaussian perturbation to the substation area load and new energy output data to generate an equiprobable stochastic scenario set, and define the operation stage stochastic scenario variables based on the stochastic scenario set; Couple the planning stage decision variables, the operation stage probabilistic constraints, and the operation stage stochastic scenario variables to form an initial two-stage chance-constrained optimization model.

4. The method according to claim 3, wherein The step of converting the probabilistic chance constraints in the target two-stage chance-constrained optimization model into deterministic bilinear constraints through the sample average approximation method to obtain a deterministic approximation model includes: Extract the probabilistic chance constraints in the target two-stage chance-constrained optimization model; Define binary identification variables for each scenario in the equiprobable stochastic scenario set; Convert the probabilistic chance constraints into bilinear inequalities including the identification variables; Construct an identification variable summation constraint based on the probability estimation principle of the sample average approximation method: Integrate the bilinear inequality and the identification variable summation constraint, and replace the probabilistic chance constraint to obtain a deterministic approximation model.

5. The method according to claim 3, characterized in that, The steps of iteratively solving the deterministic approximation model by the column constraint generation decomposition algorithm to obtain the target interconnection group scheme include: Initialize the parameters of the column constraint generation decomposition algorithm, where the parameters include a convergence threshold, an upper bound of the objective function, and a lower bound of the objective function; Construct a master problem based on the deterministic approximation model, where the master problem includes an interconnection strategy variable, a new energy consumption rate variable, and a scenario cost upper bound variable; Solve the master problem to obtain an optimal solution of the master problem, where the optimal solution of the master problem includes an interconnection strategy, a new energy consumption rate, and a scenario identifier; According to the scenario identifier, screen the target random scenarios from the equiprobable random scenario set; Construct a sub-problem for each of the target random scenarios, where the sub-problem includes a line transmission power variable and a load shedding variable; Solve the sub-problem to obtain the objective value of the sub-problem and the feasibility status; Add a cut constraint to the master problem according to the feasibility status of the sub-problem, and update the upper bound of the objective function and the lower bound of the objective function according to the optimal solution of the master problem; When the relative error between the upper bound of the objective function and the lower bound of the objective function is greater than or equal to the convergence threshold, return to the step of constructing the master problem based on the deterministic approximation model; When the relative error is less than the convergence threshold, obtain the target interconnection group scheme according to the interconnection strategy and the new energy consumption rate in the optimal solution of the master problem.

6. The method according to claim 1, wherein The steps of screening the substation pairs that meet the preset load rate condition or the over-generation condition based on the distribution network topology parameters, the substation area load, and the new energy output data to construct an initial interconnection set include: Obtain the distribution network topology parameters, the substation area load, and the new energy output data, where the distribution network topology parameters include the distribution network node connection relationship and the line impedance parameters, and the substation area load includes the substation area load power; Calculate the distribution transformer load rate of each substation area according to the substation area load power, and identify the first type of substation areas whose distribution transformer load rate exceeds the preset load rate threshold; According to the new energy output data, identify the second type of substation areas where the wind and light output exceeds the preset rated capacity; According to the distribution network node connection relationship, screen the target substation pairs that include at least one of the first type of substation areas or the second type of substation areas within the full nodes of the distribution network; Calculate the effective electrical distance of the target substation pairs according to the line impedance parameters; Eliminate the substation pairs to be eliminated in the target substation pairs to generate an initial interconnection set, where the effective electrical distance of the substation pairs to be eliminated is greater than the preset distance threshold.

7. The method according to any one of claims 1 to 6, characterized in that, The non-linear constraints include the voltage source converter transmission power constraint and the distribution transformer load rate constraint, and the power flow model includes the distribution network active power flow equation, the reactive power flow equation, and the voltage amplitude equation; The steps of performing a polyhedral approximation linearization process on the non-linear constraints in the initial two-stage chance-constrained optimization model and reconstructing the power flow model in the initial two-stage chance-constrained optimization model into a compact form to obtain the target two-stage chance-constrained optimization model include: The polyhedron envelopment method is used to linearly approximate the transmission power constraint of the voltage source converter and the distribution transformer load rate constraint, and linearized constraint conditions are generated; An impedance matrix is constructed based on the line impedance parameters in the distribution network topology parameters; According to the impedance matrix, the active power flow equation, the reactive power flow equation and the voltage amplitude equation of the distribution network are reconstructed into a node power-voltage matrix relationship; The linearized constraint conditions are integrated with the node power-voltage matrix relationship, and the non-linear constraints and power flow equations in the initial two-stage chance-constrained optimization model are replaced to obtain the target two-stage chance-constrained optimization model.

8. A flexible interconnection group planning device for a distribution transformer area oriented to energy mutual assistance, characterized in that, The device includes: An initial set construction module, configured to screen out pairs of distribution transformers that meet the preset load rate conditions or over-generation conditions based on the distribution network topology parameters, the load in the distribution transformer area, and the new energy output data, and construct an initial interconnection set; A model construction module, configured to construct an initial two-stage chance-constrained optimization model including a planning stage and an operation stage according to the initial interconnection set. The planning stage determines the interconnection strategy and the new energy consumption rate with the goal of minimizing the investment cost and the penalty cost, and the operation stage verifies the feasibility of the interconnection strategy based on random scenarios; A linearization processing module, configured to perform polyhedron approximation linearization processing on the non-linear constraints in the initial two-stage chance-constrained optimization model, and reconstruct the power flow model in the initial two-stage chance-constrained optimization model into a compact form to obtain the target two-stage chance-constrained optimization model; An equivalent conversion module, configured to convert the probabilistic chance constraints in the target two-stage chance-constrained optimization model into deterministic bilinear constraints by the sampling average approximation method to obtain a deterministic approximation model; A solution module, configured to iteratively solve the deterministic approximation model by a column constraint generation decomposition algorithm to obtain a target interconnection group scheme.

9. A flexible interconnection group planning device for a distribution transformer area oriented to energy mutual assistance, characterized in that The device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. The computer program is configured to implement the steps of the method for planning a flexible interconnection group of distribution transformer areas for energy mutual assistance according to any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for planning a flexible interconnection group of distribution transformer areas for energy mutual assistance according to any one of claims 1 to 7 are implemented.

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