Coordinated planning method, system and equipment for AC / DC power distribution network containing flexible interconnection device, and medium

Abstract

The invention discloses an AC / DC power distribution network coordinated planning method, system, equipment and medium containing a flexible interconnection device, and belongs to the technical field of AC / DC power distribution, and the method comprises the steps: inputting the basic information of a power distribution network, setting the key parameters of a hybrid optimization algorithm, generating an initial particle swarm in an upper model of a three-layer optimization model, forming an initial solution set, and carrying out the optimization of the initial solution set; based on the initial solution set, configuring a scheme for each FID, solving a middle-layer model and a lower-layer model, converting the middle-layer model and the lower-layer model into a single-layer problem through a Lagrangian relaxation technology, carrying out solving in combination with a hybrid intelligent algorithm, evaluating the fitness of each FID scheme by utilizing a simulation result, dynamically updating an optimization algorithm state, and when a termination condition is met, carrying out optimization on the FID scheme. And outputting a global optimal planning scheme. According to the method, the three-layer collaborative planning model is constructed and the hybrid intelligent algorithm is adopted for efficient solving, so that the flexible interconnection capability, the distributed energy consumption rate and the equipment safety of the AC / DC power distribution network are improved, and meanwhile, the network loss and the cost are reduced.

Description

Technical Field

[0001] This invention relates to the field of AC / DC power distribution technology, and specifically to a method, system, equipment, and medium for coordinated planning of AC / DC power distribution networks with flexible interconnection devices. Background Technology

[0002] Against the backdrop of global energy system transformation and the widespread adoption of distributed power sources, medium- and low-voltage AC / DC distribution networks, as a key component of the new power system, have achieved large-scale application. By organically integrating the characteristics of AC and DC technologies, AC / DC distribution networks not only improve power supply quality but also provide new solutions for the coordinated access of large-scale distributed energy and energy storage systems.

[0003] Flexible interconnection devices (FIDs) are gradually becoming core equipment in AC / DC hybrid distribution networks due to their excellent power regulation capabilities, significantly improving the flexibility of grid operation and the reliability of power supply. However, the dynamic regulation characteristics of FIDs differ significantly from the operating paradigm of traditional power equipment. While integrated applications enhance system flexibility, they also expose the limitations of existing coordinated planning methods in terms of dynamic characteristics and resource optimization, which urgently require technological breakthroughs.

[0004] Existing flexible interconnection planning methods for low-voltage AC / DC distribution systems have two main shortcomings in terms of multi-source and multi-load coordination optimization: First, although flexible interconnection devices have dynamic power regulation capabilities, most planning methods do not systematically integrate active power support, reactive power regulation, and bidirectional power distribution into the planning model, resulting in insufficient potential utilization. Second, traditional load transfer methods heavily rely on physical devices such as sectionalizing switches and tie switches, resulting in rigid control strategies, complex operations, and poor response timeliness, making it difficult to meet the stringent requirements of high-penetration distributed generation grid connection for system performance and power quality. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that most planning methods do not systematically integrate the characteristics of active power support, reactive power regulation and bidirectional power distribution into the planning model, resulting in the potential not being fully explored; they are highly dependent on physical devices such as sectionalizing switches and tie switches, the control strategy is rigid, the operation is complex and the response time is poor, making it difficult to meet the stringent requirements of high-penetration distributed generation grid connection for system performance and power quality.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for coordinated planning of AC / DC distribution networks including flexible interconnection devices, comprising the following steps, Input the basic information of the power distribution network and set the key parameters of the hybrid optimization algorithm. Generate an initial particle swarm in the upper-level model of the three-layer optimization model to form an initial solution set. Based on the initial solution set, solve the middle-level and lower-level models for each FID configuration scheme. Then, transform the middle and lower-level models into single-layer problems using the Lagrange relaxation technique and solve them using the hybrid intelligent algorithm. Use simulation results to evaluate the fitness of each FID scheme, dynamically update the state of the optimization algorithm, and output the globally optimal planning scheme when the termination condition is met.

[0008] As a preferred embodiment of the AC / DC distribution network coordination planning method with flexible interconnection devices described in this invention, the basic information of the distribution network includes the distribution network topology, load data, distributed power generation characteristics, and candidate installation locations of the flexible interconnection devices. Setting key parameters for the hybrid optimization algorithm includes setting the particle swarm size and the genetic algorithm parameters.

[0009] As a preferred embodiment of the AC / DC distribution network coordination planning method with flexible interconnection devices described in this invention, the generation of the initial particle swarm includes determining the optimal installation location and capacity configuration of the flexible interconnection device based on power balance constraints, active and reactive capacity limitations of the low-voltage flexible interconnection device, and minimizing the construction and operation costs of the flexible interconnection. The installation location and capacity configuration of the flexible interconnection device are represented by an initial particle swarm, and the initial particle swarm forms an initial solution set. Power balance constraints include the following: distribution network nodes must adhere to the law of power conservation, dynamically balancing the input and output of active and reactive power flows. The active power balance constraint formula is expressed as:

[0010] in, This represents the active power transmission between node i and node j. Let i be the set of adjacent nodes that are directly connected to node i. Let i be the active power output of the distributed power source at node i. Let i be the active load of node i; The reactive power balance constraint formula is:

[0011] in, Let be the reactive power transmission amount between node i and node j. For the reactive power output of the distributed power source at node i. Let i be the reactive load. Let i be the set of adjacent nodes that are directly connected to node i. The active and reactive power capacity limits of flexible interconnect devices are expressed as follows:

[0012]

[0013] in, For the active power capacity of flexible interconnect devices, For the reactive power capacity of flexible interconnect devices, This represents the maximum active power capacity. This represents the maximum reactive power capacity. Minimizing the construction and operation costs of flexible interconnection, i.e., the objective function of the upper-level model, is expressed as:

[0014] in, To minimize the construction and operation costs of flexible interconnection, The unit capacity construction cost of the k-th low-voltage flexible interconnect device, Let the capacity of the k-th flexible interconnect device be , Let i be the construction cost per unit length of the line. Let i be the length of the line at node i. It is a collection of low-voltage flexible interconnect devices. The coefficients for the sub-optimization objective. Let N be the annual operating cost of the low-voltage flexible interconnection device and DC tie line, and let N be the set of all nodes in the distribution network.

[0015] As a preferred embodiment of the AC / DC distribution network coordination planning method with flexible interconnection devices described in this invention, the objective function of the upper-level model further includes: the low-voltage flexible interconnection adopts a segmented chain-type DC bus architecture, consisting of power routing units and interconnection cables; the distance between interconnected low-voltage distribution areas is short; and the maintenance cost of DC tie lines is not included in the objective function. The annual operating cost formula for the low-voltage flexible interconnection device and DC tie lines is expressed as:

[0016] in, The interconnection status of the substations is as follows: These are the decision variables for the upper-level model. The physical distance between interconnected station areas a The cost per unit length of DC tie line. The discount rate for the time value of money. A collection of interconnectable transformer substations. The annual operating cost of low-voltage flexible interconnect devices and DC tie lines, For a collection of transformer substations where flexible interconnect devices can be installed, The service life of the DC tie line equipment. The investment cost per unit capacity of low-voltage flexible interconnect devices. The installation capacity of the flexible interconnection device in area b is [not specified]. The lifespan of flexible interconnect devices. The annual operating and maintenance cost per unit capacity of a low-voltage flexible interconnect device.

[0017] As a preferred embodiment of the AC / DC distribution network coordination planning method with flexible interconnection devices described in this invention, the solution of the intermediate model includes: generating an initial solution set representing different combinations of flexible interconnection device locations and capacities within the decision variable space of the upper model; fixing each flexible interconnection device configuration scheme in the current solution set; calculating the optimal photovoltaic installation capacity and energy storage system configuration capacity and parameters based on the flexible interconnection device configuration, system load, and distributed energy characteristics; and fixing the flexible interconnection device configuration scheme and the photovoltaic-energy storage configuration scheme obtained from the intermediate solution. Calculating the optimal photovoltaic installation capacity and energy storage system configuration capacity and parameters includes planning the DC-side photovoltaic installation capacity and energy storage device capacity and configuration parameters based on the system's load demand, distributed energy generation characteristics, and the configuration of the upper-level flexible interconnection devices. The output power of photovoltaic (PV) power generation is affected by the irradiance-temperature coupling effect. The injected power must satisfy the nodal power conservation relationship, and the output power of the PV power plant must match the overall power balance of the system. Specifically, the PV output cannot exceed the output of the maximum installed capacity. The formula is as follows:

[0018] in, The output power for the maximum installed capacity of the photovoltaic power station, The output power of photovoltaic power generation; The charging and discharging power of the energy storage device must meet the remaining energy at the moment before the state of charge and the changes in charging and discharging during the period. Simultaneously, the charging and discharging power of the energy storage cannot exceed the maximum value, and the installed capacity of the energy storage must meet the capacity constraints at both the distribution area and system levels. The formula is as follows:

[0019]

[0020]

[0021]

[0022] in, Let be the initial state of charge during time period t. The energy storage charging capacity for period t-1. For the rated capacity of the energy storage system, The charge and discharge capacity of the energy storage system within the time interval [t-1, t] is given. The minimum value set for the state of charge. The maximum value set for the state of charge. The charging power for the energy storage system, This represents the maximum charging power of the energy storage system. This represents the maximum discharge power of the energy storage system. This refers to the discharge power of the energy storage system. Energy storage installation capacity constraints are expressed as follows:

[0023]

[0024]

[0025]

[0026] in, For energy storage installation capacity, The energy storage installation capacity configured for typical daily substation area 1~a, Unit energy storage represents the single-unit capacity of an energy storage unit, which is the minimum planarable energy storage scale allowed by the system. For integer variables whose values ​​are not less than zero, For 0-1 decision variables, This indicates that energy storage is installed in transformer area a. This indicates that area a will not have energy storage installed. This represents the minimum installed energy storage capacity for transformer area a. This represents the maximum installed energy storage capacity for transformer area a. This represents the minimum total installed capacity of energy storage for all distribution areas. This represents the maximum total installed capacity of energy storage across all distribution areas. This represents the total number of stations in the area. The objective function of the mid-level model is expressed as:

[0027] in, Here, T represents the weighting coefficient, and T represents the time period. Let t be the operating cost of the energy storage device. Let t be the cost of purchasing electricity from the grid. The actual output of the photovoltaic power station at time t. This is the objective function for the mid-level model.

[0028] As a preferred embodiment of the AC / DC distribution network coordination planning method with flexible interconnection devices described in this invention, the solution of the lower-level model includes optimizing the power distribution and voltage regulation operation status of the system under different operating scenarios under the configuration schemes of upper and middle-level equipment. The voltage at each node must be maintained within the specified upper and lower limits, while the active and reactive power transmitted by the line must not exceed the rated capacity. The constraint formula is expressed as follows:

[0029] in, Let be the voltage at node i. The minimum voltage at node i. The maximum voltage at node i; The power transmitted through the line must not exceed the rated capacity. For line (m, n), the active power requirement must be met simultaneously. and reactive power Due to capacity limitations, the line power flow constraint formula is expressed as:

[0030] in, Let (m, n) be the active power transmitted by the line. The reactive power transmitted by the line (m, n) Let (m, n) be the rated capacity of the line. The behavioral constraints of low-voltage flexible interconnection are expressed as follows:

[0031] in, The apparent power of transformer a in the distribution area at time t on the high-voltage side. This refers to the active power on the high-voltage side of transformer A in the distribution area. This refers to the reactive power on the high-voltage side of transformer A in the distribution area. This is the upper limit of the design capacity of transformer A in the distribution area. The upper limit of the long-term operating load rate of the transformer is 0.8. The actual output apparent power of the low-voltage flexible interconnect device at time t. The single-unit configuration capacity of the low-voltage flexible interconnection device in transformer area a; The objective function of the lower-level model is expressed as:

[0032] in, Let W be the set of lines and T be the time period, representing the minimum value of power system network losses. Let be the active power transmitted by the line (m, n) at time t. Let be the reactive power transmitted by the line (m, n) at time t. Let be the voltage amplitude of line (m, n) at time t. Let be the resistance of the line (m, n).

[0033] As a preferred embodiment of the AC / DC distribution network coordination planning method with flexible interconnection devices described in this invention, the step of solving the problem using a hybrid intelligent algorithm includes introducing Lagrange relaxation techniques to construct the dual function of the intermediate-level planning. And establish the dual function for the lower-level operation. Model dimensionality reduction is achieved through multiplier iteration. and Merge and reorganize to form a unified single-layer optimization model, and use GA for global exploration to locate the optimal solution space, and use PSO for fine search in the local range to solve the problem; Constructing the dual function of mid-level programming The formula is expressed as:

[0034] in, For the vector of mid-level decision variables, The output power for the maximum installed capacity of the photovoltaic power station, Let be the output power of the photovoltaic power generation at time t. The objective function for the mid-level model is... To constrain the photovoltaic multiplier, As the lower limit multiplier of energy storage SOC, As the upper limit multiplier of energy storage SOC, Let A be the transformer capacity multiplier, A be the set of power distribution stations equipped with energy storage, and B be the set of power distribution stations equipped with transformers. Let O be the state of charge of the energy storage area at time t. This represents the minimum state of charge of energy storage area o. This represents the maximum state of charge (SOP) of energy storage area o. The upper limit of the long-term operating load rate of the transformer is 0.8. This represents the upper limit of the design capacity of transformer h in the low-voltage distribution area. Let H be the active power of transformer h in the distribution area during time period t. The reactive power of transformer h in the distribution area during time period t is the high-voltage side, where T is the time period. The formula for establishing the dual function of the lower-level operation is expressed as:

[0035] in, This represents the active power transmission between node i and node j. Let i be the set of adjacent nodes that are directly connected to node i. Let be the active power output of the distributed power source at node i at time t. Let be the active load of node i. Let (i, j) be the resistance of the line. Let i be the active power balance constraint multiplier at time t. Let i be the reactive power balance constraint multiplier at time t. Let be the amount of reactive power transmitted between node i and node j at time t. Let i be the reactive power output of the distributed power source at node i at time t. Let N be the reactive load of node i at time t, and let N be the set of all nodes in the distribution network. Let be the voltage lower bound constraint multiplier for node i at time t. Let be the voltage upper limit constraint multiplier for node i at time t. Let (i, j) be the line capacity constraint multiplier at time t. Let be the minimum allowable voltage at node i. Where is the maximum allowable voltage at node i. Let W be the rated apparent power capacity of line (i, j), and W be the set of lines. For the lower-level decision variable vector, Let be the current in line (i, j) at time t. Let be the voltage at node i at time t; The formation of a unified single-layer optimization model includes setting the initial values ​​of the Lagrange multipliers, determining the step size sequence, convergence threshold, and maximum number of iterations. When the iteration process begins, the current multiplier value is fixed, and the single-layer optimization is solved. After solving, the subgradient information is calculated, the optimization direction of the current solution is evaluated, and the Lagrange multipliers are updated according to the subgradient and the preset step size sequence. The termination condition is checked. If the current number of iterations is less than or equal to the maximum value, and the magnitude of the subgradient is less than or equal to the convergence threshold, the iteration stops, and the current solution is output as the optimal planning scheme. If the condition is not met, the iteration count is incremented by 1, and the single-layer problem is solved again with the multipliers fixed until the termination condition is met. The output global optimal planning scheme includes: verifying the method; after realizing DC interconnection on the low-voltage side and starting the energy router, running according to the energy management strategy; verifying the actual effect of the optimized configuration scheme; and realizing local consumption of new energy, load balancing between distribution stations, and avoiding backfeeding to the high-voltage grid. The actual effects of the optimized configuration scheme include DC interconnection on the low-voltage side, starting the energy router, and when operating normally, the DC bus load prioritizes absorbing photovoltaic power generation energy; when the DC load cannot fully absorb photovoltaic power generation, the energy storage is activated to charge the energy storage, so that the new energy power can be absorbed locally. When neither DC load nor energy storage can fully absorb photovoltaic power generation, power is supplied to the low-voltage AC side of the two distribution areas through the energy router. If the load rate difference between distribution area A and distribution area B is less than 10%, the energy router acts as a power source to provide power to the two distribution areas. When the load rate difference between the two distribution areas exceeds 20%, or when one distribution area feeds back into the grid, the energy router is responsible for energy mutual assistance and balanced distribution of the load between the two distribution areas.

[0036] This invention provides an AC / DC distribution network coordination planning system with flexible interconnection devices.

[0037] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an AC / DC distribution network coordination planning system with flexible interconnection devices, comprising: a flexible interconnection device planning module, a photovoltaic-energy storage collaborative configuration module, an operation optimization module, and a model solving and strategy generation module.

[0038] The flexible interconnection device planning module is used for the optimal installation location and capacity configuration of flexible interconnection devices. Through upper-level model decision variables and FID capacity, it provides flexible power regulation capabilities under the premise of meeting the goals of power balance, capacity constraints and cost minimization, laying the architectural foundation for AC / DC hybrid distribution networks.

[0039] The photovoltaic-energy storage collaborative configuration module is used to optimize the maximum installed capacity of the DC-side photovoltaic system and the capacity and parameters of the energy storage system based on the planning results of the upper-level model. Through the constraints of the middle-level model and the objective function, it enables the efficient consumption of new energy and the rational allocation of energy storage resources.

[0040] The operation optimization module is used to optimize power allocation, voltage regulation and power flow control for different operating scenarios. Through lower-level model constraints, FID output range and objective function, it reduces losses through real-time scheduling and balances the load of the distribution area by utilizing the dynamic adjustment capability of FID.

[0041] The model solving and strategy generation module is used to solve complex three-layer optimization problems through hierarchical model dimensionality reduction and hybrid intelligent algorithms, and output the globally optimal planning scheme and operation strategy.

[0042] The present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the AC / DC distribution network coordination planning method with flexible interconnection device.

[0043] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the aforementioned AC / DC distribution network coordination planning method with flexible interconnection device.

[0044] The beneficial effects of this invention are as follows: This invention achieves joint optimization of planning and operation through a three-layer planning model for low-voltage flexible interconnection. Layered decision-making enables a more comprehensive and rational determination of FID installation location and capacity, DC-side photovoltaic and energy storage configuration, and system operation mode, significantly enhancing the absorption capacity of distributed energy in low-voltage distribution areas and successfully achieving load redistribution of heavily loaded transformers, thereby improving the overall system operating efficiency. By integrating the middle and lower-level optimization problems into a single-layer structure through model dimensionality reduction technology and combining it with intelligent hybrid algorithms, computational efficiency is significantly improved, enabling the acquisition of optimal planning and operation schemes in a shorter time, providing an efficient solution for practical engineering applications. By comprehensively considering various constraints and optimization objectives, the planning results better meet the actual system operation requirements, improving the system's safety, stability, and reliability. Attached Figure Description

[0045] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating the overall process of a coordinated planning method for AC / DC distribution networks with flexible interconnection devices, provided as an embodiment of the present invention.

[0047] Figure 2 This is a schematic diagram of a low-voltage flexible interconnection two-layer planning model framework for a coordinated planning method for AC / DC distribution networks with flexible interconnection devices, provided as an embodiment of the present invention.

[0048] Figure 3 This is a system scheme block diagram of an AC / DC distribution network coordination planning system with flexible interconnection device provided in one embodiment of the present invention. Detailed Implementation

[0049] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0050] Example 1, referring to Figure 1 As one embodiment of the present invention, a method for coordinated planning of AC / DC distribution networks containing flexible interconnection devices is provided, comprising: S1: Input the basic information of the distribution network and set the key parameters of the hybrid optimization algorithm. Generate the initial particle swarm in the upper-level model of the three-layer optimization model to form the initial solution set.

[0051] S2: Based on the initial solution set, solve the middle-level model and the lower-level model for each FID configuration scheme, and transform the middle-level and lower-level models into a single-level problem through the Lagrange relaxation technique, and solve it in combination with the hybrid intelligent algorithm.

[0052] S3: Use simulation results to evaluate the fitness of each FID scheme, dynamically update the state of the optimization algorithm, and output the globally optimal planning scheme when the termination condition is met.

[0053] It should be noted that by comprehensively considering various operational constraints, the coordinated and optimized configuration of flexible interconnection equipment and distributed energy equipment has improved the system's flexibility, stability, and the ability to absorb distributed energy, providing strong technical support for the scientific planning and efficient operation of low-voltage AC / DC power distribution systems.

[0054] Example 2, refer to Figure 1 and Figure 2 This is a second embodiment of the present invention, which provides a coordinated planning method for AC / DC distribution networks containing flexible interconnection devices, including: In step S1, the distribution network topology, load history and forecast data are read, and the candidate installation locations of the flexible interconnection device in the low-voltage distribution area are determined. The key control parameters of the hybrid optimization algorithm are set reasonably to ensure the efficient and stable operation of the subsequent optimization process.

[0055] Furthermore, the generation of the initial particle swarm includes determining the optimal installation location and capacity configuration of the flexible interconnect device based on power balance constraints, active and reactive capacity limitations of the low-voltage flexible interconnect device, and minimizing the construction and operation costs of the flexible interconnect. The installation location and capacity configuration of a flexible interconnect device are represented by an initial particle swarm, and the initial particle swarm forms an initial solution set. Constraints: Power balance constraints include that distribution network nodes follow the law of power conservation, and that the input and output of active and reactive power flows are dynamically balanced. The active power balance constraint formula is expressed as:

[0056] in, This represents the active power transmission between node i and node j. Let i be the set of adjacent nodes that are directly connected to node i. Let i be the active power output of the distributed power source at node i. The active load of the node; The reactive power balance constraint formula is:

[0057] in, Let be the reactive power transmission amount between node i and node j. For the reactive power output of the distributed power source at node i. Let i be the reactive load. Let i be the set of adjacent nodes that are directly connected to node i. It should be noted that the active and reactive power capacity limits of flexible interconnect devices are expressed as follows:

[0058]

[0059] in, For the active power capacity of flexible interconnect devices, For the reactive power capacity of flexible interconnect devices, This represents the maximum active power capacity. This represents the maximum reactive power capacity. Minimizing the construction and operation costs of flexible interconnection, i.e., the objective function of the upper-level model, is expressed as:

[0060] in, To minimize the construction and operation costs of flexible interconnection, The unit capacity construction cost of the k-th low-voltage flexible interconnect device, Let the capacity of the k-th flexible interconnect device be , Let i be the construction cost per unit length of the line. Let be the length of the line at node i, and be the set of nodes. It is a collection of low-voltage flexible interconnect devices. The coefficients for the sub-optimization objective. Let N be the annual operating cost of the low-voltage flexible interconnection device and DC tie line, and let N be the set of all nodes in the distribution network.

[0061] Furthermore, the objective function of the upper-level model also includes the following: the low-voltage flexible interconnection adopts a segmented chain DC bus architecture, consisting of power routing units and interconnection cables. Since the distance between interconnected low-voltage distribution areas is short, the maintenance cost of the DC tie line is not included in the objective function. The annual operating cost formula for the low-voltage flexible interconnection device and the DC tie line is expressed as:

[0062] in, The interconnection status of the substations is as follows: These are the decision variables for the upper-level model. The physical distance between interconnected station areas a The cost per unit length of DC tie line. The discount rate for the time value of money. A collection of interconnectable transformer substations. The annual operating cost of low-voltage flexible interconnect devices and DC tie lines, For a collection of transformer substations where flexible interconnect devices can be installed, The service life of the DC tie line equipment. The investment cost per unit capacity of low-voltage flexible interconnect devices. The installation capacity of the flexible interconnection device in area b is [not specified]. The lifespan of flexible interconnect devices. The annual operating and maintenance cost per unit capacity of a low-voltage flexible interconnect device.

[0063] In step S2, the solution of the middle-level model includes generating an initial solution set representing different combinations of flexible interconnect device locations and capacities within the decision variable space of the upper-level model, fixing each flexible interconnect device configuration scheme in the current solution set, calculating the optimal photovoltaic installation capacity and energy storage system configuration capacity and parameters based on the flexible interconnect device configuration, system load and distributed energy characteristics, and fixing the flexible interconnect device configuration scheme and the photovoltaic-energy storage configuration scheme obtained from the middle-level solution. Calculating the optimal photovoltaic installation capacity and energy storage system configuration capacity and parameters includes planning the DC-side photovoltaic installation capacity and energy storage device capacity and configuration parameters based on the system's load demand, distributed energy generation characteristics, and the configuration of the upper-level flexible interconnection devices. Furthermore, the constraints are as follows: the output power of photovoltaic power generation is affected by the irradiance-temperature coupling effect; the injected power must satisfy the nodal power conservation relationship; and the output power of the photovoltaic power station must match the overall power balance of the system. Specifically, the photovoltaic output cannot exceed the output of the maximum installed capacity. The formula is as follows:

[0064] in, The output power for the maximum installed capacity of the photovoltaic power station, The output power of photovoltaic power generation; Overcharging or deep discharging during energy storage operation can adversely affect its lifespan. To extend battery life and ensure stable system operation, the State of Charge (SOC) range is constrained during ESS operation, limiting it to a set upper and lower bound to avoid damage to battery performance under extreme conditions. The formula is as follows:

[0065]

[0066]

[0067]

[0068] in, Let be the initial state of charge during time period t. The energy storage charging capacity for period t-1. For the rated capacity of the energy storage system, The charge and discharge capacity of the energy storage system within the time interval [t-1, t] is given. The minimum value set for the state of charge. The maximum value set for the state of charge. The charging power for the energy storage system, This represents the maximum charging power of the energy storage system. This represents the maximum discharge power of the energy storage system. This refers to the discharge power of the energy storage system. Energy storage installation capacity constraints are expressed as follows:

[0069]

[0070]

[0071]

[0072] in, For energy storage installation capacity, The energy storage installation capacity configured for typical daily substation area 1~a, Unit energy storage represents the single-unit capacity of an energy storage unit, which is the minimum planarable energy storage scale allowed by the system. For integer variables whose values ​​are not less than zero, For 0-1 decision variables, This indicates that energy storage is installed in transformer area a. This indicates that area a will not have energy storage installed. This represents the minimum installed energy storage capacity for transformer area a. This represents the maximum installed energy storage capacity for transformer area a. This represents the minimum total installed capacity of energy storage for all distribution areas. This represents the maximum total installed capacity of energy storage across all distribution areas. This represents the total number of stations in the area. The objective function of the mid-level model is expressed as:

[0073] in, Here, T represents the weighting coefficient, and T represents the time period. Let t be the operating cost of the energy storage device. Let t be the cost of purchasing electricity from the grid. The actual output of the photovoltaic power station at time t. This is the objective function for the mid-level model.

[0074] Furthermore, solving the lower-level model includes optimizing the system's power distribution and voltage regulation operation under different operating scenarios, given the configuration schemes of the upper and middle-level devices. The voltage at each node must be maintained within the specified upper and lower limits, while the active and reactive power transmitted by the line must not exceed the rated capacity. The constraint formula is expressed as follows:

[0075] in, Let be the voltage at node i. The minimum voltage at node i. The maximum voltage at node i; The power transmitted through the line must not exceed the rated capacity. For line (m, n), the active power requirement must be met simultaneously. and reactive power Due to capacity limitations, the line power flow constraint formula is expressed as:

[0076] in, Let (m, n) be the active power transmitted by the line. The reactive power transmitted by the line (m, n) Let (m, n) be the rated capacity of the line. The behavioral constraints of low-voltage flexible interconnection are expressed as follows:

[0077] in, The apparent power of transformer a in the distribution area at time t on the high-voltage side. This refers to the active power on the high-voltage side of transformer A in the distribution area. This refers to the reactive power on the high-voltage side of transformer A in the distribution area. This is the upper limit of the design capacity of transformer A in the distribution area. The upper limit of the long-term operating load rate of the transformer is 0.8. The actual output apparent power of the low-voltage flexible interconnect device at time t. The single-unit configuration capacity of the low-voltage flexible interconnection device in transformer area a; The objective function of the lower-level model is expressed as:

[0078] in, Let W be the set of lines and T be the time period, representing the minimum value of power system network losses. Let be the active power transmitted by the line (m, n) at time t. Let be the reactive power transmitted by the line (m, n) at time t. Let be the voltage amplitude of line (m, n) at time t. Let be the resistance of the line (m, n).

[0079] In step S3, the solution process using a hybrid intelligent algorithm includes introducing Lagrange relaxation techniques to transform the hierarchical optimization model into a single-layer structure. First, the dual function L1 for the intermediate-level planning is constructed, followed by the dual function L2 for the lower-level operation. Multiplier iteration is used to reduce the model's dimensionality, significantly improving solution efficiency while maintaining constraint integrity. This provides an effective optimization tool for power distribution system planning. L1 and L2 are then merged and reorganized to form a unified single-layer optimization model, simplifying computational complexity while preserving the constraints and objective characteristics of the original problem. Global exploration (GA) is used to quickly locate the optimal solution space, and PSO is then used for fine-grained searching within a local range to improve convergence accuracy and computational efficiency.

[0080] Furthermore, construct the dual function of the mid-level programming. The formula is expressed as:

[0081] in, For the vector of mid-level decision variables, The output power for the maximum installed capacity of the photovoltaic power station, Let be the output power of the photovoltaic power generation at time t. The objective function for the mid-level model is... To constrain the photovoltaic multiplier, As the lower limit multiplier of energy storage SOC, As the upper limit multiplier of energy storage SOC, Let A be the transformer capacity multiplier, A be the set of power distribution stations equipped with energy storage, and B be the set of power distribution stations equipped with transformers. Let O be the state of charge of the energy storage area at time t. This represents the minimum state of charge of energy storage area o. This represents the maximum state of charge (SOP) of energy storage area o. The upper limit of the long-term operating load rate of the transformer is 0.8. This represents the upper limit of the design capacity of transformer h in the low-voltage distribution area. Let H be the active power of transformer h in the distribution area during time period t. The reactive power of transformer h in the distribution area during time period t is the high-voltage side, where T is the time period. Establish the dual function for the lower level operation The formula is expressed as:

[0082] in, This represents the active power transmission between node i and node j. Let i be the set of adjacent nodes that are directly connected to node i. Let be the active power output of the distributed power source at node i at time t. Let be the active load of node i. Let (i, j) be the resistance of the line. Let i be the active power balance constraint multiplier at time t. Let i be the reactive power balance constraint multiplier at time t. Let be the amount of reactive power transmitted between node i and node j at time t. Let i be the reactive power output of the distributed power source at node i at time t. Let N be the reactive load of node i at time t, and let N be the set of all nodes in the distribution network. Let be the voltage lower bound constraint multiplier for node i at time t. Let be the voltage upper limit constraint multiplier for node i at time t. Let (i, j) be the line capacity constraint multiplier at time t. Let be the minimum allowable voltage at node i. Where is the maximum allowable voltage at node i. Let W be the rated apparent power capacity of line (i, j), and W be the set of lines. For the lower-level decision variable vector, Let be the current in line (i, j) at time t. Let be the voltage at node i at time t.

[0083] The formation of a unified single-layer optimization model includes setting the initial values ​​of the Lagrange multipliers, determining the step size sequence, convergence threshold, and maximum number of iterations. When the iteration process begins, the current multiplier value is fixed, and the single-layer problem is solved. After solving, the subgradient information is calculated, the optimization direction of the current solution is evaluated, and the Lagrange multipliers are updated according to the subgradient and the preset step size sequence. The termination condition is checked. If the current number of iterations is less than or equal to the maximum value, and the magnitude of the subgradient is less than or equal to the convergence threshold, the iteration stops, and the current solution is output as the optimal planning scheme. If the condition is not met, the iteration count is incremented by 1, and the single-layer problem is solved again with the multipliers fixed until the termination condition is met.

[0084] The formula for solving a single-layer problem is expressed as:

[0085] in, For the upper-level decision variable vector, For the vector of mid-level decision variables, For the lower-level decision variable vector, The objective function of the upper-level model is... For the dual function of the mid-level programming, This is the dual function of the lower-level operation.

[0086] The formula for calculating subgradient information is expressed as:

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095] in, The difference between the actual output of the photovoltaic system and the maximum installed capacity. The difference between the energy storage state of charge and the minimum allowable value. The difference between the energy storage state of charge and the maximum allowable value. This is the difference between the apparent power of the transformer and its safe capacity. This refers to the active power imbalance at the nodes. This refers to the reactive power imbalance at the node. The difference between the node voltage and the minimum allowable value. The difference between the node voltage and the maximum allowable value. This is the difference between the line's apparent power and its rated capacity.

[0096] The update multiplier formula is expressed as:

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] in, Let w be the step size for the w-th iteration. For the photovoltaic output constraint in the (w+1)th iteration, This is the lower bound constraint of the energy storage SOC for the (w+1)th iteration. This is the upper limit constraint of the energy storage SOC for the (w+1)th iteration. For the transformer capacity constraint in the (w+1)th iteration, For the active power balance constraint in the (w+1)th iteration, For the reactive power balance constraint in the (w+1)th iteration, This is the lower voltage limit constraint for the (w+1)th iteration. This is the upper limit constraint for the voltage in the (w+1)th iteration. For the (w+1)th iteration, the line capacity constraint is... For the photovoltaic output constraint in the w-th iteration, This is the lower bound constraint for the energy storage SOC in the w-th iteration. This is the upper limit constraint of the energy storage SOC for the w-th iteration. For the transformer capacity constraint in the w-th iteration, For the active power balance constraint in the w-th iteration, For the reactive power balance constraint in the w-th iteration, This is the lower voltage limit constraint for the w-th iteration. This is the upper limit constraint for the voltage in the w-th iteration. This represents the line capacity constraint for the w-th iteration.

[0106] The GA global exploration phase includes encoding the upper-level decision variables (FID installation location, capacity) and the middle-level key variables (photovoltaic and energy storage capacity), and for each individual, calling the middle and lower-level models to calculate the comprehensive cost (construction cost, operating cost, network loss). The goal is to minimize the weighted total cost. A new generation of population is generated through selection, crossover, and mutation, and iteratively until convergence is achieved, outputting a Top-M high-quality solution set.

[0107] The local fine search phase of PSO includes taking the O high-quality solutions output by GA as the initial positions of PSO particles, randomly generating the remaining particles in the neighborhood, updating the particle velocity and position according to the standard PSO rules, driving the particles to move closer to the individual historical optimum and the group historical optimum, optimizing the variable values, and when PSO converges, the solution corresponding to the group historical optimum is the optimal planning scheme.

[0108] Furthermore, to verify the method, after achieving DC interconnection on the low-voltage side and starting the energy router, the system will operate according to the following energy management strategies to verify the actual effect of the optimized configuration scheme and ensure the efficient local consumption of new energy, load balancing between distribution stations, and prevention of backfeeding to the high-voltage grid: 1. Priority should be given to local consumption of renewable energy generation: For local DC load absorption, the photovoltaic power generation is preferentially absorbed in real time by local DC loads connected to the same DC bus.

[0109] When the DC load cannot fully absorb the photovoltaic power generation, the system automatically stores the remaining electrical energy in the energy storage device, realizing on-site storage of photovoltaic power.

[0110] When the local DC load and energy storage system (considering the current charging capacity) cannot fully absorb the photovoltaic power generation, the surplus photovoltaic power is supplied to the low-voltage AC side of transformer area A and transformer area B through the energy router.

[0111] 2. Mutual assistance and balancing control of energy routers: In the uniform power supply mode, when the real-time load rate difference between transformer area A and transformer area B is less than 10%, the energy router acts as a power source, evenly injecting the surplus photovoltaic power into the low-voltage AC bus of the two transformer areas, providing support for the AC load of both transformer areas simultaneously.

[0112] In the mutual balance mode, the energy router activates the mutual balance function when any of the following conditions occur: ① The real-time load rate difference between transformer area A and transformer area B exceeds 20%.

[0113] ② There is a risk or actual backfeeding of power to the high-voltage power grid in any of the transformer areas (A or B).

[0114] The energy router supports a dual-mode switching mechanism during operation, specifically including: A1: As a power source, it supplies power to high-load transformer areas, prioritizing the delivery of surplus power from photovoltaic or remaining transformer areas to areas with higher load rates, thereby relieving transformer pressure.

[0115] A2: As a load, it absorbs electrical energy from low-load transformer areas. When necessary, it can absorb electrical energy from transformer areas with low load rates or surplus electrical energy and transfer it to high-load transformer areas through the energy router.

[0116] Core objective: To dynamically balance the load of the two distribution areas, maximize the utilization of surplus renewable energy within the distribution areas, and eliminate backfeeding to the high-voltage grid.

[0117] 3. Reverse energy flow (insufficient photovoltaic output): When the photovoltaic power generation is lower than the DC load demand, the system will compensate for the power difference according to the established control strategy: first, the energy storage device will discharge to supplement the power; when the energy storage discharge still cannot meet the load demand, the energy router will obtain power from the low-voltage AC side of the two transformer areas (prioritizing the transformer areas with low load rates) to supply the DC load.

[0118] Furthermore, to test the operational effectiveness, DC interconnection was performed on the low-voltage side, and the energy router was activated. When the system is operating normally, the DC bus load prioritizes the absorption of photovoltaic power generation energy. When the DC load cannot fully absorb the photovoltaic power generation, the energy storage is activated to charge the energy storage, thereby realizing the local absorption of new energy power.

[0119] When neither DC load nor energy storage can fully absorb photovoltaic power generation, power is supplied to the low-voltage AC side of the two distribution areas through the energy router; when the load rate difference between distribution area A and distribution area B is less than 10%, the energy router acts as a power source or load to provide power evenly to the two distribution areas; when the load rate difference between the two distribution areas exceeds 20%, or when one distribution area feeds back to the grid, the energy router is responsible for energy mutual assistance and balanced distribution of the load between the two distribution areas.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0121] Example 3, referring to Figure 3 The third embodiment of the present invention provides an AC / DC distribution network coordination planning system with flexible interconnection devices, including: a flexible interconnection device planning module, a photovoltaic-energy storage coordinated configuration module, an operation optimization module, and a model solving and strategy generation module.

[0122] The flexible interconnection device planning module is used for the optimal installation location and capacity configuration of flexible interconnection devices. Through upper-level model decision variables and FID capacity, it provides flexible power regulation capabilities under the premise of meeting the goals of power balance, capacity constraints and cost minimization, laying the architectural foundation for AC / DC hybrid distribution networks.

[0123] The photovoltaic-energy storage collaborative configuration module is used to optimize the maximum installed capacity of the DC-side photovoltaic system and the capacity and parameters of the energy storage system based on the planning results of the upper-level model. Through the constraints of the middle-level model and the objective function, it enables the efficient consumption of new energy and the rational allocation of energy storage resources.

[0124] The operation optimization module is used to optimize power allocation, voltage regulation and power flow control for different operating scenarios. Through lower-level model constraints, FID output range and objective function, it reduces losses through real-time scheduling and balances the load of the distribution area by utilizing the dynamic adjustment capability of FID.

[0125] The model solving and strategy generation module is used to solve complex three-layer optimization problems through hierarchical model dimensionality reduction and hybrid intelligent algorithms, and output the globally optimal planning scheme and operation strategy.

[0126] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0127] Example 4 is the fourth embodiment of the present invention, which differs from the previous three embodiments in that: If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0128] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0129] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0130] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

Claims

1. A method for coordinated planning of AC / DC distribution networks with flexible interconnection devices, characterized in that: include, Input the basic information of the distribution network and set the key parameters of the hybrid optimization algorithm. Generate the initial particle swarm in the upper model of the three-layer optimization model to form the initial solution set. Based on the initial solution set, for each FID configuration scheme, the middle-level model and the lower-level model are solved, and the middle-level and lower-level models are transformed into single-layer problems by Lagrange relaxation techniques, which are then solved using hybrid intelligent algorithms. The fitness of each FID scheme is evaluated using simulation results, the state of the optimization algorithm is dynamically updated, and the globally optimal planning scheme is output when the termination condition is met.

2. The AC / DC distribution network coordination planning method with flexible interconnection device as described in claim 1, characterized in that: The basic information of the distribution network includes the distribution network topology, load data, distributed power generation characteristics, and candidate installation locations for flexible interconnection devices. Setting key parameters for the hybrid optimization algorithm includes setting the particle swarm size and the genetic algorithm parameters.

3. The AC / DC distribution network coordination planning method with flexible interconnection device as described in claim 2, characterized in that: The generation of the initial particle swarm includes determining the optimal installation location and capacity configuration of the flexible interconnect device based on power balance constraints, active and reactive capacity limitations of the low-voltage flexible interconnect device, and minimizing the construction and operation costs of the flexible interconnect. The installation location and capacity configuration of a flexible interconnect device are represented by an initial particle swarm, and the initial particle swarm forms an initial solution set. Power balance constraints include the following: distribution network nodes must adhere to the law of power conservation, dynamically balancing the input and output of active and reactive power flows. The active power balance constraint formula is expressed as: ， in, This represents the active power transmission between node i and node j. Let i be the set of adjacent nodes that are directly connected to node i. Let i be the active power output of the distributed power source at node i. Let i be the active load of node i; The reactive power balance constraint formula is: ， in, Let be the reactive power transmission amount between node i and node j. For the reactive power output of the distributed power source at node i. Let i be the reactive load. Let i be the set of adjacent nodes that are directly connected to node i. The active and reactive power capacity limits of flexible interconnect devices are expressed as follows: ， ， in, For the active power capacity of flexible interconnect devices, For the reactive power capacity of flexible interconnect devices, This represents the maximum active power capacity. This represents the maximum reactive power capacity. Minimizing the construction and operation costs of flexible interconnection, i.e., the objective function of the upper-level model, is expressed as: ； in, To minimize the construction and operation costs of flexible interconnection, The unit capacity construction cost of the k-th low-voltage flexible interconnect device, Let the capacity of the k-th flexible interconnect device be , Let i be the construction cost per unit length of the line. Let be the length of the line at node i, and be the set of nodes. It is a collection of low-voltage flexible interconnect devices. The coefficients for the sub-optimization objective. Let N be the annual operating cost of the low-voltage flexible interconnection device and DC tie line, and let N be the set of all nodes in the distribution network.

4. The AC / DC distribution network coordination planning method with flexible interconnection device as described in claim 3, characterized in that: The objective function of the upper-level model also includes the following: the low-voltage flexible interconnection adopts a segmented chain DC bus architecture, consisting of power routing units and interconnection cables. Since the distance between interconnected low-voltage distribution areas is short, the maintenance cost of the DC tie line is not included in the objective function. The annual operating cost formula for the low-voltage flexible interconnection device and the DC tie line is expressed as: ； in, The interconnection status of the substations is as follows: These are the decision variables for the upper-level model. The physical distance between interconnected station areas a The cost per unit length of DC tie line. The discount rate for the time value of money. A collection of interconnectable transformer substations. The annual operating cost of low-voltage flexible interconnect devices and DC tie lines, For a collection of transformer substations where flexible interconnect devices can be installed, The service life of the DC tie line equipment. The investment cost per unit capacity of low-voltage flexible interconnect devices. The installation capacity of the flexible interconnection device in area b is [not specified]. The lifespan of flexible interconnect devices. The annual operating and maintenance cost per unit capacity of a low-voltage flexible interconnect device.

5. The AC / DC distribution network coordination planning method with flexible interconnection device as described in claim 4, characterized in that: The solution of the middle-level model includes generating an initial solution set representing different combinations of flexible interconnect device locations and capacities within the decision variable space of the upper-level model; fixing each flexible interconnect device configuration scheme in the current solution set; calculating the optimal photovoltaic installation capacity and energy storage system configuration capacity and parameters based on the flexible interconnect device configuration, system load, and distributed energy characteristics; and fixing the flexible interconnect device configuration scheme and the photovoltaic-energy storage configuration scheme obtained from the middle-level solution. Calculating the optimal photovoltaic installation capacity and energy storage system configuration capacity and parameters includes planning the DC-side photovoltaic installation capacity and energy storage device capacity and configuration parameters based on the system's load demand, distributed energy generation characteristics, and the configuration of the upper-level flexible interconnection devices. The output power of photovoltaic (PV) power generation is affected by the irradiance-temperature coupling effect. The injected power must satisfy the nodal power conservation relationship, and the output power of the PV power plant must match the overall power balance of the system. Specifically, the PV output cannot exceed the output of the maximum installed capacity. The formula is as follows: ， in, The output power for the maximum installed capacity of the photovoltaic power station, The output power of photovoltaic power generation; The charging and discharging power of the energy storage device must meet the remaining energy at the moment before the state of charge and the changes in charging and discharging during the period. Simultaneously, the charging and discharging power of the energy storage cannot exceed the maximum value, and the installed capacity of the energy storage must meet the capacity constraints at both the distribution area and system levels. The formula is as follows: ， ， ， ， in, Let be the initial state of charge during time period t. The energy storage charging capacity for period t-1. For the rated capacity of the energy storage system, The charge and discharge capacity of the energy storage system within the time interval [t-1, t] is given. The minimum value set for the state of charge. The maximum value set for the state of charge. The charging power for the energy storage system, This represents the maximum charging power of the energy storage system. This represents the maximum discharge power of the energy storage system. This refers to the discharge power of the energy storage system. Energy storage installation capacity constraints are expressed as follows: ， ， ， ， in, For energy storage installation capacity, The energy storage installation capacity configured for typical daily substation area 1~f is as follows: Unit energy storage represents the single-unit capacity of an energy storage unit, which is the minimum planarable energy storage scale allowed by the system. For integer variables whose values ​​are not less than zero, For 0-1 decision variables, This indicates that energy storage is installed in transformer area f. This indicates that energy storage will not be installed in transformer area f. This represents the minimum installed energy storage capacity for transformer area f. This represents the maximum installed energy storage capacity for area f. This represents the minimum total installed capacity of energy storage for all distribution areas. This represents the maximum total installed capacity of energy storage across all distribution areas. This represents the total number of stations in the area. The objective function of the mid-level model is expressed as: ， in, Here, T represents the weighting coefficient, and T represents the time period. Let t be the operating cost of the energy storage device. Let t be the cost of purchasing electricity from the grid. The actual output of the photovoltaic power station at time t. This is the objective function for the mid-level model.

6. The AC / DC distribution network coordination planning method with flexible interconnection device as described in claim 5, characterized in that: Solving the lower-level model includes optimizing the system's power distribution and voltage regulation operation under different operating scenarios, given the configuration schemes of the upper and middle-level devices. The voltage at each node must be maintained within the specified upper and lower limits, while the active and reactive power transmitted by the line must not exceed the rated capacity. The constraint formula is expressed as follows: ， in, Let be the voltage at node i. The minimum voltage at node i. The maximum voltage at node i; The power transmitted through the line must not exceed the rated capacity. For line (m, n), the active power requirement must be met simultaneously. and reactive power Due to capacity limitations, the line power flow constraint formula is expressed as: ， in, Let (m, n) be the active power transmitted by the line. The reactive power transmitted by the line (m, n) Let (m, n) be the rated capacity of the line. The behavioral constraints of low-voltage flexible interconnection are expressed as follows: ， in, The apparent power of transformer f in the distribution area at time t on the high-voltage side. This refers to the active power on the high-voltage side of transformer f in the distribution area. This refers to the reactive power on the high-voltage side of transformer f in the distribution area. This represents the upper limit of the design capacity of transformer f in the distribution area. The upper limit of the long-term operating load rate of the transformer is 0.

8. The actual output apparent power of the low-voltage flexible interconnect device at time t. The single-unit configuration capacity of the low-voltage flexible interconnection device in the distribution area; The objective function of the lower-level model is expressed as: ， in, Let W be the set of lines and T be the time period, representing the minimum value of power system network losses. Let be the active power transmitted by the line (m, n) at time t. Let be the reactive power transmitted by the line (m, n) at time t. Let be the voltage amplitude of line (m, n) at time t. Let be the resistance of the line (m, n).

7. The AC / DC distribution network coordination planning method with flexible interconnection device as described in claim 6, characterized in that: The solution method using hybrid intelligent algorithms includes introducing Lagrange relaxation techniques to construct the dual function of the mid-level programming. And establish the dual function for the lower-level operation. Model dimensionality reduction is achieved through multiplier iteration. and Merge and reorganize to form a unified single-layer optimization model, and use GA for global exploration to locate the optimal solution space, and use PSO for fine search in the local range to solve the problem; Constructing the dual function of mid-level programming The formula is expressed as: ； in, For the vector of mid-level decision variables, The output power for the maximum installed capacity of the photovoltaic power station, Let be the output power of the photovoltaic power generation at time t. The objective function for the mid-level model is... To constrain the photovoltaic multiplier, As the lower limit multiplier of energy storage SOC, As the upper limit multiplier of energy storage SOC, Let A be the transformer capacity multiplier, A be the set of power distribution stations equipped with energy storage, and B be the set of power distribution stations equipped with transformers. Let O be the state of charge of the energy storage area at time t. This represents the minimum state of charge of energy storage area o. This represents the maximum state of charge (SOP) of energy storage area o. The upper limit of the long-term operating load rate of the transformer is 0.

8. This represents the upper limit of the design capacity of transformer h in the low-voltage distribution area. Let H be the active power of transformer h in the distribution area during time period t. The reactive power of transformer h in the distribution area during time period t is the high-voltage side, where T is the time period. The formula for establishing the dual function of the lower-level operation is expressed as: ； in, This represents the active power transmission between node i and node j. Let i be the set of adjacent nodes that are directly connected to node i. Let be the active power output of the distributed power source at node i at time t. Let be the active load of node i. Let (i, j) be the resistance of the line. Let i be the active power balance constraint multiplier at time t. Let i be the reactive power balance constraint multiplier at time t. Let be the amount of reactive power transmitted between node i and node j at time t. Let i be the reactive power output of the distributed power source at node i at time t. Let N be the reactive load of node i at time t, and let N be the set of all nodes in the distribution network. Let be the voltage lower bound constraint multiplier for node i at time t. Let be the voltage upper limit constraint multiplier for node i at time t. Let (i, j) be the line capacity constraint multiplier at time t. Let be the minimum allowable voltage at node i. Where is the maximum allowable voltage at node i. Let W be the rated apparent power capacity of line (i, j), and W be the set of lines. For the lower-level decision variable vector, Let be the current in line (i, j) at time t. Let be the voltage at node i at time t; The formation of a unified single-layer optimization model includes setting the initial values ​​of the Lagrange multipliers, determining the step size sequence, convergence threshold, and maximum number of iterations. When the iteration process begins, the current multiplier value is fixed, and the single-layer optimization is solved. After solving, the subgradient information is calculated, the optimization direction of the current solution is evaluated, and the Lagrange multipliers are updated according to the subgradient and the preset step size sequence. The termination condition is checked. If the current number of iterations is less than or equal to the maximum value, and the magnitude of the subgradient is less than or equal to the convergence threshold, the iteration stops, and the current solution is output as the optimal planning scheme. If the condition is not met, the iteration count is incremented by 1, and the single-layer problem is solved again with the multipliers fixed until the termination condition is met. The output global optimal planning scheme includes: verifying the method; after realizing DC interconnection on the low-voltage side and starting the energy router, running according to the energy management strategy; verifying the actual effect of the optimized configuration scheme; and realizing local consumption of new energy, load balancing between distribution stations, and avoiding backfeeding to the high-voltage grid. The actual effects of the optimized configuration scheme include DC interconnection on the low-voltage side, starting the energy router, and when operating normally, the DC bus load prioritizes absorbing photovoltaic power generation energy; when the DC load cannot fully absorb photovoltaic power generation, the energy storage is activated to charge the energy storage, so that the new energy power can be absorbed locally. When neither DC load nor energy storage can fully absorb photovoltaic power generation, power is supplied to the low-voltage AC side of the two distribution areas through the energy router. If the load rate difference between distribution area A and distribution area B is less than 10%, the energy router acts as a power source to provide power to the two distribution areas. When the load rate difference between the two distribution areas exceeds 20%, or when one distribution area feeds back into the grid, the energy router is responsible for energy mutual assistance and balanced distribution of the load between the two distribution areas.

8. A coordinated planning system for AC / DC distribution networks with flexible interconnection devices, employing the coordinated planning method for AC / DC distribution networks with flexible interconnection devices as described in any one of claims 1 to 7, characterized in that, include: The module includes a flexible interconnection device planning module, a photovoltaic-energy storage collaborative configuration module, an operation optimization module, and a model solving and strategy generation module. The flexible interconnection device planning module is used for the optimal installation location and capacity configuration of flexible interconnection devices. Through upper-level model decision variables and FID capacity, it provides flexible power regulation capabilities under the premise of meeting the goals of power balance, capacity constraints and cost minimization, laying the architectural foundation for AC / DC hybrid distribution networks. The photovoltaic-energy storage collaborative configuration module is used to optimize the maximum installed capacity of the DC-side photovoltaic system and the capacity and parameters of the energy storage system based on the planning results of the upper-level model. Through the constraints and objective function of the middle-level model, it enables the efficient consumption of new energy and the rational allocation of energy storage resources. The operation optimization module is used to optimize power allocation, voltage regulation and power flow control for different operating scenarios. Through lower-level model constraints, FID output range and objective function, it reduces losses through real-time scheduling and balances the load of the distribution area by utilizing the dynamic adjustment capability of FID. The model solving and strategy generation module is used to solve complex three-layer optimization problems through hierarchical model dimensionality reduction and hybrid intelligent algorithms, and output the globally optimal planning scheme and operation strategy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the AC / DC distribution network coordination planning method with flexible interconnection device as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the AC / DC distribution network coordination planning method with flexible interconnection device as described in any one of claims 1 to 7.

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