An AC / DC power distribution network coordinated planning method, system, device and medium containing a flexible interconnection device

Abstract

The application discloses a kind of AC-DC distribution network coordination planning method, system, equipment and medium containing flexible interconnection device, belong to AC-DC distribution technology field, including: input distribution network's basic information, and set the key parameter of hybrid optimization algorithm, initial particle swarm is generated in the upper model in three-layer optimization model, form initial solution set, based on initial solution set, for each FID configuration scheme, solve middle layer model and lower layer model, and through Lagrange relaxation technique middle lower layer model is converted into single layer problem, combined with hybrid intelligent algorithm is solved, the fitness of each FID scheme is evaluated using simulation results, dynamically update optimization algorithm state, when meeting termination condition, output global optimal planning scheme.The application solves efficiently by constructing three-layer collaborative planning model and using hybrid intelligent algorithm, improves the flexible interconnection capability of AC-DC distribution network, distributed energy consumption rate and equipment safety, while reducing network loss and cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of AC / DC power distribution, in particular to an AC / DC power distribution network coordinated planning method, system, equipment and medium containing a flexible interconnection device. BACKGROUND

[0002] Under the background of global energy system reform and popularization of distributed power supply, as a key link of new power system, medium and low voltage AC / DC power distribution network has realized large-scale application. Through the organic integration of AC and DC technical characteristics, AC / DC power distribution network improves power supply quality and provides a new solution for the coordinated access of large-scale distributed energy and energy storage system.

[0003] Flexible interconnection device is gradually becoming the core equipment of AC / DC hybrid power distribution network due to its excellent power regulation capability, which significantly improves the flexibility and power supply reliability of power grid operation. However, the dynamic regulation characteristics of FID are significantly different from the operation paradigm of traditional power equipment. The integration application improves the flexibility of the system, but also exposes the adaptive limitations of existing coordinated planning methods in dynamic characteristics and resource optimization configuration, which urgently needs technical breakthrough.

[0004] The existing flexible interconnection planning method of low-voltage AC / DC power distribution system has the following two deficiencies in multi-source and multi-load coordinated optimization. On the one hand, although the flexible interconnection device has dynamic power regulation capability, most planning methods do not systematically integrate characteristics such as active power support, reactive power regulation and bidirectional power distribution into the planning model, resulting in potential not being fully tapped. On the other hand, traditional load transfer means highly depends on physical devices such as sectionalizing switches and tie switches, the regulation strategy is rigid, the operation is complex and the response timeliness is poor, which is difficult to adapt to the stringent demands of system efficiency and power supply quality of high penetration rate distributed power grid connection. SUMMARY

[0005] In view of the above problems, the present application is proposed.

[0006] Therefore, the technical problem solved by the present application is that most planning methods do not systematically integrate characteristics such as active power support, reactive power regulation and bidirectional power distribution into the planning model, resulting in potential not being fully tapped. Highly dependent on physical devices such as sectionalizing switches and tie switches, the regulation strategy is rigid, the operation is complex and the response timeliness is poor, which is difficult to adapt to the stringent demands of system efficiency and power supply quality of high penetration rate distributed power grid connection.

[0007] To solve the above technical problems, the present application provides the following technical scheme: an AC / DC power distribution network coordinated planning method containing a flexible interconnection device, comprising the following steps,

[0008] The basic information of the input power distribution network is input, and key parameters of the hybrid optimization algorithm are set, an initial particle swarm is generated in the upper model in the three-layer optimization model, and an initial solution set is formed; based on the initial solution set, the middle model and the lower model are solved for each FID configuration scheme, and the middle and lower models are converted into a single-layer problem through the Lagrange relaxation technique, and are solved by combining the hybrid intelligent algorithm; the fitness of each FID scheme is evaluated by using the simulation result, the state of the optimization algorithm is dynamically updated, and when the termination condition is met, the global optimal planning scheme is output.

[0009] As a preferred scheme of the AC / DC power distribution network coordinated planning method with the flexible interconnection device, wherein: the basic information of the power distribution network includes the topological structure of the power distribution network, load data, distributed power characteristics and candidate installation positions of the flexible interconnection device;

[0010] Setting the key parameters of the hybrid optimization algorithm includes setting the particle swarm size and genetic algorithm parameters.

[0011] As a preferred scheme of the AC / DC power distribution network coordinated planning method with the flexible interconnection device, wherein: the generation of the initial particle swarm includes determining the optimal installation position and capacity configuration of the flexible interconnection device based on the power balance constraint, the active and reactive power capacity limit of the low-voltage flexible interconnection device and the minimization of the construction and operation cost of the flexible interconnection, the installation position and capacity configuration of one flexible interconnection device are represented by an initial particle swarm, and the initial particle swarm forms an initial solution set;

[0012] The power balance constraint includes that the nodes of the power distribution network follow the power conservation law, and the input and output of the dynamic balance of active and reactive power flow, and the active power balance constraint formula is represented as:

[0013]

[0014] wherein, is the line active power transmission amount between node i and node j, is a set of adjacent nodes directly connected to node i, is the active power output of the distributed power supply at node i, is the active load of node i;

[0015] The reactive power balance constraint formula is:

[0016]

[0017] wherein, is the reactive power transmission amount between node i and node j, is the reactive power output of the distributed power supply at node i, is the reactive load of node i, a set of adjacent nodes directly connected with node i;

[0018] The active and reactive power capacity limits of the flexible interconnection device are expressed as:

[0019]

[0020]

[0021] wherein, is the active power capacity of the flexible interconnection device, is the reactive power capacity of the flexible interconnection device, is the maximum value of the active power capacity, is the maximum value of the reactive power capacity;

[0022] The construction and operation cost minimization of the flexible interconnection, i.e. the objective function of the upper model, is expressed as:

[0023]

[0024] wherein, is the minimum value of the construction and operation cost of the flexible interconnection, is the unit capacity construction cost of the kth low-voltage flexible interconnection device, is the capacity of the kth flexible interconnection device, is the unit length construction cost of the line of node i, is the length of the line of node i, is a set of low-voltage flexible interconnection devices, is a coefficient of the sub-optimization objective, is the annual operation cost of the low-voltage flexible interconnection device and the DC tie line, and N is a set of all nodes in the distribution network.

[0025] As a preferred scheme of the method for coordinating planning of an AC / DC distribution network with flexible interconnection devices, the objective function of the upper model further comprises that the low-voltage flexible interconnection adopts a segmented chain type DC bus architecture, is composed of a power routing unit and an interconnection cable, the distance between interconnected low-voltage areas is short, the DC tie line maintenance cost is not included in the objective function, and the annual operation cost formula of the low-voltage flexible interconnection device and the DC tie line is expressed as:

[0026]

[0027] wherein, is the interconnection state of the area, is a decision variable of the upper model, is the physical distance between the interconnected areas a, is the investment cost per unit length of the DC tie line, is the capital time value conversion rate, for the interconnectable transformer group combination set, for the annual operation cost of the low-voltage flexible interconnection device and the DC tie line, for the transformer group set to which the flexible interconnection device can be installed, for the equipment service life of the DC tie line, for the investment cost of the low-voltage flexible interconnection device per unit capacity, for the installation capacity of the flexible interconnection device in the transformer group b, for the equipment service life of the flexible interconnection device, for the annual operation and maintenance cost of the low-voltage flexible interconnection device per unit capacity.

[0028] As a preferred scheme of the AC / DC power distribution network coordinated planning method with a flexible interconnection device, wherein: the solving of the middle layer model includes, in the upper layer model decision variable space, generating an initial solution set representing different flexible interconnection device location and capacity combinations, 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 and energy storage configuration scheme obtained by the middle layer solving;

[0029] Calculating the optimal photovoltaic installation capacity and energy storage system configuration capacity and parameters includes, through the load demand of the system, the distributed energy generation characteristics, and the flexible interconnection device configuration generated by the upper layer, planning the installation capacity of the DC side photovoltaic and the capacity and configuration parameters of the energy storage device;

[0030] The output power of photovoltaic power generation is affected by the irradiance-temperature coupling effect, the injected power needs to meet the node power conservation relationship, and the output power of the photovoltaic power station needs to match the overall power balance of the system, wherein the photovoltaic output cannot exceed the output of the maximum installed capacity, which is expressed by the formula:

[0031]

[0032] wherein, is the output of the maximum installed capacity of the photovoltaic power station, is the output power of photovoltaic power generation;

[0033] The charge and discharge power of the energy storage device needs to meet the remaining power at the previous time of the state of charge and the charge and discharge power change in the period, while the charge and discharge power of the energy storage cannot exceed the maximum value, and the energy storage installation capacity needs to meet the capacity constraints of the transformer group and the system, which is expressed by the formula:

[0034]

[0035]

[0036]

[0037]

[0038] wherein, SoC(t) is the initial state of charge at time t, SoC(t-1) is the state of charge at time t-1, Cmax is the rated capacity of the energy storage system, Q(t) is the charge-discharge amount of the energy storage system in the time interval [t-1, t], SoCmin is the minimum value of the state of charge, SoCmax is the maximum value of the state of charge, Pcharge is the charging power of the energy storage system, Pcharge,max is the maximum charging power of the energy storage system, Pdischarge,max is the maximum discharging power of the energy storage system, Pdischarge is the discharging power of the energy storage system;

[0039] The energy storage installation capacity constraint is represented as:

[0040]

[0041]

[0042]

[0043]

[0044] wherein, C is the energy storage installation capacity, C is the energy storage installation capacity configured for the substation a on a typical day e, C is the unit energy storage representing the single-machine capacity of the energy storage unit, i.e., the minimum planned energy storage scale allowed by the system, is an integer variable with a value not less than zero, is a 0-1 decision variable, represents that the energy storage is installed in the substation a, represents that the energy storage is not installed in the substation a, Cmin is the minimum installation capacity of the energy storage for the substation a, Cmax is the maximum installation capacity of the energy storage for the substation a, Cmin is the minimum total installation capacity of the energy storage for all substations, Cmax is the maximum total installation capacity of the energy storage for all substations, N is the total number of substations;

[0045] The objective function of the middle-layer model is represented as:

[0046]

[0047] wherein, is a weight coefficient, T is a time period, is the operating cost of the energy storage device at time t, is the cost of purchasing electricity from the power grid at time t, is the actual output of the photovoltaic power station at time t, is the objective function of the middle layer model.

[0048] As a preferred scheme of the AC / DC power distribution network coordinated planning method with flexible interconnection device, wherein: solving the lower layer model includes, under the upper and middle layer device configuration scheme, optimizing the power distribution and voltage regulation of the system under different operation scenarios;

[0049] Each node voltage is kept within the specified upper and lower limit range, while the active power and reactive power transmitted by the line cannot exceed the rated capacity, and the constraint formula is represented as:

[0050]

[0051] wherein, is the voltage of node i, is the minimum voltage of node i, is the maximum voltage of node i;

[0052] The power transmitted by the line cannot exceed the rated capacity, for line (m, n), the active power and the reactive power capacity limit must be met at the same time, and the line flow constraint formula is represented as:

[0053]

[0054] wherein, is the active power transmitted by line (m, n), is the reactive power transmitted by line (m, n), is the rated capacity of line (m, n);

[0055] The low-voltage flexible interconnection behavior constraint is represented as:

[0056]

[0057] wherein, is the apparent power of the transformer on the high-voltage side of the transformer in the substation a at time t, is the active power of the high-voltage side of the transformer in the substation a, is the reactive power of the high-voltage side of the transformer in the substation a, is the upper limit of the design capacity of the transformer in the substation a, The upper limit of the long-term operation load rate of the transformer is 0.8, The actual output apparent power of the low-voltage flexible interconnection device at time t is, The single-machine configuration capacity of the low-voltage flexible interconnection device of the transformer area a is;

[0058] The objective function of the lower layer model is represented as:

[0059]

[0060] Wherein, The minimum value of the network loss of the power system is W, T is the time period, The active power transmitted by the line (m, n) at time t is, The reactive power transmitted by the line (m, n) at time t is, The voltage amplitude of the line (m, n) at time t is, The resistance of the line (m, n) is.

[0061] As a preferred scheme of the AC / DC power distribution network coordinated planning method with a flexible interconnection device, wherein: the solving by combining the mixed intelligent algorithm includes introducing the Lagrange relaxation technology, constructing the dual function of the middle layer planning And establishing the dual function of the lower layer operation Through multiplier iteration to reduce the dimension of the model, combine And And reorganize to form a unified single-layer optimization model, and use GA to perform global exploration to locate the optimal solution space, and use PSO to perform fine search in the local range to solve;

[0062] The dual function of the middle layer planning is constructed The formula is represented as:

[0063]

[0064] Wherein, The middle layer decision variable vector is, The maximum installed capacity of the photovoltaic power station is, The output power of the photovoltaic power generation at time t is, The objective function of the middle layer model is, The photovoltaic output constraint multiplier is, The lower limit multiplier of the SOC of the energy storage is, The upper limit multiplier of the SOC of the energy storage is, The transformer capacity multiplier is A, which is the set of transformer areas configured with energy storage, and B is the set of transformer areas configured with transformers, The state of charge of the energy storage transformer area o at time t is, The minimum value of the state of charge of the energy storage transformer area o is, SoCmaxis the maximum state of charge of the energy storage substation, is the upper limit of long-term load rate of transformer 0.8, is the upper limit of design capacity of low-voltage substation transformer h, is the active power of substation transformer h on the high-voltage side at time period t, is the reactive power of substation transformer h on the high-voltage side at time period t, T is the time period;

[0065] The dual function formula of the lower layer operation is established as:

[0066]

[0067] wherein, is the active power transmission between node i and node j, is the set of adjacent nodes directly connected to node i, is the active power output of the distributed power supply at node i at time t, is the active load of node i, is the resistance of line (i, j), is the active power balance constraint multiplier of node i at time t, is the reactive power balance constraint multiplier of node i at time t, is the reactive power transmission between node i and node j at time t, is the reactive power output of the distributed power supply at node i at time t, is the reactive load of node i at time t, N is the set of all nodes in the distribution network, is the lower limit voltage constraint multiplier of node i at time t, is the upper limit voltage constraint multiplier of node i at time t, is the line capacity constraint multiplier of line (i, j) at time t, is the lowest allowed voltage of node i, is the highest allowed voltage of node i, is the rated apparent power capacity of line (i, j), W is the set of lines, is the lower layer decision variable vector, is the current of line (i, j) at time t, is the voltage of node i at time t;

[0068] The forming of the unified single-layer optimization model comprises setting an initial value of a Lagrange multiplier, determining a step sequence, a convergence threshold and a maximum iteration number, fixing a current multiplier value when an iteration process starts, solving a single-layer optimization, calculating sub-gradient information after the solving, evaluating an optimization direction of the current solution, updating the Lagrange multiplier according to the sub-gradient and the preset step sequence, checking a termination condition, stopping the iteration when the current iteration number is less than or equal to the maximum value and the modulus of the sub-gradient is less than or equal to the convergence threshold, and outputting the current solution as an optimal planning scheme, and when the condition is not met, then the iteration number is increased by 1, and the single-layer problem is solved again by fixing the multiplier, until the termination condition is met.

[0069] The output global optimal planning scheme comprises method verification, after the low-voltage side is implemented with DC interconnection and the energy router is started, energy management strategy operation is performed to verify the actual effect of the optimal configuration scheme, and new energy is locally consumed, load balancing between transformer areas is achieved, and reverse power transmission to the high-voltage power grid is avoided.

[0070] The actual effect of the optimal configuration scheme comprises DC interconnection on the low-voltage side, starting of the energy router, when normal operation is performed, the DC bus load preferentially consumes photovoltaic power generation energy, when the DC load cannot completely consume the photovoltaic power generation, the energy storage is started to charge the energy storage, and the new energy power is locally consumed.

[0071] When the DC load and the energy storage cannot completely consume the photovoltaic power generation, power is supplied to two transformer area low-voltage AC sides through the energy router, the load rate difference between the transformer area A and the transformer area B is less than 10%, the energy router is used as a power supply to supply power to the two transformer areas, when the load rate difference between the two transformer areas exceeds 20% or one transformer area appears reverse power transmission to the power grid, the energy router is responsible for energy aid, and the load of the two transformer areas is balanced and distributed.

[0072] The application provides a flexible interconnection device-containing AC-DC power distribution network coordinated planning system.

[0073] To solve the above technical problems, the application provides the following technical scheme: a flexible interconnection device-containing AC-DC power distribution network coordinated planning system, 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.

[0074] The flexible interconnection device planning module is used for optimal installation position and capacity configuration of the flexible interconnection device, and provides flexible power regulation capacity under the premise of meeting power balance, capacity constraint and minimum cost minimization target through upper model decision variables and FID capacity, thereby laying a foundation for the architecture of the AC-DC hybrid power distribution network.

[0075] The photovoltaic-energy storage collaborative configuration module is configured to optimize the maximum installation capacity of the direct current side photovoltaic system and the capacity and parameters of the energy storage system based on the planning result of the upper model, and to perform reasonable allocation of new energy efficient consumption and energy storage resources through the middle model constraint and the objective function.

[0076] The operation optimization module is configured to optimize power distribution, voltage regulation and power flow control for different operation scenarios, to reduce loss through real-time scheduling through the lower model constraint, FID output range and objective function, and to balance the load of the transformer area by using the dynamic adjustment capability of the FID.

[0077] The model solving and strategy generating module is configured to solve the complex three-layer optimization problem through the hierarchical model dimension reduction and the mixed intelligent algorithm, and to output the globally optimal planning scheme and operation strategy.

[0078] The application provides a computer device, including a memory and a processor, the memory stores a computer program, and the processor realizes the steps of the coordinated planning method of the AC / DC power distribution network containing the flexible interconnection device when executing the computer program.

[0079] The application provides a computer readable storage medium, which stores a computer program, and the computer program realizes the steps of the coordinated planning method of the AC / DC power distribution network containing the flexible interconnection device when being executed by a processor.

[0080] The application has the beneficial effects that the three-layer planning model is coordinated by the low-voltage flexible interconnection, the joint optimization of planning and operation is realized, the installation position and capacity of the FID, the configuration of the direct current side photovoltaic and energy storage and the system operation mode are more comprehensively and reasonably determined through hierarchical decision, the consumption level of the low-voltage transformer area to the distributed energy is significantly enhanced, the load redistribution of the heavy load transformer is successfully realized, and the comprehensive operation efficiency of the system is improved; the middle and lower optimization problems are integrated into a single layer structure through the model dimension reduction technology, the intelligent mixed algorithm is combined for solving, the calculation efficiency is significantly improved, the optimal planning and operation scheme can be obtained in a short time, and an efficient solution scheme is provided for actual engineering application; the planning result is more in line with the operation demand of the actual system by comprehensively considering various constraint conditions and optimization objectives, and the safety, stability and reliability of the system are improved. BRIEF DESCRIPTION OF DRAWINGS

[0081] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0082] Figure 1 A general flow chart of a flexible interconnection device containing AC / DC power distribution network coordinated planning method is provided for an embodiment of the present application.

[0083] Figure 2 A low-voltage flexible interconnection double-layer planning model framework schematic diagram of a flexible interconnection device containing AC / DC power distribution network coordinated planning method is provided for an embodiment of the present application.

[0084] Figure 3 A system scheme module diagram of a flexible interconnection device containing AC / DC power distribution network coordinated planning system is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0085] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0086] Embodiment 1, refer to Figure 1 For an embodiment of the present application, a flexible interconnection device containing AC / DC power distribution network coordinated planning method is provided, comprising:

[0087] S1: input the basic information of the power 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, and form the initial solution set.

[0088] S2: based on the initial solution set, solve the middle layer model and the lower layer model for each FID configuration scheme, and convert the middle and lower layer models into a single layer problem through the Lagrange relaxation technique, and solve it combined with the hybrid intelligent algorithm.

[0089] S3: use the simulation results to evaluate the fitness of each FID scheme, dynamically update the optimization algorithm state, and output the global optimal planning scheme when the termination condition is met.

[0090] It should be noted that considering various operating constraints, the flexible interconnection equipment and distributed energy equipment are cooperatively optimized and configured, the flexibility, stability and distributed energy consumption capacity of the system are improved, and strong technical support is provided for scientific planning and efficient operation of the low-voltage AC / DC power distribution system.

[0091] Embodiment 2, refer to Figure 1 and Figure 2 For a second embodiment of the present application, the embodiment provides a flexible interconnection device containing AC / DC power distribution network coordinated planning method, comprising:

[0092] In step S1, the power distribution network topology, load history and prediction data are read, and candidate installation positions of low-voltage area flexible interconnection devices are determined, and key control parameters of the hybrid optimization algorithm are reasonably set to ensure efficient and stable operation of the subsequent optimization process.

[0093] Further, the generating of the initial particle swarm includes determining the optimal installation position and capacity configuration of the flexible interconnection device based on the power balance constraint, the active and reactive power capacity limit of the low-voltage flexible interconnection device, and the minimization of the construction and operation cost of the flexible interconnection. An installation position and capacity configuration of a flexible interconnection device is represented by an initial particle swarm, and the initial particle swarm forms an initial solution set.

[0094] Constraint condition: the power balance constraint includes that the nodes of the power distribution network comply with the law of conservation of power, and the input and output of dynamic balanced active and reactive power flow. The active power balance constraint formula is represented as:

[0095]

[0096] Wherein, is the active power transmission amount between node i and node j, is the set of adjacent nodes directly connected to node i, is the active power output of the distributed power supply at node i, is the active load of the node;

[0097] The reactive power balance constraint formula is:

[0098]

[0099] Wherein, is the reactive power transmission amount between node i and node j, is the reactive power output of the distributed power supply at node i, is the reactive load of the node i, is the set of adjacent nodes directly connected to node i;

[0100] It should be noted that the active and reactive power capacity limit of the flexible interconnection device is represented as:

[0101]

[0102]

[0103] Wherein, is the active power capacity of the flexible interconnection device, is the reactive power capacity of the flexible interconnection device, is the maximum value of the active power capacity, is the maximum value of the reactive power capacity;

[0104] The construction and operation cost minimization of flexible interconnection, i.e., the upper model objective function, is expressed as:

[0105]

[0106] wherein, is the minimum construction and operation cost of flexible interconnection, is the unit capacity construction cost of the kth low-voltage flexible interconnection device, is the capacity of the kth flexible interconnection device, is the unit length construction cost of the line of node i, is the length of the line of node i, and is the node set, is the low-voltage flexible interconnection device set, is the coefficient of the sub-optimization objective, is the annual operation cost of the low-voltage flexible interconnection device and DC tie line, and N is the set of all nodes in the distribution network.

[0107] Further, the upper model objective function further includes that the low-voltage flexible interconnection adopts a segmented chain type DC bus architecture, is composed of a power routing unit and an interconnection cable, the distance between interconnected low-voltage areas is short, the DC tie line maintenance cost is not included in the objective function, and the annual operation cost formula of the low-voltage flexible interconnection device and DC tie line is expressed as:

[0108]

[0109] wherein, is the area interconnection state, is the decision variable of the upper model, is the physical distance between interconnected areas a, is the investment cost per unit length of the DC tie line, is the capital time value conversion rate, is the set of interconnectable area combinations, is the annual operation cost of the low-voltage flexible interconnection device and DC tie line, is the set of areas where the flexible interconnection device can be installed, is the equipment service life of the DC tie line, is the investment cost per unit capacity of the low-voltage flexible interconnection device, is the installed capacity of the flexible interconnection device in area b, is the equipment service life of the flexible interconnection device, is the annual operation and maintenance cost per unit capacity of the low-voltage flexible interconnection device.

[0110] In step S2, the solving the middle layer model includes, in the upper layer model decision variable space, generating an initial solution set representing different flexible interconnection device position and capacity combinations, 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, fixing the flexible interconnection device configuration scheme and the photovoltaic and energy storage configuration scheme obtained by the middle layer solving;

[0111] Calculating the optimal photovoltaic installation capacity and energy storage system configuration capacity and parameters includes, through the load demand of the system, the distributed energy generation characteristics and the flexible interconnection device configuration generated by the upper layer, planning the installed capacity of the photovoltaic on the direct current side and the capacity and configuration parameters of the energy storage device;

[0112] Further, the constraint condition: the output power of photovoltaic power generation is affected by the irradiance-temperature coupling effect, the injected power needs to meet the node power conservation relationship, and the output power of the photovoltaic power station needs to match the overall power balance of the system, wherein the output of the photovoltaic cannot exceed the output of the maximum installed capacity, which is expressed by the formula:

[0113]

[0114] Wherein, is the output of the maximum installed capacity of the photovoltaic power station, is the output power of photovoltaic power generation;

[0115] If the energy storage is overcharged or deeply discharged during operation, it may have an adverse effect on the service life. In order to prolong the service life of the battery and ensure the stable operation of the system, the value range of SOC is constrained in the ESS operation, and it is limited to work between the upper and lower boundaries set to avoid damage to the battery performance in extreme working conditions. The formula is expressed as:

[0116]

[0117]

[0118]

[0119]

[0120] Wherein, is the initial state of charge at time t, is the charging capacity of the energy storage at time t-1, is the rated capacity of the energy storage system, is the charge and discharge capacity of the energy storage system in the time interval [t-1, t], is the minimum value of the state of charge set, a maximum value set for state of charge, a charging power of the energy storage system, a maximum charging power of the energy storage system, a maximum discharging power of the energy storage system, a discharging power of the energy storage system;

[0121] The energy storage installation capacity constraint is represented as:

[0122]

[0123]

[0124]

[0125]

[0126] wherein, an energy storage installation capacity, an energy storage installation capacity configured for a typical day e in the substation 1~a, a unit energy storage represents the single machine capacity of an energy storage unit, that is, the minimum planned energy storage scale allowed by the system, an integer variable with a value not less than zero, a 0-1 decision variable, representing that the energy storage is installed in the substation a, representing that the energy storage is not installed in the substation a, a minimum energy storage installation capacity of the substation a, a maximum energy storage installation capacity of the substation a, a minimum total energy storage installation capacity of all substations, a maximum total energy storage installation capacity of all substations, a total number of substations;

[0127] The objective function of the middle layer model is represented as:

[0128]

[0129] wherein, a weight coefficient, T is a time period, a running cost of the energy storage device at t, a cost of purchasing electricity from the power grid at t, an actual output of the photovoltaic power station at t, an objective function of the middle layer model.

[0130] Further, solving the lower layer model includes, under the upper and middle layer device configuration scheme, optimizing the power distribution and voltage regulation running state of the system for different running scenarios;

[0131] Each node voltage is kept within the specified upper and lower limit range, while the active power and reactive power transmitted by the line cannot exceed the rated capacity, and the constraint formula is expressed as:

[0132]

[0133] wherein, V is the voltage of node i, Vmin is the minimum voltage of node i, Vmax is the maximum voltage of node i;

[0134] The power transmitted by the line cannot exceed the rated capacity, and for the line (m, n), the active power and the reactive power capacity limit must be met simultaneously, and the line flow constraint formula is expressed as:

[0135]

[0136] wherein, Pmn is the active power transmitted by the line (m, n), Qmn is the reactive power transmitted by the line (m, n), Cmn is the rated capacity of the line (m, n);

[0137] The low-voltage flexible interconnection behavior constraint is expressed as:

[0138]

[0139] wherein, S is the apparent power of the transformer on the high-voltage side of the transformer in the substation a at time t, P is the active power of the transformer on the high-voltage side of the transformer in the substation a, Q is the reactive power of the transformer on the high-voltage side of the transformer in the substation a, Cmax is the upper limit of the design capacity of the transformer, C is the upper limit of the long-term operation load rate of the transformer 0.8, Sout is the actual output apparent power of the low-voltage flexible interconnection device at time t, Cout is the single-machine configuration capacity of the low-voltage flexible interconnection device in the substation a;

[0140] The lower layer model objective function is expressed as:

[0141]

[0142] wherein, Wmin is the minimum value of power system network loss, W is the line set, T is the time period, Pmn is the active power transmitted by the line (m, n) at time t, Qmn is the reactive power transmitted by the line (m, n) at time t, V (m, n, t) is the voltage amplitude of the line (m, n) at time t, R (m, n) is the resistance of the line (m, n).

[0143] In step S3, the combined mixed intelligent algorithm is solved, which includes introducing the Lagrange relaxation technology to realize the conversion of the hierarchical optimization model to a single layer structure. First, a dual function L1 of the middle layer planning is constructed, and then a dual function L2 of the lower layer operation is established. The model dimension is reduced through multiplier iteration, which significantly improves the solving efficiency while ensuring the integrity of the constraints, providing an effective optimization tool for power distribution system planning. L1 and L2 are combined and rearranged to form a unified single layer optimization model, thereby simplifying the calculation complexity while retaining the constraints and objective characteristics of the original problem. GA is used for global exploration to quickly locate the optimal solution space, and PSO is used for fine search in the local range to improve the convergence accuracy and calculation efficiency.

[0144] Further, the dual function of the middle layer planning is constructed The formula is:

[0145]

[0146] Wherein, is the middle layer decision variable vector, is the maximum output of the photovoltaic power station, is the output power of the photovoltaic power generation at time t, is the middle layer model objective function, is the photovoltaic output constraint multiplier, is the lower limit multiplier of the energy storage SOC, is the upper limit multiplier of the energy storage SOC, is the transformer capacity multiplier, A is the set of areas equipped with energy storage, B is the set of areas equipped with transformers, is the state of charge of the energy storage area o at time t, is the minimum value of the state of charge of the energy storage area o, is the maximum value of the state of charge of the energy storage area o, is the upper limit of the long-term operation load rate of the transformer 0.8, is the upper limit of the design capacity of the low-voltage transformer, is the active power of the transformer in area h on the high-voltage side at time t, is the reactive power of the transformer in area h on the high-voltage side at time t, T is the time period;

[0147] The dual function of the lower layer operation is established The formula is:

[0148]

[0149] where, is the line active power transfer between node i and node j, is the set of neighboring nodes directly connected to node i, is the active power output of distributed generation at node i at time t, is the active load at node i, is the resistance of line (i, j), is the active power balance constraint multiplier of node i at time t, is the reactive power balance constraint multiplier of node i at time t, is the reactive power transfer between node i and node j at time t, is the reactive power output of distributed generation at node i at time t, is the reactive load at node i at time t, N is the set of all nodes in the distribution network, is the lower voltage limit constraint multiplier of node i at time t, is the upper voltage limit constraint multiplier of node i at time t, is the line capacity constraint multiplier of line (i, j) at time t, is the minimum allowed voltage of node i, is the maximum allowed voltage of node i, is the rated apparent power capacity of line (i, j), W is the set of lines, is the lower layer decision variable vector, is the current of line (i, j) at time t, is the voltage of node i at time t.

[0150] The formation of the unified single-level optimization model includes setting the initial value of the Lagrange multiplier, and determining the step size sequence, the convergence threshold and the maximum number of iterations, fixing the current multiplier value when the iteration process starts, solving the single-level problem, after solving, calculating the sub-gradient information, evaluating the optimization direction of the current solution, and updating the Lagrange multiplier according to the sub-gradient and the preset step size sequence, checking the termination condition, the current iteration number is less than or equal to the maximum value, and the modulus of the sub-gradient is less than or equal to the convergence threshold, then stop iteration, output the current solution as the optimal planning scheme, when the condition is not met, then the iteration number is added by 1, and the single-level problem is solved by fixing the multiplier again, until the termination condition is met.

[0151] The formula for solving the single-level problem is:

[0152]

[0153] where, is the upper layer decision variable vector, is the middle layer decision variable vector, for the lower-level decision variable vector, for the upper-level model objective function, for the dual function of the middle-level planning, for the dual function of the lower-level operation.

[0154] The sub-gradient information formula is calculated as:

[0155]

[0156]

[0157]

[0158]

[0159]

[0160]

[0161]

[0162]

[0163]

[0164] wherein, is the difference between the actual output of the photovoltaic and the maximum installed capacity, is the difference between the state of charge of the energy storage and the minimum allowed value, is the difference between the state of charge of the energy storage and the maximum allowed value, is the difference between the apparent power of the transformer and the safe capacity, is the active power imbalance of the node, is the reactive power imbalance of the node, is the difference between the voltage of the node and the minimum allowed value, is the difference between the voltage of the node and the maximum allowed value, is the difference between the apparent power of the line and the rated capacity.

[0165] The updating multiplier formula is calculated as:

[0166]

[0167]

[0168]

[0169]

[0170]

[0171]

[0172]

[0173]

[0174]

[0175] wherein, is the step size for the wth iteration, is the photovoltaic power output constraint for the w+1th iteration, is the energy storage SOC lower bound constraint for the w+1th iteration, is the energy storage SOC upper bound constraint for the w+1th iteration, is the transformer capacity constraint for the w+1th iteration, is the active power balance constraint for the w+1th iteration, is the reactive power balance constraint for the w+1th iteration, is the voltage lower bound constraint for the w+1th iteration, is the voltage upper bound constraint for the w+1th iteration, is the line capacity constraint for the w+1th iteration, is the photovoltaic power output constraint for the wth iteration, is the energy storage SOC lower bound constraint for the wth iteration, is the energy storage SOC upper bound constraint for the wth iteration, is the transformer capacity constraint for the wth iteration, is the active power balance constraint for the wth iteration, is the reactive power balance constraint for the wth iteration, is the voltage lower bound constraint for the wth iteration, is the voltage upper bound constraint for the wth iteration, is the line capacity constraint for the wth iteration.

[0176] The GA global exploration phase includes encoding the upper-level decision variables (FID installation locations, capacities) and the middle-level key variables (photovoltaic and energy storage capacities), and for each individual, calling the middle and lower-level models to calculate the comprehensive cost (construction cost, operation cost, network loss), the goal being to minimize the weighted total cost, generating a new generation of population through selection, crossover, and mutation, iterating to convergence, and outputting a Top-M quality solution set.

[0177] The PSO local fine search stage includes taking the O high-quality solutions output by the GA as the initial positions of the PSO particles, randomly generating the remaining particles in the neighborhood, updating the particle speed and position according to the standard PSO rule, driving the particles to converge to the individual historical optimum and the group historical optimum, optimizing the variable value, and when the PSO converges, the solution corresponding to the group historical optimum is the optimal planning scheme.

[0178] Further, method verification is performed, after the low-voltage side is interconnected by direct current and the energy router is started, the system will follow the following energy management strategy to run, to verify the actual effect of the optimal configuration scheme, and ensure that new energy is efficiently consumed on site, load balancing between transformer areas, and avoiding sending electricity back to the high-voltage power grid:

[0179] 1. New energy generation is preferentially consumed on site:

[0180] Local direct current load consumption, photovoltaic power generation is preferentially consumed by local direct current load connected to the same direct current bus in real time.

[0181] When the direct current load cannot completely consume the photovoltaic power generation, the system automatically stores the remaining electric energy to the energy storage device to realize the on-site storage of photovoltaic power.

[0182] Cross-area low-voltage alternating current side power supply, when the local direct current load and the energy storage system (considering the current chargeable capacity) cannot completely consume the photovoltaic power generation, the excess photovoltaic power is supplied to the low-voltage alternating current side of the transformer area A and the transformer area B through the energy router.

[0183] 2. Mutual aid and balance control of the energy router:

[0184] 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 supply and uniformly injects the excess photovoltaic power into the low-voltage alternating current bus of the two transformer areas to simultaneously support the alternating current load of the two transformer areas.

[0185] Mutual aid and balance mode, when any of the following conditions occurs, the energy router starts the mutual aid and balance function:

[0186] ① The real-time load rate difference between transformer area A and transformer area B exceeds 20%.

[0187] ② Either transformer area (A or B) has the risk of sending electricity back to the high-voltage power grid or actually sending electricity back.

[0188] The energy router supports a double-mode switching mechanism in the running state, specifically including:

[0189] A1: As a power supply, supply power to the high-load transformer area, preferentially deliver the excess power from photovoltaic or the available excess power of the remaining transformer area to the transformer area with a higher load rate to relieve the transformer pressure.

[0190] A2: As a load, it absorbs power from low-load substations, and if necessary, it can absorb power from substations with low load rates or surplus power and transfer it to high-load substations through the energy router.

[0191] Core objective: dynamically balance the loads of the two substations, maximize the use of surplus new energy within the substations, and eliminate reverse power transmission to the high-voltage power grid.

[0192] 3. Reverse energy flow (insufficient photovoltaic output):

[0193] When the photovoltaic power is lower than the demand of the DC load, the system will compensate for the power difference according to the established control strategy: first, the energy storage device is discharged to supplement power; when the energy storage discharge still cannot meet the load demand, the energy router obtains power from the low-voltage AC side of the two substations (preferably from the substation with low load rate) to supply the DC load.

[0194] Furthermore, the operation effect is tested, the DC interconnection is performed on the low-voltage side, the energy router is started, and when the system is normally operated, the DC bus load preferentially consumes photovoltaic power; when the DC load cannot completely consume photovoltaic power, the energy storage is charged to realize local consumption of new energy power.

[0195] When the DC load and the energy storage cannot completely consume photovoltaic power, the energy router supplies power to the low-voltage AC side of the two substations; the load rate difference between substation A and substation B is less than 10%, the energy router is used as a power supply or load to uniformly supply power to the two substations; the load rate difference between the two substations exceeds 20%, or reverse power transmission to the power grid occurs in one of the substations, the energy router is responsible for energy interconnection and balanced distribution of the loads of the two substations.

[0196] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

[0197] Embodiment 3, refer to Figure 3 As a third embodiment of the present application, the embodiment provides a flexible interconnection device-containing AC-DC power distribution network coordination planning system, which comprises 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.

[0198] The flexible interconnection device planning module is configured to plan optimal installation positions and capacity configurations of the flexible interconnection device, to provide flexible power regulation capability under the premise of meeting power balance, capacity constraints and cost minimization targets, and to lay the foundation of the architecture of the AC / DC hybrid distribution network through upper model decision variables and FID capacity.

[0199] The photovoltaic- energy storage collaborative configuration module is configured to optimize the maximum installation 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 model, to perform efficient new energy consumption and reasonable allocation of energy storage resources through middle model constraints and objective functions.

[0200] The operation optimization module is configured to optimize power distribution, voltage regulation and power flow control for different operation scenarios, to reduce loss through real-time scheduling, and to balance the load of the transformer area by using the dynamic regulation capability of the FID through lower model constraints, FID output range and objective functions.

[0201] The model solving and strategy generating module is configured to solve the complex three-layer optimization problem through hierarchical model dimension reduction and hybrid intelligent algorithm, and to output the globally optimal planning scheme and operation strategy.

[0202] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

[0203] Embodiment 4 is the fourth embodiment of the present application, which is different from the first three embodiments:

[0204] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts that essentially contribute to the prior art or parts of the technical solutions can be embodied in the form of software products, which are stored in a storage medium and include instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in the embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and various program code storage media.

[0205] The logic and / or steps represented in the flow diagrams and / or described herein, for example, can be considered as a sequence of executable instructions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. Just by way of example, a computer-readable medium can be any device or apparatus that can store and convey instructions for execution by the instruction execution system, apparatus, or device. With respect to the description herein, a "computer-readable medium" can be any means that can store, communicate, propagate, or transport a program for use by or in connection with the instruction execution system, apparatus, or device.

[0206] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by a computer.

[0207] It should be understood that aspects of the application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, or combination thereof, can be used: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), 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. 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 genetic algorithm parameters; 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. For the reactive load of node i, 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 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. The annual operating cost of the low-voltage flexible interconnection device and DC tie line is given by N, which is the set of all nodes in the distribution network. 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. Annual operating and maintenance costs per unit capacity of low-voltage flexible interconnect devices; 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, Let [t-1, t] be the amount of charge and discharge of the energy storage system within the time interval [t−1, t]. 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. The objective function for the mid-level model; 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).

2. The AC / DC distribution network coordination planning method with flexible interconnection device as described in claim 1, 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 for node i. Let be 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 formula for solving a single-layer problem is expressed as: 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 mid-level programming, This is the dual function of the lower-level execution; The formula for calculating subgradient information is expressed as: 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. The difference between the apparent power of the line and its rated capacity; The update multiplier formula is expressed as: 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. For the line capacity constraint in the w-th iteration; 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 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 the Top-M high-quality solution set. The PSO local fine search phase 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. 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.

3. 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 1-2, 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.

4. 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 2.

5. 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 2.

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