Two-order robust planning method, device, equipment and medium for active distribution network energy storage

Abstract

The present invention relates to a two-stage robust planning method, device, equipment and medium for energy storage in an active distribution network, and the method comprises: collecting relevant parameters, wherein the relevant parameters include line parameters of the active distribution network, output of distributed power source access and load forecast reference value; determining the objective function and constraint conditions of the active distribution network based on the relevant parameters; constructing an active distribution network energy storage planning model based on the relevant parameters, the objective function and the constraint conditions; constructing an active distribution network energy storage two-stage robust planning model based on the uncertainty of the output of distributed power source in the active distribution network energy storage planning model; iteratively solving the active distribution network energy storage two-stage robust planning model, and outputting the battery energy storage planning result. The present invention reduces the conservatism of the battery energy storage planning result.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage planning, and in particular to a two-order robust planning method, device, equipment and medium for active distribution network energy storage. Background Art

[0002] The energy storage system is configured in the active distribution network. On the one hand, it can improve the absorption level of distributed power sources, and on the other hand, it can improve the reliability of power supply and improve the traditional single power supply mode. However, the strong randomness of the output of distributed power sources affects the location and capacity of energy storage configuration. Therefore, the energy storage planning method of active distribution network has developed from the deterministic two-level planning method to the uncertain planning method such as stochastic planning method, chance-constrained planning method, and robust planning method.

[0003] In order to overcome the drawbacks of random programming methods and chance-constrained programming methods that require precise probability distribution, traditional robust planning methods only construct uncertainty sets based on the upper and lower bounds of random variables and seek the optimal planning results under the worst scenario. Therefore, the planning results are conservative.

[0004] Therefore, how to reduce the conservatism of planning results is an urgent problem to be solved. Summary of the invention

[0005] In view of this, it is necessary to provide a two-order robust planning method, device, equipment and medium for active distribution network energy storage to solve the problem of conservative planning results in existing schemes.

[0006] In order to solve the above problems, in a first aspect, the present invention provides a two-order robust planning method for energy storage in an active distribution network, comprising:

[0007] Collect relevant parameters, wherein the relevant parameters include line parameters of the active distribution network, access output of distributed power sources and load forecast reference values;

[0008] Based on the relevant parameters, determining the objective function and constraint conditions of the active power distribution network;

[0009] Constructing an active distribution network energy storage planning model based on the relevant parameters, the objective function and the constraint conditions;

[0010] Based on the uncertainty of distributed generation output in the active distribution network energy storage planning model, a two-stage robust planning model for active distribution network energy storage is constructed.

[0011] The two-stage robust planning model of active distribution network energy storage is solved iteratively, and the battery energy storage planning result is output.

[0012] Furthermore, the objective function of the active distribution network includes that the investment cost of battery energy storage and the system operation cost during the planning period meet preset cost conditions.

[0013] Furthermore, it is characterized in that the constraints of the active distribution network include second-order cone power flow constraints, reactive power output constraints of capacitors, operation constraints of on-load tap-changing transformers, output power constraints of distributed power sources and battery energy storage operation constraints.

[0014] Furthermore, the active distribution network energy storage planning model includes battery energy storage, distributed power sources, on-load tap-changing transformers and the number of capacitor groups.

[0015] Furthermore, the two-stage robust planning model for active distribution network energy storage is constructed based on the uncertainty of the output of distributed power sources in the active distribution network energy storage planning model, including:

[0016] Establishing the budget uncertainty set of distributed generation output;

[0017] A two-stage robust planning model for active distribution network energy storage is constructed according to the budget uncertainty set.

[0018] Furthermore, the iterative solution of the active distribution network energy storage two-stage robust programming model includes:

[0019] The main problem and sub-problems of the two-stage robust programming model of active distribution network energy storage are constructed by using a column and constraint algorithm, and are solved iteratively.

[0020] Furthermore, the iterative solution of the two-stage robust programming model of the active distribution network energy storage specifically includes:

[0021] Determine the decision variables of the two-stage robust programming model of the active distribution network energy storage based on the main problem;

[0022] Determine the sub-problem according to the decision variables;

[0023] According to the strong duality, the bi-level programming problem in the sub-problem is transformed into a single-level programming problem.

[0024] In a second aspect, the present invention further provides a two-order robust planning device for energy storage in an active distribution network, comprising:

[0025] A collection module, used to collect relevant parameters, wherein the relevant parameters include line parameters of the active distribution network, access output of distributed power sources and load forecast reference values;

[0026] A determination module, used to determine the objective function and constraint conditions of the active power distribution network based on the relevant parameters;

[0027] A first model building module is used to build an active distribution network energy storage planning model based on the relevant parameters, the objective function and the constraint conditions;

[0028] The second model building module is used to build a two-stage robust planning model for active distribution network energy storage based on the uncertainty of distributed power output in the active distribution network energy storage planning model;

[0029] The solution module is used to iteratively solve the two-stage robust planning model of active distribution network energy storage and output the battery energy storage planning result.

[0030] In a third aspect, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the above-mentioned two-order robust planning method for active distribution network energy storage when executing the computer program.

[0031] In a fourth aspect, the present invention further provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned two-order robust planning method for active distribution network energy storage are implemented.

[0032] The beneficial effect of adopting the above embodiment is as follows: the present invention determines the objective function and constraints of the active distribution network according to the line parameters of the active distribution network, the access output of the distributed power source and the load forecast reference value, and constructs an active distribution network energy storage planning model according to the relevant parameters, objective functions and constraints. Then, according to the uncertainty of the output of the distributed power source in the active distribution network energy storage planning model, a two-stage robust planning model of the active distribution network energy storage is constructed, so that after knowing the realization of the uncertain parameters, the decision variables of the second stage are determined, and based on the staged decision, the conservatism of the battery energy storage planning results is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A method flow chart of an embodiment of a two-order robust planning method for active distribution network energy storage provided by the present invention;

[0034] Figure 2 A flow chart of a method for iteratively solving a two-stage robust planning model for active distribution network energy storage provided by an embodiment of the present invention;

[0035] Figure 3 A structural schematic diagram of an embodiment of a two-order robust planning device for active distribution network energy storage provided by the present invention;

[0036] Figure 4 The present invention provides a schematic structural diagram of an electronic device. DETAILED DESCRIPTION

[0037] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0038] In the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically limited. Reference to "embodiment" in this article means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0039] The present invention provides a two-order robust planning method, device, electronic device and computer storage medium for active distribution network energy storage, which are described below respectively.

[0040] See also Figure 1 , Figure 1 A flow chart of an embodiment of two-order robust planning of energy storage in an active distribution network provided by the present invention. A specific embodiment of the present invention discloses a two-order robust planning method for energy storage in an active distribution network, including:

[0041] Step S101: collecting relevant parameters, wherein the relevant parameters include line parameters of the active distribution network, the output of distributed power sources and load forecast reference values;

[0042] Step S102: determining the objective function and constraint conditions of the active distribution network based on relevant parameters;

[0043] Step S103: constructing an active distribution network energy storage planning model based on relevant parameters, objective functions and constraints;

[0044] Step S104: constructing a two-stage robust planning model for active distribution network energy storage based on the uncertainty of the output of distributed generation in the active distribution network energy storage planning model;

[0045] Step S105: Iteratively solve the two-stage robust planning model for active distribution network energy storage and output the battery energy storage planning result.

[0046] Specifically, the embodiment of the present invention first establishes the objective function and constraints of the active distribution network energy storage planning model based on the collected relevant parameters, and then vectorizes the active distribution network energy storage planning model established according to the relevant parameters, the objective function and the constraints to facilitate subsequent processing, and then introduces the uncertainty of the output of distributed power sources in the active distribution network energy storage planning model to construct a two-stage robust planning model for active distribution network energy storage, and finally iteratively solves the two-stage robust planning model for active distribution network energy storage, and outputs the battery energy storage planning result.

[0047] In order to solve the influence of the uncertainty of distributed power output on the active distribution network energy storage planning results, the present invention constructs a two-stage robust planning model for active distribution network energy storage according to the uncertainty of distributed power output in the active distribution network energy storage planning model, so that after knowing the realization of uncertain parameters, the second-stage decision variables are determined, and the conservatism of the battery energy storage planning results is reduced based on staged decision-making.

[0048] In one embodiment of the present invention, the active distribution network line parameters, the distributed power source access output and the load forecast reference value are input; the upper and lower limits of the state of charge SOC of the battery energy storage are set to 80% and 20% respectively, the charging and discharging efficiency is 95%, the power purchase cost coefficient of the distribution network to the main grid is 150$ / MWh, the system operation time period is 24 hours, and the time interval is 1h.

[0049] In one embodiment of the present invention, the objective function of the active distribution network includes that the investment cost of battery energy storage and the system operation cost during the planning period meet preset cost conditions. Specifically, the investment cost of battery energy storage and the system operation cost are minimized during the planning period.

[0050] Investment cost of battery energy storage C inv It mainly consists of two parts: capacity cost and power cost, as shown in the following formula:

[0051]

[0052] Among them, c s 、c c They are the investment cost coefficients per unit power and per unit capacity of battery energy storage converted on a daily basis; are the power and capacity of the battery energy storage respectively; the subscript i is the node to which the battery energy storage is connected, and N is the total number of battery energy storage connections.

[0053] The investment cost coefficient c of unit power and unit capacity of battery energy storage after daily conversion s 、c c The following formula can be used for calculation:

[0054]

[0055] Among them, α is the annual interest rate, τ is the service life of the battery energy storage, c s,annual 、c c,annua l is the annual investment cost coefficient per unit power and unit capacity of battery energy storage.

[0056] The operating cost of the system is mainly composed of the cost of electricity purchased by the distribution network from the main grid, as shown in the following formula:

[0057]

[0058] Among them, cp is the electricity purchase cost coefficient, G p,t is the purchased power in period t, Δt is the time interval, and T is the time period (24h).

[0059] Therefore, the distribution network energy storage planning objectives of the present invention are:

[0060] min C=C inv +C ope .

[0061] In one embodiment of the present invention, the constraints of the active distribution network include second-order cone power flow constraints, reactive power output constraints of capacitors, operation constraints of on-load tap-changing transformers, output power constraints of distributed power sources, and battery energy storage operation constraints.

[0062] For the second-order cone power flow constraint, specifically, the Distflow power flow model can be used to establish the second-order cone power flow constraint equation of the active distribution network, as shown in the following formula:

[0063] P i+1,t =P i,t -r i L i,t +p i+1,t

[0064] Q i+1,t =Q i,t -x i L i,t +q i+1,t

[0065]

[0066]

[0067]

[0068]

[0069] Among them, P i,t , Q i,t They represent the active power and reactive power flowing from node i to node i+1 during time period t; r i 、x i They represent the resistance and reactance of branch i respectively; p i,t ,q i,t They represent the injected active power and reactive power of node i in period t respectively; L i,t represents the square of the current amplitude of branch i during period t, They represent the active and reactive output of the distributed generation at node i in period t, They represent the charging power and discharging power of the battery energy storage at node i in time period t respectively; represents the reactive power output of the battery energy storage at node i during period t, represents the reactive power output of the capacitor at node i during period t; They represent the active power demand and reactive power demand of the load at the node i in the period t, respectively. In addition, all subscripts t in the present invention represent the variable value in the period t.

[0070] For the reactive power output constraint of the capacitor, specifically, the reactive power output constraint of the capacitor at node i is:

[0071]

[0072] Among them, q c is the unit reactive power of the capacitor bank; represents the number of capacitor banks at node i during period t; It is the upper limit of the reactive power of the capacitor bank.

[0073] For the operation constraints of the on-load tap-changing transformer, specifically, the operation constraints of the on-load tap-changing transformer can be expressed by the following formula:

[0074]

[0075] k tap,t =k tap,min +t tap,t Δk tap

[0076] 0≤t tap,t ≤t tap,max

[0077] Among them, U s,t is the square of the voltage amplitude at the connection point between the distribution network and the main grid, k tap,t is the tap ratio of the on-load tap-changing transformer, t tap,t Indicates the tap position of the on-load tap-changing transformer, Δk tap Indicates the tap ratio increment of each tap level, t tap,max Indicates the upper limit value of the tap of the on-load tap-changing transformer.

[0078] The operation constraint of the on-load tap-changing transformer is a nonlinear constraint, which can be further linearized using Big-M and binary expansion as follows:

[0079]

[0080]

[0081] 0≤U s,t -x k,t ≤(1-t k,t )M

[0082] 0≤x k,t ≤t k,t M

[0083]

[0084] 0≤u t -y k,t ≤(1-t k,t )M

[0085] 0≤y k,t ≤t k,t M

[0086] Among them, t k,t is a 0-1 variable, K is the maximum binary digit, and the auxiliary variable u t =U s,t k tap,t , x k,t =U s, t t k,t ,y k,t =u t t k,t , M is a sufficiently large positive number.

[0087] For the distributed power output power constraint, specifically, the distributed power output power constraint is as follows:

[0088]

[0089]

[0090] in, represents the maximum output active power of the distributed generation at node i during period t, is the apparent power of the distributed generation inverter at node i.

[0091] For the battery energy storage configuration quantity constraint, specifically, the battery energy storage configuration quantity constraint is as follows:

[0092]

[0093] Among them, N ess is the amount of energy storage configured in the active distribution network, is a 0-1 variable. Indicates that node i is equipped with battery energy storage, Indicates that node i is not equipped with battery energy storage.

[0094] For the battery energy storage operation constraints, specifically, the battery energy storage operation constraints are as follows:

[0095]

[0096]

[0097]

[0098] SOC i,end =SOC i,0 =SOC i,min

[0099]

[0100] Among them, E i,t and E i,t-1 is the energy of the battery storage at node i at time t and t-1; η is the charge and discharge efficiency of the battery storage; SOC i,min , SOC i,max are the minimum and maximum state of charge of the battery energy storage at node i; SOC i,0 and SOC i,end is the initial and final state of charge of the battery storage at node i; are the upper limits of charging power and discharging power of the battery energy storage at node i respectively.

[0101] In one embodiment of the present invention, the active distribution network energy storage planning model includes battery energy storage, distributed power sources, on-load tap-changing transformers, and the number of capacitor banks.

[0102] It can be understood that an active distribution network energy storage planning model can be constructed based on relevant parameters, objective functions and constraints. Specifically, the active distribution network energy storage deterministic planning model can be expressed in vector form as follows:

[0103]

[0104]

[0105]

[0106] Among them, x includes the location, rated power and capacity of the battery energy storage; y includes the tap ratio of the on-load tap-changing transformer, the number of capacitor banks, the active power and reactive power output of the battery energy storage, the active power and reactive power output of the distributed power source, and the purchased power from the main grid.

[0107] In one embodiment of the present invention, a two-stage robust planning model for active distribution network energy storage is constructed based on the uncertainty of the output of distributed power sources in the active distribution network energy storage planning model, including:

[0108] Establishing the budget uncertainty set of distributed generation output;

[0109] A two-stage robust planning model for active distribution network energy storage is constructed based on the budget uncertainty set.

[0110] The budget uncertainty set of distributed generation output is as follows:

[0111]

[0112] in, They represent the expected active power output and active power fluctuation of the distributed generation at node i in period t respectively, and Γ is the uncertainty budget.

[0113] The two-stage robust planning model for active distribution network energy storage is as follows:

[0114] Phase 1:

[0115]

[0116] Phase 2:

[0117]

[0118] In one embodiment of the present invention, iteratively solving a two-stage robust programming model for active distribution network energy storage includes:

[0119] The column and constraint algorithm is used to construct the main problem and subproblems of the two-stage robust programming model for active distribution network energy storage, and the solution is iteratively performed.

[0120] Among them, the main problem provides a lower bound for the two-stage robust planning model, as follows:

[0121]

[0122]

[0123] Based on the main problem, the decision variables of the two-stage robust planning model for active distribution network energy storage can be determined.

[0124] After the decision variables of the first stage are generated, the sub-problems can be determined according to the decision variables. The sub-problem optimization model of the second stage is as follows:

[0125]

[0126]

[0127] It can be understood that the above model is a two-level planning problem. The outer planning max means that the subproblem seeks the maximum value of the objective function in the budget uncertainty set, and the inner planning min means seeking the minimum value of the objective function through the secondary decision variables.

[0128] Therefore, the two-level programming problem in the sub-problem can be transformed into a single-level programming problem based on the strong duality. Specifically, the simplified sub-problem model is as follows:

[0129]

[0130]

[0131] Among them, π and λ are dual variables; σ l ,μ l is the dual variable of the second-order cone constraint.

[0132] Through the main problem and sub-problems, iterative solutions are obtained and the battery energy storage planning results are output.

[0133] See also Figure 2 , Figure 2 A flow chart of a method for iteratively solving a two-stage robust planning model for active distribution network energy storage provided by an embodiment of the present invention.

[0134] Specifically, first initialize, set LB = 0, UB = ∞, and iteration counter k = 0;

[0135] Solve the main problem and obtain a set of optimal solutions (x * ,L * ), update the lower bound LB:

[0136] LB=max{LB,c T x * +L *};

[0137] Substitute the solution of the main problem into the subproblem and solve the subproblem to obtain the objective function value of the subproblem and the value of the uncertain variable under the corresponding worst scenario, and update the upper bound UB: UB = max{UB,c T x+L *};

[0138] Determine the size of the upper bound UB and the lower bound LB. If UB-LB≤ε, output the optimal battery energy storage planning result, that is, the optimal operation strategy of the active distribution network;

[0139] If UB-LB>ε, increase the iteration counter k=k+1 and add the constraint: Continue to iterate and solve the main problem until the convergence condition UB-LB≤ε is met, and finally output the optimal battery energy storage planning result.

[0140] In general, compared with the existing distribution network energy storage planning technology, the present invention can achieve the following beneficial effects:

[0141] 1. The two-stage robust planning technology for active distribution network energy storage of the present invention constructs a coordinated planning model including battery energy storage, distributed power supply, on-load tap-changing transformer, and capacitor bank.

[0142] 2. Compared with the existing energy storage robust planning technology, the two-stage robust planning method for active distribution network energy storage of the present invention has better convergence and weaker conservatism, and the solution speed is faster.

[0143] In order to solve the impact of the uncertainty of distributed power output on the planning results of active distribution network energy storage, the present invention takes minimizing the investment cost of battery energy storage and the system operating cost as the goal, constructs a coordinated planning model including battery energy storage, distributed power sources, on-load tap-changing transformers, and capacitor banks, and invents a two-stage robust planning method for active distribution network energy storage. The first stage decides the location, rated capacity and rated power of the battery energy storage; the second stage decides the optimal operation strategy of the active distribution network. The objective function of the two-stage robust planning model for active distribution network energy storage is to minimize the sum of the battery energy storage investment cost and the expected operating cost under the worst probability.

[0144] In order to better implement the two-order robust planning method for active distribution network energy storage in the embodiment of the present invention, based on the two-order robust planning method for active distribution network energy storage, correspondingly, please refer to Figure 3 , Figure 3 This is a structural schematic diagram of an embodiment of a two-order robust planning device for energy storage in an active distribution network provided by the present invention. The embodiment of the present invention provides a two-order robust planning device 300 for energy storage in an active distribution network, including:

[0145] The acquisition module 301 is used to acquire relevant parameters, wherein the relevant parameters include line parameters of the active distribution network, the output of the distributed power source access and the load forecast reference value;

[0146] A determination module 302, configured to determine an objective function and constraint conditions of the active power distribution network based on the relevant parameters;

[0147] A first model building module 303 is used to build an active distribution network energy storage planning model based on the relevant parameters, the objective function and the constraint conditions;

[0148] The second model building module 304 is used to build a two-stage robust planning model for active distribution network energy storage based on the uncertainty of the output of distributed power sources in the active distribution network energy storage planning model;

[0149] The solution module 305 is used to iteratively solve the two-stage robust planning model of active distribution network energy storage and output the battery energy storage planning result.

[0150] It should be noted here that: the device 300 provided in the above embodiment can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above method embodiments, which will not be repeated here.

[0151] Based on the above-mentioned two-order robust planning method for active distribution network energy storage, an embodiment of the present invention also provides an electronic device accordingly, including: a processor and a memory, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the steps in the two-order robust planning method for active distribution network energy storage in the above-mentioned embodiments are implemented.

[0152] Figure 4 4 is a schematic diagram of the structure of an electronic device 400 suitable for implementing the embodiment of the present invention. The electronic device in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0153] The electronic device includes: a memory and a processor, wherein the processor here may be referred to as a processing device 401 hereinafter, and the memory may include at least one of a read-only memory (ROM) 402, a random access memory (RAM) 403, and a storage device 408 hereinafter, as specifically shown below:

[0154] like Figure 4 As shown, the electronic device 400 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage device 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0155] Typically, the following devices may be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 400 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 400 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0156] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 409, or installed from the storage device 408, or installed from the ROM402. When the computer program is executed by the processing device 401, the above-mentioned functions defined in the method of the embodiment of the present invention are executed.

[0157] Based on the above-mentioned two-order robust planning method for active distribution network energy storage, an embodiment of the present invention also provides a computer-readable storage medium accordingly, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the two-order robust planning method for active distribution network energy storage as in the above-mentioned embodiments.

[0158] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0159] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A two-order robust planning method for energy storage in active distribution networks, characterized in that: include: Collect relevant parameters, wherein the relevant parameters include line parameters of the active distribution network, access output of distributed power sources and load forecast reference values; Based on the relevant parameters, determining the objective function and constraint conditions of the active power distribution network; An active distribution network energy storage planning model is constructed based on the relevant parameters, the objective function and the constraint conditions. The active distribution network energy storage planning model includes battery energy storage, distributed power sources, on-load tap-changing transformers and the number of capacitor banks. The active distribution network energy storage deterministic planning model includes: ， Include the location, power rating and capacity of the battery storage; Includes the tap ratio of the on-load tap-changing transformer, the number of capacitor banks, the active power and reactive power output of the battery energy storage, the active power and reactive power output of the distributed power source, and the power purchased from the main grid; Establish the budget uncertainty set of distributed generation output: in, , They represent the expected active power output and active power fluctuation of the distributed generation at node i in period t, Budget for uncertainty; A two-stage robust planning model for active distribution network energy storage is constructed based on the budget uncertainty set: Phase 1: Phase 2: ； The two-stage robust planning model of active distribution network energy storage is solved iteratively, and the battery energy storage planning result is output.

2. The two-order robust planning method for active distribution network energy storage according to claim 1 is characterized in that: The objective function of the active distribution network includes that the investment cost of battery energy storage and the system operation cost during the planning period meet the preset cost conditions.

3. The two-order robust planning method for active distribution network energy storage according to claim 1 is characterized in that: The constraints of the active distribution network include second-order cone power flow constraints, reactive power output constraints of capacitors, operation constraints of on-load tap-changing transformers, output power constraints of distributed power sources, and battery energy storage operation constraints.

4. The two-order robust planning method for active distribution network energy storage according to claim 1, characterized in that: The iterative solution of the active distribution network energy storage two-stage robust programming model includes: The main problem and sub-problems of the two-stage robust programming model of active distribution network energy storage are constructed by using a column and constraint algorithm, and are solved iteratively.

5. The two-order robust planning method for active distribution network energy storage according to claim 4 is characterized in that: The iterative solution of the active distribution network energy storage two-stage robust programming model specifically includes: Determine the decision variables of the two-stage robust programming model of the active distribution network energy storage based on the main problem; Determine the sub-problem according to the decision variables; According to the strong duality, the bi-level programming problem in the sub-problem is transformed into a single-level programming problem.

6. A two-order robust planning device for active distribution network energy storage, characterized in that: include: A collection module, used to collect relevant parameters, wherein the relevant parameters include line parameters of the active distribution network, access output of distributed power sources and load forecast reference values; A determination module, used to determine the objective function and constraint conditions of the active power distribution network based on the relevant parameters; The first model building module is used to build an active distribution network energy storage planning model based on the relevant parameters, the objective function and the constraint conditions, wherein the active distribution network energy storage planning model includes battery energy storage, distributed power sources, on-load tap-changing transformers and the number of capacitor banks, wherein the active distribution network energy storage deterministic planning model includes: ， Include the location, power rating and capacity of the battery storage; Includes the tap ratio of the on-load tap-changing transformer, the number of capacitor banks, the active power and reactive power output of the battery energy storage, the active power and reactive power output of the distributed power source, and the power purchased from the main grid; The second model building module is used to establish a budget uncertainty set for distributed power output: in, , They represent the expected active power output and active power fluctuation of the distributed generation at node i in period t, Budget for uncertainty; A two-stage robust planning model for active distribution network energy storage is constructed based on the budget uncertainty set: Phase 1: Phase 2: ； The solution module is used to iteratively solve the two-stage robust planning model of active distribution network energy storage and output the battery energy storage planning result.

7. An electronic device, characterized in that: It includes a memory and a processor, wherein the memory is used to store a program; the processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the two-order robust planning method for active distribution network energy storage as described in any one of claims 1 to 5 above.

8. A computer-readable storage medium, characterized in that: Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the two-order robust planning method for active distribution network energy storage as described in any one of claims 1 to 5 above.

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