A method, device, terminal and medium for optimizing energy storage configuration in a distribution network

Abstract

The present application discloses a distribution network energy storage configuration optimization method, device, terminal and medium, which relates to the field of distribution network energy storage technology. The solution provided by the present application is first based on the topological information of the distribution network and the distribution network system parameters, and then considers explicit reliability to construct a distribution network energy storage configuration optimization model, wherein the distribution network energy storage configuration optimization model includes: an objective function and constraints, the objective function is used to minimize the expected value of system power shortage and the weighted value of energy storage installation cost, and the constraints include: distribution network system parameter constraints, topology-related constraints and explicit reliability constraints, so as to obtain the optimal energy storage configuration scheme of the distribution network by solving the distribution network energy storage configuration optimization model. This solution avoids subsequent repeated corrections and multiple calculations by directly embedding explicit reliability constraints in the optimization solution stage for modeling, thereby reducing computational complexity, improving solution efficiency, and making distribution network planning more suitable for large-scale complex systems.

Description

Technical Field

[0001] The present application relates to the field of distribution network energy storage technology, and in particular to a distribution network energy storage configuration optimization method, device, terminal and medium. Background Art

[0002] With the rapid development of modern power systems, reliability planning methods for distribution networks, especially the configuration planning of energy storage equipment in distribution networks, have been a research focus. Currently, the mainstream distribution network planning methods that consider reliability include post-planning reliability assessment and correction methods and heuristic optimization planning methods. These two methods typically adopt a "plan first, evaluate later" approach, namely, after completing the initial distribution network planning, the reliability is post-evaluated and corrected. Furthermore, to achieve reliability assessment, these methods typically rely on analytical methods or Monte Carlo simulations, requiring multiple solutions to the distribution network planning model and post-reliability assessments. This is essentially a "quasi-exhaustive" strategy. Faced with the nonlinear and non-convex nature of distribution networks and high-dimensional mixed-integer programming problems, the solution process is prone to excessive computational effort, slow convergence, and difficulty in obtaining a global optimal solution, thereby reducing the effectiveness of the optimization plan. Summary of the Invention

[0003] The present application provides a distribution network energy storage configuration optimization method, device, terminal and medium for solving the technical problem of low efficiency of existing distribution network planning optimization operations.

[0004] To solve the above technical problems, the first aspect of the present application provides a method for optimizing energy storage configuration in a distribution network, comprising:

[0005] Obtain topological information of the distribution network and distribution network system parameter information;

[0006] Based on the topology information and the distribution network system parameter information, an explicit reliability-based distribution network energy storage configuration optimization model is constructed, so that an optimal energy storage configuration scheme for the distribution network is obtained by solving the distribution network energy storage configuration optimization model. The distribution network energy storage configuration optimization model includes: an objective function and constraints. The objective function is used to minimize the weighted value of the expected value of system power shortage and the energy storage installation cost. The constraints include: distribution network system parameter constraints, topology-related constraints, and explicit reliability constraints.

[0007] Preferably, the distribution network system parameter information includes: upper and lower limits of distributed power output, upper and lower limits of energy storage charging and discharging power, maximum state of charge of energy storage, load data, line impedance and upper and lower limit data of node voltage.

[0008] Preferably, the distribution network system parameter constraints include: distributed power supply constraints, energy storage constraints, load constraints and power flow constraints.

[0009] Preferably, the objective function is specifically:

[0010]

[0011]

[0012] Where EENS is the expected value of system power shortage; is a Boolean variable used to indicate whether node i is equipped with an energy storage device. is the average cost of energy storage equipment; To convert power loss into economic loss, is the active load of load node i at time step t when it is operating normally, is the virtual power flow variable of node i under fault scenario w at time step t.

[0013] Preferably, the explicit reliability constraint is specifically:

[0014]

[0015]

[0016] Where, is the customer outage duration of node i; SAIDI is the average outage duration index of the system; is the number of users of node i; It is the upper limit of SAIDI.

[0017] Preferably, the topology-related constraints are specifically:

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027] Where, is the set of nodes in the distribution network, is a collection of fault scenarios, is the set of branches in the distribution network, is the set of time steps, is the set of DG nodes, is the set of fault branches under fault scenario w; is the set of substation nodes; is the virtual power flow variable of branch ij under fault scenario w at time step t; It is a Boolean variable indicating the switch status of branch ij during normal operation; and are the non-negative intermediate variables of branch ij under fault scenario w at time step t, is the cumulative sum of the non-negative intermediate variables of the branch ji formed by node i and each upstream node j of node i under the fault scenario w time step t, is the cumulative sum of the non-negative intermediate variables of the branch ik formed by node i and each downstream node k of node i under the fault scenario w time step t; and Respectively represent the number of all node sets and the number of substation node sets, is the virtual power flow variable of node i under fault scenario w at time step t, is the Boolean variable of the on / off state of the switch of branch ij under fault scenario w time step t, is the virtual power flow variable at the nodes x at both ends of branch ij under fault scenario w at time step t.

[0028] A second aspect of the present application provides a distribution network energy storage configuration optimization device, comprising:

[0029] A distribution network information acquisition unit, configured to acquire topology information of the distribution network and distribution network system parameter information of the distribution network;

[0030] An energy storage configuration optimization model construction unit is configured to construct, based on the topology information and the distribution network system parameter information, a distribution network energy storage configuration optimization model based on explicit reliability, so as to obtain an optimal energy storage configuration scheme for the distribution network by solving the distribution network energy storage configuration optimization model. The distribution network energy storage configuration optimization model includes an objective function and constraints, wherein the objective function is used to minimize the weighted value of the expected value of system power shortage and the energy storage installation cost, and the constraints include distribution network system parameter constraints, topology-related constraints, and explicit reliability constraints.

[0031] Preferably, the distribution network system parameter information includes: upper and lower limits of distributed power output, upper and lower limits of energy storage charging and discharging power, maximum state of charge of energy storage, load data, line impedance and upper and lower limit data of node voltage;

[0032] The distribution network system parameter constraints include: distributed power supply constraints, energy storage constraints, load constraints and flow constraints.

[0033] A third aspect of the present application provides a distribution network energy storage configuration optimization terminal, comprising: a memory and a processor, the memory and the processor being connected via a communication bus;

[0034] The memory is used to store program code, and the program code is used to implement the distribution network energy storage configuration optimization method provided in the first aspect of the present application;

[0035] The processor is configured to read and execute the program code.

[0036] The fourth aspect of the present application provides a computer-readable storage medium, in which program code is stored. The program code is used to be read and executed by a processor to implement the distribution network energy storage configuration optimization method provided in the first aspect of the present application.

[0037] It can be seen from the above technical solutions that this application has the following advantages:

[0038] The solution provided in this application is first based on the topological information and system parameters of the distribution network, and then considers explicit reliability to construct an energy storage configuration optimization model based on the distribution network, wherein the energy storage configuration optimization model of the distribution network includes: an objective function and constraints, the objective function is used to minimize the weighted value of the expected value of the system power shortage and the energy storage installation cost, and the constraints include: distribution network system parameter constraints, topology-related constraints and explicit reliability constraints, so as to obtain the optimal energy storage configuration scheme of the distribution network by solving the energy storage configuration optimization model of the distribution network. This solution avoids subsequent repeated corrections and multiple calculations by directly embedding explicit reliability constraints in the optimization solution stage for modeling, thereby reducing computational complexity and improving solution efficiency, making distribution network planning more suitable for large-scale complex systems. At the same time, while ensuring solution efficiency, it improves the global optimality of the planning scheme, ensuring that the energy storage configuration scheme achieves the optimal balance between reliability and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1A flow chart of an embodiment of a method for optimizing energy storage configuration in a distribution network provided in this application.

[0041] Figure 2 This is a structural diagram of an embodiment of a distribution network energy storage configuration optimization device provided in this application.

[0042] Figure 3 This is a structural diagram of an embodiment of a distribution network energy storage configuration optimization terminal provided in this application. DETAILED DESCRIPTION

[0043] The embodiments of the present application provide a distribution network energy storage configuration optimization method, device, terminal and medium for solving the technical problem of low efficiency of existing distribution network planning optimization operations.

[0044] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0045] First, a detailed description of an embodiment of a distribution network energy storage configuration optimization method provided by this application is as follows:

[0046] See also Figure 1 , an embodiment of the present application provides a method for optimizing energy storage configuration in a distribution network, comprising:

[0047] Step 101: Obtain topology information and system parameter information of the distribution network;

[0048] Step 102: constructing an explicit reliability-based distribution network energy storage configuration optimization model based on the topology information and distribution network system parameter information, so as to obtain an optimal energy storage configuration solution for the distribution network by solving the distribution network energy storage configuration optimization model;

[0049] The distribution network energy storage configuration optimization model includes: objective function and constraints. The objective function is used to minimize the weighted value of the expected value of system power shortage and energy storage installation cost. The constraints include: distribution network system parameter constraints, topology-related constraints and explicit reliability constraints.

[0050] It should be noted that according to the method provided in this embodiment, first, for the distribution network where energy storage equipment deployment needs to be planned, the topology information of the distribution network and the distribution network system parameter information are obtained, mainly including: the upper and lower limits of distributed power output, the upper and lower limits of energy storage charging and discharging power, the maximum charge state of energy storage, load data, line impedance, and the upper and lower limits of node voltage data.

[0051] Then, based on the topology information and distribution network system parameters obtained in the previous step, a new distribution network energy storage configuration optimization model considering explicit reliability is constructed, including constraints and an objective function. The constraints include distributed generation constraints, energy storage constraints, load constraints, power flow constraints, topology-related constraints, and explicit reliability expression constraints. The objective function is to minimize the weighted value of the expected system energy shortage (EENS) and the energy storage installation cost.

[0052] More specifically, regarding the distribution network energy storage configuration optimization model provided in this embodiment, examples of expressions for various constraints are as follows:

[0053] 1) Distributed power constraints:

[0054] (1)

[0055] (2)

[0056] Where: DG stands for distributed power generation, It is a collection of fault scenarios, including typical fault scenarios such as line short circuit and equipment failure. The specific fault scenarios can be determined based on the historical fault data and reliability analysis of the distribution network. It is a set of time steps. The time step length can be set according to actual needs. For example, you can set a time step of 10 minutes, or 15 minutes or other time values ​​as a time step to cover a planning period of 24 hours or longer. is the set of DG nodes; is the active power output of DG node i under fault scenario w at time step t; and are the upper and lower limits of active power output of DG node i respectively; is the reactive power output of DG node i under fault scenario w at time step t; and are the upper and lower limits of reactive power output of DG node i respectively.

[0057] Equations (1) and (2) are the upper and lower limit constraints of the active and reactive output of distributed generation, respectively.

[0058] 2) Energy storage constraints:

[0059] (3)

[0060] (4)

[0061] (5)

[0062] (6)

[0063] (7)

[0064] (8)

[0065] Where: ESS stands for energy storage equipment, is the set of ESS nodes; and are the charging power and discharging power of ESS node i under fault scenario w at time step t, respectively; and are the upper limit of charging power and the upper limit of discharging power of ESS node i respectively; and are Boolean variables indicating whether ESS node i is charging or discharging under fault scenario w at time step t, where 1 indicates charging / discharging, and 0 indicates not charging / discharging; is the active power of ESS node i under fault scenario w at time step t; and are the upper and lower limits of the state of charge of ESS node i respectively; is the state of charge of ESS node i under fault scenario w at time step t; and are the charging and discharging power coefficients of ESS node i, respectively. They can be obtained based on the efficiency parameters in the energy storage equipment technical manual or determined by the test methods specified in industry standards (such as GB / T36549-2018); is the virtual power flow variable of node i under fault scenario w at time step t. If it is 1, it means the node has virtual power flow, otherwise it means the node has no virtual power flow; A Boolean variable indicating whether node i is equipped with energy storage. A value of 1 indicates that the node is equipped with energy storage, whereas a value of 1 indicates that the node is not equipped with energy storage.

[0066] Among them, constraints (3) and (4) are the upper and lower limits of the energy storage's charge and discharge power; constraints (5)-(7) are the upper and lower limits of the energy storage's state of charge and the calculation formula; constraint (8) indicates that after the energy storage is installed, the energy storage will not be turned on when the energy storage node is powered off, and after power is on, it can only be in one of the charging / discharging states.

[0067] 3) Load constraints:

[0068] (9)

[0069] (10)

[0070] Where: and are respectively the active and reactive loads of load node i at time step t when it is operating normally; and are the active and reactive loads of load node i under fault scenario w at time step t; is the collection of load nodes.

[0071] Constraints (9) and (10) restrict the load to be restored only when the node is powered on.

[0072] 4) Power flow constraints:

[0073] (11)

[0074] (12)

[0075] (13)

[0076] (14)

[0077] (15)

[0078] (16)

[0079] (17)

[0080] (18)

[0081] (19)

[0082] Where: It is the collection of all nodes in the distribution network, including substation nodes, distributed power generation nodes, load nodes, etc. It is the collection of all branches in the distribution network. Branches include transmission lines, cables and other components used to connect two nodes. and are the active and reactive powers of node i under fault scenario w at time step t; and are the active and reactive powers flowing through branch ij under fault scenario w at time step t; and are the active and reactive outputs of substation node i under fault scenario w at time step t; is the square of the voltage amplitude at node i under fault scenario w at time step t; is the voltage amplitude of node i under fault scenario w at time step t; and are the upper and lower limits of the voltage at node i respectively; is the maximum power allowed to flow through branch ij; is a Boolean variable representing the on / off state of the switch of branch ij at time step t of fault scenario w, where 1 indicates the switch is closed and 0 indicates the switch is open. ij is the resistance value of branch ij, x ij is the reactance value of branch ij, M is an arbitrarily large positive number based on the "big M method", and its value is usually 10 5 , used to linearize the logical relationships in the constraints, such as the on and off states of switches.

[0083] Equations (11)-(14) are the power balance constraints of distribution network nodes; Equation (15) is the voltage drop constraint; Equations (16) and (17) are the upper and lower limit constraints of node voltage; and Equations (18)-(19) are the branch capacity constraints.

[0084] 5) Topology-related constraints:

[0085] (20)

[0086] (twenty one)

[0087] (twenty two)

[0088] (twenty three)

[0089] (twenty four)

[0090] (25)

[0091] (26)

[0092] (27)

[0093] (28)

[0094] Where, is the set of nodes in the distribution network, is a collection of fault scenarios, is the set of branches in the distribution network, is the set of time steps, is the set of fault branches under fault scenario w; is the set of substation nodes; is the virtual power flow variable of branch ij under fault scenario w at time step t; It is a Boolean variable indicating the switch status of branch ij during normal operation; and are the non-negative intermediate variables of branch ij under fault scenario w at time step t, is the cumulative sum of the non-negative intermediate variables of the branch ji formed by node i and each upstream node j of node i under the fault scenario w time step t, is the cumulative sum of the non-negative intermediate variables of the branch ik formed by node i and each downstream node k of node i under the fault scenario w time step t; and Respectively represent the number of all node sets and the number of substation node sets, is the virtual power flow variable of node i under fault scenario w at time step t, is the Boolean variable of the on / off state of the switch of branch ij under fault scenario w time step t, is the virtual power flow variable at the nodes x at both ends of branch ij under fault scenario w at time step t.

[0095] Formula (20) indicates that the virtual power flow through the fault branch is always 0; Formula (21) indicates that the virtual power flow through the substation node and the DG node is always 1; Formula (22) indicates that when the branch switch is disconnected, the virtual power flow through the branch is 0; Formula (23) indicates that when the branch switch is closed, the virtual power flow through the nodes at both ends of the branch is consistent with the virtual power flow through the branch; Formula (24) indicates that at time step t = 0, branch ij is in the fault isolation state, at which time only the switch is allowed to be disconnected; Formula (25) indicates that the node that has restored power supply is not allowed to be powered off again; Formulas (26)-(28) are the radial operation constraints of the distribution network.

[0096] 6) Display reliability constraints:

[0097] (29)

[0098] (30)

[0099] Where: is the customer outage duration of node i; SAIDI is the average outage duration index of the system; is the number of users of node i; k is the upper limit of SAIDI, which can be set according to IEEE Std 1366 or user-defined reliability targets. For example, when the system requires SAIDI not to exceed 100 minutes, k is SAIDI Can be set to 100 (min) or 6000 (s).

[0100] More specifically, regarding the distribution network energy storage configuration optimization model provided in this embodiment, an example expression of its objective function is as follows:

[0101] (31)

[0102] (32)

[0103] Where EENS is the expected value of system power shortage, is the average cost of the energy storage equipment, which can be calculated based on the purchase price of the energy storage equipment; The value of the constant used to convert power loss into economic loss can be determined based on the local electricity price and load importance. For example, it can be calculated using the weighted sum formula: , where α is the electricity price weight, λ is the local electricity price (yuan / kWh), β is the load importance weight, and γ is the load importance level or a constant preset by the administrator to measure load importance. In some implementation scenarios, conventional summation or product calculation can also be used, such as It is understandable that the distribution network energy storage configuration optimization model provided in this embodiment is a mixed integer linear programming model, which can be solved by a solver, and the optimal energy storage installation location solution of the distribution network can be obtained based on the solution results.

[0104] The new distribution network energy storage optimization configuration solution based on explicit reliability embedding provided in this embodiment has the following advantages:

[0105] By directly embedding explicit reliability modeling in the optimization solution phase, subsequent repeated corrections and multiple calculations are avoided, thereby reducing computational complexity and improving solution efficiency. This makes distribution network planning more suitable for large-scale complex systems. It overcomes the shortcomings of the traditional "planning-post-evaluation" method, which requires multiple iterations to solve the distribution network optimization model and performs reliability post-evaluation after each solution, resulting in large computational complexity and a lengthy solution process. This improves the efficiency of distribution network planning and avoids redundant calculations.

[0106] By fine-tuning the impact of topology changes on reliability, we ensure that the reliability level of the system can be accurately assessed under different distribution network structures, thereby providing a more adaptable optimization solution. This overcomes the shortcomings of existing reliability assessment methods, which are mostly based on fixed topology structures and cannot effectively cope with dynamic topology adjustments, resulting in a limited scope of application of reliability analysis. This method enhances the adaptability to distribution network topology changes and improves the accuracy of reliability assessment.

[0107] By coupling energy storage configuration with distribution network reliability objectives, the optimized system ensures that the configured energy storage system is not only economical but also provides higher power supply reliability in the event of faults or load fluctuations. This overcomes the traditional energy storage planning methods that focus primarily on economics or use heuristic optimization, but fail to explicitly consider the impact of energy storage on system reliability during the optimization process, which can easily lead to insufficient reliability of the final solution.

[0108] The optimization method uses explicit reliability embedding to improve the global optimality of the planning scheme while ensuring solution efficiency, ensuring that the energy storage configuration achieves the optimal balance between reliability and economy. This overcomes the mainstream heuristic optimization method's reliance on empirical rules, which often makes it difficult to achieve a globally optimal solution and may even result in suboptimal or locally optimal solutions in some cases.

[0109] In summary, the solution provided in this embodiment is suitable for modern distribution networks with high penetration of distributed energy, dynamic loads and intelligent scheduling needs. It can provide a more adaptable and robust energy storage optimization configuration solution when facing complex scenarios such as renewable energy fluctuations, load uncertainty, and grid topology adjustments, thereby improving the overall stability and reliability of the power grid.

[0110] The above is a detailed description of an embodiment of a distribution network energy storage configuration optimization method provided by this application. The following is a detailed description of an embodiment of a distribution network energy storage configuration optimization device provided by this application.

[0111] See also Figure 2 , an embodiment of the present application provides a distribution network energy storage configuration optimization device, comprising:

[0112] The distribution network information acquisition unit 201 is used to acquire the topology information of the distribution network and the distribution network system parameter information of the distribution network;

[0113] The energy storage configuration optimization model construction unit 202 is used to construct a distribution network energy storage configuration optimization model based on explicit reliability based on topology information and distribution network system parameter information, so as to obtain the optimal energy storage configuration plan for the distribution network by solving the distribution network energy storage configuration optimization model. The distribution network energy storage configuration optimization model includes: an objective function and constraints. The objective function is used to minimize the weighted value of the expected value of system power shortage and the energy storage installation cost. The constraints include: distribution network system parameter constraints, topology-related constraints, and explicit reliability constraints.

[0114] More specifically, the distribution network system parameter information includes: upper and lower limits of distributed power generation output, upper and lower limits of energy storage charge and discharge power, maximum state of charge of energy storage, load data, line impedance and upper and lower limits of node voltage data;

[0115] Distribution network system parameter constraints include: distributed generation constraints, energy storage constraints, load constraints and flow constraints.

[0116] like Figure 3 As shown, the present application also provides an embodiment of a terminal for optimizing energy storage configuration in a distribution network, comprising: a memory 33 and a processor 31 , the memory and the processor being connected via a communication bus 34 ;

[0117] The memory 33 is used to store program codes, and the program codes are used to implement the distribution network energy storage configuration optimization method provided in the above embodiment;

[0118] The processor 31 is used to read and execute program codes.

[0119] The present application provides an embodiment of a computer-readable storage medium, in which program code is stored. The program code is used to be read and executed by a processor to implement the distribution network energy storage configuration optimization method provided in the above embodiment.

[0120] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the terminals, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0122] The terms "first," "second," "third," "fourth," and the like (if any) in the specification of the present application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present application described herein, for example, can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus.

[0123] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0124] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0125] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0126] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0127] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing energy storage configuration in a distribution network, characterized in that: include: Obtain topological information of the distribution network and distribution network system parameter information; Based on the topology information and the distribution network system parameter information, a distribution network energy storage configuration optimization model based on explicit reliability is constructed, so that an optimal energy storage configuration scheme for the distribution network is obtained by solving the distribution network energy storage configuration optimization model, wherein the distribution network energy storage configuration optimization model includes: an objective function and constraints, and the constraints include: distribution network system parameter constraints, topology-related constraints, and explicit reliability constraints; The objective function is specifically: ； ； Where EENS is the expected value of system power shortage; is a Boolean variable used to indicate whether node i is equipped with an energy storage device. is the average cost of energy storage equipment; To convert power loss into economic loss, is the active load of load node i at time step t when it is operating normally, is the virtual power flow variable of node i under fault scenario w at time step t, is the set of time steps, is the set of ESS nodes, is the set of load nodes, is a collection of fault scenarios; The explicit reliability constraints are specifically: ； ； Where, is the customer outage duration of node i; SAIDI is the average outage duration index of the system; is the number of users of node i; is the upper limit of SAIDI, is a collection of fault scenarios, is the set of time steps, is the set of load nodes, is the virtual power flow variable of node i under fault scenario w at time step t.

2. A method for optimizing energy storage configuration in a distribution network according to claim 1, characterized in that: The distribution network system parameter information includes: upper and lower limits of distributed power output, upper and lower limits of energy storage charging and discharging power, maximum state of charge of energy storage, load data, line impedance and upper and lower limits of node voltage data.

3. A method for optimizing energy storage configuration in a distribution network according to claim 2, characterized in that: The distribution network system parameter constraints include: distributed power supply constraints, energy storage constraints, load constraints and flow constraints.

4. A method for optimizing energy storage configuration in a distribution network according to claim 1, characterized in that: The topology-related constraints are specifically: ； ； ； ； ； ； ； ； ； Where, is the set of nodes in the distribution network, is a collection of fault scenarios, is the set of branches in the distribution network, is the set of time steps, is the set of DG nodes, is the set of fault branches under fault scenario w; is the set of substation nodes; is the virtual power flow variable of branch ij under fault scenario w at time step t; It is a Boolean variable indicating the switch status of branch ij during normal operation; and are the non-negative intermediate variables of branch ij under fault scenario w at time step t, is the cumulative sum of the non-negative intermediate variables of the branch ji formed by node i and each upstream node j of node i under the fault scenario w time step t, is the cumulative sum of the non-negative intermediate variables of the branch ik formed by node i and each downstream node k of node i under the fault scenario w time step t; and Respectively represent the number of all node sets and the number of substation node sets, is the virtual power flow variable of node i under fault scenario w at time step t, is the Boolean variable of the on / off state of the switch of branch ij under fault scenario w time step t, is the virtual power flow variable at the nodes x at both ends of branch ij under fault scenario w at time step t.

5. A distribution network energy storage configuration optimization device, characterized in that: include: A distribution network information acquisition unit, used to acquire topological information and system parameters of the distribution network; an energy storage configuration optimization model construction unit, configured to construct, based on the topology information and the distribution network system parameter information, an explicit reliability-based distribution network energy storage configuration optimization model, so as to obtain an optimal energy storage configuration scheme for the distribution network by solving the distribution network energy storage configuration optimization model, wherein the distribution network energy storage configuration optimization model includes: an objective function and constraints, wherein the constraints include: distribution network system parameter constraints, topology-related constraints, and explicit reliability constraints; The objective function is specifically: ； ； Where EENS is the expected value of system power shortage; is a Boolean variable used to indicate whether node i is equipped with an energy storage device. is the average cost of energy storage equipment; To convert power loss into economic loss, is the active load of load node i at time step t when it is operating normally, is the virtual power flow variable of node i under fault scenario w at time step t, is the set of time steps, is the set of ESS nodes, is the set of load nodes, is a collection of fault scenarios; The explicit reliability constraints are specifically: ； ； Where, is the customer outage duration of node i; SAIDI is the average outage duration index of the system; is the number of users of node i; is the upper limit of SAIDI, is a collection of fault scenarios, is the set of time steps, is the set of load nodes, is the virtual power flow variable of node i under fault scenario w at time step t.

6. A distribution network energy storage configuration optimization device according to claim 5, characterized in that: The distribution network system parameter information includes: upper and lower limits of distributed power output, upper and lower limits of energy storage charge and discharge power, maximum state of charge of energy storage, load data, line impedance and upper and lower limits of node voltage; The distribution network system parameter constraints include: distributed power supply constraints, energy storage constraints, load constraints and flow constraints.

7. A distribution network energy storage configuration optimization terminal, characterized in that: include: a memory and a processor, wherein the memory and the processor are connected via a communication bus; The memory is used to store program code, and the program code is used to implement the distribution network energy storage configuration optimization method according to any one of claims 1 to 4; The processor is configured to read and execute the program code.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program code, and the program code is used to be read and executed by a processor to implement the distribution network energy storage configuration optimization method according to any one of claims 1 to 4.

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