Power distribution network energy storage configuration optimization method and device, terminal and medium

Abstract

The invention discloses a power distribution network energy storage configuration optimization method and device, a terminal and a medium, and relates to the technical field of power distribution network energy storage. According to the scheme provided by the invention, an energy storage configuration optimization model based on a power distribution network is firstly constructed based on topological information of the power distribution network and system parameters of the power distribution network by considering explicit reliability, the power distribution network energy storage configuration optimization model comprises an objective function and constraint conditions, the objective function is used for minimizing an expected value of system power shortage and a weighted value of energy storage installation cost, and the constraint conditions comprise power distribution network system parameter constraint, topology correlation constraint and explicit reliability constraint, so that the energy storage configuration optimization of the power distribution network can be realized through calculation of the power distribution network energy storage configuration optimization model. And obtaining an optimal energy storage configuration scheme of the power distribution network. According to the scheme, modeling is carried out by directly embedding the explicit reliability constraint in the optimization solution stage, and subsequent repeated correction and multiple times of calculation are avoided, so that the calculation complexity is reduced, the solution efficiency is improved, and power distribution network planning is more suitable for a large-scale complex system.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network energy storage, and particularly to a method, device, terminal and medium for optimizing the configuration of distribution network energy storage. Background Art

[0002] In the rapid development process of modern power systems, the reliability planning method of distribution networks, especially the configuration planning of distribution network energy storage devices, has always been the focus of research. Currently, the mainstream distribution network planning methods considering reliability mainly include: the post-evaluation and correction method of reliability evaluation after planning and the heuristic optimization planning method. These two types of methods usually adopt the idea of "planning first and then evaluating", that is, after completing the preliminary distribution network planning, the reliability is evaluated and corrected afterwards. Moreover, to achieve reliability evaluation, these methods usually rely on the analytical method or the Monte Carlo simulation method, and need to solve the distribution network planning model multiple times and conduct reliability post-evaluation. Essentially, it is a "quasi-exhaustive" strategy. Facing the non-linear, non-convex and high-dimensional mixed integer programming problems of distribution networks, the solution process is extremely prone to problems such as excessive computational complexity, slow convergence speed, and difficulty in obtaining the global optimal solution, thus reducing the effectiveness of the optimization planning. Summary of the Invention

[0003] The present application provides a method, device, terminal and medium for optimizing the configuration of distribution network energy storage, which is used to solve the technical problem of low operation efficiency of the existing distribution network planning optimization.

[0004] To solve the above technical problem, in the first aspect of the present application, a method for optimizing the configuration of distribution network energy storage is provided, including:

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

[0006] According to the topological information and the distribution network system parameter information, constructing a distribution network energy storage configuration optimization model based on explicit reliability, so as to obtain the optimal energy storage configuration scheme of the distribution network through the solution of the distribution network energy storage configuration optimization model. Among them, the distribution network energy storage configuration optimization model includes: an objective function and constraint conditions. 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 constraint conditions 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 charge and discharge 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 constraints, energy storage constraints, load constraints and power flow constraints.

[0009] Preferably, the objective function is specifically:

[0010]

[0011]

[0012] In the formula, EENS is the expected value of power shortage of the system; is a Boolean variable used to represent whether the energy storage device is installed at node i, is the average cost of the energy storage device; is a constant for converting power loss into economic loss, is the active load of load node i at time step t under normal operation, is the virtual power flow variable of node i at time step t in fault scenario w.

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

[0014]

[0015]

[0016] In the formula, is the customer interruption duration of node i; SAIDI is the system average interruption duration index; is the number of users of node i; is the upper limit value of SAIDI.

[0017] Preferably, the topology-related constraint is specifically:

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027] In the formula, is the set of nodes in the distribution network, is a set of fault scenarios, is a set of branches in the distribution network, is a set of time steps, is a set of DG nodes, is the set of fault branches under fault scenario w; is a set of substation nodes; is the virtual power flow variable of branch ij at time step t under fault scenario w; is a Boolean variable of the switch state when branch ij operates normally; and are respectively the non - negative intermediate variables of branch ij at time step t under fault scenario w, is the sum of the non - negative intermediate variables of the branches ji formed by node i and its upstream nodes j at time step t under fault scenario w, is the sum of the non - negative intermediate variables of the branches ik formed by node i and its downstream nodes k at time step t under fault scenario w; 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 at time step t under fault scenario w, is the on - off state Boolean variable of the switch of branch ij at time step t under fault scenario w, is the virtual power flow variable of node x at both ends of branch ij at time step t under fault scenario w.

[0028] The second aspect of this application provides a distribution network energy storage configuration optimization device, including:

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

[0030] An energy storage configuration optimization model construction unit, configured to construct an explicit - reliability - based distribution network energy storage configuration optimization model according to the topological information and the distribution network system parameter information, so as to obtain the optimal energy storage configuration plan of the distribution network through the solution of the distribution network energy storage configuration optimization model. Among them, the distribution network energy storage configuration optimization model includes: an objective function and constraint conditions. 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 constraint conditions 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 generation output, upper and lower limits of energy storage charge - discharge 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 source constraints, energy storage constraints, load constraints, and power flow constraints.

[0033] The third aspect of this application provides an optimization terminal for energy storage configuration in a distribution network, including: a memory and a processor, and the memory and the processor are connected through a communication bus;

[0034] The memory is used to store program codes, and the program codes are used to implement the energy storage configuration optimization method provided in the first aspect of this application;

[0035] The processor is used to read and execute the program codes.

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

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

[0038] The solution provided by this application first is based on the topological information of the distribution network and the distribution network system parameters, and then constructs an energy storage configuration optimization model for the distribution network considering explicit reliability. Among them, the energy storage configuration optimization model for the distribution network includes: an objective function and constraint conditions. 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 constraint conditions include: distribution network system parameter constraints, topology-related constraints, and explicit reliability constraints, so as to obtain the optimal energy storage configuration scheme for the distribution network through the solution of the energy storage configuration optimization model for the distribution network. This solution directly embeds explicit reliability constraints into the modeling during the optimization solution stage, avoiding subsequent repeated corrections and multiple calculations, thereby reducing the computational complexity and improving the solution efficiency, making the distribution network planning more applicable to large-scale complex systems. At the same time, while ensuring the 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. Description of the Drawings

[0039] In order to more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0040] Figure 1A flowchart of an embodiment of an optimized method for configuring energy storage in a distribution network provided by this application.

[0041] Figure 2 A structural diagram of an embodiment of an optimized device for configuring energy storage in a distribution network provided by this application.

[0042] Figure 3 A structural diagram of an embodiment of an optimized terminal for configuring energy storage in a distribution network provided by this application. Detailed implementation manners

[0043] Embodiments of this application provide an optimized method, device, terminal, and medium for configuring energy storage in a distribution network, which are used to solve the technical problem of low operation efficiency in the existing distribution network planning and optimization.

[0044] To make the invention objectives, features, and advantages of this application more obvious and understandable, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the embodiments described below are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

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

[0046] Please refer to Figure 1 , an optimized method for configuring energy storage in a distribution network provided by an embodiment of this application includes:

[0047] Step 101, obtain the topological information of the distribution network and the distribution network system parameter information;

[0048] Step 102, construct an optimized model for configuring energy storage in the distribution network based on explicit reliability according to the topological information and the distribution network system parameter information, so as to obtain the optimal energy storage configuration plan for the distribution network through the solution of the optimized model for configuring energy storage in the distribution network.

[0049] Among them, the optimized model for configuring energy storage in the distribution network includes: an objective function and constraint conditions. 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 constraint conditions 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 that needs to plan the deployment of energy storage devices, the topological structure information and distribution network system parameter information of the distribution network are obtained, mainly including: 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 limit data of node voltage.

[0051] Then, based on the topological structure information and distribution network system parameter information obtained in the previous step, a new distribution network energy storage configuration optimization model considering explicit reliability is constructed, including constraint conditions and an objective function. Among them, the constraint conditions include: distributed power 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 energy not served (EENS) of the system and the energy storage installation cost.

[0052] More specifically, regarding the distribution network energy storage configuration optimization model provided in this embodiment, the expression examples of its respective constraint conditions are as follows:

[0053] 1) Distributed power constraints:

[0054] (1)

[0055] (2)

[0056] In the formula: DG represents the distributed power, is the set of fault scenarios, and the fault scenarios include typical fault scenarios such as line short circuits and equipment failures, which can be specifically determined according to the historical fault data and reliability analysis of the distribution network; is the set of time steps, and the time step length can be set according to actual needs. For example, 10 minutes can be set as one time step, or 15 minutes or other time values can be set as one time step, covering a planning period of 24 hours a day or longer; is the set of DG nodes; is the active power output of DG node i at time step t in fault scenario w; and are respectively the upper and lower limits of the active power output of DG node i; is the reactive power output of DG node i at time step t in fault scenario w; and are respectively the upper and lower limits of the reactive power output of DG node i.

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

[0058] 2) Energy storage constraints:

[0059] (3)

[0060] (4)

[0061] (5)

[0062] (6)

[0063] (7)

[0064] (8)

[0065] In the formula: ESS represents the energy storage device, is the set of ESS nodes; and are the charging power and discharging power of ESS node i at time step t in fault scenario w, respectively; and are the upper limits of the charging power and discharging power of ESS node i, respectively; and are Boolean variables indicating whether ESS node i is charging or discharging at time step t in fault scenario w. A value of 1 means it is charging / discharging, and a value of 0 means it is not charging / discharging; is the active power of ESS node i at time step t in fault scenario w; 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 at time step t in fault scenario w; and are the charging and discharging power coefficients of ESS node i, respectively, which can be obtained according to the efficiency parameters in the technical manual of the energy storage device or determined by the test methods specified in industry standards (such as GB / T36549-2018); is the virtual power flow variable of node i at time step t in fault scenario w. A value of 1 means that the node has virtual power flow, and vice versa; is a Boolean variable indicating whether node i is equipped with energy storage. A value of 1 means that the node is equipped with energy storage, and vice versa.

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

[0067] 3) Load constraint:

[0068] (9)

[0069] (10)

[0070] Wherein: and are the active and reactive power loads of load node i at time step t during normal operation, respectively; and are the active and reactive power loads of load node i at time step t in fault scenario w, respectively; is the set of load nodes.

[0071] Constraints (9) and (10) limit the restoration of the load only when the node is energized.

[0072] 4) Power flow constraints:

[0073] (11)

[0074] (12)

[0075] (13)

[0076] (14)

[0077] (15)

[0078] (16)

[0079] (17)

[0080] (18)

[0081] (19)

[0082] Wherein: is the set of all nodes in the distribution network, including substation nodes, distributed power generation nodes, load nodes, etc.; is the set of all branches in the distribution network. Branches include components such as transmission lines and cables used to connect two nodes; and are the active and reactive power of node i at time step t in fault scenario w, respectively; and are the active and reactive power flowing through branch ij at time step t in fault scenario w, respectively; and are the active and reactive power outputs of substation node \(i\) at time step \(t\) in fault scenario \(w\), respectively; is the square of the voltage magnitude of node \(i\) at time step \(t\) in fault scenario \(w\); is the voltage magnitude of node \(i\) at time step \(t\) in fault scenario \(w\); and are the upper and lower voltage limits of node \(i\), respectively; is the maximum power that can flow through branch \(ij\); is the on - off state Boolean variable of the switch of branch \(ij\) at time step \(t\) in fault scenario \(w\), where 1 means the switch is closed and 0 means the switch is open, \(r\) ij is the resistance value of branch \(ij\), \(x\) ij is the reactance value of branch \(ij\), and \(M\) is an arbitrarily large positive number based on the "big M method", and its value is usually \(10\) 5 , which is used to linearize the logical relationships in the constraint conditions, such as the on - off state of the switch.

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

[0084] 5) Topology - related constraints:

[0085] (20)

[0086] (21)

[0087] (22)

[0088] (23)

[0089] (24)

[0090] (25)

[0091] (26)

[0092] (27)

[0093] (28)

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

[0095] Equation (20) indicates that the virtual power flow through the fault branch is always 0; Equation (21) indicates that the virtual power flow of substation nodes and DG nodes is always 1; Equation (22) indicates that when the branch switch is off, the virtual power flow through the branch is 0; Equation (23) indicates that when the branch switch is on, the virtual power flow through the two - end nodes of the branch is the same as the virtual power flow through the branch; Equation (24) indicates that at time step t = 0, branch ij is in the fault isolation state, and only the switch is allowed to be disconnected at this time; Equation (25) indicates that the nodes that have been powered on are not allowed to be powered off again; Equations (26) - (28) are the radial operation constraints of the distribution network.

[0096] 6) Display the reliability constraint:

[0097] (29)

[0098] (30)

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

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

[0101] (31)

[0102] (32)

[0103] In the formula, EENS is the expected value of the system's power shortage, is the average cost of the energy storage device, and its value can be calculated according to the purchase price of the energy storage device; is a constant for converting power loss into economic loss, and its value can be determined according to the local electricity price and load importance. For example, it can be calculated according to the weighted sum calculation formula: , where α is the electricity price weight, λ is the local electricity price (yuan / kWh), β is the load importance weight, γ is the load importance level or a constant preset by the administrator to measure the load importance. In some implementation scenarios, it can also be calculated by conventional summation or multiplication methods, such as . It can be understood 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 plan of the distribution network can be obtained according to the solution result.

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

[0105] By directly embedding explicit reliability modeling in the optimization solution stage, subsequent repeated corrections and multiple calculations are avoided, thereby reducing the computational complexity and improving the solution efficiency, making the distribution network planning more applicable to large-scale complex systems. It overcomes the shortcomings of the traditional "planning - post-evaluation" method that requires multiple iterative solutions of the distribution network optimization model and performs reliability post-evaluation after each solution, with a large amount of calculation and a long solution process, achieving the effect of improving the distribution network planning solution efficiency and avoiding redundant calculations;

[0106] By refining the modeling of the impact of topological changes on reliability, it ensures that the reliability level of the system can be accurately evaluated under different distribution network structures, thereby providing a more adaptable optimization plan. This overcomes the shortcoming that most existing reliability assessment methods are based on fixed topological structures and are difficult to effectively respond to topological dynamic adjustments, resulting in limited applicability of reliability analysis, enhances the adaptability to distribution network topological changes, and improves the accuracy of reliability assessment;

[0107] Coupling the energy storage configuration with the reliability goal of the distribution network for optimization ensures that the configured energy storage system not only has economy but also can provide higher power supply reliability in case of faults or load fluctuations. This overcomes the shortcoming that traditional energy storage planning methods mainly focus on economy or use heuristic optimization and do not explicitly consider the impact of energy storage on system reliability during the optimization process, which easily leads to insufficient reliability of the final plan;

[0108] Adopting an optimization method with explicit reliability embedding improves the global optimality of the planning plan while ensuring the solution efficiency, and ensures that the energy storage configuration plan reaches an optimal balance between reliability and economy. This overcomes the shortcoming that mainstream heuristic optimization methods rely on empirical rules and their planning plans often cannot guarantee global optimality, and may even obtain suboptimal or local optimal solutions in some cases;

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

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

[0111] Please refer to Figure 2 , a device for optimizing the energy storage configuration of a distribution network provided by an embodiment of this application includes:

[0112] A distribution network information acquisition unit 201, configured to acquire the topological 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 according to the topology information and distribution network system parameter information, so as to obtain the optimal energy storage configuration plan of the distribution network through the solution of the distribution network energy storage configuration optimization model. The distribution network energy storage configuration optimization model includes: an objective function and constraint conditions. 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 constraint conditions 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 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 limit data of node voltage;

[0115] The distribution network system parameter constraints include: distributed power constraints, energy storage constraints, load constraints, and power flow constraints.

[0116] As Figure 3 shown, the present application also provides an embodiment of a distribution network energy storage configuration optimization terminal, including: a memory 33 and a processor 31, and the memory and the processor are connected through 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 the program codes.

[0119] An embodiment of a computer-readable storage medium provided by the present application stores program codes in the computer-readable storage medium, and the program codes are 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 can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described terminal, device, and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0121] In several embodiments provided by the present application, it should be understood that the disclosed terminal, device, and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, 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 displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, and the indirect coupling or communication connection of the device or unit may be in electrical, mechanical, or other forms.

[0122] In the description of this application and the above-mentioned accompanying drawings, terms such as "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of this application described here, for example, can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that comprises a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0123] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally means that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or its similar expressions refer to any combination of these items, including any combination of single item (one) or plural items (ones). For example, at least one (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 unit described as a separate component may or may not be physically separated, and the component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0125] In addition, the functional units in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0126] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[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 foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. An optimization method for energy storage configuration in a distribution network, characterized in that Including: Obtain the topological information of the distribution network and the distribution network system parameter information; According to the topological information and the distribution network system parameter information, construct an explicit reliability-based distribution network energy storage configuration optimization model, so as to obtain the optimal energy storage configuration plan of the distribution network through the solution of the distribution network energy storage configuration optimization model. Among them, the distribution network energy storage configuration optimization model includes: an objective function and constraint conditions. 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 constraint conditions include: distribution network system parameter constraints, topology-related constraints, and explicit reliability constraints.

2. The optimization method for energy storage configuration of a distribution network according to claim 1, wherein 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 limit data of node voltage.

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

4. A method for optimizing the energy storage configuration of a distribution network according to claim 1, characterized in that, The specific form of the objective function is: Where EENS is the expected value of power shortage of the system; is a Boolean variable used to indicate whether the energy storage device is installed at node i, is the average cost of the energy storage device; is a constant for converting power loss into economic loss, is the active power load of load node i at time step t under normal operation, is the virtual power flow variable of node i at time step t in fault scenario w, is the set of time steps, is the set of ESS nodes, is the set of load nodes, is the set of fault scenarios.

5. The optimization method for energy storage configuration of a distribution network according to claim 1, wherein The specific form of the explicit reliability constraint is: Wherein, is the customer interruption duration of node i; SAIDI is the system's average interruption duration index; is the number of users of node i; is the upper limit value of SAIDI, is the set of fault scenarios, is the set of time steps, is the set of load nodes, is the virtual power flow variable of node i at time step t in fault scenario w.

6. The optimization method for energy storage configuration of a distribution network according to claim 1, characterized in that The specific form of the topology-related constraint is: In the formula, is the set of nodes in the distribution network, is the set 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 at time step t under fault scenario w; is the Boolean variable of the switch state when branch ij operates normally; and are respectively the non - negative intermediate variables of branch ij at time step t under fault scenario w, is the sum of the non - negative intermediate variables of branches ji formed by node i and its upstream nodes j at time step t under fault scenario w, is the sum of the non - negative intermediate variables of branches ik formed by node i and its downstream nodes k at time step t under fault scenario w; 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 at time step t under fault scenario w, is the on - off state Boolean variable of the switch of branch ij at time step t under fault scenario w, is the virtual power flow variable of node x at both ends of branch ij at time step t under fault scenario w.

7. A device for optimizing the energy storage configuration of a distribution network, characterized in that, Including: A distribution network information acquisition unit for acquiring the topological information of the distribution network and the distribution network system parameters; An energy storage configuration optimization model construction unit for constructing an explicit reliability-based distribution network energy storage configuration optimization model according to the topological information and the distribution network system parameter information, so as to obtain the optimal energy storage configuration plan of the distribution network through the solution of the distribution network energy storage configuration optimization model. Among them, the distribution network energy storage configuration optimization model includes: an objective function and constraint conditions. 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 constraint conditions include: distribution network system parameter constraints, topology-related constraints, and explicit reliability constraints.

8. An optimized device for energy storage configuration in a distribution network according to claim 7, 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 limit data of node voltage; The distribution network system parameter constraints include: distributed power constraints, energy storage constraints, load constraints, and power flow constraints.

9. An optimized terminal for energy storage configuration in a distribution network, characterized in that, Including: A memory and a processor, the memory and the processor are connected through a communication bus; The memory is used to store program codes, and the program codes are used to implement the distribution network energy storage configuration optimization method described in any one of claims 1 to 6; The processor is used to read and execute the program codes.

10. A computer-readable storage medium, characterized in that, The program codes are stored in the computer-readable storage medium and are used to be read and executed by the processor to implement the distribution network energy storage configuration optimization method described in any one of claims 1 to 6.

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