Full peer-to-peer power distribution system equipment capacity planning method and device, equipment and storage medium

Abstract

The invention belongs to the technical field of power distribution network planning, and particularly relates to a full peer-to-peer power distribution system equipment capacity planning method and device, equipment and a storage medium. The method comprises the steps of obtaining data information of a to-be-planned all-peer power distribution system; based on the data information, taking the minimum annual comprehensive cost of the full peer-to-peer power distribution system as an objective function, and taking the intelligent soft switch, the distributed photovoltaic and the energy storage capacity as variables, constructing a full peer-to-peer power distribution system planning model; constructing a distributed planning model based on the plurality of power distribution networks and the full peer-to-peer power distribution system planning model; and based on the distributed planning model and a preset constraint condition, determining the planning of the equipment capacity of the all-peer-to-peer power distribution system. According to the method, the capacity of the intelligent soft switch, the distributed photovoltaic device and the energy storage device in the full-peer-to-peer power distribution system is reasonably planned, the operation efficiency and the overall stability of the power distribution system are improved, and meanwhile the solving efficiency of the planning model is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of distribution network planning, and in particular, relates to a planning method, device, equipment and storage medium for equipment capacity of a fully-equivalent distribution system. Background Art

[0002] In response to the energy crisis and environmental pollution, the distribution system is gradually evolving into a new form of multi-faceted interaction with new energy as the main body and multiple types of flexible resource access. However, distributed photovoltaic power generation has significant intermittency and volatility, which will have a certain impact on the safe and stable operation of the distribution network. To this end, the coordinated planning of energy storage can promote the consumption of renewable energy and achieve source-load balance by peak shaving and valley filling. On the other hand, new power electronic equipment such as intelligent soft switches are connected to the distribution network on a large scale to achieve flexible closed-loop operation, which can quickly and flexibly adjust the network flow and improve the photovoltaic absorption capacity of the distribution network.

[0003] The fully peer-to-peer distribution system realizes the flexible interconnection and power mutual assistance of multiple distribution networks based on intelligent soft switches. It adopts the idea of ​​peer-to-peer communication. There is no distinction between primary and secondary between the distribution networks, and they are in a peer-to-peer relationship. However, when using a centralized optimization method to solve the planning model of the fully peer-to-peer distribution system, it is necessary to upload data to a unified main system to make planning decisions for multiple distribution networks. However, for large-scale distribution systems, considering communication limitations and solution efficiency, the applicability of centralized optimization methods is reduced, and the mutual interference between the distribution networks causes the overall stability of the entire system to be poor.

[0004] Therefore, how to use distributed optimization methods to achieve distributed optimization planning of a fully peer-to-peer distribution system and improve the operating efficiency and overall stability of the distribution system has become an urgent problem to be solved. Summary of the invention

[0005] The present application provides a planning method, device, equipment and storage medium for the equipment capacity of a fully peer-to-peer distribution system, which uses a distributed optimization method to achieve distributed optimization planning of a fully peer-to-peer distribution system, thereby improving the operating efficiency and overall stability of the distribution system.

[0006] In a first aspect, an embodiment of the present application provides a planning method for the equipment capacity of a fully peer-to-peer distribution system, which is applied to a fully peer-to-peer distribution system containing multiple distribution networks, and the method includes: step S1, obtaining data information of the fully peer-to-peer distribution system to be planned; step S2, based on the data information, taking the minimization of the annual comprehensive cost of the fully peer-to-peer distribution system as the objective function, and taking intelligent soft switches, distributed photovoltaics and energy storage capacity as variables, to construct a planning model for the fully peer-to-peer distribution system; step S3, based on the multiple distribution networks and the planning model of the fully peer-to-peer distribution system, constructing a distributed planning model; step S4, based on the distributed planning model and preset constraints, determining the planning of the equipment capacity of the fully peer-to-peer distribution system, and the constraints include: flow constraints, node voltage constraints, branch current constraints, intelligent soft switch capacity and power constraints, distributed photovoltaic capacity and power constraints, energy storage capacity and power constraints.

[0007] In one possible implementation, based on the distributed planning model and preset constraints, determining the planning of the equipment capacity of the fully symmetrical distribution system includes: using convex relaxation and linearization technology to linearize and approximate the nonlinear terms in the power flow constraint and the intelligent soft switch power constraint, and determining the linearized constraints of the power flow constraint and the intelligent soft switch power constraint; based on the distributed planning model, the power flow constraint, the linearized constraints of the intelligent soft switch power constraint, the node voltage constraint, the branch current constraint, the intelligent soft switch power constraint, the distributed photovoltaic capacity and power constraint, and the energy storage capacity and power constraint, the planning of the equipment capacity of the fully symmetrical distribution system is determined by the prediction-correction alternating direction multiplier method.

[0008] In a possible implementation, the data information in step S1 includes: network topology parameters, load data; investment and operation and maintenance costs, and operating parameters of intelligent soft switches, distributed photovoltaic and energy storage equipment; electricity purchase price parameters; and load and distributed photovoltaic output coefficients.

[0009] In a possible implementation, based on the multiple distribution networks and the full peer-to-peer distribution system planning model, constructing a distributed planning model includes: based on the full peer-to-peer distribution system planning model, decoupling the multiple distribution networks in the full peer-to-peer distribution system from the common DC bus of the multi-port intelligent soft switch to construct a distributed planning model; the full peer-to-peer distribution system planning model is:

[0010] minF=C inv +C om +C p

[0011] In the formula, F, C inv , C om and C pThey represent the annual comprehensive cost of the fully equitable distribution system, the equipment planning investment cost, the equipment operation and maintenance cost, and the cost of purchasing electricity from the superior power grid;

[0012] The distributed planning model is:

[0013]

[0014] In the formula, F n , and They represent the annual comprehensive cost of distribution network n, equipment planning investment cost, equipment operation and maintenance cost, and the cost of purchasing electricity from the superior power grid; It represents the cost of electricity traded between distribution network n and other distribution networks. The specific calculation formula is:

[0015]

[0016] In the formula, p s represents the probability of scene s, Indicates the intelligent soft switching power.

[0017] In a possible implementation, the power flow constraint is:

[0018]

[0019] Where P ij,t,s and Q ij,t,s Respectively represent the active and reactive power transmitted on branch ij; I ij,t,s Represents branch current; r ij and x ij Represent branch resistance and reactance respectively; branch ij is connected to branch jw, P j,t,s and Q j,t,s denote the active and reactive net loads of node j respectively; and are the active and reactive loads of node j respectively; and They represent the power purchased and sold by the distribution network to the upper grid respectively; Indicates the reactive power input from the upper power grid; and Respectively represent the active and reactive power output by the intelligent soft switch; and They represent the active and reactive power output of distributed photovoltaics respectively; and Respectively represent the charging and discharging power of energy storage; V i,t,s Represents node voltage; Ω SOP ,Ω PV ,Ω ESS and ΩSUB Respectively represent the access SOP, PV, ESS and substation node set; Ω l Indicates a branch set.

[0020] In a possible implementation, the intelligent soft switch capacity and power constraints are:

[0021]

[0022] In the formula, and Respectively represent the installation capacity and upper capacity limit of the intelligent soft switch; and A sop Respectively represent the active power loss and loss coefficient of the intelligent soft switch;

[0023] The distributed photovoltaic capacity and power constraints are:

[0024]

[0025] In the formula, Indicates the maximum installed capacity of PV; Indicates photovoltaic output coefficient; Indicates the minimum power factor of the PV inverter.

[0026] In a possible implementation, the energy storage capacity and power constraints are:

[0027]

[0028] In the formula, and Respectively represent the installed power and capacity of energy storage; and Respectively represent the energy storage installation power and capacity upper limit; η ess,c and η ess,d Respectively represent the charging and discharging efficiency of energy storage; Indicates the amount of electricity stored in the energy storage; D ess represents the energy storage discharge depth, and t represents the time.

[0029] In a second aspect, an embodiment of the present application provides a planning device for the equipment capacity of a fully peer-to-peer distribution system, the device comprising: an acquisition module for acquiring data information of a fully peer-to-peer distribution system to be planned; a construction module for constructing a planning model of a fully peer-to-peer distribution system based on the data information, with the minimization of the annual comprehensive cost of the fully peer-to-peer distribution system as the objective function, and with intelligent soft switches, distributed photovoltaics and energy storage capacity as variables; the construction module is also used to construct a distributed planning model based on the multiple distribution networks and the planning model of the fully peer-to-peer distribution system; a planning module is used to determine the planning of the equipment capacity of the fully peer-to-peer distribution system based on the distributed planning model and preset constraints, the constraints comprising: flow constraints, node voltage constraints, branch current constraints, intelligent soft switch capacity and power constraints, distributed photovoltaic capacity and power constraints, energy storage capacity and power constraints.

[0030] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in the first aspect or the second aspect or any one of the implementation methods thereof is implemented.

[0031] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect or the second aspect or any one of the implementation methods thereof is implemented.

[0032] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on an electronic device, the electronic device executes the method described in the first aspect or any one of the implementation methods thereof.

[0033] Compared with the prior art, the embodiments of the present application have the following beneficial effects: obtaining data information of the fully symmetrical distribution system to be planned; based on the data information, taking the minimum annual comprehensive cost of the fully symmetrical distribution system as the objective function, and taking the intelligent soft switch, distributed photovoltaic and energy storage capacity as variables, constructing a fully symmetrical distribution system planning model; constructing a distributed planning model based on multiple distribution networks and the fully symmetrical distribution system planning model; determining the planning of the equipment capacity of the fully symmetrical distribution system based on the distributed planning model and preset constraints, thereby reasonably planning the equipment capacity of each distribution network in the fully symmetrical distribution system. The operating efficiency and overall stability of the distribution system are improved, and the efficiency of solving the planning model is also improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 work.

[0035] Figure 1 A schematic diagram of a flow chart of a method for planning equipment capacity of a fully symmetrical power distribution system provided in an embodiment of the present application;

[0036] Figure 2 A schematic diagram of a fully symmetrical power distribution system to be planned with multiple distribution networks provided for this application;

[0037] Figure 3 Schematic diagram of photovoltaic output and load curve provided for this application;

[0038] Figure 4 A schematic diagram of the topology of the planned fully-equivalent power distribution system provided for this application;

[0039] Figure 5 A structural block diagram of a device for planning capacity of equipment in a fully symmetrical power distribution system provided in one embodiment of the present application;

[0040] Figure 6 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0041] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0042] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0043] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0044] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0045] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0046] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0047] Figure 1 A flowchart of a method for planning equipment capacity of a fully symmetrical power distribution system provided in an embodiment of the present application is applied to a fully symmetrical power distribution system including multiple distribution networks. The specific method includes:

[0048] Step S1, obtaining data information of a fully symmetrical power distribution system to be planned.

[0049] In a possible implementation, the data information in step S1 includes: network topology parameters, load data; investment and operation and maintenance costs, and operating parameters of intelligent soft switches, distributed photovoltaic and energy storage equipment; electricity purchase price parameters; and load and distributed photovoltaic output coefficients.

[0050] Step S2: Based on the data information, taking the minimization of the annual comprehensive cost of the fully symmetrical distribution system as the objective function, and taking the intelligent soft switch, distributed photovoltaic and energy storage capacity as variables, a fully symmetrical distribution system planning model is constructed.

[0051] For example, the fully peer-to-peer distribution system realizes the flexible interconnection and power mutual assistance of multiple distribution networks based on intelligent soft switches, adopts the peer-to-peer communication concept, and there is no primary and secondary distinction between the distribution networks, but a peer-to-peer relationship. The planning model of the fully peer-to-peer distribution system is:

[0052] minF=C inv +C om +C p

[0053] In the formula, F, C inv , C om and C p They respectively represent the annual comprehensive cost of the fully symmetrical distribution system, equipment planning investment cost, equipment operation and maintenance cost, and the cost of purchasing electricity from the superior power grid.

[0054] in,

[0055]

[0056] In the formula, λ sop , pv and λ ess They represent the annual value coefficients of the smart soft switch (Soft OpenPoint, SOP), distributed photovoltaic (Photovoltaic, PV) and energy storage equipment (Energy Storage System, ESS); c sop,inv 、c pv ,inv 、c s,inv and c e,inv They represent the SOP unit capacity, PV unit capacity, ESS unit power and investment cost per unit capacity respectively; and Respectively represent the installed SOP capacity, PV capacity, ESS power and capacity; c sop 、c pv and c ess They represent the operation and maintenance costs per unit capacity of SOP, per unit electricity of PV and ESS respectively; p s represents the probability of scene s; and They represent PV active output, ESS charging and discharging power respectively; and They represent the prices of electricity purchased and sold by the distribution network to the upper-level power grid respectively; and They represent the power purchased and sold by the distribution network to the upper grid respectively; a , r and y a Respectively represent the annual value coefficient, depreciation rate and service life of equipment a; Ω SOP ,Ω PV ,Ω ESS and Ω SUB They represent the access SOP, PV, ESS and substation node sets respectively.

[0057] Step S3: construct a distributed planning model based on multiple distribution networks and a fully peer-to-peer distribution system planning model.

[0058] Exemplarily, based on the fully peer-to-peer distribution system planning model, multiple distribution networks in the fully peer-to-peer distribution system are decoupled from the multi-port intelligent soft switch common DC bus, and the SOP power of the input distribution network n (n = 1, 2, ..., N) is defined as Construct a distributed planning model, the distributed planning model (planning model of distribution network n) is:

[0059]

[0060] In the formula, F n , and They represent the annual comprehensive cost of distribution network n, equipment planning investment cost, equipment operation and maintenance cost, and the cost of purchasing electricity from the superior power grid; It represents the cost of electricity traded between distribution network n and other distribution networks. The specific calculation formula is:

[0061]

[0062] In the formula, p s represents the probability of scene s, Indicates the intelligent soft switching power.

[0063] Step S4: Based on the distributed planning model and preset constraints, determine the planning of the equipment capacity of the full-equivalent distribution system, where the constraints include: flow constraints, node voltage constraints, branch current constraints, intelligent soft switch capacity and power constraints, distributed photovoltaic capacity and power constraints, and energy storage capacity and power constraints.

[0064] For example, the power flow constraint is:

[0065]

[0066] Where P ij,t,s and Q ij,t,s Respectively represent the active and reactive power transmitted on branch ij; I ij,t,s Represents branch current; r ij and x ij Represent branch resistance and reactance respectively; branch ij is connected to branch jw, P j,t,s and Q j,t,s denote the active and reactive net loads of node j respectively; and are the active and reactive loads of node j respectively; and They represent the power purchased and sold by the distribution network to the upper grid respectively; Indicates the reactive power input from the upper power grid; and Respectively represent the active and reactive power output by the intelligent soft switch; and They represent the active and reactive power output of distributed photovoltaics respectively; and Respectively represent the charging and discharging power of energy storage; V i,t,s Represents node voltage; Ω SOP ,Ω PV ,Ω ESS and Ω SUB Respectively represent the access SOP, PV, ESS and substation node set; Ω l Indicates a branch set.

[0067] Exemplarily, the node voltage constraint is:

[0068]

[0069] Where V i max and V i min Represent the upper and lower limits of the node voltage respectively.

[0070] Exemplarily, the branch current constraint is:

[0071]

[0072] In the formula, Indicates the maximum value of branch current.

[0073] Exemplarily, the smart soft switch capacity and power constraints are:

[0074]

[0075] In the formula, and Respectively represent the installation capacity and upper capacity limit of the intelligent soft switch; and A sop Respectively represent the active loss and loss coefficient of the intelligent soft switch.

[0076] Exemplarily, the distributed photovoltaic capacity and power constraints are:

[0077]

[0078] In the formula, Indicates the maximum installed capacity of PV; Indicates photovoltaic output coefficient; Indicates the minimum power factor of the PV inverter.

[0079] Exemplarily, the energy storage capacity and power constraints are:

[0080]

[0081] In the formula, and Respectively represent the installed power and capacity of energy storage; and Respectively represent the energy storage installation power and capacity upper limit; η ess,c and η ess,d Respectively represent the charging and discharging efficiency of energy storage; Indicates the amount of electricity stored in the energy storage; D ess represents the energy storage discharge depth, and t represents the time.

[0082] In one possible implementation, based on the distributed planning model and preset constraints, determining the planning of the equipment capacity of the fully symmetrical distribution system includes: using convex relaxation and linearization technology to linearize and approximate the nonlinear terms in the power flow constraint and the intelligent soft switch power constraint, and determining the linearized constraints of the power flow constraint and the intelligent soft switch power constraint; based on the distributed planning model, the power flow constraint, the linearized constraints of the intelligent soft switch power constraint, the node voltage constraint, the branch current constraint, the intelligent soft switch power constraint, the distributed photovoltaic capacity and power constraint, and the energy storage capacity and power constraint, the planning of the equipment capacity of the fully symmetrical distribution system is determined by the prediction-correction alternating direction multiplier method.

[0083] Specifically, the nonlinear terms in the power flow constraint and the intelligent soft switch power constraint are linearized and approximated by using convex relaxation and linearization technology, and the linearized constraints of the power flow constraint and the intelligent soft switch power constraint are determined to include:

[0084] ν i,t,s and Replace the power flow constraint and General Decomposed into two second-order cone constraints:

[0085]

[0086] In the formula, O ij,t,s is the introduced intermediate variable.

[0087] The general formula of the above two second-order cone constraints can be expressed as The polyhedron approximation method can be used to introduce a set of equality and inequality constraints to transform them into linear constraints:

[0088]

[0089] In the formula, ξ 0 , χ 0 , β , χ β , ν and χ ν These are additional variables introduced.

[0090] Considering that the power loss coefficient of SOP is generally small, the arithmetic mean value is used instead of the RMS value.

[0091]

[0092] For absolute value terms and Introduce non-negative intermediate variables to replace absolute value terms:

[0093]

[0094] In the formula, and They are all introduced intermediate variables.

[0095] Similarly, the polyhedron approximation method is used to deal with the SOP power constraint

[0096] Exemplarily, the planning of equipment capacity of the full-equivalent distribution system is determined by a prediction-correction alternating direction multiplier method, including:

[0097] Introducing decoupled variables The Lagrangian equation of the objective function of the planning model of the distribution network n can be expressed as:

[0098]

[0099] In the formula, λ n,t,s and μ represent the Lagrange multiplier and penalty factor respectively.

[0100] Taking N = 3 distribution networks as an example, the variables and Lagrange multiplier prediction and correction formulas based on the prediction-correction alternating direction multiplier method (PCB-ADMM) are as follows:

[0101]

[0102] In the formula, and They represent the predicted values ​​of the distribution network n variables and the Lagrange multiplier at the kth iteration respectively, and α represents the correction step size.

[0103] The convergence conditions of PCB-ADMM are as follows:

[0104]

[0105] In the formula, δ 1 and δ 2 represents the residual, and ε represents the convergence threshold.

[0106] Furthermore, the distributed planning model is solved, and the fully symmetrical distribution system planning is carried out according to the optimal solution, that is, the intelligent soft switches, distributed photovoltaics and energy storage capacity are planned.

[0107] Figure 2 The diagram is a schematic diagram of a fully symmetrical distribution system to be planned with multiple distribution networks. The fully symmetrical distribution system example includes two IEEE 33-node distribution networks and one IEEE 69-node distribution network, and three-port SOP is used to achieve flexible interconnection. Node 32 of the IEEE 33 distribution network and node 68 of the IEEE 69 distribution network are selected as the access locations of the three-port SOP, and the tie switch in each distribution network is selected as the candidate location of the two-port SOP. Nodes 3, 9, 15 and 29 of distribution network 1 are set as candidate nodes for PV, and nodes 4, 10, 16 and 30 are set as candidate locations for ESS, with a maximum photovoltaic penetration rate of 50%; assuming that distribution network 2 does not have the conditions for accessing photovoltaics and energy storage, no candidate nodes are set; for distribution network 3, nodes 14, 24, 31 and 52 are selected as candidate nodes for PV, and candidate nodes for ESS are nodes 15, 25, 32 and 53, and the maximum photovoltaic penetration rate is set to 75%. The prices of electricity purchased and sold by the distribution network to the upper grid are shown in Table 1, and other system parameters are shown in Table 2. The photovoltaic output and load curves under three typical scenarios are shown in Figure 3 As shown, the scene probabilities are set to 0.5, 0.25, and 0.25, respectively.

[0108] Table 1

[0109]

[0110] Table 2

[0111]

[0112]

[0113] In order to verify the effectiveness of the proposed distributed optimization planning method, the centralized optimization and distributed optimization methods are used to solve the planning model, and the planning results are compared and analyzed. The structure of the planned distribution system is as follows: Figure 4 The comparison of the annual comprehensive cost results is shown in Table 3, and the comparison of SOP, PV and ESS equipment configuration results is shown in Table 4.

[0114] Table 3

[0115] Cost (ten thousand yuan) Distribution network 1 Distribution network 2 Distribution network 3 sum Centralized 841.8538 1146.6136 738.6564 2727.1238 distributed 841.8594 1146.6208 738.6439 2727.1241

[0116] Table 4

[0117]

[0118] As shown in Table 3, the annual comprehensive costs of each distribution network obtained by the centralized and distributed optimization solutions are basically the same, and the total cost of the distribution system under distributed optimization is only 0.0003 million yuan more than the centralized optimization result. As shown in Table 4, except for the slight difference of 1 unit value in the optimized configuration capacity of a few devices in distribution network 1, the equipment planning results of other distribution networks are consistent. This shows that the proposed distributed optimization planning model and solution method are correct and effective.

[0119] The present application obtains data information of a fully symmetrical distribution system to be planned; based on the data information, takes the minimization of the annual comprehensive cost of the fully symmetrical distribution system as the objective function, and takes the intelligent soft switch, distributed photovoltaic and energy storage capacity as variables to construct a planning model for the fully symmetrical distribution system; constructs a distributed planning model based on multiple distribution networks and the planning model of the fully symmetrical distribution system; based on the distributed planning model and preset constraints, determines the planning of the equipment capacity of the fully symmetrical distribution system, fully considers the communication limitation problem of the planning of large-scale fully symmetrical distribution system, and constructs a distributed optimization planning model for the fully symmetrical distribution system; solves and optimizes the configuration of the intelligent soft switch, distributed photovoltaic and energy storage capacity according to the optimal solution, thereby rationally planning the equipment capacity of the fully symmetrical distribution system, improving the investment and operation benefits of the distribution system, and at the same time improving the efficiency of solving the planning model.

[0120] Figure 5 This is a structural block diagram of a device for planning the capacity of equipment in a fully symmetrical power distribution system provided by an embodiment of the present application. For the sake of convenience, only the part related to the embodiment of the present application is shown. Figure 5 The planning device 500 for the equipment capacity of a full-peer distribution system may include an acquisition module 501 , a construction module 502 , and a planning module 503 .

[0121] The acquisition module 501 is used to acquire data information of the fully equitable power distribution system to be planned.

[0122] The construction module 502 is used to construct a planning model of a fully symmetrical distribution system based on data information, with the minimization of the annual comprehensive cost of the fully symmetrical distribution system as the objective function, and with intelligent soft switches, distributed photovoltaics and energy storage capacity as variables.

[0123] The construction module 502 is further configured to construct a distributed planning model based on the multiple distribution networks and the full peer-to-peer distribution system planning model.

[0124] The planning module 503 is used to determine the planning of the equipment capacity of the fully symmetrical distribution system based on the distributed planning model and preset constraints, wherein the constraints include: flow constraints, node voltage constraints, branch current constraints, intelligent soft switch capacity and power constraints, distributed photovoltaic capacity and power constraints, and energy storage capacity and power constraints.

[0125] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0126] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0127] Figure 6 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Figure 6 As shown, the electronic device 6 of this embodiment includes: at least one processor 60 ( Figure 6 Only one is shown in the figure), a memory 61, and a computer program 62 stored in the memory 61 and executable on at least one processor 60, the processor 60 executes the computer program 62 to implement the above Figure 1 The steps in the method embodiment, or the implementation of the above Figure 5 Functions of each module / unit in the device embodiment.

[0128] The electronic device 6 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 6 may include but is not limited to a processor 60 and a memory 61. Those skilled in the art will appreciate that Figure 6 It is only an example of the electronic device 6 and does not constitute a limitation on the electronic device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0129] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0130] In some embodiments, the memory 61 may be an internal storage unit of the electronic device 6, such as a hard disk or memory of the electronic device 6. In other embodiments, the memory 61 may also be an external storage device of the electronic device 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the electronic device 6. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as program codes of a computer program. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0131] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0132] If 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 this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include at least: any entity or device capable of carrying the computer program code to an electronic device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0133] A computer-readable storage medium provided in an embodiment of the present application has the same beneficial effects as the above-mentioned method for planning the equipment capacity of a fully-equivalent power distribution system.

[0134] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0135] A computer program product provided in an embodiment of the present application has the same beneficial effects as the above-mentioned method for planning the equipment capacity of a fully-equivalent power distribution system.

[0136] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0137] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0138] In the embodiments provided in the present application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, 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 through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0139] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. 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, and should all be included in the protection scope of the present application.

Claims

1. A method for planning equipment capacity of a fully symmetrical power distribution system, characterized in that: Applied to a fully peer-to-peer power distribution system including multiple power distribution networks, the method comprises: Step S1, obtaining data information of a fully equitable power distribution system to be planned; Step S2: Based on the data information, a planning model for a fully symmetrical distribution system is constructed with the minimum annual comprehensive cost of the fully symmetrical distribution system as the objective function and with intelligent soft switches, distributed photovoltaics and energy storage capacity as variables; Step S3: constructing a distributed planning model based on the multiple distribution networks and the full peer-to-peer distribution system planning model; Step S4: Based on the distributed planning model and preset constraints, determine the planning of the equipment capacity of the fully equivalent distribution system, wherein the constraints include: flow constraints, node voltage constraints, branch current constraints, intelligent soft switch capacity and power constraints, distributed photovoltaic capacity and power constraints, and energy storage capacity and power constraints.

2. The method according to claim 1, characterized in that The data information in step S1 includes: network topology parameters, load data; investment and operation and maintenance costs, and operating parameters of intelligent soft switches, distributed photovoltaic and energy storage equipment; electricity purchase price parameters; and load and distributed photovoltaic output coefficients.

3. The method according to claim 1, characterized in that The step S3 comprises: Based on the full-peer distribution system planning model, multiple distribution networks in the full-peer distribution system are decoupled from a common DC bus of a multi-port intelligent soft switch to construct a distributed planning model; The full peer-to-peer distribution system planning model is: minF=C inv +C om +C p In the formula, F, C inv , C om and C p They represent the annual comprehensive cost of the fully equitable distribution system, the equipment planning investment cost, the equipment operation and maintenance cost, and the cost of purchasing electricity from the superior power grid; The distributed planning model is: In the formula, F n , and They represent the annual comprehensive cost of distribution network n, equipment planning investment cost, equipment operation and maintenance cost, and the cost of purchasing electricity from the superior power grid; It represents the cost of electricity traded between distribution network n and other distribution networks. The specific calculation formula is: In the formula, p s represents the probability of scene s, Indicates the intelligent soft switching power.

4. The method according to claim 1, characterized in that: The step S4 comprises: Using convex relaxation and linearization technology to linearize and approximate the nonlinear terms in the power flow constraint and the intelligent soft switch power constraint, and determine the linearized constraints of the power flow constraint and the intelligent soft switch power constraint; Based on the distributed planning model, the power flow constraints, the linearized constraints of the intelligent soft switch power constraints, the node voltage constraints, the branch current constraints, the intelligent soft switch power constraints, the distributed photovoltaic capacity and power constraints and the energy storage capacity and power constraints, the planning of the equipment capacity of the fully equivalent distribution system is determined by the prediction-correction alternating direction multiplier method.

5. The method according to claim 4, characterized in that The power flow constraint is: Where P ij,t,s and Q ij,t,s Respectively represent the active and reactive power transmitted on branch ij; I ij,t,s Represents branch current; r ij and x ij They represent branch resistance and reactance respectively; Branch ij is connected to branch jw, P j,t,s and Q j,t,s denote the active and reactive net loads of node j respectively; and are the active and reactive loads of node j respectively; and They represent the power purchased and sold by the distribution network to the upper grid respectively; Indicates the reactive power input from the upper power grid; and Respectively represent the active and reactive power output by the intelligent soft switch; and They represent the active and reactive power output of distributed photovoltaics respectively; and Respectively represent the charging and discharging power of energy storage; V i,t,s Represents node voltage; Ω SOP ,Ω PV ,Ω ESS and Ω SUB Respectively represent the access SOP, PV, ESS and substation node set; Ω l Indicates a branch set.

6. The method according to claim 5, characterized in that The capacity and power constraints of the intelligent soft switch are: In the formula, and Respectively represent the installation capacity and upper capacity limit of the intelligent soft switch; and A sop Respectively represent the active power loss and loss coefficient of the intelligent soft switch; The distributed photovoltaic capacity and power constraints are: In the formula, Indicates the maximum installed capacity of PV; Indicates photovoltaic output coefficient; Indicates the minimum power factor of the PV inverter.

7. The method according to claim 6, characterized in that The energy storage capacity and power constraints are: In the formula, and Respectively represent the installed power and capacity of energy storage; and Respectively represent the energy storage installation power and capacity upper limit; η ess,c and η ess,d They represent the charging and discharging efficiency of energy storage respectively; Indicates the amount of electricity stored in the energy storage; D ess represents the energy storage discharge depth, and t represents the time.

8. A planning device for equipment capacity of a fully symmetrical power distribution system, applied to a fully symmetrical power distribution system containing multiple distribution networks, characterized in that: The device comprises: An acquisition module is used to acquire data information of a fully equitable power distribution system to be planned; A construction module is used to construct a planning model of a fully symmetrical distribution system based on the data information, taking the minimization of the annual comprehensive cost of the fully symmetrical distribution system as the objective function, and taking the intelligent soft switch, distributed photovoltaic and energy storage capacity as variables; The construction module is further used to construct a distributed planning model based on the multiple distribution networks and the full peer-to-peer distribution system planning model; A planning module is used to determine the planning of the equipment capacity of the fully-equivalent distribution system based on the distributed planning model and preset constraints, wherein the constraints include: flow constraints, node voltage constraints, branch current constraints, intelligent soft switch capacity and power constraints, distributed photovoltaic capacity and power constraints, and energy storage capacity and power constraints.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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