Method, device and storage medium for planning capacity of full peer power distribution system equipment

Abstract

The application belongs to the technical field of power distribution network planning, and particularly relates to a full-peer power distribution system equipment capacity planning method, device, equipment and storage medium. Data information of a to-be-planned full-peer power distribution system is acquired; based on the data information, a full-peer power distribution system planning model is constructed with the minimum annual comprehensive cost of the full-peer power distribution system as an objective function and the intelligent soft switch, distributed photovoltaic and energy storage capacity as variables; based on multiple power distribution networks and the full-peer power distribution system planning model, a distributed planning model is constructed; and based on the distributed planning model and a preset constraint condition, the planning of the full-peer power distribution system equipment capacity is determined. The capacity of the intelligent soft switch, distributed photovoltaic and energy storage equipment in the full-peer power distribution system is reasonably planned, the operation efficiency and overall stability of the power distribution system are improved, and the solving efficiency of the planning model is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power distribution network planning, and particularly relates to a planning method and device for equipment capacity of a full-peer power distribution system, equipment and a storage medium. BACKGROUND

[0002] To cope with energy crisis and environmental pollution, the power distribution system is gradually evolving into a new type of multi-element interaction mode with new energy as the main body and multiple types of flexible resources being accessed. However, distributed photovoltaic power generation has significant intermittency and volatility, which will have a certain impact on the safe and stable operation of the power distribution network. Therefore, by coordinating the planning of energy storage, renewable energy consumption can be promoted, and source-load power balance can be achieved through peak clipping and valley filling. On the other hand, new power electronic devices such as smart soft switches are connected to the power distribution network to realize flexible closed-loop operation, which can quickly and flexibly adjust network power flow and improve the photovoltaic power consumption capacity of the power distribution network.

[0003] The full-peer power distribution system realizes flexible interconnection and power interconnection of multiple power distribution networks based on smart soft switches, adopts the idea of peer-to-peer communication, and there is no primary and secondary relationship between the power distribution networks, which are in a peer-to-peer relationship. However, when using a centralized optimization method to solve the planning model of the full-peer power distribution system, the planning decisions of multiple power distribution networks need to be made by a unified main system through uploading data. However, for large-scale power distribution systems, considering the communication limitations and solving efficiency, the applicability of the centralized optimization method is reduced, and the overall stability of the entire system is poor due to mutual interference between the power distribution networks.

[0004] Therefore, how to use a distributed optimization method to realize distributed optimization planning of the full-peer power distribution system and improve the operation efficiency and overall stability of the power distribution system has become a problem to be solved. SUMMARY

[0005] The application provides a planning method and device for equipment capacity of a full-peer power distribution system, equipment and a storage medium, which realizes distributed optimization planning of the full-peer power distribution system using a distributed optimization method and improves the operation efficiency and overall stability of the power distribution system.

[0006] In a first aspect, the embodiments of the present application provide a planning method for equipment capacity of a full peer-to-peer power distribution system. The method is applied to a full peer-to-peer power distribution system including a plurality of power distribution networks. The method includes: step S1, obtaining data information of a full peer-to-peer power distribution system to be planned; step S2, based on the data information, constructing a full peer-to-peer power distribution system planning model, with the minimum annual comprehensive cost of the full peer-to-peer power distribution system as an objective function, and with intelligent soft switches, distributed photovoltaic and energy storage capacities as variables; step S3, based on the plurality of power distribution networks and the full peer-to-peer power distribution system planning model, constructing a distributed planning model; and step S4, based on the distributed planning model and a preset constraint condition, determining a planning for the equipment capacity of the full peer-to-peer power distribution system, the constraint condition including a power flow constraint, a node voltage constraint, a branch current constraint, an intelligent soft switch capacity and power constraint, a distributed photovoltaic capacity and power constraint, and an energy storage capacity and power constraint.

[0007] In a possible implementation, based on the distributed planning model and the preset constraint condition, determining the planning for the equipment capacity of the full peer-to-peer power distribution system includes: linearizing and approximating non-linear terms in the power flow constraint and the intelligent soft switch power constraint by using a convex relaxation and linearization technique to determine linearized constraints of the power flow constraint and the intelligent soft switch power constraint; and based on the distributed planning model, the linearized constraints of the power flow constraint and 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, determining the planning for the equipment capacity of the full peer-to-peer power distribution system by using a predictive-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 cost, and operation parameters of intelligent soft switches, distributed photovoltaic and energy storage equipment; power purchase price parameters; load and distributed photovoltaic output coefficient.

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

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

[0011] In the formula, F, C inv , C om , and C prespectively represent the annual comprehensive cost, equipment planning investment cost, equipment operation and maintenance cost and the cost of purchasing electricity from the upper grid of the full peer-to-peer power distribution system;

[0012] The distributed planning model is:

[0013]

[0014] In the formula, F n 、 and respectively represent the annual comprehensive cost, equipment planning investment cost, equipment operation and maintenance cost and the cost of purchasing electricity from the upper grid of the power distribution network n; represent the cost of trading electric energy between the power distribution network n and other power distribution networks, and the specific calculation formula is:

[0015]

[0016] In the formula, p s represents the probability of scenario s, represents the intelligent soft switch power.

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

[0018]

[0019] In the formula, P ij,t,s and Q ij,t,s respectively represent the active and reactive power transmitted on the branch ij; I ij,t,s represents the branch current; r ij and x ij respectively represent the branch resistance and reactance; the branch ij is connected with the branch jw, P j,t,s and Q j,t,s respectively represent the active and reactive net load of the node j; and respectively represent the active and reactive load of the node j; and respectively represent the power purchased and sold by the power distribution network from the upper grid; represents the reactive power input by the upper grid; and respectively represent the active and reactive power output by the intelligent soft switch; and respectively represent the active and reactive power output by the distributed photovoltaic; and respectively represent the charging and discharging power of the energy storage; V i,t,s represents the node voltage; Ω SOP , Ω PV , Ω ESS and ΩSUB respectively represent the set of access SOPs, PVs, ESSs and substation nodes; Ω l represent the set of branches.

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

[0021]

[0022] wherein, and respectively represent the installed capacity and the capacity upper limit of the intelligent soft-switching; and A sop respectively represent the active power loss and the loss coefficient of the intelligent soft-switching;

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

[0024]

[0025] wherein, represents the maximum installed capacity of the PV; represents the photovoltaic output coefficient; represents the minimum power factor of the photovoltaic inverter.

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

[0027]

[0028] wherein, and respectively represent the installed power and the capacity of the energy storage; and respectively represent the installed power and the capacity upper limit of the energy storage; η ess,c and η ess,d respectively represent the charging and discharging efficiency of the energy storage; represents the amount of electricity stored by the energy storage; D ess represents the depth of discharge of the energy storage, and t represents time.

[0029] In a second aspect, an embodiment of the present application provides a device capacity planning apparatus for a full peer-to-peer power distribution system, the apparatus comprising: an acquisition module configured to acquire data information of a full peer-to-peer power distribution system to be planned; a construction module configured to construct, based on the data information, a full peer-to-peer power distribution system planning model with a minimum annual comprehensive cost of the full peer-to-peer power distribution system as an objective function and with intelligent soft switches, distributed photovoltaic and energy storage capacities as variables; the construction module is further configured to construct, based on the multiple power distribution networks and the full peer-to-peer power distribution system planning model, a distributed planning model; and a planning module configured to determine, based on the distributed planning model and a preset constraint condition, a planning of the device capacity of the full peer-to-peer power distribution system, the constraint condition comprising: a power flow constraint, a node voltage constraint, a branch current constraint, an intelligent soft switch capacity and power constraint, a distributed photovoltaic capacity and power constraint, and an energy storage capacity and power constraint.

[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 capable of running on the processor, and the processor implements the method according to the first aspect or the second aspect or any of the implementation manners thereof when executing the computer program.

[0031] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, the computer readable storage medium storing a computer program, and the computer program is executed by a processor to implement the method according to the first aspect or the second aspect or any of the implementation manners thereof.

[0032] In a fifth aspect, an embodiment of the present application provides a computer program product, when the computer program product is run on an electronic device, the electronic device executes the method according to the first aspect or any of the implementation manners thereof.

[0033] Compared with the prior art, the embodiment of the present application has the beneficial effects that: the data information of the full peer-to-peer power distribution system to be planned is acquired; based on the data information, a full peer-to-peer power distribution system planning model is constructed with a minimum annual comprehensive cost of the full peer-to-peer power distribution system as an objective function and with intelligent soft switches, distributed photovoltaic and energy storage capacities as variables; based on the multiple power distribution networks and the full peer-to-peer power distribution system planning model, a distributed planning model is constructed; and based on the distributed planning model and a preset constraint condition, a planning of the device capacity of the full peer-to-peer power distribution system is determined, so that the device capacities of the multiple power distribution networks in the full peer-to-peer power distribution system are reasonably planned. The operation efficiency and overall stability of the power distribution system are improved, and the solving efficiency of the planning model is also improved. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative effort.

[0035] Figure 1 A flowchart of a planning method of equipment capacity of a full peer-to-peer power distribution system according to an embodiment of the present application is shown in FIG. 1.

[0036] Figure 2 A full peer-to-peer power distribution system to be planned according to the present application is shown in FIG. 2.

[0037] Figure 3 A photovoltaic output and load curve according to the present application is shown in FIG. 3.

[0038] Figure 4 A topological structure of a full peer-to-peer power distribution system after planning according to the present application is shown in FIG. 4.

[0039] Figure 5 A structural block diagram of a planning device of equipment capacity of a full peer-to-peer power distribution system according to an embodiment of the present application is shown in FIG. 5.

[0040] Figure 6 A structural diagram of an electronic device according to an embodiment of the present application is shown in FIG. 6. DETAILED DESCRIPTION

[0041] In the following description, specific details are set forth in order to provide a thorough understanding of embodiments of the present application. However, persons having ordinary skill in the art will readily understand that embodiments of the present application can be practiced without these specific details, which are set forth for purposes of explanation only. In other instances, well-known systems, devices, circuits, and methods have not been described in detail so as not to unnecessarily obscure aspects of the present application.

[0042] It should be understood that the term "comprising" as used in the specification and in the claims indicates the presence of the recited features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0043] It should also be understood that the term "and / or" as used herein refers to any combination of associated listed items, and all possible combinations, and includes these combinations.

[0044] As used in this application and the appended claims, the term “if’ can be construed to mean “when” or “once” or “in response to a determination” or “in response to a detection” that a described condition precedent has been accomplished, unless context dictates otherwise. Similarly, the phrases “if it is determined” or “if [a described condition or event] is detected” can be construed to mean “once it is determined” or “in response to a determination” or “once [the described condition or event] is detected” or “in response to the detection [of the described condition or event]”, depending on the context.

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

[0046] Reference in the specification to “one embodiment” or “some embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase “in one embodiment” or “in some embodiments” in various places in the specification are not necessarily all referring to the same embodiment, although it can. The terms “comprising”, “including”, “having” and their variants, mean “including but not limited to”, unless otherwise expressly specified and are not excluding additional, unrecited elements or method steps.

[0047] Figure 1 A flowchart of a planning method for the capacity of a full peer-to-peer power distribution system device is provided for an embodiment of the present application. The method is applied to a full peer-to-peer power distribution system containing multiple power distribution networks. The specific method includes:

[0048] Step S1, obtaining data information of a full peer-to-peer power distribution system to be planned.

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

[0050] Step S2, based on the data information, taking the minimum annual comprehensive cost of the full peer-to-peer power distribution system as the objective function, and taking the capacities of the intelligent soft switches, distributed photovoltaic and energy storage devices as variables, a full peer-to-peer power distribution system planning model is constructed.

[0051] Exemplarily, the full peer-to-peer power distribution system realizes flexible interconnection and power mutual aid of multiple power distribution networks based on intelligent soft switches. The peer-to-peer communication idea is adopted, and there is no primary and secondary relationship between the power distribution networks, but a peer-to-peer relationship. The full peer-to-peer power distribution system planning model is:

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

[0053] where F, C inv , C om and C p represent the annual comprehensive cost, equipment planning investment cost, equipment operation and maintenance cost and power purchase cost from the upper grid of the full peer-to-peer power distribution system, respectively.

[0054] where,

[0055]

[0056] where λ sop , λ pv and λ ess represent the annual equivalent coefficient of the intelligent soft switch (Soft Open Point, SOP), distributed photovoltaic (Photovoltaic, PV) and energy storage equipment (Energy Storage System, ESS), respectively; c sop,inv , c pv ,inv , c s,inv and c e,inv represent the investment cost of the unit capacity of the SOP, the unit capacity of the PV, the unit power and the unit capacity of the ESS, respectively; and represent the installed SOP capacity, PV capacity, ESS power and capacity, respectively; c sop , c pv and c ess represent the operation and maintenance cost of the unit capacity of the SOP, the PV and the ESS, respectively; p s represents the probability of the scenario s; and represent the active power output of the PV, the charging and discharging power of the ESS, respectively; and represent the power purchase and sale price of the distribution network from the upper grid, respectively; and represent the power purchase and sale power of the distribution network from the upper grid, respectively; λ a , r and y a represent the annual equivalent coefficient, depreciation rate and service life of the equipment a, respectively; Ω SOP , Ω PV , Ω ESS and Ω SUB represent the access SOP, PV, ESS and substation node set, respectively.

[0057] Step S3, based on the multiple power distribution networks and the full peer-to-peer power distribution system planning model, a distributed planning model is constructed.

[0058] For example, based on the full peer-to-peer power distribution system planning model, the multiple power distribution networks in the full peer-to-peer power distribution system are decoupled from the multi-port intelligent soft switch public DC bus, and the SOP power of the input power distribution network n (n = 1, 2, …, N) is defined as The distributed planning model is constructed, and the distributed planning model (planning model of power distribution network n) is:

[0059]

[0060] In the formula, F n 、 and respectively represent the annual comprehensive cost, equipment planning investment cost, equipment operation and maintenance cost, and power purchase cost from the upper-level power grid of the power distribution network n; represents the cost of trading electric energy between the power distribution network n and other power distribution networks, and the specific calculation formula is:

[0061]

[0062] In the formula, p s represents the probability of scenario s, represents the intelligent soft switch power.

[0063] Step S4, based on the distributed planning model and the preset constraint condition, the planning of the device capacity of the full peer-to-peer power distribution system is determined, and the constraint condition includes: power flow constraint, node voltage constraint, branch current constraint, intelligent soft switch capacity and power constraint, distributed photovoltaic capacity and power constraint, and energy storage capacity and power constraint.

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

[0065]

[0066] In the formula, P ij,t,s and Q ij,t,s respectively represent the active and reactive power transmitted on the branch ij; I ij,t,s represents the branch current; r ij and x ij respectively represent the resistance and reactance of the branch; the branch ij is connected with the branch jw, P j,t,s and Q j,t,s respectively represent the active and reactive net load of the node j; and respectively represent the active and reactive load of the node j; and respectively represent the power purchase and power sale of the power distribution network to the upper-level power grid; PQin represents the reactive power injected by the upstream grid; and PQout and represent the active and reactive power output by the intelligent soft switch, respectively; and PQout and represent the active and reactive power output by the distributed PV, respectively; and PQin and represent the charging and discharging power of the energy storage, respectively; V i,t,s V represents the node voltage; Ω SOP , Ω PV , Ω ESS , and Ω SUB represent the set of SOP, PV, ESS, and substation nodes, respectively; Ω l represents the set of branches.

[0067] Exemplarily, the node voltage constraint is:

[0068]

[0069] wherein, 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] wherein, represents the maximum branch current.

[0073] Exemplarily, the intelligent soft switch capacity and power constraint is:

[0074]

[0075] wherein, and represent the installed capacity and capacity upper limit of the intelligent soft switch, respectively; and A sop represent the active loss and loss coefficient of the intelligent soft switch, respectively.

[0076] Exemplarily, the distributed PV capacity and power constraint is:

[0077]

[0078] wherein, represents the maximum installed capacity of PV; represents the PV output coefficient; represents the minimum power factor of the PV inverter.

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

[0080]

[0081] wherein, and respectively represent the energy storage installed power and capacity; and respectively represent the upper limits of the energy storage installed power and capacity; η ess,c and η ess,d respectively represent the charging and discharging efficiencies of the energy storage; represents the amount of electricity stored by the energy storage; D ess represents the depth of discharge of the energy storage, and t represents time.

[0082] In one possible implementation, based on the distributed planning model and the preset constraint conditions, determining the planning of the equipment capacity of the full peer-to-peer power distribution system comprises: linearizing and approximating the nonlinear terms in the power flow constraint and the smart soft switch power constraint by using a convex relaxation and linearization technique to determine the linearized constraints of the power flow constraint and the smart soft switch power constraint; based on the distributed planning model, the linearized constraints of the power flow constraint and the smart soft switch power constraint, the node voltage constraint, the branch current constraint, the smart soft switch power constraint, the distributed photovoltaic capacity and power constraint, and the energy storage capacity and power constraint, determining the planning of the equipment capacity of the full peer-to-peer power distribution system by using a prediction-correction alternating direction multiplier method.

[0083] Specifically, linearizing and approximating the nonlinear terms in the power flow constraint and the smart soft switch power constraint by using a convex relaxation and linearization technique to determine the linearized constraints of the power flow constraint and the smart soft switch power constraint comprises:

[0084] respectively replacing the nonlinear terms in the power flow constraint with i,t,s and and decomposing the formula into two second-order cone constraint formulas:

[0085]

[0086] wherein, O ij,t,s is an introduced intermediate variable.

[0087] The general formula of the above two second-order cone constraint formulas can be expressed as A polyhedral approximation method can be used to introduce a set of equality and inequality constraints to convert them into linearized constraints:

[0088] ​

[0089] where ξ 0 , χ 0 , ξ β , χ β , ξ ν and χ ν are introduced extra variables.

[0090] Considering the small power loss coefficient of SOP, the arithmetic mean is used to replace the root mean square value

[0091]

[0092] For the absolute value term and introduce non-negative intermediate variables to replace the absolute value term:

[0093]

[0094] where and are introduced intermediate variables.

[0095] Similarly, the polyhedral approximation method is used to process the SOP power constraint

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

[0097] Decoupling variables are introduced The Lagrange equation of the objective function of the planning model of the power distribution network n can be expressed as:

[0098]

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

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

[0101]

[0102] where and represent the predicted values of the variable and the Lagrange multiplier of the power distribution network n at the kth iteration, respectively, and α represents the correction step size.

[0103] The convergence condition of the PCB-ADMM is as follows:

[0104]

[0105] where δ 1 and δ 2 denotes the residual error, and ε denotes the convergence threshold.

[0106] Further, the distributed planning model is solved, and the full peer-to-peer power distribution system is planned according to the optimal solution, that is, the intelligent soft switch, distributed photovoltaic and energy storage capacity are planned.

[0107] Figure 2 For the full peer-to-peer power distribution system to be planned containing multiple distribution networks, the full peer-to-peer power distribution system example contains two IEEE 33-node distribution networks and one IEEE 69-node distribution network, and the three-port SOP is used to realize flexible interconnection. The nodes 32 of the IEEE 33 distribution network and the nodes 68 of the IEEE 69 distribution network are selected as the access positions of the three-port SOP, and the tie-in switches in each distribution network are selected as the candidate positions of the two-port SOP. The nodes 3, 9, 15 and 29 of the distribution network 1 are set as the candidate nodes of the PV, and the nodes 4, 10, 16 and 30 are set as the candidate positions of the ESS, and the maximum photovoltaic penetration rate is 50%; it is assumed that the distribution network 2 does not have the condition of accessing photovoltaic and energy storage, and no candidate node is set; for the distribution network 3, the nodes 14, 24, 31 and 52 are selected as the candidate nodes of the PV, and the nodes 15, 25, 32 and 53 are selected as the candidate nodes of the ESS, and the maximum photovoltaic penetration rate is set to 75%. The price of the distribution network to the upper-level power grid is 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 , and the scenario probabilities are set to 0.5, 0.25 and 0.25 respectively.

[0108] Table 1

[0109]

[0110] Table 2

[0111]

[0112]

[0113] 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 respectively, and the planning results are compared and analyzed. The planned power distribution system structure is shown in Figure 4 , the obtained annual comprehensive cost results are shown in Table 3, and the SOP, PV and ESS device configuration results are shown in Table 4.

[0114] Table 3

[0115] Cost (ten thousand yuan) Power distribution network 1 Power distribution network 2 Power distribution network 3 Total Centralized 841.8538 1146.6136 738.6564 2727.1238 Distributed 841.8594 1146.6208 738.6439 2727.1241

[0116] Table 4

[0117]

[0118] From Table 3, it can be seen that the annual comprehensive costs of each distribution network obtained by the centralized and distributed optimization solving methods are basically the same, and the total cost of the distribution system under the distributed optimization is only 0.003 yuan more than the centralized optimization result. From Table 4, it can be seen that, except for the optimization configuration capacity of a small number of devices in distribution network 1 with a subtle difference of 1 unit value, the device planning results of other distribution networks are consistent. This shows that the proposed distributed optimization planning model and solving method are correct and effective.

[0119] The application obtains data information of a to-be-planned full peer-to-peer power distribution system; based on the data information, a full peer-to-peer power distribution system planning model is constructed with the minimum annual comprehensive cost of the full peer-to-peer power distribution system as an objective function and the intelligent soft switch, distributed photovoltaic and energy storage capacity as variables; based on the multiple distribution networks and the full peer-to-peer power distribution system planning model, a distributed planning model is constructed; based on the distributed planning model and a preset constraint condition, the planning of the device capacity of the full peer-to-peer power distribution system is determined, the communication limitation problem of large-scale full peer-to-peer power distribution system planning is fully considered, the distributed optimization planning model of the full peer-to-peer power distribution system is constructed, the intelligent soft switch, distributed photovoltaic and energy storage capacity are optimized and configured through solving and according to the optimal solution, so that the device capacity of the full peer-to-peer power distribution system is reasonably planned, the investment operation benefit of the power distribution system is improved, and the solving efficiency of the planning model is improved.

[0120] Figure 5 The structure block diagram of the planning device of the device capacity of the full peer-to-peer power distribution system provided by an embodiment of the application is shown, only the parts related to the embodiments of the application are shown for the convenience of description. Figure 5 The planning device of the device capacity of the full peer-to-peer power distribution system 500 can include an acquisition module 501, a construction module 502 and a planning module 503.

[0121] The acquisition module 501 is configured to acquire data information of a to-be-planned full peer-to-peer power distribution system.

[0122] The construction module 502 is configured to construct a full peer-to-peer power distribution system planning model based on the data information, with the minimum annual comprehensive cost of the full peer-to-peer power distribution system as an objective function and the intelligent soft switch, distributed photovoltaic 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 power distribution system planning model.

[0124] The planning module 503 is used to determine the planning of the equipment capacity of the fully peer-to-peer power distribution system based on the distributed planning model and preset constraints. The constraints include: power flow constraints, node voltage constraints, branch current constraints, intelligent soft switching 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 and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0126] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to 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 embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0127] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this 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 diagram), memory 61, and a computer program 62 stored in memory 61 and executable on at least one processor 60, wherein the processor 60 executes the computer program 62 to implement the above. Figure 1 The steps in the method embodiments, or the implementation of the above Figure 5 The functions of each module / unit in the device embodiment.

[0128] Electronic device 6 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. This electronic device 6 may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that... Figure 6 This is merely an example of electronic device 6 and does not constitute a limitation on electronic device 6. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0129] The processor 60 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0130] The memory 61 can be an internal storage unit of the electronic device 6, such as a hard disk or a memory of the electronic device 6 in some embodiments. The memory 61 can 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 in some other embodiments. Further, the memory 61 can include both the internal storage unit and the external storage device of the electronic device 6. The memory 61 is used to store an operating system, application programs, a boot loader, data, and other programs, such as program codes of computer programs, etc. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0131] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps in the above-mentioned various method embodiments.

[0132] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms. The computer readable medium at least includes 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), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. For example, a U disk, a mobile hard disk, a magnetic disk or an optical disk, etc.

[0133] The computer readable storage medium provided by the embodiment of the present application has the same beneficial effects as the above-mentioned full peer-to-peer power distribution system device capacity planning method.

[0134] The computer program product provided by the embodiment of the present application includes a computer program, which can implement the steps in each method embodiment when executed by a processor.

[0135] The computer program product provided by the embodiment of the present application has the same beneficial effects as the above-mentioned full peer-to-peer power distribution system device capacity planning method.

[0136] In the above-mentioned embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0137] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0138] In the embodiments of the present application, it should be understood that the disclosed apparatus / electronic device and method can be implemented in other manners. For example, the embodiments of the apparatus / electronic device described above are merely schematic; for example, the division of the modules or units is merely logical function division; an actual mapping of physical boundaries can be different, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not implemented. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, or among them, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0139] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0140] The above embodiments are merely used to describe the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for planning of equipment capacity in a full peer-to-peer power distribution system, characterized by, The method is applied to a full peer-to-peer power distribution system containing multiple power distribution networks, and the method comprises the following steps: Step S1, obtaining data information of a full peer-to-peer power distribution system to be planned; Step S2, based on the data information, constructing a full peer-to-peer power distribution system planning model with the minimum annual comprehensive cost of the full peer-to-peer power distribution system as an objective function and the capacities of intelligent soft switches, distributed photovoltaic devices and energy storage devices as variables; Step S3, based on the multiple power distribution networks and the full peer-to-peer power distribution system planning model, constructing a distributed planning model; the step S3 comprises: Based on the full peer-to-peer power distribution system planning model, decoupling the multiple power distribution networks from the multi-port intelligent soft switch common DC bus in the full peer-to-peer power distribution system to construct a distributed planning model; The full peer-to-peer power distribution system planning model is: min F = C inv + C om + C p In the formula, F, C inv , C om and C p respectively represent the annual comprehensive cost, equipment planning investment cost, equipment operation and maintenance cost and power purchase cost from the upper grid of the full peer-to-peer power distribution system. The distributed planning model is: In the formula, F n , and respectively represent the annual comprehensive cost, equipment planning investment cost, equipment operation and maintenance cost and power purchase cost from the upper grid of the distribution network n; represents the cost of trading power with other distribution networks, and the specific calculation formula is: In the formula, p s represent the probability of the scene s, represent the intelligent soft switch power; Step S4, based on the distributed planning model and a preset constraint condition, determining the planning of the capacities of the full peer-to-peer power distribution system devices, wherein the constraint condition comprises a power flow constraint, a node voltage constraint, a branch current constraint, an intelligent soft switch capacity and power constraint, a distributed photovoltaic capacity and power constraint, and an energy storage capacity and power constraint; the step S4 comprises: Using convex relaxation and linearization techniques to linearize and approximate the nonlinear terms in the power flow constraint and the intelligent soft switch power constraint, to determine the linearized constraints of the power flow constraint and the intelligent soft switch power constraint; Based on the distributed planning model, the linearized constraints of the power flow constraint and 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 capacities of the full peer-to-peer power distribution system devices is determined by using a prediction-correction alternating direction multiplier method.

2. The method of claim 1, wherein, The data information in the step S1 comprises network topology parameters and load data, investment and operation costs and operating parameters of intelligent soft switches, distributed photovoltaic devices and energy storage devices, power purchase price parameters, load and distributed photovoltaic output coefficients.

3. The method of claim 1, wherein, The power flow constraint is: where P ij,t,s and Q ij,t,s represent the active and reactive power transmitted on branch ij, respectively; I ij,t,s represents the branch current; r ij and x ij represent the branch resistance and reactance, respectively. Branch ij is connected to branch jw, P j,t,s and Q j,t,s represent the active and reactive net load of node j, respectively; and are the active and reactive load of node j, respectively; and represent the active and reactive power purchased and sold by the distribution network from the upper-level grid, respectively; represents the reactive power input from the upper-level grid; and represent the active and reactive power output by the smart soft switch, respectively; and represent the active and reactive power output by the distributed photovoltaic, respectively; and represent the charging and discharging power of the energy storage, respectively;V i,t,s represents the node voltage;Ω SOP , Ω PV , Ω ESS and Ω SUB represent the set of SOP, PV, ESS and substation nodes, respectively;Ω l represents the set of branches.

4. The method of claim 3, wherein, The intelligent soft switch capacity and power constraint is: In the formula, and respectively represent the installed capacity and the capacity upper limit of the intelligent soft switch; and A sop respectively represent the active loss and the loss coefficient of the intelligent soft switch; The distributed photovoltaic capacity and power constraint is: wherein represents the PV maximum installed capacity; represents the PV power output coefficient; represents the PV inverter minimum power factor.

5. The method of claim 4, wherein, The energy storage capacity and power constraint is: wherein, and denote the installed power and capacity of energy storage, respectively; and denote the upper limit of installed power and capacity of energy storage, respectively; η ess,c and η ess,d denote the charging and discharging efficiency of energy storage, respectively; denotes the amount of electricity stored by energy storage; D ess denotes the depth of discharge of energy storage, and t denotes time.

6. A device for planning the capacity of a full peer-to-peer power distribution system, adapted to the method of claim 1, for use in a full peer-to-peer power distribution system comprising a plurality of power distribution networks, characterized in that, The apparatus comprises: an obtaining module configured to obtain data information of a full peer-to-peer power distribution system to be planned; a constructing module configured to, based on the data information, construct a full peer-to-peer power distribution system planning model with the minimum annual comprehensive cost of the full peer-to-peer power distribution system as an objective function and the capacities of intelligent soft switches, distributed photovoltaic devices and energy storage devices as variables; The constructing module is further configured to, based on the multiple power distribution networks and the full peer-to-peer power distribution system planning model, construct a distributed planning model; a planning module configured to, based on the distributed planning model and a preset constraint condition, determine the planning of the capacities of the full peer-to-peer power distribution system devices, wherein the constraint condition comprises a power flow constraint, a node voltage constraint, a branch current constraint, an intelligent soft switch capacity and power constraint, a distributed photovoltaic capacity and power constraint, and an energy storage capacity and power constraint.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor implements the method according to any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium storing a computer program, the computer-readable storage medium comprising: The computer program, which when executed by a processor, implements the method as claimed in any of claims 1 to 5.

Images