A medium and low voltage AC-DC power distribution network planning method and device

Abstract

The application discloses a kind of low-voltage AC-DC distribution network planning method and device, for taking into account the privacy right of each market participant and the benefit of each planning subject, so as to improve the economy of low-voltage AC-DC distribution network planning scheme, improve the benefit of each planning subject.

Description

Technical Field

[0001] This invention relates to the field of AC / DC distribution network planning technology, and more particularly to a method and apparatus for planning medium and low voltage AC / DC distribution networks. Background Technology

[0002] With the construction of new power systems, a large number of independent entities such as distributed power generation investment operators and microgrid investment operators will emerge in the distribution network. These entities have different interests and their supply and demand relationships affect each other. Therefore, it is necessary to study and consider the multi-entity collaborative planning of market transactions.

[0003] Existing multi-stakeholder collaborative planning methods for distribution networks, such as the two-level game planning method with distribution networks and microgrids as the main stakeholders, and the method for establishing distributed generation and distribution network expansion planning with demand-side management operators and distribution networks as different stakeholders, all employ centralized solutions, neglecting the privacy rights of various market participants and failing to consider the interests of each planning entity. Furthermore, the aforementioned multi-stakeholder collaborative planning methods for distribution networks that consider market transactions have not yet addressed the AC / DC distribution network domain. Summary of the Invention

[0004] This invention provides a method and apparatus for planning medium and low voltage AC / DC distribution networks, which addresses the problem that existing AC / DC distribution network planning processes neglect the impact of multi-entity market transactions and individual interests on planning schemes.

[0005] In a first aspect, the present invention provides a method for planning medium- and low-voltage AC / DC distribution networks, comprising:

[0006] Obtain typical time-series scenarios of distributed photovoltaic and load at a preset time scale, and obtain the cooperative game planning ideas of multiple investment and operation entities in medium and low voltage AC and DC distribution networks;

[0007] Taking the minimum investment and operation and maintenance costs, electricity purchase costs, and bilateral market transaction costs of medium-voltage distribution networks as the objective function of medium-voltage distribution networks, and combining the pre-set first constraint, a coordinated planning model for distributed photovoltaic and energy storage in medium-voltage AC / DC distribution networks is established.

[0008] Using the minimum investment and operation and maintenance costs of low-voltage distribution networks and the minimum transaction costs of the bilateral market of low-voltage distribution networks as the objective function of low-voltage distribution networks, and combined with the pre-set second constraint, a coordinated planning model for distributed photovoltaic and energy storage in low-voltage AC / DC distribution networks is established.

[0009] Based on the aforementioned cooperative game planning approach, the coordinated planning model of distributed photovoltaic and energy storage in medium-voltage AC / DC distribution networks and the coordinated planning model of distributed photovoltaic and energy storage in low-voltage AC / DC distribution networks are transformed into a cooperative game planning model of medium-voltage and low-voltage distribution networks based on Nash bargaining.

[0010] Based on the typical time-series scenario, the cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network is solved sequentially to obtain the planned capacity of distributed photovoltaic and energy storage, the bilateral transaction electricity volume, and the bilateral transaction electricity price.

[0011] Optionally, typical time-series scenarios of distributed photovoltaic power and loads at a preset time scale can be obtained, as well as cooperative game planning strategies for multiple investment and operation entities in medium- and low-voltage AC / DC distribution networks, including:

[0012] Obtain historical data on the distributed photovoltaic power output and the load from the historical database;

[0013] Using a time-series scene clustering algorithm, the historical data is reduced to the typical time-series scene at the preset time scale.

[0014] Optionally, the investment and operation and maintenance costs of the medium-voltage distribution network include: the investment costs of photovoltaic and energy storage in the medium-voltage distribution network, and the operation and maintenance costs of photovoltaic, energy storage and converter stations in the medium-voltage distribution network; the investment and operation and maintenance costs of the low-voltage distribution network include: the investment costs of photovoltaic and energy storage in the low-voltage distribution network, and the operation and maintenance costs of photovoltaic, energy storage and converter stations in the low-voltage distribution network.

[0015] The objective function for the medium-voltage distribution network is:

[0016]

[0017] The objective function for the low-voltage distribution network is:

[0018]

[0019] in, For the investment costs of photovoltaic and energy storage in medium-voltage distribution networks, To cover the operation and maintenance costs of photovoltaic, energy storage, and converter stations in medium-voltage distribution networks, For the cost of purchasing electricity for medium-voltage distribution networks, For the bilateral market transaction costs of medium-voltage distribution networks, For the investment costs of photovoltaics and energy storage, To reduce the operation and maintenance costs of photovoltaic, energy storage, and converter stations in low-voltage distribution networks, This reduces the transaction costs in the bilateral market for low-voltage distribution networks.

[0020] Optionally, based on the typical time-series scenario, the cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network is solved sequentially to obtain the planned capacity of distributed photovoltaic and energy storage, the bilateral transaction electricity volume, and the bilateral transaction electricity price, including:

[0021] By introducing linearization and equivalent decomposition methods, the cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network is transformed into a social cost minimization problem and a sub-problem of minimizing the costs of each subject.

[0022] The social cost minimization problem and the subproblems of minimizing the costs of each subject are solved sequentially using the alternating direction multiplier method to obtain the planned capacity of distributed photovoltaic and energy storage, the bilateral transaction electricity volume, and the bilateral transaction electricity price.

[0023] Secondly, the present invention also provides a medium- and low-voltage AC / DC distribution network planning device, comprising:

[0024] The acquisition module is used to acquire typical time-series scenarios of distributed photovoltaic and load at a preset time scale, as well as to acquire the cooperative game planning ideas of multiple investment and operation entities in medium and low voltage AC and DC distribution networks.

[0025] The medium-voltage model building module is used to establish a coordinated planning model for distributed photovoltaic and energy storage in medium-voltage AC / DC distribution networks, with the objective function of minimizing the investment and operation and maintenance costs, electricity purchase costs, and bilateral market transaction costs of medium-voltage distribution networks, combined with the pre-set first constraint conditions.

[0026] The low-voltage model building module is used to establish a coordinated planning model for distributed photovoltaic and energy storage in low-voltage AC / DC distribution networks, with the objective function of minimizing the investment and operation and maintenance costs and the transaction costs of the bilateral market of low-voltage distribution networks, combined with the pre-set second constraint conditions.

[0027] The model conversion module is used to convert the medium-voltage AC / DC distribution network distributed photovoltaic and energy storage coordination planning model and the low-voltage AC / DC distribution network distributed photovoltaic and energy storage coordination planning model into a medium-voltage distribution network and low-voltage distribution network cooperative game planning model based on Nash bargaining, based on the cooperative game planning idea.

[0028] The solution module is used to sequentially solve the cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network based on the typical time-series scenario, and obtain the planned capacity of distributed photovoltaic and energy storage, bilateral transaction electricity volume and bilateral transaction price in turn.

[0029] Optionally, the acquisition module includes:

[0030] The acquisition submodule is used to acquire historical data of the distributed photovoltaic power output and the load from the historical database;

[0031] The time-series scene generation submodule is used to reduce the historical data to the typical time-series scene at the preset time scale by using a time-series scene clustering algorithm.

[0032] Optionally, the investment and operation and maintenance costs of the medium-voltage distribution network include: the investment costs of photovoltaic and energy storage in the medium-voltage distribution network, and the operation and maintenance costs of photovoltaic, energy storage and converter stations in the medium-voltage distribution network; the investment and operation and maintenance costs of the low-voltage distribution network include: the investment costs of photovoltaic and energy storage in the low-voltage distribution network, and the operation and maintenance costs of photovoltaic, energy storage and converter stations in the low-voltage distribution network.

[0033] The objective function for the medium-voltage distribution network is:

[0034]

[0035] The objective function for the low-voltage distribution network is:

[0036]

[0037] in, For the investment costs of photovoltaic and energy storage in medium-voltage distribution networks, To cover the operation and maintenance costs of photovoltaic, energy storage, and converter stations in medium-voltage distribution networks, For the cost of purchasing electricity for medium-voltage distribution networks, For the bilateral market transaction costs of medium-voltage distribution networks, For the investment costs of photovoltaics and energy storage, To reduce the operation and maintenance costs of photovoltaic, energy storage, and converter stations in low-voltage distribution networks, To reduce transaction costs in the bilateral market of low-voltage distribution networks.

[0038] Optionally, the solution module includes:

[0039] The model transformation submodule is used to introduce linearization and equivalent decomposition methods to transform the cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network into a social cost minimization problem and a sub-problem of cost minimization for each subject.

[0040] The planning submodule is used to sequentially solve the social cost minimization problem and the subproblems of minimizing the costs of each subject using the alternating direction multiplier method, so as to obtain the planned capacity of distributed photovoltaic and energy storage, the bilateral transaction electricity volume, and the bilateral transaction electricity price.

[0041] A third aspect of this application provides an electronic device, the device including a processor and a memory;

[0042] The memory is used to store program code and transmit the program code to the processor;

[0043] The processor is used to execute the medium- and low-voltage AC / DC distribution network planning method described in the first aspect according to the instructions in the program code.

[0044] The fourth aspect of this application provides a computer-readable storage medium for storing program code for executing the medium- and low-voltage AC / DC distribution network planning method described in the first aspect.

[0045] As can be seen from the above technical solutions, the present invention has the following advantages:

[0046] This invention obtains typical time-series scenarios of distributed photovoltaic (PV) power and loads at a preset time scale, and acquires cooperative game planning ideas for multiple investment and operation entities in medium- and low-voltage AC / DC distribution networks. It establishes a coordinated planning model for distributed PV and energy storage in medium-voltage AC / DC distribution networks, using the minimum investment and operation costs, electricity purchase costs, and bilateral market transaction costs of the medium-voltage distribution network as the objective function, combined with pre-set first constraints. Similarly, it uses the minimum investment and operation costs and bilateral market transaction costs of the low-voltage distribution network as the objective function, combined with pre-set... The second constraint is to establish a coordinated planning model for distributed photovoltaic (PV) and energy storage in low-voltage AC / DC distribution networks. Based on the aforementioned cooperative game theory planning approach, the coordinated planning models for distributed PV and energy storage in medium-voltage AC / DC distribution networks and low-voltage AC / DC distribution networks are transformed into cooperative game theory planning models for medium-voltage and low-voltage distribution networks based on Nash bargaining. Based on the aforementioned typical time-series scenarios, the cooperative game theory planning models for medium-voltage and low-voltage distribution networks are solved sequentially to obtain the planned capacity for distributed PV and energy storage, the bilateral transaction volume, and the bilateral transaction price. This approach balances the privacy rights of various market participants with the interests of all planning entities, thereby improving the economic efficiency of medium- and low-voltage AC / DC distribution network planning schemes and enhancing the interests of all planning entities. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating the steps of an embodiment of a medium- and low-voltage AC / DC distribution network planning method according to the present invention.

[0049] Figure 2 This is a schematic diagram of a typical chain-type medium-low voltage AC / DC distribution network according to an embodiment of the medium-low voltage AC / DC distribution network planning method of the present invention;

[0050] Figure 3 This is a typical daily load scenario time-series curve according to an embodiment of the medium- and low-voltage AC / DC distribution network planning method of the present invention;

[0051] Figure 4 This is a typical daily photovoltaic scenario time-series curve according to an embodiment of the medium- and low-voltage AC / DC distribution network planning method of the present invention;

[0052] Figure 5 This is a schematic diagram of a computational system structure according to an embodiment of the medium- and low-voltage AC / DC distribution network planning method of the present invention.

[0053] Figure 6 This is a diagram illustrating the electricity trading volume of a medium- and low-voltage AC / DC distribution network, as an embodiment of the planning method for medium- and low-voltage AC / DC distribution networks according to the present invention.

[0054] Figure 7 This is a diagram illustrating the electricity price for medium- and low-voltage AC / DC distribution networks, representing an embodiment of the present invention's planning method for medium- and low-voltage AC / DC distribution networks.

[0055] Figure 8 This is a residual iteration diagram of subproblem 1 in the cooperative game planning of a medium- and low-voltage AC / DC distribution network according to an embodiment of the present invention.

[0056] Figure 9 This is a cost iteration diagram of subproblem 2 in the cooperative game planning of a medium- and low-voltage AC / DC distribution network according to an embodiment of the present invention.

[0057] Figure 10 This is a structural block diagram of an embodiment of a medium- and low-voltage AC / DC distribution network planning device according to the present invention. Detailed Implementation

[0058] This invention provides a method and apparatus for planning medium and low voltage AC / DC distribution networks, which addresses the problem that existing AC / DC distribution network planning processes neglect the impact of multi-entity market transactions and individual interests on planning schemes.

[0059] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0060] Please see Figure 1 , Figure 1 This is a flowchart illustrating the steps of an embodiment of a medium- and low-voltage AC / DC distribution network planning method according to the present invention, applied to... Figure 2In the typical chain-type medium- and low-voltage AC / DC distribution network diagram shown, 1 represents the AC network, 2 represents the medium-voltage AC bus, 3 represents the medium-voltage DC bus, 4 represents the low-voltage distribution network DC load, 5 represents the low-voltage distribution network distributed photovoltaic system, 6 represents the low-voltage distribution network DC energy storage system, 7 represents the low-voltage distribution network AC load, 8 represents the medium-voltage distribution network distributed photovoltaic system, 9 represents the medium-voltage distribution network DC energy storage system, and 10 represents the industrial DC load. The low-voltage distribution network DC load, low-voltage distribution network distributed photovoltaic system, low-voltage distribution network DC energy storage system, and low-voltage distribution network AC load are all connected to the low-voltage DC bus. The method may specifically include the following steps:

[0061] Step S101: Obtain typical time-series scenarios of distributed photovoltaic and load at a preset time scale, and obtain the cooperative game planning ideas of multiple investment and operation entities in medium and low voltage AC / DC distribution networks.

[0062] Specifically, this involves obtaining typical time-series scenarios for distributed photovoltaic (PV) power and loads over a preset time scale, as well as developing cooperative game-theoretic planning strategies for multiple investment and operation entities in medium- and low-voltage AC / DC distribution networks, including:

[0063] Obtain historical data on the distributed photovoltaic power output and the load from the historical database;

[0064] Using a time-series scene clustering algorithm, the historical data is reduced to the typical time-series scene at the preset time scale.

[0065] Please see Figure 3 and Figure 4 , Figure 3 This is a typical daily load scenario time-series curve according to an embodiment of the medium- and low-voltage AC / DC distribution network planning method of the present invention. Figure 4 This is a time-series curve of a typical daily photovoltaic scenario in an embodiment of a medium- and low-voltage AC / DC distribution network planning method of the present invention, where the horizontal axis represents time (h), and... Figure 3 The vertical axis represents the per-unit value of photovoltaic processing. Figure 4 The vertical axis represents the per-unit value of load demand. In this embodiment of the invention, 8760 hours of historical data on distributed photovoltaic output and load are imported. A time-series scenario clustering algorithm is used to reduce the original data, generating multiple typical time-series scenarios of photovoltaic and load with a time scale of 24, thereby generating... Figure 3 and Figure 4 .

[0066] In this embodiment of the invention, the cooperative game planning approach for multiple investment and operation entities in medium- and low-voltage distribution networks is as follows: both medium- and low-voltage distribution networks directly participate in bilateral market transactions as investment entities, plan distributed photovoltaic and energy storage within their respective systems, and decide on the transaction volume and price based on the energy storage operation strategy and the operation status of distributed photovoltaic. To ensure the fairness and rationality of the game planning decision-making scheme for investment operators in medium- and low-voltage distribution networks, Nash bargaining theory is introduced to construct a cooperative game planning model. The establishment of a cooperative alliance requires two basic conditions: 1) overall rationality, meaning the overall revenue of the cooperative alliance must be higher than the sum of the revenues of each participant operating individually; 2) individual rationality, meaning that for each participant in the alliance, the final revenue allocated after participating in the cooperative alliance is greater than that without participating in the cooperative alliance.

[0067] Step S102: Using the minimum investment and operation and maintenance costs of medium-voltage distribution networks, the electricity purchase costs of medium-voltage distribution networks, and the bilateral market transaction costs of medium-voltage distribution networks as the objective function of medium-voltage distribution networks, and combining the pre-set first constraint conditions, establish a coordinated planning model for distributed photovoltaic and energy storage in medium-voltage AC / DC distribution networks.

[0068] Specifically, the investment and operation and maintenance costs of the medium-voltage distribution network include: the investment costs of photovoltaic and energy storage in the medium-voltage distribution network, as well as the operation and maintenance costs of photovoltaic, energy storage and converter stations in the medium-voltage distribution network; the investment and operation and maintenance costs of the low-voltage distribution network include: the investment costs of photovoltaic and energy storage in the low-voltage distribution network, as well as the operation and maintenance costs of photovoltaic, energy storage and converter stations in the low-voltage distribution network.

[0069] The objective function for the medium-voltage distribution network is:

[0070]

[0071] The objective function for the low-voltage distribution network is:

[0072]

[0073] in, For the investment costs of photovoltaic and energy storage in medium-voltage distribution networks, To cover the operation and maintenance costs of photovoltaic, energy storage, and converter stations in medium-voltage distribution networks, For the cost of purchasing electricity for medium-voltage distribution networks, For the bilateral market transaction costs of medium-voltage distribution networks, For the investment costs of photovoltaics and energy storage, To reduce the operation and maintenance costs of photovoltaic, energy storage, and converter stations in low-voltage distribution networks, To reduce transaction costs in the bilateral market of low-voltage distribution networks.

[0074] In this embodiment of the invention, the costs of the objective function of the medium-voltage distribution network are determined according to the following formulas:

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] in, and These represent the investment costs for photovoltaic and energy storage systems in medium-voltage power distribution networks, respectively. and Representing the photovoltaic installed capacity, energy storage installed capacity, and apparent power of energy storage installed at node i, respectively; c pv c e and c s λ, r, and y represent the unit investment cost of photovoltaic capacity, energy storage capacity, and apparent power of energy storage, respectively; λ, r, and y represent the annual value factor of the equipment, the depreciation rate, and the average service life, respectively. and These represent the operation and maintenance costs of photovoltaic, energy storage, AC / DC converter stations, and DC / DC converter stations, respectively; p s Indicates the probability of a typical scenario s; and These represent photovoltaic power generation, energy storage charging and discharging power, respectively; τ pv , and These represent the operation and maintenance costs per unit of electricity for photovoltaic, energy storage charging, and discharging, respectively. and τ ac-dc These represent the power output of the AC / DC converter station between nodes ij and the maintenance cost per unit of electricity, respectively. and τ dc-dc These represent the initial power of the DC / DC converter station and the unit power operation and maintenance cost, respectively. and These represent the cost of purchasing and selling electricity per unit of electricity, respectively. and These represent the purchased and sold power outputs, respectively. These represent the unit cost of electricity in a bilateral transaction; Ω PV Ω BES Ω AC-DC Ω DC-DC and Ω sub These represent the sets of nodes connected to photovoltaic, energy storage, AC / DC converter stations, DC / DC converter stations, and AC substations, respectively; h represents the location of the low-voltage distribution network node connecting to the medium-voltage distribution network. It should be noted that the operation and maintenance costs of the DC / DC converter station are shared equally by the medium-voltage distribution network investment operator and the low-voltage distribution network investment operator.

[0087] In addition, the pre-defined first constraints include: medium-voltage AC distribution network constraints and medium-voltage DC distribution network constraints. The medium-voltage AC distribution network constraints include power flow constraints, voltage / current constraints, PV installed capacity and active-reactive power constraints, AC-side energy storage installed capacity and active-reactive power constraints, and converter station constraints. The power flow constraints of the medium-voltage AC distribution network are power flow constraints described by a branch power flow model, as follows:

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094] Where s represents the scene, t represents time; r ij and x ij These represent the resistance and reactance of branch ij, respectively, and branch jw is connected to branch ij; P ij,t,s and Q ij,t,s P represents the active and reactive power transmitted in branch ij, respectively; j,t,s and Q j,t,s These represent the net active and reactive loads of node j, respectively. and These are the active and reactive power of the load, respectively. This refers to the reactive power of the AC-side nodes in the AC / DC converter station. and These are the active and reactive power input from the upstream power grid, respectively. The reactive power output of the PV; and These represent the active and reactive power outputs of the BES, respectively; V i,t,s and I ij,t,s These represent node voltage and branch current, respectively; Ω l This is a collection of medium-voltage AC distribution network lines.

[0095] The constraints that the current in each branch and the voltage at each node must satisfy are as follows:

[0096]

[0097]

[0098] in: This indicates the upper limit of the current in branch ij; and These represent the upper and lower limits of the voltage at node i, respectively.

[0099] The PV installation capacity and active-reactive power constraints are as follows:

[0100]

[0101]

[0102]

[0103] In the above formula, Maximum PV installation capacity; The per-unit value represents the PV output curve. This represents the minimum power factor at which the PV inverter operates;

[0104] The AC-side energy storage installation capacity and active-reactive power constraints are as follows:

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] in: and These are the maximum installable apparent power and capacity of the BES, respectively. Let η be the charge at time t; BES,C and η BES,D These represent charging and discharging efficiencies, respectively; D BES The depth of discharge for energy storage.

[0114] Meanwhile, the constraints of the medium-voltage DC distribution network include power flow constraints, voltage / current constraints, PV installed capacity and active power constraints, and DC-side energy storage installed capacity and active power constraints. Among these, the DC power flow constraints are still described using the branch power flow model, as detailed below:

[0115]

[0116]

[0117]

[0118]

[0119] in: The active power of the DC-side node of the AC / DC converter station; Ω L This is a collection of medium-voltage DC distribution network lines.

[0120] The constraints that the current in each branch and the voltage at each node must satisfy are as follows:

[0121]

[0122]

[0123] in: This indicates the upper limit of the current in branch ij; and These represent the upper and lower limits of the voltage at node i, respectively.

[0124] The PV installation capacity and active power constraints are as follows:

[0125]

[0126]

[0127] In the above formula, Maximum PV installation capacity; The per-unit value represents the PV output curve;

[0128] The DC-side energy storage installation capacity and active power constraints are as follows:

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137] in: and These are the maximum installable apparent power and capacity of the BES, respectively. Let η be the charge at time t; BES,C and η BES,D These represent charging and discharging efficiencies, respectively; D BES The depth of discharge for energy storage.

[0138] The AC / DC converter station is a power conversion unit between the AC subgrid and the DC subgrid within the medium-voltage distribution network area, and its corresponding constraints are as follows:

[0139]

[0140]

[0141]

[0142] in: For the power loss of the AC / DC converter station; η AC-DC The loss factor of the AC / DC converter station; This represents the maximum apparent power transmitted by the AC / DC converter station.

[0143] Step S103: Using the minimum investment and operation and maintenance costs of the low-voltage distribution network and the minimum transaction costs of the bilateral market of the low-voltage distribution network as the objective function of the low-voltage distribution network, and combining the pre-set second constraint conditions, establish a coordinated planning model for distributed photovoltaic and energy storage in the low-voltage AC / DC distribution network.

[0144] In this embodiment of the invention, the costs of the objective function of the low-voltage distribution network are determined according to the following formulas:

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154] in: This indicates the operating cost of the DC / AC converter station; and τ dc-ac These represent the initial power of the DC / AC converter station and the unit power operation and maintenance cost, respectively.

[0155] In addition, the pre-defined second set of constraints includes: power balance constraints, photovoltaic installation capacity and active power output constraints, energy storage installation capacity and active power output constraints, DC / DC converter station constraints, and DC / AC converter station constraints. Among them, the photovoltaic installation capacity and active power output constraints, and the energy storage installation capacity and active power output constraints are consistent with those of the medium-voltage DC distribution network.

[0156] The power balance constraint is:

[0157]

[0158]

[0159] in: This refers to the active power on the low-voltage side of the DC / DC converter station. This refers to the active power on the AC side of the DC / AC converter station. and These represent the active power of DC and AC loads in the low-voltage distribution network, respectively. It should be noted that due to the relatively short length of the low-voltage distribution network lines, their line parameters are ignored.

[0160] The DC / DC converter station is a DC / DC converter station connecting the medium-voltage distribution network and the low-voltage distribution network, and its constraints are as follows:

[0161]

[0162]

[0163] in: The power loss of the DC / DC converter station; η DC-DC This represents the loss factor of the DC / DC converter station.

[0164] The DC / AC converter station is a converter station in a low-voltage distribution network area that supplies power from the DC subgrid to the AC load. Its constraints are as follows:

[0165]

[0166]

[0167] in: For the power loss of the DC / AC converter station; η DC-AC This represents the loss factor of the DC / AC converter station.

[0168] Step S104: Based on the cooperative game planning idea, the coordinated planning model of distributed photovoltaic and energy storage in medium-voltage AC / DC distribution network and the coordinated planning model of distributed photovoltaic and energy storage in low-voltage AC / DC distribution network are transformed into a cooperative game planning model of medium-voltage distribution network and low-voltage distribution network based on Nash bargaining.

[0169] Step S105: Based on the typical time series scenario, the cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network is solved sequentially to obtain the planned capacity of distributed photovoltaic and energy storage, bilateral transaction electricity volume and bilateral transaction electricity price.

[0170] Specifically, by introducing linearization and equivalent decomposition methods, the cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network is transformed into a social cost minimization problem and a sub-problem of cost minimization for each subject.

[0171] The social cost minimization problem and the subproblems of minimizing the costs of each subject are solved sequentially using the alternating direction multiplier method to obtain the planned capacity of distributed photovoltaic and energy storage, the bilateral transaction electricity volume, and the bilateral transaction electricity price.

[0172] In this embodiment of the invention, the cooperative game planning model of medium-voltage and low-voltage distribution networks contains a large number of variables. Nonlinear constraints such as power flow constraints, energy storage operation constraints, and converter station operation constraints make the model a non-convex, nonlinear, NP-hard problem, making it difficult to find the optimal solution. Therefore, second-order cone relaxation and linearization are applied to the power flow constraints, and the remaining constraints are linearized to facilitate rapid solution finding in the model. The specific process is as follows:

[0173] First, the model is transformed into a cooperative game planning model based on Nash bargaining:

[0174]

[0175] in: C represents the total cost of the medium- and low-voltage distribution networks at the point of negotiation breakdown, with the planning outcome of no negotiation between the medium- and low-voltage distribution networks considered as the point of negotiation breakdown; M C L,h These represent the total costs of the medium- and low-voltage distribution networks when participating in price negotiations, respectively. The following conditions must be met:

[0176]

[0177]

[0178] Then, the quadratic term variable is replaced by the quadratic term present in the power flow constraint formula of the cooperative game model: and Using ν i,t,s and l ij,t,s replace and Convert to:

[0179]

[0180]

[0181]

[0182]

[0183]

[0184]

[0185]

[0186] Next, the power flow constraints are relaxed using a second-order cone method, specifically as follows:

[0187]

[0188] Convert to the following formula:

[0189]

[0190]

[0191] And the above formula:

[0192]

[0193] Converted to the following formula:

[0194]

[0195] Then, second-order conical linearization is performed, which involves linearizing the transformed power flow constraints, energy storage operation constraints, and converter station operation constraints.

[0196] Among them, the following constraints apply:

[0197]

[0198] The polyhedral approximation method can be used to introduce a set of inequalities to linearize it, specifically:

[0199]

[0200]

[0201]

[0202] Where: ξ β and χ β It is represented as an intermediate variable for linearization; the parameter ν determines the number of additional constraints and variables added to the linearized expression.

[0203] Then, the absolute value term is linearized. That is, for constraint terms containing absolute values, the following absolute value transformation method is introduced for linearization:

[0204]

[0205] Among them, X + X and X- are two non-negative intermediate variables;

[0206] Finally, the model is equivalently decomposed into easily solvable subproblems of minimizing social costs and minimizing the costs of each agent, including:

[0207] Sub-problem 1: Minimize social cost, i.e.:

[0208]

[0209] Subproblem 2: Minimize the costs of each entity, i.e.:

[0210]

[0211] In summary, the method involves replacing the square terms of the variables in the power flow equations of the medium- and low-voltage AC / DC distribution network planning model considering multi-agent cooperative game theory with linear variables; relaxing the quadratic constraints and using the polyhedral approximation method to transform the quadratic expression into multiple inequality expression constraints; introducing intermediate variables to transform the absolute value constraint terms into linearized expressions; and for the form of multiplying the traded electricity volume with the traded electricity price, introducing an equivalent decomposition method to transform the Nash bargaining problem into an equivalent subproblem of maximizing social benefits (i.e., minimizing social costs) and maximizing energy payment benefits (i.e., minimizing the costs of each agent), thereby linearizing the non-convex and nonlinear parts of the medium- and low-voltage AC / DC distribution network planning model considering multi-agent cooperative game theory.

[0212] Meanwhile, in this embodiment of the invention, to protect the decision-making autonomy and information privacy of each investment entity, the Alternating Directional Multiplier Method (ADMM), which has good convergence for large-scale variable optimization problems, is used to solve the two sub-problems sequentially. Based on the principle of the ADMM algorithm, auxiliary variables need to be introduced to decouple the trading electricity volume and trading price variables of the medium and low voltage distribution networks. The augmented Lagrangian function, variable update formula, and iterative convergence condition for each sub-problem are given, thus improving the traditional ADMM algorithm. According to the formula: ρ1(k)=ρ1e (k-1) The value of the penalty factor is dynamically adjusted to accelerate the convergence of the ADMM algorithm and improve the solution efficiency. The specific steps include:

[0213] 1) Introduce auxiliary variables To decouple the traded electricity volume, the objective function of subproblem 1 is modified to the augmented Lagrangian function of the medium- and low-voltage AC / DC distribution networks, as shown in the following formula. The initial iteration count is set to 1, and the Lagrange multipliers, coupling variables, and penalty factor are initialized as follows:

[0214]

[0215]

[0216] in: Let ρ1 represent the Lagrange multiplier for subproblem 1 with respect to electricity trading; ρ1 is the penalty factor for subproblem 1.

[0217] 2) Each entity updates its trading volume strategy locally. In each iteration, it updates the variables and Lagrange multipliers according to the following formula:

[0218]

[0219] Where k is the number of distributed iterations for subproblem 1.

[0220] 3) Determine the convergence of the algorithm using the following formula. If the convergence condition is met, the iteration stops; otherwise, update the iteration count k = k + 1, adjust the penalty factor, return to the previous step, and re-enter the next iteration until the convergence condition or the set maximum number of iterations is met. The formula is:

[0221]

[0222] Where ε1 is the residual convergence threshold of subproblem 1.

[0223] 4) After solving subproblem 1, the optimized cost will be... Trading volume Pass it to subproblem 2. Introduce auxiliary variables. To decouple the electricity trading price, the objective function of sub-problem 2 in step S3 is modified to the augmented Lagrangian function of the medium- and low-voltage AC / DC distribution networks shown in the following formula. The initial iteration count is set to 1, and the Lagrange multipliers, coupling variables, and penalty factors are initialized. The formula is:

[0224]

[0225]

[0226] in, Let ρ2 denote the Lagrange multiplier of subproblem 2; ρ2 is the penalty factor for subproblem 2.

[0227] 5) Each entity updates its trading volume strategy locally. In each iteration, it updates the variables and Lagrange multipliers according to the formula in step 2), i.e.:

[0228]

[0229] Where k is the number of distributed iterations for subproblem 2.

[0230] 6) Determine the convergence status of the algorithm using the following formula. If the convergence condition is met, the iteration stops; otherwise, update the iteration count k = k + 1, adjust the penalty factor, and return to step 2) to re-enter the next iteration round until the convergence condition or the set maximum number of iterations is met. The formula is:

[0231]

[0232] In this context, ε2 is the residual convergence threshold for subproblem 2.

[0233] Please see Figure 5 , Figure 5This is a system structure diagram illustrating a computational example of a medium- and low-voltage AC / DC distribution network planning method according to the present invention. Simulation analysis is performed using an improved 17-node medium-voltage AC / DC distribution network and a low-voltage AC / DC distribution network as two investment and operation entities. It is assumed that the medium-voltage AC distribution network has photovoltaic nodes 4, 11, and 13 with capacities of 400, 600, and 500 kW respectively, and node 12 has 400 kWh / 200 kVA of energy storage. The load values ​​of nodes 2-6 in the medium-voltage distribution network are 160 + j60 kVA, nodes 7-13 are 265 + j90 kVA, and nodes 14-17 are 240 + j75 kVA. Reactive power is not considered for DC nodes. The DC and AC loads of the low-voltage distribution network are 200 kW and 100 kW respectively. The reference value for the AC voltage of the medium-voltage distribution network is 12.66 kV, the reference value for the DC voltage is 25.32 kV, and the reference power is 1 MVA. The minimum power factor of the photovoltaic inverter is set to 0.9. The apparent power and unit capacity of the energy storage unit are set at 50kVA and 50kWh, respectively, with a charge / discharge efficiency of 0.95 and a depth of discharge of 0.9. The loss factors for the AC / DC converter station, DC / DC converter station, and DC / AC converter station are 0.01, 0.04, and 0.02, respectively. The system pricing parameters are shown in the table below:

[0234]

[0235]

[0236] Meanwhile, the ADMM parameters are set as follows: the convergence threshold is 10⁻³, and the initial values ​​of the Lagrange multipliers and the penalty factor are set to 0 and 0.1, respectively.

[0237] To verify the effectiveness of the planning method proposed in this embodiment, five examples are set up for comparative analysis: 1) Example 1: Photovoltaic and energy storage planning is carried out only in the medium-voltage distribution network, with distributed solution; 2) Example 2: Photovoltaic and energy storage planning is carried out only in the low-voltage distribution network, with distributed solution; 3) Example 3: Photovoltaic planning is carried out only in both the medium-voltage and low-voltage distribution networks, with distributed solution; 4) Example 4: Photovoltaic and energy storage planning is carried out in both the medium-voltage and low-voltage distribution networks, with distributed solution; 5) Example 5: Photovoltaic and energy storage planning is carried out in both the medium-voltage and low-voltage distribution networks, with centralized solution. The impact of photovoltaic-energy storage coordination and bilateral coordination on the cooperative game planning results of the medium-voltage and low-voltage AC / DC distribution networks is studied, and the planning results are shown in the table. As can be seen from Examples 1, 2, and 4 in the table, bilateral coordination planning in the medium-voltage and low-voltage AC / DC networks can not only increase the photovoltaic configuration capacity but also effectively reduce the total system cost; as can be seen from Examples 3 and 4 in the table, photovoltaic-energy storage coordinated planning can effectively increase the photovoltaic planning capacity while effectively reducing the total system cost. This indicates that while the planning model of this invention cannot guarantee optimal investment benefits for both medium- and low-voltage distribution network operators, it does reduce overall energy purchase costs and total costs, and effectively increases photovoltaic penetration. Furthermore, as shown in Examples 4 and 5 of Table 1, the results of solving the multi-investment entity planning for medium- and low-voltage AC / DC distribution networks using the distributed optimization method are correct and effective. The distributed solution method employed in this invention ensures a fair and reasonable distribution of benefits among the entities, while also protecting the privacy of each entity.

[0238] To analyze the role of bilateral market transactions, the results of traditional market transactions (where the medium-voltage distribution network dominates and the low-voltage distribution network sells surplus electricity to the medium-voltage distribution network at a lower price) and bilateral market transactions (where the medium- and low-voltage distribution networks are equal in status and negotiate prices) were also studied, as shown in the table below:

[0239]

[0240] As can be seen, although the introduction of bilateral market transactions increases the cost of medium-voltage distribution networks, the planning model of this invention optimizes the transaction price through Nash bargaining, thereby enhancing the enthusiasm of low-voltage distribution network investors and operators, promoting the participation of low-voltage distribution networks in market transactions, achieving photovoltaic-friendly integration, and improving social benefits. Please refer to... Figure 6 and Figure 7 , Figure 6 This is a diagram illustrating the electricity trading volume of a medium- and low-voltage AC / DC distribution network, as an embodiment of the medium- and low-voltage AC / DC distribution network planning method of the present invention. Figure 7 This is a price chart for medium- and low-voltage AC / DC distribution networks, illustrating the electricity volume and price transactions in a bilateral trading market for medium- and low-voltage distribution networks, as per an embodiment of a medium- and low-voltage AC / DC distribution network planning method of the present invention. Please refer to... Figure 8 and Figure 9 , Figure 8This is a residual iteration diagram of subproblem 1 in the cooperative game planning of a medium- and low-voltage AC / DC distribution network according to an embodiment of the present invention. Figure 9 This diagram illustrates the cost iteration of sub-problem 2 in a cooperative game planning approach for medium- and low-voltage AC / DC distribution networks, according to an embodiment of the present invention. The diagrams show the residual iteration for sub-problem 1 and the total cost iteration for sub-problem 2. Sub-problem 1 reaches convergence after 23 iterations, while sub-problem 2 meets the convergence condition after 19 iterations. At this point, the costs of the medium- and low-voltage distribution networks are 9,958,270 yuan and 840,880 yuan, respectively. The improved ADMM algorithm of this invention exhibits strong convergence performance and solution efficiency.

[0241] This invention, through obtaining typical time-series scenarios of distributed photovoltaic (PV) power and loads at a preset time scale, and acquiring cooperative game planning strategies for multiple investment and operation entities in medium- and low-voltage AC / DC distribution networks, establishes a coordinated planning model for distributed PV and energy storage in medium-voltage AC / DC distribution networks, using the minimum investment and operation costs, electricity purchase costs, and bilateral market transaction costs of medium-voltage distribution networks as the objective function, combined with pre-set first constraints. Similarly, it uses the minimum investment and operation costs and bilateral market transaction costs of low-voltage distribution networks as the objective function, combined with pre-set... Under the given second constraint, a coordinated planning model for distributed photovoltaic (PV) and energy storage in low-voltage AC / DC distribution networks is established. Based on the cooperative game theory planning approach, the coordinated planning models for distributed PV and energy storage in medium-voltage AC / DC distribution networks and low-voltage AC / DC distribution networks are transformed into cooperative game theory planning models for medium-voltage and low-voltage distribution networks based on Nash bargaining. Based on the typical time-series scenarios, the cooperative game theory planning models for medium-voltage and low-voltage distribution networks are solved sequentially to obtain the planned capacity for distributed PV and energy storage, the bilateral transaction volume, and the bilateral transaction price. This approach balances the privacy rights of various market participants and the interests of all planning entities, thereby improving the economic efficiency of medium- and low-voltage AC / DC distribution network planning schemes and enhancing the interests of all planning entities.

[0242] Please see Figure 10 The diagram shows a structural block diagram of an embodiment of a medium- and low-voltage AC / DC distribution network planning device, which includes the following modules:

[0243] The acquisition module 301 is used to acquire typical time-series scenarios of distributed photovoltaic and load at a preset time scale, as well as to acquire the cooperative game planning ideas of multiple investment and operation entities in medium and low voltage AC and DC distribution networks.

[0244] The medium-voltage model establishment module 302 is used to establish a coordinated planning model for distributed photovoltaic and energy storage in medium-voltage AC / DC distribution networks, with the objective function of minimizing the investment and operation and maintenance costs, electricity purchase costs, and bilateral market transaction costs of medium-voltage distribution networks, combined with the pre-set first constraint conditions.

[0245] The low-voltage model establishment module 303 is used to establish a coordinated planning model for distributed photovoltaic and energy storage in low-voltage AC / DC distribution networks, with the objective function of minimizing the investment and operation and maintenance costs and the transaction costs of the bilateral market of low-voltage distribution networks, combined with the pre-set second constraint conditions.

[0246] The model conversion module 304 is used to convert the medium-voltage AC / DC distribution network distributed photovoltaic and energy storage coordination planning model and the low-voltage AC / DC distribution network distributed photovoltaic and energy storage coordination planning model into a medium-voltage distribution network and low-voltage distribution network cooperative game planning model based on the cooperative game planning idea.

[0247] The solution module 305 is used to sequentially solve the cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network based on the typical time-series scenario, and obtain the planned capacity of distributed photovoltaic and energy storage, bilateral transaction electricity volume and bilateral transaction price in sequence.

[0248] In an optional embodiment, the acquisition module 301 includes:

[0249] The acquisition submodule is used to acquire historical data of the distributed photovoltaic power output and the load from the historical database;

[0250] The time-series scene generation submodule is used to reduce the historical data to the typical time-series scene at the preset time scale by using a time-series scene clustering algorithm.

[0251] In one optional embodiment, the investment and operation and maintenance costs of the medium-voltage distribution network include: the investment costs of photovoltaic and energy storage in the medium-voltage distribution network, and the operation and maintenance costs of photovoltaic, energy storage and converter stations in the medium-voltage distribution network; the investment and operation and maintenance costs of the low-voltage distribution network include: the investment costs of photovoltaic and energy storage in the low-voltage distribution network, and the operation and maintenance costs of photovoltaic, energy storage and converter stations in the low-voltage distribution network.

[0252] The objective function for the medium-voltage distribution network is:

[0253]

[0254] The objective function for the low-voltage distribution network is:

[0255]

[0256] in, For the investment costs of photovoltaic and energy storage in medium-voltage distribution networks, To cover the operation and maintenance costs of photovoltaic, energy storage, and converter stations in medium-voltage distribution networks, For the cost of purchasing electricity for medium-voltage distribution networks, For the bilateral market transaction costs of medium-voltage distribution networks, For the investment costs of photovoltaics and energy storage, To reduce the operation and maintenance costs of photovoltaic, energy storage, and converter stations in low-voltage distribution networks, This reduces the transaction costs in the bilateral market for low-voltage distribution networks.

[0257] In an optional embodiment, the solver module 305 includes:

[0258] The model transformation submodule is used to introduce linearization and equivalent decomposition methods to transform the cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network into a social cost minimization problem and a sub-problem of cost minimization for each subject.

[0259] The planning submodule is used to sequentially solve the social cost minimization problem and the subproblems of minimizing the costs of each subject using the alternating direction multiplier method, so as to obtain the planned capacity of distributed photovoltaic and energy storage, the bilateral transaction electricity volume, and the bilateral transaction electricity price.

[0260] This application also provides an electronic device, which includes a processor and a memory;

[0261] The memory is used to store program code and transfer the program code to the processor;

[0262] The processor is used to execute the medium and low voltage AC / DC distribution network planning method in the above method embodiments according to the instructions in the program code.

[0263] This application also provides a computer-readable storage medium for storing program code for executing the medium- and low-voltage AC / DC distribution network planning method in the above method embodiments.

[0264] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0265] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0266] Furthermore, the functional units in the various embodiments of this application 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.

[0267] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of this application through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0268] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A planning method for medium and low voltage AC / DC distribution networks, characterized in that, include: Obtain typical time-series scenarios of distributed photovoltaic and load at a preset time scale, and obtain the cooperative game planning ideas of multiple investment and operation entities in medium and low voltage AC and DC distribution networks; Taking the minimum investment and operation and maintenance costs, electricity purchase costs, and bilateral market transaction costs of medium-voltage distribution networks as the objective function of medium-voltage distribution networks, and combining the pre-set first constraint, a coordinated planning model for distributed photovoltaic and energy storage in medium-voltage AC / DC distribution networks is established. Using the minimum investment and operation and maintenance costs of low-voltage distribution networks and the minimum transaction costs of the bilateral market of low-voltage distribution networks as the objective function of low-voltage distribution networks, and combined with the pre-set second constraint, a coordinated planning model for distributed photovoltaic and energy storage in low-voltage AC / DC distribution networks is established. Based on the aforementioned cooperative game planning approach, the coordinated planning model of distributed photovoltaic and energy storage in medium-voltage AC / DC distribution networks and the coordinated planning model of distributed photovoltaic and energy storage in low-voltage AC / DC distribution networks are transformed into a cooperative game planning model of medium-voltage and low-voltage distribution networks based on Nash bargaining. Based on the typical time series scenario, the cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network is solved sequentially to obtain the planned capacity of distributed photovoltaic and energy storage, the bilateral transaction electricity volume and the bilateral transaction electricity price. Based on the aforementioned typical time-series scenario, the cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network is solved sequentially to obtain the planned capacity of distributed photovoltaic and energy storage, the bilateral transaction volume, and the bilateral transaction price, including: A linearization method is introduced to linearize the cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network. The equivalent decomposition method is then used to transform the linearized cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network into a social cost minimization problem and a sub-problem of cost minimization for each subject. The social cost minimization problem and the subproblems of minimizing the costs of each subject are solved sequentially using the alternating direction multiplier method to obtain the planned capacity of distributed photovoltaic and energy storage, the bilateral transaction electricity volume, and the bilateral transaction electricity price. The first set of constraints includes power flow constraints, voltage / current constraints, PV installed capacity and active-reactive power constraints, AC side energy storage installed capacity and active-reactive power constraints, converter station constraints, and DC power flow constraints. The DC power flow constraints are power flow constraints described by the branch power flow model. The second set of constraints includes power balance constraints, photovoltaic installation capacity and active power output constraints, energy storage installation capacity and active power output constraints, DC / DC converter station constraints, and DC / AC converter station constraints.

2. The medium- and low-voltage AC / DC distribution network planning method according to claim 1, characterized in that, Obtain typical time-series scenarios for distributed photovoltaic (PV) power and loads at a preset time scale, and acquire cooperative game planning strategies for multiple investment and operation entities in medium- and low-voltage AC / DC distribution networks, including: Historical data on the output and load of the distributed photovoltaic system are obtained from the historical database. Using a time-series scene clustering algorithm, the historical data is reduced to the typical time-series scene at the preset time scale.

3. The method for planning medium and low voltage AC / DC distribution networks according to claim 1, characterized in that, The investment and operation and maintenance costs of the medium-voltage distribution network include: the investment costs of photovoltaic and energy storage in the medium-voltage distribution network, as well as the operation and maintenance costs of photovoltaic, energy storage and converter stations in the medium-voltage distribution network; the investment and operation and maintenance costs of the low-voltage distribution network include: the investment costs of photovoltaic and energy storage in the low-voltage distribution network, as well as the operation and maintenance costs of photovoltaic, energy storage and converter stations in the low-voltage distribution network. The objective function for the medium-voltage distribution network is: ； The objective function for the low-voltage distribution network is: ； in, For the investment costs of photovoltaic and energy storage in medium-voltage distribution networks, To cover the operation and maintenance costs of photovoltaic, energy storage, and converter stations in medium-voltage distribution networks, For the cost of purchasing electricity for medium-voltage distribution networks, For the bilateral market transaction costs of medium-voltage distribution networks, To reduce the investment costs of photovoltaic and energy storage in low-voltage distribution networks, To reduce the operation and maintenance costs of photovoltaic, energy storage, and converter stations in low-voltage distribution networks, This reduces the transaction costs in the bilateral market for low-voltage distribution networks.

4. A medium- and low-voltage AC / DC distribution network planning device, characterized in that, include: The acquisition module is used to acquire typical time-series scenarios of distributed photovoltaic and load at a preset time scale, as well as to acquire the cooperative game planning ideas of multiple investment and operation entities in medium and low voltage AC and DC distribution networks. The medium-voltage model building module is used to establish a coordinated planning model for distributed photovoltaic and energy storage in medium-voltage AC / DC distribution networks, with the objective function of minimizing the investment and operation and maintenance costs, electricity purchase costs, and bilateral market transaction costs of medium-voltage distribution networks, combined with the pre-set first constraint conditions. The low-voltage model building module is used to establish a coordinated planning model for distributed photovoltaic and energy storage in low-voltage AC / DC distribution networks, with the objective function of minimizing the investment and operation and maintenance costs and the transaction costs of the bilateral market of low-voltage distribution networks, combined with the pre-set second constraint conditions. The model conversion module is used to convert the medium-voltage AC / DC distribution network distributed photovoltaic and energy storage coordination planning model and the low-voltage AC / DC distribution network distributed photovoltaic and energy storage coordination planning model into a medium-voltage distribution network and low-voltage distribution network cooperative game planning model based on Nash bargaining, based on the cooperative game planning idea. The solution module is used to sequentially solve the cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network based on the typical time-series scenario, and obtain the planned capacity of distributed photovoltaic and energy storage, bilateral transaction electricity volume and bilateral transaction electricity price in turn; The solution module includes: The model transformation submodule is used to introduce a linearization method to linearize the cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network, and to use the equivalent decomposition method to transform the linearized cooperative game planning model of the medium-voltage distribution network and the low-voltage distribution network into a social cost minimization problem and a sub-problem of cost minimization for each subject. The planning submodule is used to sequentially solve the social cost minimization problem and the subproblems of minimizing the costs of each subject using the alternating direction multiplier method, so as to obtain the planned capacity of distributed photovoltaic and energy storage, the bilateral transaction electricity volume, and the bilateral transaction electricity price.

5. The medium- and low-voltage AC / DC distribution network planning device according to claim 4, characterized in that, The acquisition module includes: The acquisition submodule is used to obtain historical data on the output and load of the distributed photovoltaic system from the historical database. The time-series scene generation submodule is used to reduce the historical data to the typical time-series scene at the preset time scale by using a time-series scene clustering algorithm.

6. The medium- and low-voltage AC / DC distribution network planning device according to claim 4, characterized in that, The investment and operation and maintenance costs of the medium-voltage distribution network include: the investment costs of photovoltaic and energy storage in the medium-voltage distribution network, as well as the operation and maintenance costs of photovoltaic, energy storage and converter stations in the medium-voltage distribution network; the investment and operation and maintenance costs of the low-voltage distribution network include: the investment costs of photovoltaic and energy storage in the low-voltage distribution network, as well as the operation and maintenance costs of photovoltaic, energy storage and converter stations in the low-voltage distribution network. The objective function for the medium-voltage distribution network is: ； The objective function for the low-voltage distribution network is: ； in, For the investment costs of photovoltaic and energy storage in medium-voltage distribution networks, To cover the operation and maintenance costs of photovoltaic, energy storage, and converter stations in medium-voltage distribution networks, For the cost of purchasing electricity for medium-voltage distribution networks, For the bilateral market transaction costs of medium-voltage distribution networks, To reduce the investment costs of photovoltaic and energy storage in low-voltage distribution networks, To reduce the operation and maintenance costs of photovoltaic, energy storage, and converter stations in low-voltage distribution networks, This reduces the transaction costs in the bilateral market for low-voltage distribution networks.

7. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer-readable instructions, which, when executed by the processor, perform the method as described in any one of claims 1-3.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by this processor, it performs the method as described in any one of claims 1-3.

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