Distribution network electric energy transaction planning method, device and non-volatile storage medium

Abstract

The present invention discloses a method, device and non-volatile storage medium for planning power trading in a distribution network. The method includes: obtaining power information of a target distribution network; dividing the target distribution network into multiple partitions based on the power information; establishing corresponding calculation models for the target distribution network, shared energy storage power station and photovoltaic community; using master-slave game theory to construct a multi-layer master-slave game model including a master-slave relationship between the calculation models corresponding to the target distribution network, shared energy storage power station and photovoltaic community; based on the multi-layer master-slave game model, respectively calculating the power selling and purchasing prices of the distribution network, the maximum revenue of the shared energy storage service provider, the real-time service fee, and the maximum consumer surplus for each of the multiple partitions, and determining the power trading plan within the target distribution network accordingly. The present invention solves the technical problem that the rationality of the power trading plan of the distribution network is low due to the lack of comprehensive consideration of the impact between the distribution network, shared energy storage power station and photovoltaic community.

Description

Technical Field

[0001] The present invention relates to the field of power system energy storage technology, and in particular to a distribution network electric energy trading planning method, device and non-volatile storage medium. Background Art

[0002] In the power industry, new energy sources are gradually replacing fossil energy power generation. At the same time, problems with new energy sources, such as volatility and randomness, also bring new challenges to the safe and stable operation of the power system. Energy storage technology has become an important technical means for large-scale grid connection of new energy sources, which can effectively reduce the impact of new energy volatility and randomness on the power grid.

[0003] With the rise of renewable energy, energy storage has become a crucial component of the power system. The study "Coordinated Optimization of Distribution Network Energy Storage Equipment Operation Strategies and Capacity Considering Demand-Side Electricity Prices" established a coordinated optimization model for demand-side electricity prices, energy storage operation strategies, and capacity allocation, maximizing distribution network revenue. However, energy storage investment costs are high, and independently building energy storage equipment cannot achieve energy complementarity among multiple participants.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] Embodiments of the present invention provide a distribution network power trading planning method, device and non-volatile storage medium to at least solve the technical problem that the rationality of distribution network power trading planning is low due to the lack of comprehensive consideration of the impact between the distribution network, shared energy storage power station and photovoltaic community.

[0006] According to one aspect of an embodiment of the present invention, a distribution network electric energy transaction planning method is provided, comprising: obtaining power information of a target distribution network; dividing the target distribution network into a plurality of partitions according to the power information; establishing corresponding calculation models for the target distribution network, the shared energy storage power station contained in the target distribution network, and the photovoltaic community contained in the area covered by the target distribution network, wherein the calculation model corresponding to the target distribution network comprises distribution network constraints and distribution network objective functions, the calculation model corresponding to the shared energy storage power station comprises shared energy storage power station constraints, shared energy storage service provider constraints, and shared energy storage service provider objective functions, and the calculation model corresponding to the photovoltaic community comprises photovoltaic community constraints and photovoltaic community objective functions; and adopting the master-slave game theory to construct a multi-layer master-slave game model including a master-slave relationship among the calculation models corresponding to the target distribution network, the shared energy storage power station, and the photovoltaic community, wherein the multi-layer master-slave game model includes ... The multi-layer master-slave game model includes a first-layer master-slave game model and a second-layer master-slave game model. The first-layer master-slave game model uses the calculation model corresponding to the target distribution network as the upper-layer leadership model, and the calculation model corresponding to the shared energy storage power station as the lower-layer follower model. The second-layer master-slave game model uses the calculation model corresponding to the shared energy storage power station as the upper-layer leadership model, and the calculation model corresponding to the photovoltaic community as the lower-layer follower model. Based on the multi-layer master-slave game model, the distribution network electricity sales and purchase prices, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximization of consumer surplus of each partition in multiple partitions are calculated respectively. Among them, the multi-layer master-slave game model is solved using Stackelberg equilibrium. According to the distribution network electricity sales and purchase prices, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximization of consumer surplus of each partition in multiple partitions, the electricity trading plan within the target distribution network is determined.

[0007] Optionally, based on the multi-layer master-slave game model, the distribution network electricity sales and purchase prices, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximization of consumer surplus for each of the multiple partitions are calculated respectively, including: obtaining multiple groups of distribution network parameters corresponding one-to-one to the multiple partitions; inputting the multiple groups of distribution network parameters into the first-layer master-slave game model respectively, and inputting the output results of the first-layer master-slave game model into the second-layer master-slave game model to obtain the distribution network electricity sales and purchase prices, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximization of consumer surplus for each of the multiple partitions.

[0008] Optionally, multiple groups of distribution network parameters are respectively input into the first-layer master-slave game model, and the output results of the first-layer master-slave game model are input into the second-layer master-slave game model to obtain the distribution network electricity sales and purchase prices, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximized consumer surplus of each of the multiple partitions, including: inputting multiple groups of distribution network parameters corresponding to the multiple partitions into the calculation model corresponding to the target distribution network in the first-layer master-slave game model, and the calculation model corresponding to the target distribution network outputs multiple first transaction electricity prices corresponding to the multiple partitions; inputting multiple first transaction electricity prices into the calculation model corresponding to the shared energy storage power station in the first-layer master-slave game model, and the calculation model corresponding to the shared energy storage power station outputs the output results of the first-layer master-slave game model, wherein the output results include the multiple partitions respectively. Corresponding multiple first revenues and multiple first service fees; the output result of the first-layer master-slave game model is input into the calculation model corresponding to the photovoltaic community in the second-layer master-slave game model, and the calculation model corresponding to the photovoltaic community outputs multiple first consumer surpluses corresponding to multiple partitions; the multi-layer master-slave game model adjusts the output result of the calculation model corresponding to the shared energy storage power station according to the multiple first consumer surpluses corresponding to the multiple partitions, and then adjusts the output result of the calculation model corresponding to the target distribution network according to the output result of the calculation model corresponding to the shared energy storage power station, until the output result of the multi-layer master-slave game model satisfies the Stackelberg equilibrium, the multi-layer master-slave game model outputs the distribution network electricity sales and purchase prices, the maximum revenue of the shared energy storage service provider, the real-time service fee, and the maximized consumer surplus for each partition in the multiple partitions.

[0009] Optionally, the calculation model corresponding to the target distribution network includes: the distribution network objective function is: Where: is the distribution network revenue of the nth partition; and are the electricity selling price and electricity purchasing price of the target distribution network at time t respectively; and are the power transmitted outward and input to the target distribution network at time t, respectively. T is the number of moments, and Δt is the time interval between two adjacent moments. The distribution network constraints are: Where: and are the maximum selling price and the maximum purchasing price of the target distribution network, and are the electricity selling price and electricity purchasing price of the target distribution network respectively.

[0010] Optionally, the calculation model corresponding to the shared energy storage power station includes: the objective function of the shared energy storage service provider is: Where: The revenue of the shared energy storage service provider in the nth partition; ess (t) is the service fee of the shared energy storage service provider at time t; and are the charging power and discharging power of the shared energy storage power station at time t, is the electricity price of the target distribution network at time t, and Δt is the time interval between two adjacent times. The constraints of the shared energy storage power station include energy conservation constraint, capacity constraint, charge and discharge power conservation constraint, and charge and discharge power limit constraint. Among them, the energy conservation constraint is: Where: E(t+1) is the power of the shared energy storage station at time t+1; E(t) is the power of the shared energy storage station at time t; u is the self-discharge rate of the shared energy storage station; η c and η d are the charging efficiency and discharging efficiency of the shared energy storage power station respectively; the capacity constraint is: E min ≤E(t)≤E max , where: E min and E max are the lower and upper capacity limits of the shared energy storage power station; the charge and discharge power conservation constraints are: The charge and discharge power limit constraints are: U c +U d ≤1, U c ∈{0,1},U d ∈{0,1}, where: P max The maximum charging and discharging power allowed for the shared energy storage power station, and are the charging power and discharging power of the shared energy storage power station respectively; U c and U d The charging and discharging status of the shared energy storage power station; the shared energy storage service provider constraints are: Where: is the maximum service fee of the shared energy storage service provider, λ ess It is the service fee of the shared energy storage service provider.

[0011] Optionally, the calculation model corresponding to the photovoltaic community includes: the initial load of the photovoltaic community is: P i,0 (t) = P i,base0 (t)+P i,loss0 (t)+P i,shift0 (t), where: P i,0 (t) is the original load of the ith photovoltaic community at time t, P i,base0 (t), P i,loss0 (t) and P i,shift0(t) are the base load, curtailable load and transferable load of the ith photovoltaic community at time t; the load adjusted by the photovoltaic community is: P i,L (t) = P i,base0 (t)+P i,loss0 (t)+P i,shift0 (t)-P i,loss (t)-P i,shift (t), where: P i,L (t) is the load of the ith photovoltaic community after adjustment at time t; P i,loss (t) and P i,shift (t) are the load reduction and transfer power of the ith photovoltaic community at time t; the photovoltaic community constraint condition is: 0≤P i,loss (t)≤P i,loss0 (t), 0≤P i,shift (t)≤P i,shift0 (t), Where: P c (t) and P d (t) are the charging power and discharging power of the shared energy storage power station at time t; P i,pv (t) is the photovoltaic power generation power of community i at time t; N is the number of photovoltaics; the photovoltaic community objective function is: Where: is the maximum consumer surplus of the photovoltaic community in the nth partition; α i is the preference coefficient for consuming electric energy, and Δt is the time interval between two adjacent moments.

[0012] Optionally, dividing the target distribution network into a plurality of partitions according to the power information includes: obtaining a plurality of partition constraints; dividing the target distribution network into the plurality of partitions according to the power information and the plurality of partition constraints, wherein the plurality of partition constraints include: Where: P i,0 (t) is the original load of PV community i at time t; k is the ratio coefficient between energy storage capacity and load; M is the number of PV communities; is the upper limit of the capacity of the nth shared energy storage power station; is the power limit of the nth shared energy storage power station; Where: P min and P max are the valley and peak values ​​of the target distribution network, respectively; Where: P n (t) is the total load of the nth partition at time t.

[0013] According to another aspect of an embodiment of the present invention, a distribution network electric energy trading planning device is also provided, including: an acquisition module for acquiring power information of a target distribution network, wherein the target distribution network includes a shared energy storage power station, and the area covered by the target distribution network includes a photovoltaic community, and the power information includes the daily load demand of the target distribution network, the number of shared energy storage power stations, the capacity of the shared energy storage power station, the power of the shared energy storage power station, and the photovoltaic daily output curve of the photovoltaic community; a partitioning module for dividing the target distribution network into multiple partitions according to the power information; a first modeling module for establishing calculation models corresponding to the target distribution network, the shared energy storage power station and the photovoltaic community, wherein the calculation model corresponding to the target distribution network includes distribution network constraints and distribution network objective functions, the calculation model corresponding to the shared energy storage power station includes shared energy storage power station constraints, shared energy storage service provider constraints and shared energy storage service provider objective functions, and the calculation model corresponding to the photovoltaic community includes shared energy storage power station constraints, shared energy storage service provider constraints and shared energy storage service provider objective functions, and the calculation model corresponding to the photovoltaic community includes shared energy storage power station constraints, shared energy storage service provider constraints and shared energy storage service provider objective functions. The second modeling module is used to construct a multi-layer master-slave game model including the master-slave relationship between the calculation models corresponding to the target distribution network, the shared energy storage power station and the photovoltaic community using the master-slave game theory, wherein the multi-layer master-slave game model includes a first-layer master-slave game model and a second-layer master-slave game model. The first-layer master-slave game model uses the calculation model corresponding to the target distribution network as the upper-layer leadership model, and the calculation model corresponding to the shared energy storage power station as the lower-layer follower model. The second-layer master-slave game model uses the calculation model corresponding to the shared energy storage power station as the upper-layer leadership model, and the calculation model corresponding to the photovoltaic community as the lower-layer follower model. The calculation module is used to calculate the distribution network electricity sales and purchase prices, the maximum revenue of the shared energy storage service provider, the real-time service fee, and the maximization of consumer surplus for each of the multiple partitions based on the multi-layer master-slave game model. The multi-layer master-slave game model is solved using Stackelberg equilibrium.

[0014] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is also provided, which includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute any one of the above-mentioned distribution network power trading planning methods.

[0015] According to another aspect of an embodiment of the present invention, a computer device is provided. The computer device includes a processor, and the processor is used to run a program, wherein when the program is run, any one of the above-mentioned distribution network power transaction planning methods is executed.

[0016] In an embodiment of the present invention, a multi-layer master-slave game model is established to unify the calculation models corresponding to the target distribution network, shared energy storage power station and photovoltaic community. By dividing the target distribution network into different partitions, the multi-layer master-slave game model is used to calculate the distribution network electricity sales and purchase prices, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximization of consumer surplus in different partitions, thereby achieving the purpose of comprehensively considering the maximization of the interests of all parts in the power grid transaction, thereby achieving the technical effect of improving the rationality of planning distribution network power transactions, and further solving the technical problem of low rationality of distribution network power transaction planning due to failure to comprehensively consider the influence between the distribution network, shared energy storage power station and photovoltaic community. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 A hardware structure block diagram of a computer terminal for implementing a method for planning power transaction in a distribution network is shown;

[0019] Figure 2 1 is a flow chart of a method for planning power transaction in a distribution network according to an embodiment of the present invention;

[0020] Figure 3 is a schematic diagram of a multi-layer master-slave game model provided according to an optional embodiment of the present invention;

[0021] Figure 4 is a schematic diagram of constructing a multi-layer master-slave game model according to an optional embodiment of the present invention;

[0022] Figure 5 is a schematic diagram of distribution network transaction optimization provided according to an optional embodiment of the present invention;

[0023] Figure 6 It is a structural block diagram of a distribution network electric energy transaction planning device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] According to an embodiment of the present invention, a method embodiment of power distribution network trading planning is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal for implementing a distribution network power transaction planning method is shown. Figure 1 As shown, the computer terminal 10 may include one or more (illustrated as 102a, 102b, ..., 102n in the figure) processors (the processor may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices), a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0028] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10. As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0029] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the distribution network power transaction planning method in the embodiment of the present invention. The processor executes the software programs and modules stored in the memory 104 to perform various functional applications and data processing, that is, to implement the distribution network power transaction planning method of the above-mentioned application. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely located relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0030] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0031] Figure 2 FIG. 1 is a flow chart of a method for planning power distribution network energy transactions according to an embodiment of the present invention. Figure 2 As shown, the method includes the following steps:

[0032] Step S202: Acquire power information of the target distribution network.

[0033] In this step, the target distribution network is the entire power grid in a region. A shared energy storage power station operated by a shared energy storage service provider is established in this region, and a photovoltaic community with its own photovoltaic power station is also established. When the photovoltaic power station can be self-sufficient for the photovoltaic community, the photovoltaic community does not need to obtain electricity from the distribution network. When the photovoltaic power station has overflow electricity, the overflow electricity can be sold to the distribution network. When the photovoltaic power station cannot be self-sufficient for the photovoltaic community, the photovoltaic community needs to purchase electricity from the distribution network. The shared energy storage power station can store electricity when the power supply overflows and provide electricity to the load when the load demand in the distribution network is large. Among them, the target distribution network includes the shared energy storage power station, and the area covered by the target distribution network includes the photovoltaic community. The power information includes the daily load demand of the target distribution network, the number of shared energy storage power stations, the capacity of the shared energy storage power station, the power of the shared energy storage power station, and the photovoltaic daily output curve of the photovoltaic community.

[0034] Step S204: Divide the target distribution network into multiple partitions according to the power information.

[0035] In this step, the target distribution network can be dynamically partitioned reasonably on a daily basis based on the number, capacity, and power of shared energy storage power stations in the target distribution network, the daily load demand, and the daily output curve of the photovoltaic community.

[0036] Step S206: Establish corresponding calculation models for the target distribution network, the shared energy storage power station included in the target distribution network, and the photovoltaic community included in the area covered by the target distribution network. The calculation model corresponding to the target distribution network includes distribution network constraints and distribution network objective functions, the calculation model corresponding to the shared energy storage power station includes shared energy storage power station constraints, shared energy storage service provider constraints, and shared energy storage service provider objective functions, and the calculation model corresponding to the photovoltaic community includes photovoltaic community constraints and photovoltaic community objective functions.

[0037] In this step, different business model planning strategies can be formulated for the distribution network, shared energy storage power station, and photovoltaic community based on their different characteristics. That is, optimization models for the distribution network, shared energy storage power station, and photovoltaic community can be established, and their respective benefits can be maximized or costs can be minimized by adding different objective functions and constraints.

[0038] Step S208: A multi-layer master-slave game model is constructed using master-slave game theory, including the master-slave relationship between the calculation models corresponding to the target distribution network, the shared energy storage power station, and the photovoltaic community. The multi-layer master-slave game model includes a first-layer master-slave game model and a second-layer master-slave game model. The first-layer master-slave game model uses the calculation model corresponding to the target distribution network as the upper-layer leader model and the calculation model corresponding to the shared energy storage power station as the lower-layer follower model. The second-layer master-slave game model uses the calculation model corresponding to the shared energy storage power station as the upper-layer leader model and the calculation model corresponding to the photovoltaic community as the lower-layer follower model.

[0039] In this step, the optimization models of the distribution network, shared energy storage power station and photovoltaic community can be combined and connected, and a multi-layer master-slave game model between the three optimization models can be constructed using the master-slave game theory.

[0040] Step S210 , based on a multi-layer master-slave game model, respectively calculates the electricity sales and purchase prices of the distribution network for each of the multiple partitions, the maximum revenue of the shared energy storage service provider, the real-time service fee, and the maximum consumer surplus, wherein the multi-layer master-slave game model is solved using Stackelberg equilibrium.

[0041] In this step, based on the zoning results of the target distribution network, each zone is planned based on the business model planning strategy and optimization model of the distribution network, shared energy storage service provider, and photovoltaic community, and the carbon emissions of the distribution network are taken into account. The daily revenue and cost of the distribution network, shared energy storage service provider, and photovoltaic community are obtained respectively. Then, using a multi-layer master-slave game model, the real-time electricity price of the distribution network and the real-time service fee of the shared energy storage service provider are obtained.

[0042] Step S212: Determine the electricity trading plan within the target distribution network based on the electricity sales and purchase prices of the distribution network in each of the multiple partitions, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximum consumer surplus.

[0043] In the embodiment of the present invention, a multi-layer master-slave game model is established to unify the calculation models corresponding to the target distribution network, shared energy storage power station and photovoltaic community. By dividing the target distribution network into different partitions, the multi-layer master-slave game model is used to respectively calculate the distribution network electricity sales and purchase prices, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximization of consumer surplus in different partitions, thereby achieving the purpose of comprehensively considering the maximization of the interests of all parts in the power grid transaction, thereby achieving the technical effect of improving the rationality of planning distribution network power transactions, and further solving the technical problem of low rationality of distribution network power transaction planning due to failure to comprehensively consider the influence between the distribution network, shared energy storage power station and photovoltaic community.

[0044] As an optional embodiment, based on a multi-layer master-slave game model, the distribution network electricity sales and purchase prices, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximization of consumer surplus for each of the multiple partitions are calculated respectively, including: obtaining multiple groups of distribution network parameters corresponding to the multiple partitions one by one; inputting the multiple groups of distribution network parameters into the first-layer master-slave game model respectively, and inputting the output results of the first-layer master-slave game model into the second-layer master-slave game model to obtain the distribution network electricity sales and purchase prices, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximization of consumer surplus for each of the multiple partitions.

[0045] Alternatively, to maximize the benefits of the distribution network, the shared energy storage service provider, and the consumer surplus of the user aggregator, this paper introduces the concept of a master-slave game. In a master-slave game, the leader has a leadership advantage, enabling them to seize the initiative or a favorable position in the game. Followers follow the leader in their game, and the leader ultimately makes the most advantageous decision based on the followers' decisions. The core idea is to have the leader and followers adjust their strategies based on each other's strategies to maximize their respective interests. For the distribution network and shared energy storage service provider, the former acts as the leader, and the latter as the follower. For the shared energy storage service provider and the user aggregator, the former acts as the leader, and the latter as the follower.

[0046] Figure 3 Schematic diagram of a multi-layer master-slave game model according to an optional embodiment of the present invention. Figure 3 As shown, the multi-layer master-slave game model includes a distribution network calculation model, a shared energy storage power station service provider model, and a user aggregator model. The distribution network calculation model aims to maximize revenue, with the purchase and sales prices as variables. The shared energy storage power station service provider calculation model aims to maximize revenue, with the real-time service fee as a variable. The user aggregator calculation model aims to maximize consumer surplus, with the shiftable load as a variable. The multi-layer master-slave game model is mainly divided into two layers. The first layer is a game model between the distribution network and the shared energy storage power station service provider, with the distribution network as the leader and the shared energy storage power station service provider as the follower. The second layer is a game model between the shared energy storage power station service provider and the user aggregator, with the shared energy storage power station service provider as the leader and the user aggregator as the follower.

[0047] Figure 4 Schematic diagram of constructing a multi-layer master-slave game model according to an optional embodiment of the present invention. Figure 4 As shown in the figure, when establishing a multi-layer master-slave game model, it is necessary to determine the participants of the game, the boundary conditions of the game and the payoff function of each player, and then establish a non-cooperative game with a master-slave structure, and solve the equilibrium solution of the game according to the Stackelberg equilibrium strategy.

[0048] The master-slave game model solution process is a process from the upper layer to the lower layer and then back to the upper layer to form a cycle. The leader and follower adjust their own strategies according to the other party's strategy to maximize their respective interests. The mathematical expression of the nth game of the master-slave game is as follows: j∈N has:

[0049] Leader: Follower:

[0050] Where: M and N are the leaders and followers respectively; x n and y n is the strategy of the leader and follower in the nth game; S(x n-1 ) is the n-1th strategy generated by the follower based on the leader’s n-1 games; S(y n-1 ) is the nth strategy generated by the leader based on the follower’s n-1 games, F i (x n ,y n-1 ) is the strategy of the game leader and follower, respectively, x n ,y n-1 The interests of the leaders, i (x n ,y n ) is the strategy of the game leader and follower, respectively, x n ,y n interests of followers.

[0051] The Stackelberg equilibrium is defined as follows: j∈N has:

[0052]

[0053]

[0054] Where (x * ,y * ) is the Stackelberg equilibrium solution of this game.

[0055] Where: is the optimal strategy of all leaders except individual i; is the optimal strategy of all followers except individual j; is the optimal strategy of leader individual i; is the optimal strategy of follower individual j; x i is the strategy of leader individual i; y j is the strategy of follower individual j.

[0056] As an optional embodiment, multiple groups of distribution network parameters are respectively input into the first-layer master-slave game model, and the output results of the first-layer master-slave game model are input into the second-layer master-slave game model to obtain the distribution network electricity sales and purchase prices of each of the multiple partitions, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximized consumer surplus, including: inputting multiple groups of distribution network parameters corresponding to the multiple partitions into the calculation model corresponding to the target distribution network in the first-layer master-slave game model, and the calculation model corresponding to the target distribution network outputs multiple first transaction electricity prices corresponding to the multiple partitions; inputting multiple first transaction electricity prices into the calculation model corresponding to the shared energy storage power station in the first-layer master-slave game model, and the calculation model corresponding to the shared energy storage power station outputs the output results of the first-layer master-slave game model, wherein the output results include multiple partitions. The output results of the first-layer master-slave game model are input into the calculation model corresponding to the photovoltaic community in the second-layer master-slave game model, and the calculation model corresponding to the photovoltaic community outputs multiple first consumer surpluses corresponding to the multiple partitions; the multi-layer master-slave game model adjusts the output results of the calculation model corresponding to the shared energy storage power station according to the multiple first consumer surpluses corresponding to the multiple partitions, and then adjusts the output results of the calculation model corresponding to the target distribution network according to the output results of the calculation model corresponding to the shared energy storage power station, until the output results of the multi-layer master-slave game model meet the Stackelberg equilibrium, the multi-layer master-slave game model outputs the distribution network electricity sales and purchase prices, the maximum income of the shared energy storage service provider, the real-time service fee, and the maximized consumer surplus for each partition in the multiple partitions.

[0057] Figure 5 Schematic diagram of distribution network transaction optimization according to an optional embodiment of the present invention. Figure 5 As shown in the figure, according to the zoning results of the target distribution network, each zone is based on the business model planning strategy and optimization model of the distribution network, shared energy storage service provider and photovoltaic community, and the carbon emissions of the distribution network are taken into account. The daily revenue and cost of the distribution network, shared energy storage service provider and photovoltaic community are obtained respectively. Then, the multi-layer master-slave game model is used to obtain the real-time electricity price of the distribution network and the real-time service fee of the shared energy storage service provider.

[0058] As an optional embodiment, the calculation model corresponding to the target distribution network includes: the distribution network objective function is: Where: is the distribution network revenue of the nth partition; and are the electricity selling price and electricity purchasing price of the target distribution network at time t respectively; and are the power transmitted outward and input to the target distribution network at time t, respectively. T is the number of moments, and Δt is the time interval between two adjacent moments. The distribution network constraints are: Where: and are the maximum selling price and the maximum purchasing price of the target distribution network, and are the electricity selling price and electricity purchasing price of the target distribution network respectively.

[0059] As an optional embodiment, the calculation model corresponding to the shared energy storage power station includes: the objective function of the shared energy storage service provider is: Where: The revenue of the shared energy storage service provider in the nth partition; ess (t) is the service fee of the shared energy storage service provider at time t; and are the charging power and discharging power of the shared energy storage power station at time t, is the electricity price of the target distribution network at time t, and Δt is the time interval between two adjacent times. The constraints of the shared energy storage power station include energy conservation constraint, capacity constraint, charge and discharge power conservation constraint, and charge and discharge power limit constraint. Among them, the energy conservation constraint is: Where: E(t+1) is the power of the shared energy storage station at time t+1; E(t) is the power of the shared energy storage station at time t; u is the self-discharge rate of the shared energy storage station; η c and η d are the charging efficiency and discharging efficiency of the shared energy storage power station respectively; the capacity constraint is: E min ≤E(t)≤E max , where: E min and E max are the lower and upper capacity limits of the shared energy storage power station; the charge and discharge power conservation constraints are: The charge and discharge power limit constraints are: U c +U d ≤1, U c ∈{0,1},U d ∈{0,1}, where: P max The maximum charging and discharging power allowed for the shared energy storage power station, and are the charging power and discharging power of the shared energy storage power station respectively; U c and U d The charging and discharging status of the shared energy storage power station; the shared energy storage service provider constraints are: Where: is the maximum service fee of the shared energy storage service provider, λ ess It is the service fee of the shared energy storage service provider.

[0060] As an optional embodiment, the calculation model corresponding to the photovoltaic community includes: the initial load of the photovoltaic community is: P i,0 (t) = P i,base0 (t)+P i,loss0 (t)+P i,shift0 (t), where: P i,0 (t) is the original load of the ith photovoltaic community at time t, P i,base0 (t), P i,loss0 (t) and P i,shift0 (t) are the base load, curtailable load and transferable load of the ith photovoltaic community at time t; the load adjusted by the photovoltaic community is: P i,L (t) = P i,base0 (t)+P i,loss0 (t)+P i,shift0 (t)-P i,loss (t)-P i,shift (t), where: P i,L (t) is the load of the ith photovoltaic community after adjustment at time t; P i,loss (t) and P i,shift (t) are the load reduction and transfer power of the ith photovoltaic community at time t; the photovoltaic community constraint condition is: 0≤P i,loss (t)≤P i,loss0 (t), 0≤P i,shift (t)≤P i,shift0 (t), Where: P c (t) and P d (t) are the charging power and discharging power of the shared energy storage power station at time t; P i,pv (t) is the photovoltaic power generation power of community i at time t; N is the number of photovoltaics; the photovoltaic community objective function is: Where: is the maximum consumer surplus of the photovoltaic community in the nth partition; α i is the preference coefficient for consuming electric energy, and Δt is the time interval between two adjacent moments.

[0061] As an optional embodiment, dividing the target distribution network into multiple partitions based on power information includes: obtaining multiple partition constraints; dividing the target distribution network into multiple partitions based on the power information and the multiple partition constraints, wherein the multiple partition constraints include: Where: P i,0 (t) is the original load of PV community i at time t; k is the ratio coefficient between energy storage capacity and load; M is the number of PV communities; is the upper limit of the capacity of the nth shared energy storage power station; is the power limit of the nth shared energy storage power station; Where: P min and P max are the valley and peak values ​​of the target distribution network, respectively; Where: P n (t) is the total load of the nth partition at time t.

[0062] Optionally, the number of shared energy storage power stations can be used as the number of partitions based on the number, capacity and power of shared energy storage power stations. The load can be reasonably distributed based on the capacity and power status of each shared energy storage. The complementary load characteristics of the photovoltaic community can be used to divide the loads with complementary characteristics into a partition, which can effectively improve the peak-to-valley difference between the distribution network and the shared energy storage power supply. The load balancing characteristics of each partition should be considered. The power supply capacity of the shared energy storage should be considered. The total daily load of each partition should be kept balanced, and the partitioning should be carried out accordingly.

[0063] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0064] Through the description of the above implementation methods, those skilled in the art can clearly understand that the distribution network power transaction planning method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0065] According to an embodiment of the present invention, a distribution network power transaction planning device for implementing the above-mentioned distribution network power transaction planning method is also provided. Figure 6 FIG. 1 is a structural block diagram of a distribution network power energy transaction planning device according to an embodiment of the present invention. Figure 6 As shown, the distribution network electric energy transaction planning device includes: an acquisition module 60, a partitioning module 62, a first modeling module 64, a second modeling module 66 and a calculation module 68. The distribution network electric energy transaction planning device is described below.

[0066] An acquisition module 60 is configured to acquire power information of a target distribution network, wherein the target distribution network includes a shared energy storage power station, and the area covered by the target distribution network includes a photovoltaic community. The power information includes the daily load demand of the target distribution network, the number of shared energy storage power stations, the capacity of the shared energy storage power stations, the power of the shared energy storage power stations, and the daily photovoltaic output curve of the photovoltaic community.

[0067] The partitioning module 62 is connected to the acquisition module 60 and is used to divide the target distribution network into multiple partitions according to the power information.

[0068] The first modeling module 64 is connected to the partitioning module 62 and is used to establish calculation models corresponding to the target distribution network, shared energy storage power station and photovoltaic community. The calculation model corresponding to the target distribution network includes distribution network constraints and distribution network objective functions, the calculation model corresponding to the shared energy storage power station includes shared energy storage power station constraints, shared energy storage service provider constraints and shared energy storage service provider objective functions, and the calculation model corresponding to the photovoltaic community includes photovoltaic community constraints and photovoltaic community objective functions.

[0069] The second modeling module 66 is connected to the first modeling module 64 and is used to use the master-slave game theory to construct a multi-layer master-slave game model including the master-slave relationship between the calculation models corresponding to the target distribution network, the shared energy storage power station and the photovoltaic community. The multi-layer master-slave game model includes a first-layer master-slave game model and a second-layer master-slave game model. The first-layer master-slave game model uses the calculation model corresponding to the target distribution network as the upper-layer leadership model and the calculation model corresponding to the shared energy storage power station as the lower-layer follower model. The second-layer master-slave game model uses the calculation model corresponding to the shared energy storage power station as the upper-layer leadership model and the calculation model corresponding to the photovoltaic community as the lower-layer follower model.

[0070] The calculation module 68 is connected to the second modeling module 66 and is used to calculate the distribution network electricity sales and purchase prices, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximum consumer surplus for each of the multiple partitions based on a multi-layer master-slave game model, wherein the multi-layer master-slave game model is solved using Stackelberg equilibrium.

[0071] The determination module 70 is connected to the calculation module 68 and is used to determine the electricity trading plan within the target distribution network based on the distribution network electricity sales and purchase prices of each partition in the multiple partitions, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximization of consumer surplus.

[0072] It should be noted that the acquisition module 60, partitioning module 62, first modeling module 64, second modeling module 66, calculation module 68, and determination module 70 described above correspond to steps S202 to S212 in the embodiment. The examples and application scenarios implemented by these modules and corresponding steps are the same, but are not limited to those disclosed in the above embodiment. It should be noted that the above modules, as part of the apparatus, can be run in the computer terminal 10 provided in the embodiment.

[0073] An embodiment of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0074] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the distribution network power transaction planning method and device in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned distribution network power transaction planning method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely located relative to the processor, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0075] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: obtaining the power information of the target distribution network, wherein the target distribution network includes a shared energy storage power station, the area covered by the target distribution network includes a photovoltaic community, and the power information includes the daily load demand of the target distribution network, the number of shared energy storage power stations, the capacity of the shared energy storage power station, the power of the shared energy storage power station, and the photovoltaic daily output curve of the photovoltaic community; dividing the target distribution network into multiple partitions according to the power information; establishing calculation models corresponding to the target distribution network, the shared energy storage power station and the photovoltaic community respectively, wherein the calculation model corresponding to the target distribution network includes distribution network constraints and distribution network objective functions, the calculation model corresponding to the shared energy storage power station includes shared energy storage power station constraints, shared energy storage service provider constraints and shared energy storage service provider objective functions, and the calculation model corresponding to the photovoltaic community includes photovoltaic community constraints. District constraints and photovoltaic community objective functions; the master-slave game theory is used to construct a multi-layer master-slave game model including the master-slave relationship between the calculation models corresponding to the target distribution network, shared energy storage power station and photovoltaic community, wherein the multi-layer master-slave game model includes a first-layer master-slave game model and a second-layer master-slave game model. The first-layer master-slave game model uses the calculation model corresponding to the target distribution network as the upper-layer leadership model, and the calculation model corresponding to the shared energy storage power station as the lower-layer follower model. The second-layer master-slave game model uses the calculation model corresponding to the shared energy storage power station as the upper-layer leadership model, and the calculation model corresponding to the photovoltaic community as the lower-layer follower model; based on the multi-layer master-slave game model, the distribution network electricity sales and purchase prices, the maximum revenue of the shared energy storage service provider, the real-time service fee, and the maximized consumer surplus of each partition in multiple partitions are calculated respectively. wherein the multi-layer master-slave game model is solved using Stackelberg equilibrium.

[0076] In an embodiment of the present invention, a multi-layer master-slave game model is established to unify the calculation models corresponding to the target distribution network, shared energy storage power station and photovoltaic community. By dividing the target distribution network into different partitions, the multi-layer master-slave game model is used to calculate the distribution network electricity sales and purchase prices, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximization of consumer surplus in different partitions, thereby achieving the purpose of comprehensively considering the maximization of the interests of all parts in the power grid transaction, thereby achieving the technical effect of improving the rationality of planning distribution network power transactions, and further solving the technical problem of low rationality of distribution network power transaction planning due to failure to comprehensively consider the influence between the distribution network, shared energy storage power station and photovoltaic community.

[0077] A person skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a non-volatile storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0078] The embodiment of the present invention further provides a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store the program code executed by the distribution network power transaction planning method provided in the above embodiment.

[0079] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0080] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: obtaining power information of the target distribution network, wherein the target distribution network includes a shared energy storage power station, the area covered by the target distribution network includes a photovoltaic community, and the power information includes the daily load demand of the target distribution network, the number of shared energy storage power stations, the capacity of the shared energy storage power station, the power of the shared energy storage power station, and the photovoltaic daily output curve of the photovoltaic community; dividing the target distribution network into multiple partitions according to the power information; establishing calculation models corresponding to the target distribution network, the shared energy storage power station and the photovoltaic community respectively, wherein the calculation model corresponding to the target distribution network includes distribution network constraints and distribution network objective functions, the calculation model corresponding to the shared energy storage power station includes shared energy storage power station constraints, shared energy storage service provider constraints and shared energy storage service provider objective functions, and the calculation model corresponding to the photovoltaic community includes photovoltaic constraints. The constraints of the photovoltaic community and the objective function of the photovoltaic community are constructed; the master-slave game theory is used to construct a multi-layer master-slave game model including the master-slave relationship between the calculation models corresponding to the target distribution network, the shared energy storage power station and the photovoltaic community, wherein the multi-layer master-slave game model includes a first-layer master-slave game model and a second-layer master-slave game model. The first-layer master-slave game model uses the calculation model corresponding to the target distribution network as the upper-layer leadership model, and the calculation model corresponding to the shared energy storage power station as the lower-layer follower model. The second-layer master-slave game model uses the calculation model corresponding to the shared energy storage power station as the upper-layer leadership model, and the calculation model corresponding to the photovoltaic community as the lower-layer follower model; based on the multi-layer master-slave game model, the distribution network electricity sales and purchase prices, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximized consumer surplus of each partition in multiple partitions are calculated respectively. wherein the multi-layer master-slave game model is solved using Stackelberg equilibrium.

[0081] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0082] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0084] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0085] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0086] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, and other media that can store program code.

[0087] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for planning power trading in a distribution network, characterized in that: include: Obtain power information of the target distribution network; Dividing the target distribution network into a plurality of partitions according to the power information; Establishing calculation models corresponding to the target distribution network, the shared energy storage power station included in the target distribution network, and the photovoltaic community included in the area covered by the target distribution network, wherein the calculation model corresponding to the target distribution network includes distribution network constraints and distribution network objective functions, the calculation model corresponding to the shared energy storage power station includes shared energy storage power station constraints, shared energy storage service provider constraints, and shared energy storage service provider objective functions, and the calculation model corresponding to the photovoltaic community includes photovoltaic community constraints and photovoltaic community objective functions; A multi-layer master-slave game model is constructed using master-slave game theory, including a master-slave relationship between the calculation models corresponding to the target distribution network, the shared energy storage power station, and the photovoltaic community. The multi-layer master-slave game model includes a first-layer master-slave game model and a second-layer master-slave game model. The first-layer master-slave game model uses the calculation model corresponding to the target distribution network as the upper-layer leader model and the calculation model corresponding to the shared energy storage power station as the lower-layer follower model. The second-layer master-slave game model uses the calculation model corresponding to the shared energy storage power station as the upper-layer leader model and the calculation model corresponding to the photovoltaic community as the lower-layer follower model. Based on the multi-layer master-slave game model, the power sales and purchase prices of the distribution network, the maximum revenue of the shared energy storage service provider, the real-time service fee, and the maximized consumer surplus are calculated for each of the multiple partitions, wherein the multi-layer master-slave game model is solved using Stackelberg equilibrium; Determining an energy trading plan within the target distribution network based on the electricity sales and purchase prices of the distribution network in each of the multiple subareas, the maximum revenue of the shared energy storage service provider, the real-time service fee, and maximizing consumer surplus; Among them, the distribution network objective function is: Where: is the distribution network revenue of the nth partition; and are the electricity selling price and electricity purchasing price of the target distribution network at time t respectively; and are the power transmitted outward and the power input into the target distribution network at time t, respectively, T is the number of moments, and Δt is the time interval between two adjacent moments; The objective function of the shared energy storage service provider is: Where: The revenue of the shared energy storage service provider in the nth partition; ess (t) is the service fee of the shared energy storage service provider at time t; and are respectively the charging power and discharging power of the shared energy storage power station at time t; The photovoltaic community objective function is: Where: is the maximum consumer surplus of the photovoltaic community in the nth partition; α i is the preference coefficient for consuming electric energy; P i,0 (t) is the original load of the ith photovoltaic community at time t; P i,L (t) is the load of the ith photovoltaic community after adjustment at time t; P c (t) and P d (t) are the charging power and discharging power of the shared energy storage power station at time t respectively.

2. The method according to claim 1, characterized in that The method of calculating the electricity sales and purchase prices of the distribution network for each of the multiple partitions, the maximum revenue of the shared energy storage service provider, the real-time service fee, and maximizing consumer surplus based on the multi-layer master-slave game model includes: Acquire multiple groups of distribution network parameters corresponding one-to-one to the multiple partitions; The multiple groups of distribution network parameters are respectively input into the first-layer master-slave game model, and the output results of the first-layer master-slave game model are input into the second-layer master-slave game model to obtain the distribution network electricity sales and purchase prices, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximized consumer surplus for each of the multiple partitions.

3. The method according to claim 2, characterized in that Inputting the multiple groups of distribution network parameters into the first-layer master-slave game model respectively, and inputting the output results of the first-layer master-slave game model into the second-layer master-slave game model, to obtain the distribution network electricity sales and purchase prices, the maximum revenue of the shared energy storage service provider, the real-time service fee, and the maximum consumer surplus for each of the multiple partitions, including: Inputting the multiple groups of distribution network parameters corresponding to the multiple partitions into the calculation model corresponding to the target distribution network in the first-layer master-slave game model, and the calculation model corresponding to the target distribution network outputs multiple first transaction electricity prices corresponding to the multiple partitions; Inputting the multiple first transaction electricity prices into the calculation model corresponding to the shared energy storage power station in the first-layer master-slave game model, respectively, so that the calculation model corresponding to the shared energy storage power station outputs the output result of the first-layer master-slave game model, wherein the output result includes the multiple first revenues and multiple first service fees corresponding to the multiple partitions respectively; Inputting the output result of the first-layer master-slave game model into the calculation model corresponding to the photovoltaic community in the second-layer master-slave game model, and the calculation model corresponding to the photovoltaic community outputs a plurality of first consumer surpluses corresponding to the plurality of partitions respectively; The multi-layer master-slave game model adjusts the output result of the calculation model corresponding to the shared energy storage power station according to the multiple first consumer surpluses corresponding to the multiple partitions, and then adjusts the output result of the calculation model corresponding to the target distribution network according to the output result of the calculation model corresponding to the shared energy storage power station, until the output result of the multi-layer master-slave game model satisfies the Stackelberg equilibrium. The multi-layer master-slave game model outputs the distribution network electricity sales and purchase prices, the maximum profit of the shared energy storage service provider, the real-time service fee, and the maximized consumer surplus for each partition in the multiple partitions.

4. The method according to claim 1, wherein The calculation model corresponding to the target distribution network includes: the distribution network constraint conditions are: Where: and are the maximum electricity selling price and the maximum electricity purchasing price of the target distribution network, and are the electricity selling price and electricity purchasing price of the target distribution network respectively.

5. The method according to claim 1, wherein The calculation model corresponding to the shared energy storage power station includes: The shared energy storage power station constraints include energy conservation constraints, capacity constraints, charge and discharge power conservation constraints, and charge and discharge power limit constraints, wherein the energy conservation constraints are: Where: E(t+1) is the power of the shared energy storage station at time t+1; E(t) is the power of the shared energy storage station at time t; u is the self-discharge rate of the shared energy storage station; η c and η d are respectively the charging efficiency and the discharging efficiency of the shared energy storage power station; The capacity constraints are: E min ≤E(t)≤E max , Where: E min and E max The lower and upper capacity limits of the shared energy storage power station; The charge and discharge power conservation constraint is: The charge and discharge power limit constraints are: IN c +U d ≤1 IN c ∈{0,1},U d ∈{0,1}, Where: P max The maximum charge and discharge power allowed by the shared energy storage power station, and are respectively the charging power and discharging power of the shared energy storage power station; U c and U d charging and discharging status of the shared energy storage power station; The shared energy storage service provider constraints are: Where: is the maximum service fee of the shared energy storage service provider, λ ess It is the service fee of the shared energy storage service provider.

6. The method according to claim 1, characterized in that The calculation model corresponding to the photovoltaic community includes: The initial load of the photovoltaic community is: P i,0 (t)=P i,base0 (t)+P i,loss0 (t)+P i,shift0 (t), Where: P i,base0 (t), P i,loss0 (t) and P i,shift0 (t) are the base load, curtailable load and transferable load of the ith PV community at time t; The load adjusted by the photovoltaic community is: P i,L (t)=P i,base0 (t)+P i,loss0 (t)+P i,shift0 (t)-P i,loss (t)-P i,shift (t), Where: P i,loss (t) and P i,shift (t) are the load reduction and transfer power of the ith PV community at time t; The photovoltaic community constraints are: 0≤P i,loss (t)≤P i,loss0 (t), 0≤P i,shift (t)≤P i,shift0 (t), Where: P i,pv (t) is the photovoltaic power generation power of community i at time t; N is the number of photovoltaic cells.

7. The method according to claim 1, characterized in that The step of dividing the target distribution network into a plurality of partitions according to the power information includes: Get multiple partition constraints; Divide the target distribution network into a plurality of partitions according to the power information and the plurality of partition constraints, wherein the plurality of partition constraints include: Where: k is the ratio coefficient of energy storage capacity to load; M is the number of photovoltaic communities; is the upper limit of the capacity of the nth shared energy storage power station; is the power limit of the nth shared energy storage power station; T is the time number; Where: P min and P max are the valley value and peak value of the target distribution network respectively; Where: P n (t) is the total load of the nth partition at time t.

8. A distribution network electric energy transaction planning device, characterized in that: include: An acquisition module is used to obtain power information of the target distribution network; a partitioning module, configured to divide the target distribution network into a plurality of partitions according to the power information; A first modeling module is configured to establish a computing model corresponding to each of the target distribution network, the shared energy storage power station included in the target distribution network, and the photovoltaic community included in the area covered by the target distribution network, wherein the computing model corresponding to the target distribution network includes distribution network constraints and a distribution network objective function, the computing model corresponding to the shared energy storage power station includes shared energy storage power station constraints, shared energy storage service provider constraints, and a shared energy storage service provider objective function, and the computing model corresponding to the photovoltaic community includes photovoltaic community constraints and a photovoltaic community objective function; A second modeling module is used to construct a multi-layer master-slave game model using master-slave game theory, including a master-slave relationship between the calculation models corresponding to the target distribution network, the shared energy storage power station, and the photovoltaic community. The multi-layer master-slave game model includes a first-layer master-slave game model and a second-layer master-slave game model. The first-layer master-slave game model uses the calculation model corresponding to the target distribution network as an upper-layer leadership model and the calculation model corresponding to the shared energy storage power station as a lower-layer follower model. The second-layer master-slave game model uses the calculation model corresponding to the shared energy storage power station as an upper-layer leadership model and the calculation model corresponding to the photovoltaic community as a lower-layer follower model. a calculation module for calculating, based on the multi-layer master-slave game model, the electricity sales and purchase prices of the distribution network for each of the multiple partitions, the maximum revenue of the shared energy storage service provider, the real-time service fee, and the maximum consumer surplus, wherein the multi-layer master-slave game model is solved using Stackelberg equilibrium; a determination module, configured to determine an energy trading plan within the target distribution network based on the electricity sales and purchase prices of the distribution network in each of the multiple partitions, the maximum revenue of the shared energy storage service provider, the real-time service fee, and the maximum consumer surplus; Among them, the distribution network objective function is: Where: is the distribution network revenue of the nth partition; and are the electricity selling price and electricity purchasing price of the target distribution network at time t respectively; and are the power transmitted outward and the power input into the target distribution network at time t, respectively, T is the number of moments, and Δt is the time interval between two adjacent moments; The objective function of the shared energy storage service provider is: Where: The revenue of the shared energy storage service provider in the nth partition; ess (t) is the service fee of the shared energy storage service provider at time t; and are respectively the charging power and discharging power of the shared energy storage power station at time t; The photovoltaic community objective function is: Where: is the maximum consumer surplus of the photovoltaic community in the nth partition; α i is the preference coefficient for consuming electric energy; P i,0 (t) is the original load of the ith photovoltaic community at time t; P i,L (t) is the load of the ith photovoltaic community after adjustment at time t; P c (t) and P d (t) are the charging power and discharging power of the shared energy storage power station at time t respectively.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the distribution network power transaction planning method according to any one of claims 1 to 7.

10. A computer device, characterized in that: include: memory and processor, The memory stores a computer program; The processor is used to execute the computer program stored in the memory, and when the computer program is running, the processor executes the distribution network power transaction planning method according to any one of claims 1 to 7.

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