A graph-based method and system for optimizing the profitability of energy storage market participation

Abstract

This invention discloses a method for optimizing the market participation energy storage profit based on graph computation. The method includes: acquiring power system physical model data and relevant data required for energy storage computation; analyzing the data structures of the power system physical model and the energy storage model; constructing a power system physical graph model and an energy storage graph model; merging the power system physical graph model and the energy storage graph model to form a unified graph model; obtaining power grid power flow and state estimation results based on graph computation; establishing a linear programming objective function to maximize energy storage profit based on market price fluctuations and changes in electricity demand, under the constraint of ensuring the safe and stable operation of the power grid and energy storage; performing graph-parallel computation based on the unified graph model, combined with the objective function and constraints, to determine the energy storage power supply and discharge power to optimize the market participation energy storage profit; and returning the optimal energy storage profit and charging / discharging power strategy results.

Description

Technical Field

[0001] This invention relates to the field of energy storage optimization technology in the power market, and in particular to a method and system for optimizing the profit of market-participating energy storage based on graph computation. Background Technology

[0002] Energy storage technology plays a crucial role in new power systems. With the rapid development of renewable energy, energy storage systems have become key to balancing supply and demand, improving power quality, and responding to electricity market demands. By storing renewable energy and releasing it when needed, energy storage systems help balance the power system and improve the utilization rate of renewable energy. To maximize the profitability of energy storage, it is necessary to consider the technological and policy context, including market intelligence, data analysis, market participation strategies, integrated energy management systems, virtual power plants, and market incentives.

[0003] However, existing technologies still have some shortcomings. Currently, multiple constraints of energy storage systems are not comprehensively considered, such as storage capacity, charging and discharging power, charging and discharging efficiency, energy storage grid exchange power balance, energy storage energy balance, and electricity market prices. Furthermore, relational data-based technologies perform poorly in maximizing energy storage profits under these multiple constraints. Therefore, these issues need to be addressed to achieve higher efficiency when developing new technologies and algorithms. Summary of the Invention

[0004] In view of the problems existing in the field, which do not comprehensively consider the stable operation of the power system, market price fluctuations, and multiple constraints of energy storage systems, this invention is proposed.

[0005] Therefore, the problem to be solved by this invention is how to implement a method for maximizing the profit of energy storage based on graph databases, taking into account market price fluctuations and changes in electricity demand.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a method for optimizing the market participation energy storage profit based on graph computing. This method includes: acquiring power system physical model data for market participation and relevant data required for energy storage calculation; parsing the power system physical model data, constructing corresponding power system physical nodes and node attributes in a graph database model according to different data types and definitions, and parsing the energy storage model data structure to define the graph nodes and node attributes of the energy storage system; merging the power system physical graph and the energy storage graph model to form a unified graph model; establishing a linear programming objective function to maximize energy storage profit based on market price fluctuations and changes in electricity demand; performing calculations based on the unified graph model, combining the objective function and constraints, and determining energy storage power supply and discharge strategies to optimize the market participation energy storage profit; and returning the optimal energy storage profit strategy result.

[0007] As a preferred embodiment of the graph-based method for optimizing market participation in energy storage profits according to the present invention, the specific formula of the objective function is as follows: in, Describe the objective function. This indicates the total number of steps in the planning time. Indicates the unit time step, This represents the charging / discharging power of the stored energy at time t. express Energy storage charging power at any time, express Energy storage and discharge power at any given time express Real-time energy storage and electricity sales market electricity prices express Real-time energy storage electricity purchase market electricity price, express The cost of on-demand energy storage and charging express Cost of storing and discharging energy at all times.

[0008] As a preferred embodiment of the graph-based market participation energy storage profit optimization method of the present invention, the constraints include: energy storage capacity constraints, charging and discharging power constraints, charging and discharging efficiency constraints, energy storage grid exchange power balance constraints, energy storage energy balance constraints, initial and final energy level constraints of the energy storage system, electricity market price constraints, energy storage operating time constraints, and energy storage charge and discharge times constraints.

[0009] As a preferred embodiment of the graph-based market participation energy storage profit optimization method described in this invention, the specific formula for the energy storage energy balance constraint is as follows: in, express Stores energy at all times. express Energy storage charging power at any time, express Energy storage and discharge power at any given time Indicates the unit time step, Indicates the maximum discharge power of the energy storage. Indicates the maximum charging power of the energy storage. express The cost of on-demand energy storage and charging express Cost of storing and discharging energy at all times.

[0010] As a preferred embodiment of the graph-based market participation energy storage profit optimization method described in this invention, the specific formula for the energy storage charge / discharge cycle constraint is as follows: in, Indicates at time Whether the energy storage is charged or discharged, This indicates the maximum allowed number of total charge and discharge cycles for energy storage. This indicates the total number of steps in the planning time. Indicates the unit time step, express Energy storage charging power at any time, express The energy storage and discharge power at any given moment.

[0011] As a preferred embodiment of the graph-based computation-based method for optimizing the profit of market-participating energy storage, the method comprises the following steps: determining the energy storage power supply and discharge strategies to optimize the profit of market-participating energy storage; performing graph computation based on a pre-established unified graph model and obtaining the power flow calculation results and state estimation results of the power grid; calculating the power supply and consumption of the energy storage area based on the power flow and state estimation results, and performing short-term load forecasting, writing the forecast results into the attributes of the load nodes in the corresponding area or into additional load forecasting nodes; reading the price attributes of real-time price nodes, the load attributes of load nodes, and all constraints defined on the power grid nodes and energy storage nodes from the unified graph model; performing optimization function parsing by customizing the internal optimization calculation function of the graph database or calling optimization software using the API interface of the graph database, and generating optimization problems; using the parallel computing capabilities of the graph database to quickly iterate and solve the problem to obtain the charging and discharging strategies that maximize the energy storage revenue under different price periods; and writing the solved charging and discharging strategies into the attributes of the energy storage nodes in the corresponding period as suggestions for subsequent charging and discharging control of energy storage.

[0012] As a preferred embodiment of the graph-based market participation energy storage profit optimization method described in this invention, the specific formula for the energy storage grid exchange power balance constraint is as follows: in, express Energy storage exchanges power with the grid at all times. express Total load of the power grid at any given time express Total power generation of the power grid at any given time express Energy storage charging power at any time, express The energy storage and discharge power at any given moment.

[0013] Secondly, embodiments of the present invention provide a market participation energy storage profit optimization system based on graph computing, which includes a data acquisition module for acquiring power system physical model data for market participation and relevant data required for energy storage calculation; a model parsing and fusion module for parsing the energy storage model data structure to define the nodes and node attributes of the energy storage system, and fusing the power system physical graph and the energy storage graph model to form a unified graph model; and a profit optimization calculation module for establishing an energy storage profit optimization analysis function in the graph database, combining the unified graph model, objective function, and constraints, calling a custom optimization function or the graph database API interface to call optimization software, and calculating to determine the energy storage power supply and discharge strategy to optimize the market participation energy storage profit.

[0014] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the graph-based market participation energy storage profit optimization method as described in the first aspect of the present invention.

[0015] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of the graph computing-based market participation energy storage profit optimization method as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: It establishes a unified graph model integrating power grid and energy storage, enabling a deep digital description of the physical characteristics of the power grid and the characteristics of energy storage, providing high-quality data support for energy storage planning; leveraging the distributed computing capabilities of graph database parallel computing, it achieves high-speed parallel processing of massive amounts of equipment and their constraints, significantly improving computing performance; employing the customizable extension capabilities of graph computing functions, it achieves seamless integration of power system power flow calculation in the graph database, improving the support for power-related professional characteristics; combining graph models with algorithms, it achieves the organic integration of digital modeling and efficient computing of power physical systems, promoting the coordinated development of intelligent, flexible, and economical power grids. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments 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. Wherein: Figure 1 This is a flowchart of a graph-based method for optimizing the profitability of market-participated energy storage.

[0018] Figure 2 This document outlines the graph model construction process for a graph-based method to optimize the profitability of energy storage through market participation. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example 1 Reference Figures 1-2 This is the first embodiment of the present invention, which provides a method for optimizing market participation in energy storage profits based on graph computation, including: S1: Obtain physical model data of the power system participating in the market and relevant data required for energy storage calculations.

[0023] Specifically, the physical model data of the power system participating in the market includes the topological relationships of various devices that construct the physical grid topology of the participating market, such as generators, transformers, switches, disconnectors, transmission lines, energy storage, loads, etc., as well as equipment parameters and operating parameters; the relevant data required for energy storage calculations include energy storage device parameters and constraint parameters.

[0024] S2: Parse the power system physical model data, construct the corresponding power system physical nodes and node attributes in the graph database model according to different data types and definitions, and parse the energy storage model data structure to define the graph nodes and node attributes of the energy storage system.

[0025] Preferably, based on different equipment data types, such as generators, transformers, switches, disconnectors, transmission lines, energy storage, and loads, corresponding graph equipment nodes and node attributes are defined. In the process of parsing the power system physical model data, various physical attributes involved in the equipment data are considered, including the power grid's operating parameters, logical attributes, logical management relationships, computational attributes, and parameters required for power flow calculations. Based on these physical attributes, various types of equipment are transformed into graph nodes in the graph database, and the corresponding attributes are added to the nodes. Finally, the edges of the graph nodes are constructed based on the physical topology connections between the equipment to establish the power system physical graph model.

[0026] It should be noted that the method for analyzing the data structure of the energy storage model is similar to that for analyzing the data of the power system physical model, defining the nodes and node attributes of the energy storage system.

[0027] S3: Integrate the physical graph model of the power system and the graph model of energy storage to form a unified graph model.

[0028] Specifically, in the power system physical graph model, nodes for equipment such as generators, transformers, lines, and switches have been established, and attributes such as equipment parameters have been added. In the energy storage graph model, nodes for energy storage facilities have been established, and constraints such as energy storage capacity, charging and discharging power, and minimum and maximum energy storage have been added. When integrating the two, it is necessary to establish the connection relationship between energy storage facility nodes and grid nodes, including associating them with graph nodes involved in physical topology, logical management, and optimization calculations, i.e., determining the location of energy storage access to the grid and its access method (access to the high-voltage side or low-voltage side of the transformer, etc.). The attributes of energy storage facility nodes are adjusted to add market transaction attributes, such as bidding functions and participation methods. The attributes of grid nodes are adjusted to add attribute identifiers for participating in market transactions. In the calculation module, collaborative simulation calculations between energy storage nodes and grid nodes are implemented, so that the charging and discharging behavior of energy storage can influence and participate in the power flow calculation and optimal scheduling of the grid. Finally, a unified graph database model containing the grid and energy storage is formed to support subsequent market simulation calculations.

[0029] S4: Based on market price fluctuations and changes in electricity demand, establish a linear programming objective function to maximize energy storage profits.

[0030] Specifically, the formula for the objective function is as follows: in, Describe the objective function. This indicates the total number of steps in the planning time. Indicates the unit time step, This represents the charging / discharging power of the stored energy at time t. express Energy storage charging power at any time, express Energy storage and discharge power at any given time express Real-time energy storage and electricity sales market electricity prices express Real-time energy storage electricity purchase market electricity price, express The cost of on-demand energy storage and charging express Cost of storing and discharging energy at all times.

[0031] S5: Based on a unified graph model, calculations are performed using objective functions and constraints to determine energy storage charging and discharging strategies, thereby optimizing the profits of market participants in energy storage.

[0032] Specifically, it includes the following steps: S5.1: Perform graph calculations based on a pre-established unified graph model and obtain power flow calculation results and state estimation results of the power grid.

[0033] Specifically, the constructed power grid topology diagram model (node ​​switch diagram) is transformed into a power grid calculation diagram model (bus branch diagram) through a custom graph topology analysis function. Then, the power flow and state estimation results are obtained by quickly calculating the power flow and state estimation results through a custom graph power flow and graph state estimation function.

[0034] S5.2: Based on the power flow and state estimation results, calculate the power supply and consumption situation of the connected energy storage area, perform short-term load forecasting, and write the forecast results into the attributes of the load nodes in the corresponding area.

[0035] Preferably, based on the power flow and state estimation results within a continuous time segment, the load volume of the continuous time segment within the energy storage area is obtained, and statistical analysis is performed on the time-series load volume. Short-term load forecasting can be achieved through traditional load forecasting methods or graph machine learning methods.

[0036] S5.3: Read the price attributes of real-time price nodes, the load attributes of load nodes, and all constraints defined on grid nodes and energy storage nodes from the unified graph model.

[0037] Furthermore, the constraints include energy storage capacity constraints, charging and discharging power constraints, charging and discharging efficiency constraints, energy storage grid exchange power balance constraints, energy storage energy balance constraints, initial and final energy level constraints of the energy storage system, electricity market price constraints, energy storage operating time constraints, and energy storage charge and discharge cycles constraints.

[0038] Specifically, since energy storage has a maximum storage capacity, it cannot store electrical energy indefinitely. Therefore, the specific formula for the energy storage capacity constraint is as follows: in, Indicates the minimum energy level for energy storage. Indicates the maximum energy level of energy storage. Indicates energy storage The electrical energy at any given moment, and .

[0039] It should be noted that in this embodiment, the minimum energy level of energy storage means that when the remaining capacity drops to 50% of the rated capacity after energy storage discharge, discharging will stop; the maximum energy level of energy storage means that when the capacity rises to 95% of the rated capacity after energy storage charging, charging will stop.

[0040] Furthermore, the charging and discharging power constraints require that the charging and discharging power of energy storage must be within allowable ranges, as specified in the following formulas: in, Indicates the maximum discharge power of the energy storage. Indicates the maximum charging power of the energy storage. express Energy storage charging power at any time, express The energy storage and discharge power at any given moment.

[0041] Furthermore, energy storage experiences energy loss during charging and discharging, so charging and discharging efficiency constraints need to be provided. These are represented by efficiency parameters, using the ratio of actual charging / discharging power to calculated charging / discharging power. The specific formula is as follows: in, Indicates the maximum discharge power of the energy storage. This indicates the maximum charging power of the energy storage.

[0042] Furthermore, the specific formula for the power balance constraint of the energy storage grid is as follows: in, express Energy storage exchanges power with the grid at all times. express Total load of the power grid at any given time express Total power generation of the power grid at any given time express Energy storage charging power at any time, express The energy storage and discharge power at any given moment.

[0043] Preferably, the specific formula for the energy balance constraint of energy storage is as follows: in, express Stores energy at all times. express Energy storage charging power at any time, express Energy storage and discharge power at any given time Indicates the unit time step, Indicates the maximum discharge power of the energy storage. Indicates the maximum charging power of the energy storage. express The cost of on-demand energy storage and charging express Cost of storing and discharging energy at all times.

[0044] Furthermore, the specific formulas for the initial and final energy level constraints of the energy storage system are as follows: in, Indicates the initial energy storage capacity. Indicates the final amount of energy stored. Indicates the time.

[0045] Furthermore, energy storage operation needs to consider real-time electricity market prices, including the rate of change of electricity market prices and peak prices. The specific formula for electricity market price constraints is as follows: in, express Real-time energy storage for buying or selling electricity prices. Indicates the peak price in the electricity market. This indicates the maximum rate of change in electricity prices in the electricity market.

[0046] Preferably, the charging and discharging power of the energy storage must be within the allowable range; therefore, the specific formula for the energy storage operating time constraint is as follows: in, Indicates in Whether the energy storage is running at all times This indicates the maximum allowable total operating time for energy storage. This indicates the total number of steps in the planning time. Indicates the unit time step, express Energy storage charging power at any time, express The energy storage and discharge power at any given moment.

[0047] It should be noted that, It is a binary variable judgment function used to determine whether energy storage is operating.

[0048] Furthermore, the specific formula for the energy storage cycle constraint is as follows: in, Indicates at time Whether the energy storage is charged or discharged, This indicates the maximum allowed number of total charge and discharge cycles for energy storage. This indicates the total number of steps in the planning time. Indicates the unit time step, express Energy storage charging power at any time, express The energy storage and discharge power at any given moment.

[0049] It should be noted that, It is a binary variable judgment function used to determine whether the energy storage is charging or discharging.

[0050] Furthermore, all constraint-related attributes are defined in the constraint graph nodes in the form of attribute name + data type. Then, during the data import process, the corresponding data is mapped to the specified attributes. Since the constraint graph nodes are associated with the specified physical graph nodes, when parsing the optimization results, it is only necessary to directly read the relevant constraint values ​​from the nodes during the process of writing the graph database call CPLEX to achieve graph parallel (parallel processing of all constraints) optimization calculation.

[0051] S5.4: The optimization software is called through the API interface of the graph database to parse the optimization function and generate optimization problems.

[0052] It should be noted that the optimization function refers to the function that includes the objective function and all constraint functions.

[0053] S5.5: Utilize the parallel computing capabilities of graph databases to quickly iterate and solve for the optimal charging and discharging strategy for energy storage profit under different electricity price periods.

[0054] Preferably, a graph model is used to determine the grid topology and energy storage access location, clarify the scope of load impact, realize real-time power flow and state estimation based on graph calculation, and refine short-term load forecasting; based on real-time market electricity prices and energy storage charging and discharging costs in different time intervals, graph calculation is used to quickly analyze and maximize energy storage profits under the constraints of stable power system operation, safe and stable operation of energy storage system, and compliance with energy storage management policies, and to give the optimal charging and discharging strategy for the current time period.

[0055] S5.6: Write the obtained charging and discharging strategy into the energy storage node attributes of the corresponding time period as a suggestion for subsequent charging and discharging control of energy storage.

[0056] S6: Returns the result of the optimal energy storage profit strategy.

[0057] Furthermore, based on business needs, it provides the optimal profit results for the energy storage system in the current or multiple time periods, along with corresponding charge and discharge power recommendations. These results can help users make reasonable charging and discharging decisions at different times to maximize the economic benefits of the energy storage system.

[0058] Furthermore, this embodiment also provides a market participation energy storage profit optimization system based on graph computing, including a data acquisition module for acquiring power system physical model data for market participation and relevant data required for energy storage calculation; a model parsing and fusion module for parsing the energy storage model data structure to define the nodes and node attributes of the energy storage system, and fusing the power system physical graph and the energy storage graph model to form a unified graph model; and a profit optimization calculation module for establishing an energy storage profit optimization analysis function in the graph database, combining the unified graph model, objective function, and constraints, calling a custom optimization function or the graph database API interface to call optimization software, and calculating to determine the energy storage power supply and discharge strategy to optimize the market participation energy storage profit.

[0059] This embodiment also provides a computer device applicable to the market participation energy storage profit optimization method based on graph computing, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the market participation energy storage profit optimization method based on graph computing as proposed in the above embodiment.

[0060] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0061] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, performs the following steps: acquiring power system physical model data for market participation and relevant data required for energy storage calculation; parsing the power system physical model data, defining corresponding nodes and node attributes according to different data types, and parsing the energy storage model data structure to define the nodes and node attributes of the energy storage system; merging the power system physical graph and the energy storage graph model to form a unified graph model; establishing a linear programming objective function to maximize energy storage profits based on market price fluctuations and changes in electricity demand; performing calculations based on the unified graph model, combined with the objective function and constraints, and determining energy storage power supply and discharge strategies to optimize the profits of market-participating energy storage; and returning the optimal energy storage profit strategy result.

[0062] In summary, this invention establishes a unified graph model integrating power grid and energy storage, enabling a deep digital description of the physical characteristics of the power grid and the characteristics of energy storage, providing high-quality data support for energy storage planning. Leveraging the distributed computing capabilities of graph database parallel computing, it achieves high-speed parallel processing of massive amounts of equipment and their constraints, significantly improving computational performance. Utilizing the customizable extension capabilities of graph computation functions, it achieves seamless integration of power system power flow calculations into the graph database, enhancing the support for power-related professional characteristics. By combining the graph model with algorithms, it achieves the organic integration of digital modeling and efficient computation of the power physical system, promoting the coordinated development of a smart, flexible, and economical power grid.

[0063] Example 2 Reference Figures 1-2 This is the second embodiment of the present invention, which provides a method for optimizing the market participation energy storage profit based on graph computation. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0064] Specifically, taking a feeder with an energy storage system of a provincial power grid company as an example, the load data of the feeder for one day with a cycle of 15 minutes is obtained by graph calculation, as shown in Table 1.

[0065] Table 1. Load data for one day, with each cycle lasting 15 minutes.

[0066] Furthermore, the daily market electricity purchase price data with a cycle of 15 minutes (peak price 0.6224, off-peak price 0.175 from 1:00 to 6:00, and normal price 0.273 for the rest of the time) is shown in Table 2.

[0067] Table 2. Daily market electricity purchase price data with a 15-minute cycle.

[0068] The preferred daily market electricity price data, with each cycle lasting 15 minutes (peak price 0.7231, off-peak price 0.173 from 1:00 to 6:00, and normal price 0.281 for the rest of the time), is shown in Table 3.

[0069] Table 3. Daily market electricity price data with a 15-minute cycle.

[0070] Furthermore, the daily energy storage charging cost data, with each cycle lasting 15 minutes, is shown in Table 4.

[0071] Table 4. Daily energy storage charging cost data with each cycle lasting 15 minutes.

[0072] Preferably, the daily energy storage discharge cost with a cycle of 15 minutes is shown in Table 5.

[0073] Table 5. Daily energy storage charging cost data with each cycle lasting 15 minutes.

[0074] Preferably, the energy storage charging and discharging capacity is limited to 95% and 50%, respectively. That is, charging stops when the energy storage capacity reaches 95% and discharging stops when it reaches 50%. The maximum number of charging and discharging times per day is set to 3. The entire provincial network system has 12,923 nodes and 14,785 edges. Graph topology analysis and state estimation are completed within 600ms, and graph power flow calculation is completed within 400ms. In this example, the feeder system has 18 nodes and 21 edges, and the optimization calculation is completed within 100ms.

[0075] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing market participation profits in energy storage based on graph computation, characterized in that: include, Obtain physical model data of the power system for market participation and relevant data required for energy storage calculations; The power system physical model data is parsed, and the corresponding power system physical nodes and node attributes are constructed in the graph database model according to different data types and definitions. The energy storage model data structure is also parsed to define the graph nodes and node attributes of the energy storage system. The power system physical diagram and energy storage diagram model are integrated to form a unified diagram model; Based on market price fluctuations and changes in electricity demand, establish a linear programming objective function to maximize energy storage profits; The specific formula for the objective function is as follows: in, Describe the objective function. This indicates the total number of steps in the planning time. Indicates the unit time step, This represents the charging / discharging power of the stored energy at time t. express Energy storage charging power at any time, express Energy storage and discharge power at any given time express Real-time energy storage and electricity sales market electricity prices express Real-time energy storage electricity purchase market electricity price, express The cost of on-demand energy storage and charging express Cost of storing and discharging energy at any time; Based on a unified graph model, calculations are performed by combining the objective function and constraints, and energy storage power supply and discharge strategies are determined to optimize the profits of market participants in energy storage. Determining the energy storage power supply and discharge strategy to optimize the profitability of market participants in energy storage includes the following steps: Graph computation is performed based on a pre-established unified graph model, and power flow calculation results and state estimation results of the power grid are obtained. Based on the power flow and state estimation results, the power supply and consumption situation of the connected energy storage area is calculated, and short-term load forecasting is performed. The forecast results are written into the attributes of the load nodes in the corresponding area or into additional load forecasting nodes. Read the electricity price attributes of the real-time electricity price node, the load attributes of the load node, and all constraints defined on the grid node and energy storage node from the unified graph model. The optimization problem can be generated by using a custom internal optimization calculation function of the graph database or by using the API interface of the graph database to call the optimization software to parse the optimization function. By leveraging the parallel computing capabilities of graph databases, a fast iterative solution is obtained to achieve a charging and discharging strategy that maximizes energy storage revenue under different electricity price periods. The obtained charging and discharging strategies are written into the energy storage node attributes for the corresponding time period as suggestions for subsequent charging and discharging control of energy storage. Return the results of the optimal energy storage profit strategy.

2. The method for optimizing market participation energy storage profits based on graph computation as described in claim 1, characterized in that: The constraints include energy storage capacity constraints, charging and discharging power constraints, charging and discharging efficiency constraints, energy storage grid exchange power balance constraints, energy storage energy balance constraints, initial and final energy level constraints of the energy storage system, electricity market price constraints, energy storage operating time constraints, and energy storage charge and discharge cycles constraints.

3. The method for optimizing market participation energy storage profits based on graph computation as described in claim 2, characterized in that: The specific formula for the energy storage energy balance constraint is as follows: in, express Stores energy at all times. express Energy storage charging power at any time, express Energy storage and discharge power at any given time Indicates the unit time step, Indicates the maximum discharge power of the energy storage. Indicates the maximum charging power of the energy storage. express The cost of on-demand energy storage and charging express Cost of storing and discharging energy at all times.

4. The method for optimizing market participation energy storage profits based on graph computation as described in claim 2, characterized in that: The specific formula for the constraint on the number of energy storage charge-discharge cycles is as follows: in, Indicates at time Whether the energy storage is charged or discharged, This indicates the maximum allowed number of total charge and discharge cycles for energy storage. This indicates the total number of steps in the planning time. Indicates the unit time step, express Energy storage charging power at any time, express The energy storage and discharge power at any given moment.

5. The method for optimizing market participation energy storage profits based on graph computation as described in claim 2, characterized in that: The specific formula for the power balance constraint of the energy storage grid is as follows: in, express Energy storage exchanges power with the grid at all times. express Total load of the power grid at any given time express Total power generation of the power grid at any given time express Energy storage charging power at any time, express The energy storage and discharge power at any given moment.

6. A graph-based energy storage profit optimization system for market participation, based on the graph-based energy storage profit optimization method according to any one of claims 1 to 5, characterized in that: It also includes, The data acquisition module is used to acquire physical model data of the power system participating in the market and relevant data required for energy storage calculations; The model parsing and fusion module is used to parse the energy storage model data structure to define the nodes and node attributes of the energy storage system, and to fuse the power system physical diagram and the energy storage diagram model to form a unified graph model. The profit optimization calculation module is used to establish energy storage profit optimization analysis functions in the graph database. It combines a unified graph model, objective function and constraints, calls custom optimization functions or graph database API interface to call optimization software, and calculates to determine the energy storage power supply and discharge strategy to optimize the profit of market participants in energy storage.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the graph-based energy storage profit optimization method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the graph-based market participation energy storage profit optimization method according to any one of claims 1 to 5.

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