Method for evaluating and configuring general energy storage technology economy based on dynamic knowledge graph

Abstract

The application discloses a kind of based on dynamic knowledge graph's general energy storage technology economic evaluation and configuration method, belong to electric power system dispatching field.The method is used to generate the configuration scheme of each type of energy storage unit in electric power system;Method includes constructing dynamic knowledge graph based on the information of energy storage unit;Again based on dynamic knowledge graph, economic dynamic evaluation is carried out to energy storage unit under multiple time scales;Then with the economic optimality of energy storage system formed by energy storage unit, investment cost is lowest and system reliability is highest as objective function, constructs multi-objective optimization configuration model;Finally, multi-objective optimization algorithm is used to solve multi-objective optimization configuration model, and the optimal configuration scheme of energy storage system is obtained.The application realizes unified quantitative evaluation and dynamic adaptive configuration to general energy storage system, significantly improves the utilization efficiency and planning scientificity of energy storage resource of electric power system.

Description

Technical Field

[0001] This invention belongs to the field of power system dispatching, and in particular, it is a method for economic evaluation and configuration of general energy storage technology based on dynamic knowledge graph. Background Technology

[0002] With the global energy transition and the large-scale development of renewable energy, the operation of power systems faces unprecedented challenges. In particular, the intermittency and volatility of renewable energy sources such as wind and solar power make traditional power system dispatching methods ill-suited to rapidly changing electricity demand and supply conditions. Therefore, energy storage technology, as an important means to improve power system stability, regulate the volatility of renewable energy, and ensure power supply reliability, has become one of the key technologies for power system development.

[0003] Currently, there are many types of energy storage technologies, including lithium-ion batteries, hydrogen energy storage, pumped hydro storage, and flywheel energy storage. Each technology has its own advantages and disadvantages, and is suitable for different scenarios. For example, lithium-ion batteries have high energy density and fast response capabilities, making them suitable for short-term regulation; hydrogen energy storage excels in long-term, large-scale energy storage; pumped hydro storage is suitable for large-scale, long-term energy storage; and flywheel energy storage provides rapid power response. In practical applications, the selection and configuration of appropriate energy storage technologies has become a crucial factor affecting the economy, reliability, and stability of power systems.

[0004] Despite significant progress in the research and application of energy storage technologies, existing methods for economic evaluation and configuration optimization still have some limitations. Current evaluation methods typically analyze single energy storage technologies, lacking comprehensive assessments of combined applications of multiple technologies. Furthermore, many evaluation methods fail to consider the dynamic changes of energy storage technologies during actual grid operation, thus failing to adequately address the real-time operational needs of the power system. In addition, existing optimization methods often focus on a single objective, failing to comprehensively consider multiple aspects such as investment costs, operating costs, economic benefits, and system reliability within a unified framework, resulting in evaluation results that lack comprehensiveness and flexibility.

[0005] Furthermore, current optimization of energy storage technology configurations often overlooks the interactions and synergistic effects between different energy storage technologies. The overall effectiveness of an energy storage system depends not only on the performance of individual technologies but also on the comprehensive coordination of various technologies. Therefore, understanding how to integrate the advantages and disadvantages of multiple energy storage technologies through reasonable evaluation methods and fully utilize their synergistic effects is crucial for improving the economic efficiency and reliability of energy storage technologies in power systems. Summary of the Invention

[0006] To address the problems in existing technologies, this invention provides a method for economic evaluation and configuration of general energy storage technologies based on dynamic knowledge graphs.

[0007] The technical solution adopted in this invention is as follows:

[0008] This invention discloses an economic evaluation and configuration method for general energy storage technologies based on dynamic knowledge graphs, used to generate configuration schemes for various types of energy storage units in power systems; the method includes the following steps:

[0009] Step 1: Obtain the power system to be optimized and construct a dynamic knowledge graph based on the information of the energy storage units in the power system;

[0010] Step 2: Based on the dynamic knowledge graph obtained in Step 1, establish a multi-timescale economic evaluation model and use the model to conduct dynamic economic evaluation of various types of energy storage units at multiple time scales.

[0011] Step 3: Based on the economic dynamic evaluation results obtained in Step 2, construct a multi-objective optimization configuration model with the objective functions of achieving the best economic performance, lowest investment cost, and highest system reliability of the energy storage system composed of energy storage units.

[0012] Step 4: Solve the multi-objective optimization configuration model using a multi-objective optimization algorithm to obtain the optimal configuration scheme of the energy storage system; wherein, the optimal configuration scheme includes the installed capacity ratio and power configuration of each type of energy storage unit in the power system, as well as the multi-timescale charging and discharging operation strategy corresponding to each type of energy storage unit.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0014] This invention proposes a method for economic evaluation and configuration of general energy storage technologies based on dynamic knowledge graphs. First, by constructing a dynamic knowledge graph encompassing various energy storage units, it addresses the problem that existing energy storage system evaluation methods cannot uniformly consider multiple types of energy storage technologies. Through this dynamic knowledge graph, standardized modeling of energy storage units corresponding to different energy storage technologies such as lithium-ion batteries, hydrogen storage, pumped hydro storage, and flywheel storage can be achieved, enabling dynamic updates and status tracking of each energy storage unit, providing accurate data support for subsequent economic evaluation and configuration optimization. Second, by establishing a multi-timescale economic evaluation model, this invention achieves dynamic analysis of the entire lifecycle cost and multi-dimensional benefits of the energy storage system, better adapting to grid load fluctuations and market price changes, ensuring maximum economic efficiency of the energy storage system. Furthermore, this invention employs a multi-objective optimization algorithm for energy storage system configuration optimization, generating multiple Pareto optimal solution sets through non-dominated sorting and congestion calculation, providing decision-makers with more alternatives. By introducing weight allocation and decision preference methods, the optimal energy storage configuration scheme can be flexibly selected according to actual needs, ensuring the best balance between system economy, investment cost, and reliability. In summary, this invention introduces dynamic modeling, multi-objective optimization, and multi-timescale analysis into the evaluation and configuration optimization of energy storage technologies, significantly improving the scientific rigor, adaptability, and economy of energy storage systems, and providing innovative technical support for energy transition and smart grid construction. Attached Figure Description

[0015] Figure 1 This is a flowchart of the economic evaluation and configuration method for pan-energy storage technology based on dynamic knowledge graph of the present invention.

[0016] Figure 2 These are typical daily load demand curves and wind and solar power output curves;

[0017] Figure 3 The graph shows the changes in key indicators and approximate hypervolume as a function of iteration number.

[0018] Figure 4 The diagram shows the optimal energy storage configuration.

[0019] Figure 5 A bar chart showing the health status of different energy storage systems in different scenarios;

[0020] Figure 6 A bar chart showing the SOC (State of Charge) of different energy storage systems in different scenarios. Detailed Implementation

[0021] The present invention will be further described and illustrated below with reference to specific embodiments. The embodiments described are merely examples of the content of this disclosure and do not limit the scope of the invention. The technical features of each embodiment in the present invention can be combined accordingly, provided that there is no mutual conflict.

[0022] This invention relates to a method for economic evaluation and configuration of general energy storage technologies based on dynamic knowledge graphs. It aims to address the shortcomings of existing methods for economic evaluation and configuration optimization of energy storage technologies, providing a more comprehensive, flexible, and dynamic energy storage configuration solution. Specifically, this invention constructs an energy storage technology model based on a dynamic knowledge graph, extracts key parameters affecting the economics and operational efficiency of the energy storage system in real time, and comprehensively optimizes the energy storage technology across multiple dimensions, including economics, investment cost, and system reliability, based on a multi-objective optimization method. This invention can comprehensively consider multiple factors such as the economics, investment cost, and system reliability of energy storage technologies, and can perform real-time optimization and configuration of energy storage technologies in a dynamic environment, thereby providing more scientific and comprehensive energy storage configuration decision support for the power system.

[0023] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0024] like Figure 1 The flowchart shown is a process for the economic evaluation and configuration method of pan-energy storage technology based on dynamic knowledge graphs according to the present invention. The method of the present invention is used to generate configuration schemes for various types of energy storage units in a power system, and includes the following steps:

[0025] Step 1: Obtain the power system to be optimized, construct a knowledge graph based on the information of each type of energy storage unit in the power system, and dynamically update the knowledge graph according to the real-time information of each type of energy storage unit in the power system.

[0026] This step involves constructing a dynamic knowledge graph encompassing various energy storage technologies in the power system (i.e., the energy storage technologies corresponding to energy storage units). By constructing this dynamic knowledge graph, the performance of the energy storage system comprised of all energy storage units and its relationship with the power grid can be comprehensively reflected, ensuring accurate assessment of the energy storage system's status during power system dispatch. The construction of the dynamic knowledge graph involves unified modeling of various energy storage technologies in the power system, establishing a structured representation of the attributes, operating status, and relationships with the power grid of each energy storage unit.

[0027] Unified modeling of various energy storage technologies in the power system is achieved through attribute vectorization modeling. For any energy storage unit... (i.e., the first) Type of energy storage unit), which in The state vector at time t is defined as:

[0028]

[0029] In the formula, for At any given time, the energy storage unit The state vector; for At any given time, the energy storage unit The instantaneous discharge power; for At any given time, the energy storage unit Instantaneous charging power; for At any given time, the energy storage unit Available energy; for At any given time, the energy storage unit Energy conversion efficiency; For energy storage units The unit capacity investment cost; For energy storage units Life cycle; for At any given time, the energy storage unit The specific attribute vector of energy storage technology.

[0030] The specific attribute vector of the energy storage technology The definition is as follows:

[0031]

[0032] in, When the value is 1, the energy storage unit It is a lithium-ion battery; for At any given time, the charge / discharge rate of the lithium-ion battery; for The battery temperature of the lithium-ion battery at a given time; This represents the capacity decay coefficient of a lithium-ion battery. When the value is 2, the energy storage unit For hydrogen energy storage units; Electrolysis efficiency of the hydrogen energy storage unit; , They are respectively At any given time, the energy absorption and release power of the hydrogen energy storage unit; When the value is 3, the energy storage unit It is a pumped storage unit; , They are respectively At any given time, the upper and lower water levels of the pumped storage unit; The hydraulic efficiency of a pumped storage unit; When the value is 4, the energy storage unit It is a flywheel energy storage unit; for At time t, the flywheel angular velocity of the flywheel energy storage unit; The moment of inertia of the flywheel in the flywheel energy storage unit; This represents the friction loss factor of the flywheel energy storage unit.

[0033] Various energy storage units have different physical characteristics, economic parameters and operating mechanisms. Therefore, it is necessary to model each type of energy storage unit separately and monitor and update its dynamic operating status in real time.

[0034] (1) Lithium-ion batteries

[0035] For lithium-ion batteries, modeling focuses on their charge / discharge efficiency and state of health. The charging and discharging efficiencies of a lithium-ion battery are expressed as follows: and The state of charge (SOC) of a lithium-ion battery changes over time. The power during the charging and discharging process of a lithium-ion battery can be expressed as:

[0036]

[0037]

[0038] in, for The power output during the charging process of a lithium-ion battery at a given time; for The input power of the lithium-ion battery at any given time; for The power of a lithium-ion battery during discharge at a given time. for At any given time, the output power of the lithium-ion battery.

[0039] The state of charge (SOC) of a lithium-ion battery updates over time, as shown by the formula:

[0040]

[0041] in, for The charging state of the lithium-ion battery at any given time; This refers to the total energy storage capacity of the lithium-ion battery. For time step.

[0042] In a specific embodiment of the present invention, the operating state of a lithium-ion battery is jointly determined by its charging state and discharging state, and is characterized by the battery's charging and discharging power and SOC: In the charging state, the battery operates at an input power... The battery absorbs and stores electrical energy from the system; in the discharge state, it outputs power to the outside. The battery's state of energy (SOC) is dynamically updated over time, and its change between adjacent moments is determined by the discharge power and the battery's rated energy storage capacity. A joint decision.

[0043] (2) Hydrogen energy storage unit

[0044] The modeling of the hydrogen energy storage unit considers the efficiency of hydrogen production, storage, and conversion. The energy conversion efficiency during the hydrogen production process in the hydrogen energy storage unit is: The efficiency of releasing electrical energy from hydrogen in a hydrogen energy storage unit is... The charging and discharging process of a hydrogen energy storage unit can be described by the following formula:

[0045]

[0046]

[0047] in, for At any given moment, the energy level of the hydrogen storage unit; for At what moment, the energy released by the hydrogen energy storage unit; and They are respectively At any given time, the hydrogen production power of the hydrogen energy storage unit and the discharge power of the hydrogen energy storage unit are measured.

[0048] In a specific embodiment of the present invention, the operating state of the hydrogen energy storage unit can be divided into two types: charging state (hydrogen production) and discharging state (hydrogen power generation). In the charging state, the hydrogen energy storage unit uses input electrical power to produce hydrogen, converting electrical energy into hydrogen energy and storing it. In the discharging state, the stored hydrogen is released through an energy conversion device and converted into electrical energy. The energy released at any given moment is determined by the discharge power. through The charging and discharging of the hydrogen energy storage unit at different times together characterize its operating state, thus reflecting its energy conversion and storage characteristics in the system.

[0049] (3) Pumped storage unit

[0050] Modeling pumped storage units involves pump efficiency. and power generation efficiency During charging, the energy of the pump is converted into the potential energy of the water and stored in the upper reservoir; during discharging, it is converted into electrical energy through the water flow. The power conversion of pumped-storage hydroelectric power can be expressed as:

[0051]

[0052]

[0053] in, for At any given moment, the pumped storage unit is charged with energy; for At any given moment, the energy released by the pumped storage unit; and They are respectively At any given time, the pumped storage unit's pumping power and power generation power are measured.

[0054] In a specific embodiment of the present invention, the pumped storage unit can operate in two states: charging (pumping) and discharging (generating electricity). In the charging state, the pumped storage unit consumes electrical energy to drive a water pump, pumping water from the lower reservoir to the upper reservoir, thus converting electrical energy into the potential energy of the water. In the discharging state, water in the upper reservoir is discharged through a turbine, converting potential energy into electrical energy. These two operating states are distinguished by power direction and energy conversion efficiency, and together they characterize the operating characteristics of the pumped storage unit at different times.

[0055] (4) Flywheel energy storage unit

[0056] The modeling of the flywheel energy storage unit focuses on the flywheel's energy storage capacity and rotational speed variations. The expression for flywheel energy storage is:

[0057]

[0058] in, for At any given moment, the energy stored in the flywheel energy storage unit; The flywheel moment of inertia of the flywheel energy storage unit. for At time t, the flywheel angular velocity of the flywheel energy storage unit.

[0059] The charging and discharging power of a flywheel energy storage unit is closely related to its energy state changes, as expressed below:

[0060]

[0061]

[0062] in, for At any given time, the charging power of the flywheel energy storage unit; for The discharge power of the flywheel energy storage unit at a given time; and These are the charging and discharging efficiency coefficients of the flywheel energy storage unit, respectively. for At any given time, the input power of the flywheel energy storage unit; for At time t, the output power of the flywheel energy storage unit.

[0063] In a specific embodiment of the present invention, the operating state of the flywheel energy storage unit can be divided into two types: charging state and discharging state, which are characterized by the energy storage and release process corresponding to the change in its rotational speed. In the charging state, the flywheel is powered by input power. Electrical energy is converted into mechanical kinetic energy and stored in the flywheel energy storage unit. In the discharge state, the flywheel decelerates, releasing the stored mechanical kinetic energy and converting it back into electrical energy. Therefore, the operating state of the flywheel energy storage unit at different times can be characterized by the flywheel's moment of inertia, angular velocity, and charging / discharging power, reflecting its high-speed, short-term energy storage and rapid regulation characteristics.

[0064] The modeling results for each type of energy storage unit will be integrated into a knowledge graph structure using a "node-edge-attribute-weight" framework. The dynamic knowledge graph uses various types of energy storage units in the power system as nodes, each node including the state vector and operating state of the corresponding energy storage unit. Based on this, by defining relational edges between nodes, the coupling relationships between different energy storage units in terms of technical characteristics, operational behavior, and scheduling functions are characterized. The edge weights are dynamically calculated based on the similarity of specific attribute vectors of the energy storage units. Through the combined effect of relational edges and edge weights, the economic information of each energy storage unit is disseminated and aggregated in the knowledge graph, thereby achieving unified integration and collaborative evaluation of the modeling results for multiple types of energy storage. In other words, the knowledge graph includes nodes and edges; when constructing the knowledge graph, various types of energy storage units are used as nodes, and the attributes of each node include the information and weight of the energy storage unit. The connections between energy storage units are used as edges, and the attributes of the edges are their weights. The weight of an energy storage unit is obtained based on its rated power, energy conversion efficiency, available energy, and unit capacity investment cost. The weight of an edge is obtained based on the state vectors of the energy storage units corresponding to the two nodes connected by the edge. To achieve a quantitative comparison of energy storage technologies within a unified semantic space, the comprehensive performance function of an energy storage unit is defined as follows:

[0065]

[0066] in, for At any given time, the energy storage unit Overall performance; for At any given time, the energy storage unit Rated power; for At any given time, the energy storage unit Energy conversion efficiency; for At any given time, the energy storage unit Available energy; For energy storage units The unit capacity investment cost; , , , These are the weighting coefficients for power, efficiency, energy utilization, and cost, respectively. , , Energy storage units Maximum rated power, rated capacity, and maximum investment cost.

[0067] Energy storage unit The overall performance is used to generate dynamic weights for each node in the dynamic knowledge graph, representing the techno-economic contribution of the energy storage unit corresponding to that node. The dynamic weights of the nodes can be represented as follows:

[0068]

[0069] in, for At any given time, the energy storage unit The weight of the corresponding node in the knowledge graph; for At any given time, the energy storage unit Its overall performance.

[0070] The edge weights of the relationships between nodes in the knowledge graph for each energy storage unit are determined by the following formula:

[0071]

[0072] in, for At any given time, the energy storage unit Corresponding nodes and energy storage units The edge weights of the edges between corresponding nodes; It is the Sigmoid normalization function; for At any given time, the energy storage unit State vector; and energy storage unit The inner product of the transpose of the state vector is used to measure its coupling strength.

[0073] As power system operation data is updated in real time, the attributes and weights of nodes, as well as the edge weights between nodes, are adjusted over time to achieve dynamic evolution of the knowledge graph. When a new energy storage unit is added to the power system, new nodes and their relationships are automatically generated; when an energy storage unit is decommissioned, the corresponding node is marked as invalid and the structure of the knowledge graph is adjusted. This method achieves unified semantic modeling and dynamic updating of diverse and heterogeneous energy storage units, such as lithium-ion batteries, hydrogen energy storage units, pumped hydro storage units, and flywheel energy storage units. This provides standardized knowledge input and quantitative feature support for subsequent economic assessment and configuration optimization, thereby improving the intelligence and interpretability of the comprehensive evaluation of energy storage systems.

[0074] Step 2: Based on the dynamic knowledge graph obtained in Step 1, calculate the full life cycle cost and multi-dimensional benefits of various types of energy storage units, then establish a multi-timescale economic evaluation model, and use this model to conduct dynamic economic evaluation at multiple time scales.

[0075] The purpose of this step is to construct a multi-timescale economic evaluation model, aiming to assess the economics of energy storage units from multiple dimensions. This multi-timescale economic evaluation model extracts key parameters affecting economics based on a dynamic knowledge graph and considers the total lifecycle cost and multi-dimensional benefits of energy storage units, thus forming a comprehensive evaluation index system. Based on this, the multi-timescale economic evaluation model performs dynamic evaluations at different time scales, ensuring that the evaluation results accurately reflect the economic performance of the energy storage system composed of all energy storage units at different operational stages.

[0076] The multi-timescale economic evaluation model considers the performance of energy storage technology across multiple operating cycles, encompassing initial investment, operation and maintenance costs, revenue, and other economic factors. Step two establishes a comprehensive evaluation index system that includes two main categories of indicators: total lifecycle cost and multi-dimensional revenue.

[0077] The Life Cycle Cost (LCC) mainly includes the initial investment cost, operation and maintenance costs, and decommissioning and scrapping costs. Its calculation formula is as follows:

[0078]

[0079] in, For energy storage units The total lifecycle cost; For energy storage units The initial investment cost includes the cost of equipment purchase, installation, and commissioning; For energy storage units The operating and maintenance costs; For energy storage units The costs of retirement and scrapping. The design lifespan of the energy storage unit; for At any given time, the energy storage unit The operating and maintenance costs of coupling knowledge graphs.

[0080] Initial investment costs and decommissioning and scrapping costs are typically static parameters that do not directly involve the dynamic impact of other energy storage units. However, the calculation of operation and maintenance costs requires consideration of the dynamic impact of other energy storage units and can be expressed by the following formula:

[0081]

[0082] In the formula, For energy storage units The operating and maintenance costs; The coupling coefficient for operation and maintenance costs; For energy storage units The set of adjacent nodes of the corresponding node; for At any given time, the energy storage unit Corresponding nodes and energy storage units The edge weights of the edges between corresponding nodes; for At any given time, the energy storage unit The weight of the corresponding node in the knowledge graph; For energy storage units The operating and maintenance costs.

[0083] Multiple Dimension Revenue (MDR) primarily includes electricity revenue, ancillary service revenue, and carbon trading revenue. MDR is the weighted sum of electricity revenue, ancillary service revenue, and carbon trading revenue.

[0084]

[0085] in, for At any given time, the energy storage unit Multi-dimensional benefits; for At any given time, the energy storage unit Electricity revenue; for At any given time, the energy storage unit Ancillary service revenue; for At any given time, the energy storage unit Carbon trading revenue; , , These are the weighting coefficients for electricity revenue, ancillary service revenue, and carbon trading revenue, respectively.

[0086] The formula for calculating electricity revenue is:

[0087]

[0088] In the formula, for At any given time, the electricity price in the electricity market.

[0089] The formula for calculating revenue from ancillary services is:

[0090]

[0091] In the formula, Ancillary service revenue per unit power; for At any given time, the energy storage unit The weight of the corresponding node in the knowledge graph.

[0092] The formula for calculating carbon trading revenue is:

[0093]

[0094] In the formula, This represents the carbon emission reduction factor per unit of electrical energy. for At any given time, the carbon price in the carbon trading market.

[0095] Based on the above-mentioned benefits, a comprehensive economic evaluation index system can be established to help comprehensively assess the economic benefits of energy storage systems. A multi-timescale economic evaluation model can be developed, which can dynamically evaluate the economics of energy storage units based on changes over different time periods.

[0096] The core of the multi-timescale economic evaluation model lies in:

[0097] (1) Based on time series analysis, consider factors such as fluctuations in electricity market prices, changes in the efficiency of energy storage units, and changes in load demand at different time scales;

[0098] (2) Real-time optimization scheduling: Based on the evaluation results at different time scales, dynamically adjust the operation strategy of the energy storage system to achieve the optimal balance between cost and benefit;

[0099] (3) Lifespan decay and investment recovery: By using a dynamic lifespan prediction model, the use and maintenance strategies of energy storage units can be adjusted to extend the service life of the equipment and optimize the investment recovery period.

[0100] Specifically, the mathematical expression for the multi-timescale economic evaluation model is as follows:

[0101]

[0102]

[0103] in, for At any given time, the energy storage unit In time scale The comprehensive economic evaluation value across multiple time scales; A set of time scales; Time scale The corresponding weighting coefficients; for At any given time, the energy storage unit In time scale The economic performance score below; for At any given time, the energy storage unit In time scale The multi-dimensional benefits; and They are respectively in At time 10, all energy storage units are on the time scale The maximum and minimum values ​​of multi-dimensional returns; Time scale The cost penalty coefficient is as follows; for At any given time, the energy storage unit In time scale The total lifecycle cost; and They are respectively At time 10, all energy storage units are on the time scale The maximum and minimum total lifecycle costs under the given conditions.

[0104] The economic performance of energy storage systems varies depending on the needs at different time scales. Short-term assessments primarily consider the immediate returns of energy storage systems; medium-term assessments focus on seasonal fluctuations and market cycles; and long-term assessments comprehensively consider the entire lifecycle costs and benefits of energy storage systems to make systematic economic forecasts. This hierarchical, multi-time-scale assessment allows for a comprehensive understanding of the economic performance of energy storage systems over different time periods, providing multi-dimensional data support for optimal allocation and investment decisions.

[0105] Step 3: Based on the economic dynamic evaluation results obtained in Step 2, a multi-objective optimization configuration model is constructed with the objective function of achieving the optimal economic efficiency, lowest investment cost, and highest system reliability of the energy storage system composed of energy storage units. The constraints are energy storage unit charging and discharging power constraints, energy storage unit capacity constraints, grid operation constraints, environmental constraints, energy storage system reliability constraints, and energy storage unit health status constraints.

[0106] The purpose of this step is to construct a multi-objective optimization configuration model that comprehensively considers the economy, investment cost, and system reliability of the energy storage system, thereby achieving the optimal configuration of the energy storage system.

[0107] The specific objective function includes the following three aspects:

[0108] (1) Optimal economic efficiency

[0109] The economic optimization objective aims to maximize the revenue of the energy storage system and minimize its investment cost, ultimately achieving economic benefits. Considering the total lifecycle cost and multi-dimensional benefits, the economic sub-objective function can be expressed as:

[0110]

[0111] in, The sub-objective function for optimizing the economic performance of the energy storage system; To obtain the maximum value of the function; For energy storage units The total lifecycle cost.

[0112] By optimizing this sub-objective function, an energy storage configuration scheme that maximizes revenue and minimizes investment costs within a specific time frame can be obtained.

[0113] (2) Lowest investment cost

[0114] The goal of minimizing investment costs is to ensure that the initial investment in an energy storage system is as low as possible, while guaranteeing the system's economic viability and reliability. Its objective function is:

[0115]

[0116] in, The sub-objective function is the one that minimizes investment costs; To obtain the minimum value of the function.

[0117] This objective function aims to reduce the initial capital investment of energy storage systems by optimizing the selection of energy storage units.

[0118] Specifically, the initial investment cost of each energy storage unit can be calculated using the following formula, taking into account the specific investment parameters of the energy storage technology corresponding to each energy storage unit.

[0119] a) Lithium-ion batteries

[0120] Initial investment cost of lithium-ion batteries This mainly includes the unit capacity cost of lithium-ion batteries. and the energy storage capacity of lithium-ion batteries The formula is as follows:

[0121]

[0122] b) Hydrogen storage unit

[0123] Initial investment cost of hydrogen energy storage units This mainly includes the cost of hydrogen production equipment and storage facilities. The calculation formula is as follows:

[0124]

[0125] in, This refers to the unit capacity investment cost of a hydrogen energy storage unit. The electrical energy capacity of the hydrogen energy storage unit; The investment cost per unit hydrogen storage capacity; This refers to the hydrogen storage capacity of the hydrogen energy storage unit.

[0126] c) Pumped storage unit

[0127] Initial investment cost of pumped storage units This mainly includes the construction costs of the water pump system and power generation equipment. The calculation formula is as follows:

[0128]

[0129] in, The unit power investment cost of the pump equipment for pumped storage units; This refers to the power of the pumped storage unit; The unit capacity investment cost of the power generation equipment for pumped storage units; This refers to the energy capacity of the pumped storage unit.

[0130] d) Flywheel energy storage unit

[0131] Initial investment cost of flywheel energy storage units This mainly includes the unit capacity cost of flywheel energy storage units. and the capacity of flywheel energy storage units The calculation formula is as follows:

[0132]

[0133] (3) Energy storage systems have the highest reliability

[0134] The system reliability objective aims to improve the reliability of energy storage systems during grid operation, namely, increasing the availability of energy storage units and the fault tolerance of the energy storage system. The reliability of the energy storage system is measured by the reliability indicators of the energy storage units and the power supply capacity of the energy storage system. This objective function can be expressed as:

[0135]

[0136] in, The sub-objective function that maximizes the reliability of the energy storage system; and They are respectively Availability factors of the energy storage system at time t, representing the state of discharge and the state of charge.

[0137] The objective function aims to maximize the availability of the energy storage system, that is, to improve the reliability of energy storage units in grid regulation through efficient scheduling.

[0138] Furthermore, to ensure the feasibility and rationality of the multi-objective optimization configuration model, the following are the definitions of various constraints in the model. These constraints ensure that each part of the energy storage system meets physical, economic, and operational requirements, thereby achieving optimal configuration of the energy storage system.

[0139] (1) Energy storage unit charging and discharging power constraints

[0140] The charging and discharging power of the energy storage unit should be within the specified range:

[0141]

[0142]

[0143] in, and They are respectively At any given time, the energy storage unit Minimum charging power and minimum discharging power; and They are respectively At any given time, the energy storage unit Maximum charging power and maximum discharging power;

[0144] This constraint ensures that the power of each energy storage unit does not exceed its physical limits, preventing operation beyond its capacity and ensuring the stability of the energy storage system.

[0145] (2) Energy storage unit capacity constraints

[0146] The available energy of each energy storage unit needs to be between its maximum and minimum energy storage capacity:

[0147]

[0148] in, For energy storage units Minimum energy storage capacity; For energy storage units The maximum energy storage capacity.

[0149] In addition, the total capacity of the energy storage system should meet the needs of the grid load, and the overall energy storage capacity should meet the following conditions:

[0150]

[0151] in, This is the total energy storage capacity required by the power grid; This represents the total number of energy storage units in the energy storage system.

[0152] (3) Power grid operation constraints

[0153] The dispatching of energy storage systems should be matched with the grid's load demand, frequency regulation, and voltage requirements. Specifically, the overall equivalent power injection of the energy storage system into the grid should meet the requirements of the grid load demand and operational safety boundaries, ensuring operation within the range of grid load demand.

[0154]

[0155] in, for The system must meet the grid load demand at all times to ensure that the charging and discharging power of the energy storage unit matches the grid demand. This is the maximum regulating power of the energy storage system, ensuring that the energy storage unit can provide sufficient power support when grid demand fluctuates.

[0156] This constraint ensures that the energy storage system can effectively regulate the grid load without exceeding the grid's maximum regulation requirements.

[0157] (4) Reliability constraints of energy storage systems

[0158] The availability of an energy storage unit is typically determined by its state of health (SOH) and failure rate. This can be modeled using the following formula:

[0159]

[0160] in, for At any given time, the energy storage unit Availability represents the probability that an energy storage device can operate normally and provide power; for At any given time, the energy storage unit The failure rate is typically estimated based on the technical parameters, usage frequency, and environmental conditions of the energy storage unit. It can be obtained through historical data or statistical analysis and is generally related to the type and intensity of use of the energy storage unit. It is usually a value less than 1, representing the probability of equipment failure. for At any given time, the energy storage unit The health status indicates the remaining health level of the energy storage unit at that moment. As time goes by, the health status of the energy storage unit will decay.

[0161] The reliability requirements of an energy storage system necessitate that each energy storage unit can provide sufficient power within its predetermined timeframe, ensuring that the availability of each energy storage unit is not lower than the corresponding minimum availability threshold, i.e.:

[0162]

[0163] in, For energy storage units The minimum availability threshold.

[0164] This constraint ensures that the energy storage system has sufficient reliability during operation, thereby avoiding failure of the energy storage system due to faults or inefficient operation.

[0165] (5) Energy storage unit health status (SOH) constraints

[0166] The State of Health (SOH) of an energy storage unit gradually decays over time. Each energy storage unit must meet a corresponding minimum health condition to ensure normal operation. This decay can be modeled using the following relationship:

[0167]

[0168] in, At the initial moment, the energy storage unit ; health status; For energy storage units The decay rate.

[0169] To ensure the energy storage unit can operate normally throughout its entire lifespan, the minimum health status must meet the following constraints:

[0170]

[0171] in, for At any given time, the energy storage unit The minimum health status threshold.

[0172] This constraint ensures that the energy storage unit will not experience performance degradation or failure due to excessively low health status during operation.

[0173] (6) Environmental constraints

[0174] The environmental impact of energy storage systems must also be considered, particularly carbon emissions and energy consumption. By setting environmental constraints, it can be ensured that energy storage units meet environmental requirements while satisfying economic and reliability standards. For example, the carbon emissions of an energy storage system should be below the maximum allowable value:

[0175]

[0176] in, for At any given time, the energy storage unit Carbon emissions; This represents the maximum permissible carbon emissions for an energy storage system.

[0177] Step 4: Solve the multi-objective optimization configuration model using a multi-objective optimization algorithm to obtain the optimal configuration scheme for the energy storage system. The optimal configuration scheme includes the installed capacity ratio and power configuration of various types of energy storage units in the power system, as well as the multi-timescale charging and discharging operation strategies corresponding to each type of energy storage unit. The purpose of this step is to solve the constructed multi-objective optimization configuration model using a multi-objective optimization algorithm. The optimization aims to maximize the economic efficiency, minimize the investment cost, and maximize the system reliability of the energy storage system while satisfying various constraints. Commonly used multi-objective optimization algorithms such as the Non-Dominated Sorting Genetic Algorithm (NSGA-II) are mainly used. A Pareto optimal solution set is generated through non-dominated sorting and congestion calculation. Based on weight allocation or decision preferences, the final recommended energy storage configuration scheme is determined from the Pareto optimal solution set.

[0178] In step four, the NSGA-II algorithm is first used to solve the multi-objective optimization model established in step three. The NSGA-II algorithm is a widely used evolutionary algorithm, particularly suitable for multi-objective optimization problems. Its core idea is to gradually select the optimal solution through the evolutionary process of the population, and present the final result in the form of a Pareto optimal solution set. Specific steps include:

[0179] (1) Initialize a population of n individuals, each representing a possible energy storage system configuration. These individuals are randomly initialized in the population to ensure solution diversity, and a convergence error limit is set;

[0180] (2) Evaluate the objective function for each individual in the population and calculate the value of the individual under the three objectives of economy, investment cost and system reliability;

[0181] (3) Perform non-dominated ordination on the population. Non-dominated ordination is the core step of NSGA-II. It divides individuals in the population into different levels by comparing the dominance relationships between individuals. The dominance relationship is defined as follows: If an individual Not inferior to individuals in all goals And superior to at least one objective ,but Dominate ;if Do not dominate ,and Do not dominate Then it is called and These are non-dominated solutions, meaning they lie on the Pareto front. By orienting the population according to non-dominated solutions, we can divide the population into multiple fronts. The first front contains the solutions with the least domination, the second front contains solutions dominated by the first front, and so on.

[0182] (4) To maintain the diversity of solutions, NSGA-II evaluates solutions through crowding calculation. Crowding reflects the distribution of solutions in the solution space, and the calculation method is as follows:

[0183]

[0184] in, To solve The degree of congestion; For the target m in the solution The function value at that point, and These are the solutions in the sorted solution set. The meaning of "after" and "before". Let be the objective dimension of the multi-objective optimization problem.

[0185] (5) Based on the non-dominated sorting and crowding calculation, new populations are generated through operations such as selection, crossover and mutation;

[0186] (6) After multiple generations of evolution, the NSGA-II algorithm will generate a set of Pareto optimal solutions. These solutions represent the best trade-offs between different objectives and form the Pareto front, which is the set of solutions that cannot be dominated by other solutions on all objectives.

[0187] After multiple generations of evolution, the NSGA-II algorithm generates a set of Pareto optimal solutions. These solutions represent the best trade-offs among multiple objectives, and cannot be dominated by other solutions on all objectives, forming a Pareto front. Specifically, the Pareto optimal solution set satisfies the following conditions: maximizing the economics of the energy storage system, minimizing the investment cost of the energy storage system, and maximizing the reliability of the energy storage system. For each generated Pareto optimal solution, the corresponding objective function value can be obtained, and these solution sets provide different energy storage configuration schemes.

[0188] Each solution in the generated Pareto optimal solution set represents a different energy storage system configuration. Since there may be conflicts between objectives, the final selection of the optimal energy storage configuration usually depends on the decision-maker's preferences. In this case, a weighted allocation method is used to aid decision-making. This involves obtaining the economic efficiency, investment cost, and system reliability of the energy storage system in each configuration scheme corresponding to each solution in the Pareto optimal solution set, and then performing a weighted sum to obtain the comprehensive evaluation value corresponding to the configuration scheme of each solution in the Pareto optimal solution set. The configuration scheme with the highest comprehensive evaluation value is selected as the optimal energy storage system configuration. The specific calculation formula is as follows:

[0189]

[0190] in, This is the comprehensive evaluation value corresponding to the configuration scheme of the solutions in the Pareto optimal solution set; , , These are values ​​for economic efficiency, investment cost, and system reliability, respectively. , , These are the weighting coefficients corresponding to economic efficiency, investment cost, and system reliability, respectively.

[0191] The optimal configuration scheme for energy storage systems includes the installed capacity ratio and power configuration of various types of energy storage units in the power system, as well as the multi-timescale charging and discharging operation strategies corresponding to each type of energy storage unit.

[0192] To verify the effectiveness of this invention, simulation analysis was conducted based on numerical examples. The simulation analysis focused on a hybrid energy storage system using four different energy storage technologies.

[0193] Figure 2 The six sub-graphs depict the operational characteristics of the energy storage system from two dimensions: time scale and energy type. The hourly weekly load curve reflects the overall change pattern of electricity demand within a week, exhibiting obvious periodicity. The hourly weekly wind power output shows strong randomness and volatility. The hourly weekly photovoltaic output exhibits a typical "daytime generation, nighttime shutdown" characteristic. The corresponding minute-level daily load curve, daily wind power fluctuation curve, and daily photovoltaic curve further reveal the high-frequency fluctuation characteristics at short time scales, reflecting the uncertainty of renewable energy output and load within short periods. These differentiated characteristics collectively illustrate the coexistence of fluctuations at multiple time scales, which is a key basis for configuring multiple types of energy storage to achieve peak shaving and valley filling, smooth fluctuations, and ensure stable system operation. Meanwhile, Table 1 provides the technical parameters of each energy storage system, i.e., the basic node attributes in the knowledge graph.

[0194] Table 1 Energy Storage Technology Parameters

[0195]

[0196] like Figure 3 As shown in the figure, this embodiment illustrates the dynamic convergence process of various performance indicators as the number of iterations changes when configuring a generalized energy storage system based on multi-objective optimization. The figure contains two parts: the upper part shows the evolution of net present value and system investment cost over 100 iterations; the lower part shows the overall fluctuation trend of the comprehensive objective function value.

[0197] The upper half of the graph shows that the net present value fluctuates significantly in the early stages, but the curve tends to stabilize as the number of iterations increases, indicating that the algorithm gradually finds a more profitable energy storage configuration. The red curve corresponds to the investment cost, which exhibits considerable randomness between different iterations. This is due to the multi-objective search repeatedly weighing solutions with lower cost objectives against solutions with higher returns, consistent with the expected algorithm behavior. As iterations proceed, the upper and lower bounds of the indicators gradually converge, indicating that the algorithm is approaching a more reliable solution set. The final estimated net present value is 4.8421 million yuan, and the estimated total investment is 5.8775 million yuan.

[0198] The lower half of the figure shows the changes in the comprehensive objective function during the iteration process. The curve initially exhibits large fluctuations, then gradually enters a narrower range, indicating that the optimization population has approached the Pareto front. In the later stages of iteration, although the comprehensive objective value still changes to some extent, the fluctuation amplitude decreases significantly, indicating that the algorithm has entered a local fine-grained search phase. The above iterative trends demonstrate that the proposed generalized energy storage configuration optimization method can effectively explore the trade-offs between economy, reliability, and operational performance of different energy storage combinations, and gradually approach the optimal solution set through an evolutionary mechanism, achieving efficient configuration of the energy storage system. This figure serves as one of the verification bases for the algorithm's performance stability and optimization convergence.

[0199] Figure 4 To achieve the optimal energy storage configuration, the system underwent optimization calculations, ultimately outputting a power capacity configuration scheme centered on pumped hydro storage (67%), hydrogen storage (16%), lithium-ion batteries (13%), and flywheel storage (4%). This scheme fully embodies the synergistic complementarity of multi-level and multi-timescale energy storage: pumped hydro storage serves as a large-scale, long-term energy storage baseload, undertaking the main functions of energy time-shifting and system backup; hydrogen storage provides key support for cross-seasonal energy storage, enhancing the long-term absorption capacity of renewable energy; lithium-ion batteries leverage their short-term and high-efficiency advantages to achieve flexible intraday regulation and power support; while the small but crucial flywheel storage provides the system with millisecond-level rapid frequency response, ensuring the dynamic stability of the power grid. By comprehensively balancing economy, reliability, and technical applicability through a multi-objective optimization model, this configuration forms a coordinated system with "long-term energy storage as the foundation, medium- and short-term regulation as an auxiliary, and rapid response as a supplement." It is particularly suitable for grid scenarios with both energy balance and power stability requirements under high-proportion renewable energy access, verifying the effectiveness and advancement of the method of this invention in achieving scientific energy storage configuration and improving the overall system efficiency.

[0200] Figure 5 and Figure 6This demonstrates that during dynamic operation, the knowledge graph enables real-time status monitoring and intelligent reasoning across different scenarios, revealing significant scenario-adaptive patterns in the SOC and health of each energy storage unit. During periods of high photovoltaic power generation, hydrogen storage maintains a high SOC level, reflecting the system's proactive use of its function to absorb excess renewable energy over long periods and on a large scale. In scenarios with frequency fluctuations, flywheel storage exhibits the most active SOC changes, reflecting its core role in millisecond-level rapid frequency regulation. Under peak load conditions, pumped hydro storage and lithium-ion batteries show a coordinated decrease in SOC, directly reflecting their joint discharge scheduling strategy as the primary regulation means. Simultaneously, the health of each energy storage technology remains stable and within a high range across different scenarios, verifying that the system effectively ensures the reliability of energy storage assets throughout their entire lifecycle by achieving multi-objective optimized configuration through state awareness and operational strategy optimization.

[0201] The overall status monitoring view shows that the system constructed by this invention can not only provide static optimization configuration schemes, but also realize intelligent collaborative operation and status maintenance in dynamic scenarios, ensuring the efficient and reliable execution of the configuration scheme in the actual power grid environment.

[0202] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. Those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for economic evaluation and configuration of general energy storage technologies based on dynamic knowledge graphs, used to generate configuration schemes for various types of energy storage units in a power system; characterized in that, The method includes the following steps: Step 1: Obtain the power system to be optimized and construct a dynamic knowledge graph based on the information of the energy storage units in the power system; Step 2: Based on the dynamic knowledge graph obtained in Step 1, establish a multi-timescale economic evaluation model and use the model to conduct dynamic economic evaluation of various types of energy storage units at multiple time scales. Step 3: Based on the economic dynamic evaluation results obtained in Step 2, construct a multi-objective optimization configuration model with the objective functions of achieving the best economic performance, lowest investment cost, and highest system reliability of the energy storage system composed of energy storage units. Step 4: Solve the multi-objective optimization configuration model using a multi-objective optimization algorithm to obtain the optimal configuration scheme of the energy storage system; wherein, the optimal configuration scheme includes the installed capacity ratio and power configuration of each type of energy storage unit in the power system, as well as the multi-timescale charging and discharging operation strategy corresponding to each type of energy storage unit; In step one, constructing the dynamic knowledge graph includes constructing a knowledge graph based on the information of energy storage units in the power system, and dynamically updating the knowledge graph according to the real-time information of the energy storage units. The knowledge graph includes nodes and edges. When constructing the knowledge graph, each type of energy storage unit is used as a node. The attributes of the node include the information of the energy storage unit and the weight of the energy storage unit. The lines between energy storage units are used as edges, and the attributes of the edges are the weights of the edges. The weight of an energy storage unit is obtained based on its rated power, energy conversion efficiency, available energy, and unit capacity investment cost. The weight of an edge is obtained based on the state vectors of the energy storage units corresponding to the two nodes connected by the edge. In step three, the economically optimal sub-objective function for: in, To obtain the maximum value of the function; The design lifespan of the energy storage unit; for At any given time, the energy storage unit In time scale The comprehensive economic evaluation value across multiple time scales; , , These are the weighting coefficients for electricity revenue, ancillary service revenue, and carbon trading revenue, respectively. for At any given time, the energy storage unit Electricity revenue; for At any given time, the energy storage unit Ancillary service revenue; for At any given time, the energy storage unit Carbon trading revenue; For energy storage units The total lifecycle cost; Minimum investment cost sub-objective function for: in, To find the minimum value of the function; For energy storage units The initial investment cost; The sub-objective function for maximizing system reliability for: in, and They are respectively Availability factors of the energy storage system's state of discharge and state of charge at any given time; for At any given time, the energy storage unit The instantaneous discharge power; for At any given time, the energy storage unit Instantaneous charging power.

2. The method for economic evaluation and configuration of pan-energy storage technologies based on dynamic knowledge graphs according to claim 1, characterized in that, In step one, the various types of energy storage units in the power system include lithium-ion batteries, hydrogen energy storage units, pumped hydro storage units, and flywheel energy storage units; the information of the energy storage unit includes a state vector and an operating state. The energy storage unit state vector includes instantaneous discharge power, instantaneous charging power, available energy, energy conversion efficiency, unit capacity investment cost, life cycle, and specific attribute vectors. The operating state includes charging state and discharging state.

3. The method for economic evaluation and configuration of pan-energy storage technologies based on dynamic knowledge graphs according to claim 2, characterized in that, The specific attribute vectors of the lithium-ion battery include charge / discharge rate, battery temperature, and capacity decay coefficient; the specific attribute vectors of the hydrogen energy storage unit include electrolysis efficiency, energy absorption power, and energy release power; the specific attribute vectors of the pumped hydro storage unit include upper water level, lower water level, and hydraulic efficiency; and the specific attribute vectors of the flywheel energy storage unit include flywheel angular velocity, flywheel moment of inertia, and friction loss factor.

4. The method for economic evaluation and configuration of pan-energy storage technologies based on dynamic knowledge graphs according to claim 1, characterized in that, Step two, which involves establishing a multi-timescale economic evaluation model based on the dynamic knowledge graph obtained in step one, includes: The full life cycle cost and multi-dimensional benefits of various types of energy storage units are calculated by combining the dynamic knowledge graph obtained in step one, and then a multi-time scale economic evaluation model is established based on the obtained full life cycle cost and multi-dimensional benefits.

5. The method for economic evaluation and configuration of pan-energy storage technologies based on dynamic knowledge graphs according to claim 4, characterized in that, The full life cycle cost of the energy storage unit is calculated as follows: First, the weight of the energy storage unit and the edge weights between the node corresponding to the energy storage unit and each node in the set of its neighboring nodes are obtained from the dynamic knowledge graph. Then, the operation and maintenance cost of the energy storage unit after coupling with the knowledge graph is obtained by combining the operation and maintenance cost of the energy storage unit. Finally, the initial investment cost of the energy storage unit, the integral value of the coupled operation and maintenance cost of the energy storage unit at all time points within the design life cycle, and the decommissioning and scrapping cost of the energy storage unit are added together to obtain the full life cycle cost.

6. The method for economic evaluation and configuration of pan-energy storage technologies based on dynamic knowledge graphs according to claim 4, characterized in that, The multi-dimensional revenue of the energy storage unit is calculated as follows: First, the energy revenue of the energy storage unit is obtained based on its discharge power and energy conversion efficiency, as well as the electricity price in the electricity market; then, the ancillary service revenue of the energy storage unit is obtained based on its weight in the dynamic knowledge graph, its discharge power, and the electricity price in the electricity market; next, the carbon trading revenue of the energy storage unit is obtained based on its weight in the dynamic knowledge graph, its discharge power, and the carbon price in the carbon trading market; finally, the multi-dimensional revenue is obtained based on the energy revenue, ancillary service revenue, and carbon trading revenue.

7. The method for economic evaluation and configuration of pan-energy storage technologies based on dynamic knowledge graphs according to claim 4, characterized in that, The expression for the multi-timescale economic evaluation model is as follows: ； ； in, for At any given time, the energy storage unit In time scale The comprehensive economic evaluation value across multiple time scales; A set of time scales; Time scale The corresponding weighting coefficients; for At any given time, the energy storage unit In time scale The economic performance score below; for At any given time, the energy storage unit In time scale The multi-dimensional benefits; and They are respectively in At time 10, all energy storage units are on the time scale The maximum and minimum values ​​of multi-dimensional returns; Time scale The cost penalty coefficient is as follows; for At any given time, the energy storage unit In time scale The total lifecycle cost; and They are respectively At that moment, all energy storage units are on the time scale The maximum and minimum total lifecycle costs under the given conditions.

8. The method for economic evaluation and configuration of pan-energy storage technologies based on dynamic knowledge graphs according to claim 7, characterized in that, In step three, the economic efficiency of the energy storage system is obtained based on a comprehensive economic evaluation value across multiple time scales, total life cycle cost, and multi-dimensional benefits; the investment cost of the energy storage system is the sum of the initial investment costs of each energy storage unit in the energy storage system; and the system reliability of the energy storage system is obtained based on the charging power and discharging power of each energy storage unit. The constraints when constructing a multi-objective optimization configuration model include energy storage unit charging and discharging power constraints, energy storage unit capacity constraints, grid operation constraints, environmental constraints, energy storage system reliability constraints, and energy storage unit health status constraints.

9. The method for economic evaluation and configuration of pan-energy storage technologies based on dynamic knowledge graphs according to claim 1, characterized in that, In step four, a multi-objective optimization algorithm is used to solve the multi-objective optimization configuration model to obtain the optimal solution set. Then, the economic efficiency, investment cost, and system reliability of the energy storage system under the configuration scheme corresponding to each solution in the optimal solution set are obtained and weighted summed to obtain the comprehensive evaluation value corresponding to the configuration scheme of each solution in the optimal solution set. The configuration scheme with the highest comprehensive evaluation value is taken as the optimal configuration scheme of the energy storage system.

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