A hybrid energy storage integration method and device based on energy storage selection, a terminal device, and a storage medium

Abstract

The application discloses a hybrid energy storage integration method and device based on energy storage selection, a terminal device and a storage medium, and belongs to the technical field of energy storage. The method comprises the following steps: calculating the scene matching degree of the to-be-integrated energy storage and the to-be-integrated energy storage application scene according to power grid energy storage data, real-time sensor data and simulation data, and screening out a target energy storage type according to the degree of electricity cost in the operation cost data; constructing a power distribution model and operation power, equipment state safety, energy storage operation environment and application scene constraints according to power grid topology data, power grid energy storage data, real-time sensor data and operation cost data; taking the minimum comprehensive operation cost as a target, solving the model under each constraint to obtain a current power distribution amount, calculating a selection adaptation degree according to real-time sensor data, energy storage rated power, the current comprehensive operation cost and the average degree of electricity cost; and determining a final power distribution amount and integrating operation according to the selection adaptation degree. Through implementation of the application, the problem that existing technologies cannot dynamically integrate energy storage is solved.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, and in particular to a hybrid energy storage integration method, device, terminal equipment, and storage medium based on energy storage selection. Background Technology

[0002] While wind power, photovoltaic (PV), and other new energy power generation projects have achieved large-scale grid connection, their output exhibits significant intermittency, fluctuation, and randomness, posing severe challenges to grid frequency stability, voltage regulation, and efficient absorption of new energy. Energy storage technology, as a core supporting means to smooth out fluctuations in new energy output, enhance grid operational flexibility, and achieve cross-period energy allocation, has become a key foundational equipment for the construction of new power systems. To address the needs of different scenarios, hybrid energy storage combines two or more different types of energy storage technologies, complementing their respective shortcomings to achieve optimal overall benefits.

[0003] Currently, existing hybrid energy storage system technologies have been initially applied in scenarios such as grid frequency regulation, new energy peak shaving, and industrial and commercial power supply. Most hybrid energy storage products on the market adopt a hierarchical topology and fixed-ratio power allocation design approach, mainly relying on engineering experience to select energy storage combinations, and then connecting different energy storage units to the grid through an energy storage converter (PCS), equipped with basic charging and discharging control function modules to perform single-unit parameter verification on each energy storage unit and allocate charging and discharging power according to a fixed ratio.

[0004] However, relying on engineering experience to select energy storage combinations and then allocating power according to a fixed ratio lacks dynamic optimization, cannot adapt to the real-time fluctuations of new energy output and grid load, and is difficult to meet the needs of new power systems for energy storage security and coordination. Summary of the Invention

[0005] This invention provides a hybrid energy storage integration method, device, terminal equipment, and storage medium based on energy storage selection. It can effectively solve the problem that existing technologies rely on engineering experience to select energy storage combinations and then allocate power according to a fixed ratio, which lacks dynamic optimization and makes it difficult to meet the needs of new power systems for energy storage safety and coordination.

[0006] One embodiment of the present invention provides a hybrid energy storage integration method based on energy storage selection, comprising: Based on the energy storage application scenarios to be integrated, real-time sensor data, sensor simulation data, grid energy storage data, grid topology data, and operating cost data are acquired. Based on the real-time data from the sensors, the sensor simulation data, and the grid energy storage data, the scenario matching degree between the energy storage to be integrated and the application scenario to be integrated is calculated. Based on the scenario matching degree, the preset matching degree threshold, and the cost per kilowatt-hour in the operating cost data, the target energy storage type is selected. Based on grid topology data, grid energy storage data, real-time sensor data, and operating cost data, a power allocation model is constructed based on the target energy storage type, along with corresponding operating power constraints, equipment state safety constraints, energy storage operating environment constraints, and application scenario constraints. Under constraints of operating power, equipment state safety, energy storage operating environment, and application scenario, with the goal of minimizing overall operating cost, the power allocation model is solved to obtain the current power allocation of each application level of the hybrid energy storage system. The selection suitability is calculated based on the current power allocation, the real-time data from the sensors, the rated power corresponding to the target energy storage type, the current comprehensive operating cost, and the average cost per kilowatt-hour in the operating cost data. If the selection compatibility is greater than or equal to the preset compatibility threshold, the final power allocation is obtained; otherwise, the target energy storage type is re-selected and the current power allocation is updated. The corresponding integrated energy storage will be scheduled for operation based on the final power allocation.

[0007] Furthermore, based on the real-time data from the sensors, the sensor simulation data, and the grid energy storage data, the scenario matching degree between the energy storage to be integrated and the application scenario to be integrated is calculated, including: Based on the real-time data from the sensors, the simulation data from the sensors, and the grid energy storage data, extract the required parameters for the energy storage application scenarios to be integrated. The expert scoring method is used to compare and score the requirement parameters pairwise, generating a judgment matrix; The normalized eigenvector is calculated based on the judgment matrix. The normalized eigenvector is used as the initial importance score for each requirement parameter. The initial importance score is then normalized to obtain the final importance score. The scenario matching degree between the energy storage to be integrated and its application scenario is obtained by weighting the final importance score, the real-time sensor data corresponding to the demand parameters, and the corresponding sensor simulation data.

[0008] Furthermore, based on the scenario matching degree, the preset matching degree threshold, and the cost per kilowatt-hour in the operating cost data, target energy storage types are selected, including: Based on the scene matching degree and the preset matching degree threshold, the energy storage to be integrated with a scene matching degree greater than or equal to the preset matching degree threshold is selected as the first integrated energy storage. For each application level, the comprehensive cost per kilowatt-hour of each first integrated energy storage in the application level of the energy storage application scenario to be integrated is calculated based on the cost per kilowatt-hour in the operating cost data. The first integrated energy storage with the lowest comprehensive cost per kilowatt-hour is taken as the target energy storage type of the application level of the energy storage application scenario to be integrated.

[0009] Furthermore, the application layers of hybrid energy storage systems include: instantaneous response layer, short-term peak shaving layer, and medium- to long-term energy storage layer; Under constraints of operating power, equipment state safety, energy storage operating environment, and application scenario, with the objective of minimizing overall operating cost, the power allocation model is solved to obtain the current power allocation at each application level of the hybrid energy storage system, including: The first initial power allocation of the instantaneous response layer is calculated based on the real-time load fluctuation value in the real-time data of the sensor, the initial state of charge of the target energy storage type in the instantaneous response layer, and the preset instantaneous response weight. The second initial allocation power of the short-term peak shaving layer is calculated based on the load average value in the real-time data of the sensor, the first initial allocation power, the initial health status of the target energy storage type of the short-term peak shaving layer, and the preset short-term peak shaving weight. The third initial allocation power of the medium- and long-term energy storage layer is calculated based on the total daily load demand, the first initial allocation power, the second initial allocation power, and the preset medium- and long-term energy storage weight in the real-time data of the sensor. With the goal of minimizing overall operating costs, the constraints of operating power, equipment state safety, energy storage operating environment, and application scenario are used as the solution boundary conditions. The solution is iteratively obtained based on the first initial power allocation, the second initial power allocation, and the third initial power allocation to obtain the current power allocation of each application level of the hybrid energy storage system.

[0010] Furthermore, based on the current power allocation, the real-time data from the sensors, the rated power corresponding to the target energy storage type, the current comprehensive operating cost, and the average cost per kilowatt-hour in the operating cost data, the selection suitability is calculated, including: Based on the current power allocation, the actual operating data of each application level target energy storage type in the real-time data of the sensors, the rated power corresponding to the target energy storage type, and the demand threshold of the energy storage application scenario to be integrated, calculate the parameter compliance rate of each application level target energy storage type. The equipment utilization rate is calculated based on the current power allocation, the rated power corresponding to the target energy storage type, and the actual output power of each application level in the real-time data of the sensors. The current comprehensive cost per kilowatt-hour is determined based on the current comprehensive operating cost. The cost optimization coefficient is calculated based on the current comprehensive cost per kilowatt-hour and the average cost per kilowatt-hour of a single energy storage unit in the operating cost data. The selection suitability is obtained by weighting and summing the parameter compliance rate, equipment utilization rate, and cost optimization coefficient.

[0011] Furthermore, the power allocation model is as follows: ; in, For overall operating costs; The equipment depreciation cost for the j-th target energy storage type; The operation and maintenance cost for the j-th target energy storage type; The energy consumption cost of the j-th target energy storage type; This represents the total power generation of the hybrid energy storage system.

[0012] Furthermore, based on the final power allocation, the corresponding integrated energy storage systems to be integrated are scheduled for operation, including: Based on the final power allocation, charge and discharge control commands for energy storage units in each application level are generated. The system schedules energy storage units at each application level according to the charge and discharge control commands, and obtains real-time operating data after the energy storage units are integrated and running. The system calculates the response deviation based on the real-time operating data and the final power allocation. The constraints of the power allocation model are modified based on the response deviation.

[0013] As an improvement to the above solution, another embodiment of the present invention provides a hybrid energy storage integrated device based on energy storage selection, comprising: The power grid data acquisition module is used to acquire real-time sensor data, sensor simulation data, power grid energy storage data, power grid topology data, and operating cost data based on the energy storage application scenarios to be integrated. The target energy storage type screening module is used to calculate the scenario matching degree between the energy storage to be integrated and the application scenario to be integrated based on the real-time data of the sensor, the simulation data of the sensor, and the grid energy storage data, and to screen out the target energy storage type based on the scenario matching degree, the preset matching degree threshold, and the cost per kilowatt-hour in the operating cost data. The model and constraint construction module is used to construct a power allocation model and corresponding operating power constraints, equipment state safety constraints, energy storage operating environment constraints and application scenario constraints based on the target energy storage type, according to grid topology data, grid energy storage data, real-time sensor data and operating cost data. The current power allocation solution module is used to solve the power allocation model under the constraints of operating power, equipment state safety, energy storage operating environment and application scenario, with the goal of minimizing the overall operating cost, to obtain the current power allocation of each application level of the hybrid energy storage system. The selection compatibility calculation module is used to calculate the selection compatibility based on the current power allocation, the real-time data of the sensor, the rated power corresponding to the target energy storage type, the current comprehensive operating cost, and the average cost per kilowatt-hour in the operating cost data. The final power allocation determination module is used to obtain the final power allocation if the selection adaptability is greater than or equal to a preset adaptability threshold; otherwise, the target energy storage type is re-selected and the current power allocation is updated. The integrated operation module is used to schedule the integrated operation of the corresponding energy storage to be integrated based on the final power allocation.

[0014] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a hybrid energy storage integration method based on energy storage selection as described in the above embodiments.

[0015] Another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the hybrid energy storage integration method based on energy storage selection described in the above embodiment.

[0016] By implementing this invention, at least the following beneficial effects are achieved: This invention provides a hybrid energy storage integration method, device, terminal equipment, and storage medium based on energy storage selection. The method quantifies scenario matching based on real-time sensor data, sensor simulation data, grid energy storage data, grid topology data, and operating cost data. It selects target energy storage types by combining levelized cost per kilowatt-hour (LCOE), eliminating the use of fixed charge / discharge power ratios and the subjectivity of manual experience-based selection. Based on the target energy storage type, it constructs a power allocation model by integrating grid topology data, real-time sensor data, and operating cost data, and superimposes multiple constraints including operating power constraints, equipment state safety constraints, energy storage operating environment constraints, and application scenario constraints. The model is solved with the goal of minimizing overall operating cost, yielding real-time power allocation at each application level. This allocation can be dynamically adjusted according to real-time fluctuations in renewable energy output and grid load, rather than a static fixed allocation. This overcomes the shortcomings of traditional fixed-ratio allocation, which cannot adapt to dynamic grid changes, achieving dynamic optimization and adaptation. A selection matching index is introduced for closed-loop verification. If the matching degree is insufficient, the energy storage type is re-selected and the power allocation is updated, ensuring safe, collaborative, and adaptable operation of the energy storage system. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a hybrid energy storage integration method based on energy storage selection according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a hybrid energy storage integrated device based on energy storage selection according to an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See Figure 1 To address the problem that existing technologies rely on engineering experience to select energy storage combinations and then allocate power according to a fixed ratio, lacking dynamic optimization and failing to meet the requirements of new power systems for energy storage safety and coordination, an embodiment of the present invention provides a flowchart of a hybrid energy storage integration method based on energy storage selection, including: S1. Acquire real-time sensor data, sensor simulation data, grid energy storage data, grid topology data, and operating cost data based on the energy storage application scenarios to be integrated; Specifically, the energy storage application scenarios to be integrated refer to specific power application scenarios that require the deployment of hybrid energy storage systems, including five categories: grid frequency regulation, new energy inter-day peak shaving, urban industrial and commercial users, power supply in remote areas, and data center backup power. Based on load level, failure consequences, and policy guidance, these can be divided into critical scenarios and ordinary scenarios. Grid frequency regulation, new energy inter-day peak shaving, and data center backup power are critical scenarios, while urban industrial and commercial users and power supply in remote areas are ordinary scenarios. Real-time sensor data refers to dynamic data related to the scenario and energy storage operation collected in real time by on-site sensors, including real-time load power, grid voltage, grid frequency, energy storage unit operating status data (including state of charge (SOC), state of health (SOH), and charge / discharge power), and environmental parameters (including temperature, humidity, and altitude). Sensor simulation data, also known as simulation output data, refers to simulated data output by power system simulation software to supplement insufficient real-time data coverage, including future load forecast curves, grid extreme operating condition parameters, energy storage device performance degradation data, and environmental impact simulation data. Grid energy storage data refers to industry-recognized standardized parameters for energy storage technologies, grid connection technical specifications, safety and reliability standards, cost and operation and maintenance benchmark parameters, and other industry benchmark data. Grid topology data refers to the physical topology information of the power grid corresponding to the target application scenario, including the grid structure, node parameters, access point locations, transformer station architecture, and distribution network architecture. Operating cost data refers to cost-related data involved in the entire life cycle of the energy storage system, including unit investment cost, annual operation and maintenance cost, equipment self-consumption, local electricity price, design lifespan, and average cost per kilowatt-hour for a single energy storage technology solution.

[0020] To illustrate, the first step is to determine the type and level of the energy storage application scenario to be integrated. The determination rules are as follows: scenarios involving primary and secondary loads are directly classified as critical scenarios; scenarios where a fault would cause serious impacts on the power grid or society are classified as critical scenarios; and scenarios related to renewable energy consumption and power grid security are prioritized as critical scenarios. Secondly, real-time data such as load power, grid voltage, grid frequency, and ambient temperature and humidity are collected using field-deployed sensors for voltage, current, temperature, and power. Simulation data on future load forecasts, extreme operating conditions, and equipment performance degradation are generated using power simulation software. Grid energy storage data is obtained from industry standards, energy storage technology white papers, and power grid company technical specifications. Grid topology data for the target access point is obtained from the power grid company. Operating cost data is obtained from equipment manufacturers and industry statistics. Finally, the collected multi-source data is integrated and verified using a multi-source data fusion algorithm. in, This represents the final scene parameter value, with the unit determined based on the specific parameter type. This represents the real-time data collected by the sensor, with units of and . Maintain consistency; This represents the weighting coefficient of real-time data; This represents the output data of the simulation, with units equal to... Maintain consistency; This represents the weighting coefficient of the simulation data, used to supplement insufficient scene coverage in real-time data. This represents industry benchmark data, with units and... Maintain consistency; This represents the weighting coefficient of the benchmark data, used to ensure that the data conforms to common industry standards; This represents the data deviation coefficient, which has no unit and is taken as a value between 0.9 and 1.1 depending on the data reliability. The higher the reliability, the closer the coefficient is to 1.

[0021] In a preferred embodiment of the present invention, the various energy storage application scenarios are described as follows: In the grid frequency regulation scenario, the core requirements for energy storage are millisecond to second-level response and high-frequency regulation. Quantitative parameters include response delay ≤1.2s, single regulation power fluctuation ±(40-60)MW, and annual regulation frequency ≥1.3×10 5 Secondary load fluctuation coefficient λ≤0.1, where λ is the ratio of actual load to rated load; For new energy cross-day peak shaving scenarios, the core requirements for energy storage are long-duration operation and high recovery rate. Quantitative parameters include energy storage duration ≥14.4h, energy storage capacity ≥(80-120)MWh, charge / discharge efficiency ≥88%, and electrolyte recovery rate ≥99.75%. For urban industrial and commercial users, the core requirements for energy storage are small size and high temperature resistance, with quantifiable parameters including a footprint of ≤0.18m² / kWh, thermal runaway temperature ≥315℃, and annual failure rate ≤0.08%. In remote power supply scenarios, the core requirements for energy storage are low maintenance frequency and wide temperature range. Quantitative parameters include cost per kilowatt-hour ≤ 0.735 yuan / kWh, operating temperature range -30℃ to 50℃, and annual maintenance frequency ≤ 2 times / set. Data center backup power scenario: The core requirements for energy storage are zero-interruption application and short latency. Quantitative parameters include power interruption time ≤0.055s, backup power duration ≥2.4h, and reliability ≥99.999%.

[0022] S2. Based on the real-time data from the sensors, the simulation data from the sensors, and the energy storage data from the power grid, calculate the scenario matching degree between the energy storage to be integrated and the application scenario of the energy storage to be integrated. Based on the scenario matching degree, the preset matching degree threshold, and the cost per kilowatt-hour in the operating cost data, select the target energy storage type. Specifically, scenario matching degree is a quantitative indicator representing the degree of adaptability between the energy storage technology to be integrated and the requirements of the target application scenario. The value ranges from 0 to 1, with a higher value indicating better adaptability. A preset matching degree threshold is a critical value used to screen qualified energy storage technologies, fixed at 0.8. That is, only energy storage technologies with a scenario matching degree ≥ 0.8 are eligible as candidates. The target energy storage type is the type of energy storage technology suitable for the corresponding application level, determined after scenario matching degree screening and economic evaluation. This includes flywheel energy storage, superconducting energy storage, lithium-ion batteries, sodium-ion batteries, zinc-iron flow batteries, vanadium redox flow batteries, compressed air energy storage, and mainstream semi-solid-state battery energy storage technologies.

[0023] To illustrate, core requirement parameters of the scenario are extracted from the scenario data after multi-source fusion. The weight of each requirement parameter is calculated by the analytic hierarchy process. Then, the scenario matching degree between each type of energy storage technology to be integrated and the scenario is calculated. A preset matching degree threshold of 0.8 is set, and candidate energy storage technologies with a scenario matching degree ≥ 0.8 are selected. Then, the comprehensive levelized cost of electricity of each candidate technology is calculated, and the one with the lowest comprehensive levelized cost of electricity is selected as the target energy storage type.

[0024] In a preferred embodiment of the present invention, the quantitative parameters of eight mainstream energy storage technologies are formed into a standardized library, as shown in Table 1 below: Table 1 Energy Storage Technology Parameters Preferably, based on the real-time data from the sensors, the simulation data from the sensors, and the grid energy storage data, the scenario matching degree between the energy storage to be integrated and the application scenario to be integrated is calculated, including: Based on the real-time data from the sensors, the simulation data from the sensors, and the grid energy storage data, extract the required parameters for the energy storage application scenarios to be integrated. The expert scoring method is used to compare and score the requirement parameters pairwise, generating a judgment matrix; The normalized eigenvector is calculated based on the judgment matrix. The normalized eigenvector is used as the initial importance score for each requirement parameter. The initial importance score is then normalized to obtain the final importance score. The scenario matching degree between the energy storage to be integrated and its application scenario is obtained by weighting the final importance score, the real-time sensor data corresponding to the demand parameters, and the corresponding sensor simulation data.

[0025] Specifically, the demand parameters represent the quantitative indicators of the core requirements of the energy storage application scenarios to be integrated, corresponding one-to-one with the scenario type. These include response speed, energy storage duration, charging and discharging efficiency, floor space, thermal runaway temperature, levelized cost of electricity (LCOE), maintenance frequency, and power outage time. The judgment matrix is ​​used to represent the relative importance of each demand parameter under the same criterion layer. It is generated by summing scores from pairwise comparisons by experts. The normalized feature vector is a vector representing the relative importance of each demand parameter, obtained by solving the judgment matrix. After normalization, the sum of all elements is 1, corresponding to the initial importance score of each parameter.

[0026] In a preferred embodiment of the present invention, primary requirement parameters are extracted from the multi-source fused scenario data, targeting the core operational objectives of the scenario, ensuring a one-to-one correspondence between the parameters and the scenario's quantitative requirements. For example, in a data center backup power scenario, requirement parameters such as power outage time, backup power duration, reliability, and response speed are extracted; in a remote area power supply scenario, requirement parameters such as cost per kilowatt-hour, operating temperature range, and annual maintenance frequency are extracted. Industry experts are invited to compare each requirement parameter pairwise under the same criterion level, using a 1-9 scale to score them, where 1 indicates that both are equally important, and 9 indicates that the former is extremely more important than the latter. The average of all expert scores is taken to form an n-order judgment matrix, where n is the number of requirement parameters. Then, linear algebra methods are used to solve for the largest eigenvalue of the judgment matrix and the corresponding eigenvector. The eigenvector is then normalized to obtain the initial importance score for each requirement parameter. The judgment matrix undergoes a consistency check, and the random consistency ratio (CR) is calculated. When CR < 0.1, the judgment matrix passes the consistency check, and the initial importance scores are normalized again to obtain the final importance score. If CR ≥ 0.1, experts are invited to readjust the scores until the consistency check is passed. Finally, the scene matching degree is calculated using the scene matching degree calculation formula, and the scores of all parameters are weighted and summed to obtain the final scene matching degree. Scene matching degree S: Where S represents the matching degree between energy storage technology and scenario requirements, and its value ranges from 0 to 1; This represents the weight of the i-th requirement parameter; This represents a fixed coefficient, with a value of 1, used to ensure the baseline range for matching degree calculation; This represents the actual value of the energy storage technology on the i-th demand parameter, i.e., the real-time sensor data corresponding to the energy storage to be integrated on the demand parameter, in units of 1 and 2. Maintain consistency; This represents the quantized value of the scenario at the i-th requirement parameter, i.e., the corresponding sensor simulation data, in units of 1 and 2. Maintain consistency; if If the requirement exceeds the tolerance range of the scenario, the score for this item will be recorded as 0.

[0027] Preferably, the target energy storage type is selected based on the scenario matching degree, a preset matching degree threshold, and the cost per kilowatt-hour in the operating cost data, including: Based on the scene matching degree and the preset matching degree threshold, the energy storage to be integrated with a scene matching degree greater than or equal to the preset matching degree threshold is selected as the first integrated energy storage. For each application level, the comprehensive cost per kilowatt-hour of each first integrated energy storage in the application level of the energy storage application scenario to be integrated is calculated based on the cost per kilowatt-hour in the operating cost data. The first integrated energy storage with the lowest comprehensive cost per kilowatt-hour is taken as the target energy storage type of the application level of the energy storage application scenario to be integrated.

[0028] Specifically, the first integrated energy storage technology refers to the energy storage technologies to be integrated after being screened for scenario matching, with a scenario matching degree greater than or equal to a preset matching degree threshold; these are the candidate energy storage technologies. The comprehensive levelized cost of electricity (LCOE) characterizes the total cost per unit of electricity generated throughout the entire life cycle of an energy storage technology. It is a core indicator for measuring the economics of energy storage, and its calculation formula is the total cost throughout the entire life cycle divided by the total electricity generated throughout the entire life cycle.

[0029] In a preferred embodiment of the present invention, the comprehensive cost per kilowatt-hour of each first integrated energy storage system is calculated based on the functional requirements of different application levels. The calculation formula is: C = (C d +C o +C e ) / E0, C is the comprehensive cost per kilowatt-hour (yuan / kWh) C d C represents the total lifecycle depreciation cost (in yuan). d = Initial purchase cost ÷ Design service life × Calculation period; C o The total lifecycle maintenance cost (in yuan) is C. o =Annual maintenance unit price × Calculation period; C e C represents the total lifecycle energy cost (in yuan). e=Annual self-consumption × Local electricity price × Calculation period; E is the total power generation (kWh) within the calculation period, E0 = Rated power × Annual charge / discharge duration × Charge / discharge efficiency. For the instantaneous response layer, the comprehensive cost per kilowatt-hour (LQU) under high-frequency regulation scenarios is calculated first; for the medium- and long-term energy storage layer, the comprehensive LQU under long-term charge / discharge scenarios is calculated first. Finally, the first integrated energy storage with the lowest comprehensive LQU in each application layer is determined as the target energy storage type for that layer.

[0030] In a preferred embodiment of the present invention, an energy storage technology with a scenario matching degree S≥0.8 is selected. If multiple technologies meet the condition, the final type is determined according to the principle of prioritizing economic efficiency. An example is given below: For grid frequency regulation scenarios: flywheel energy storage S=0.92 (response speed matching), superconducting energy storage S=0.88 (better response speed but higher cost), the final selection is flywheel energy storage; For new energy cross-day peak shaving scenarios: Zinc-iron flow battery S=0.90 (energy storage duration + recovery rate matching), vanadium redox flow battery S=0.85 (duration matching but high cost), the final selection is zinc-iron flow battery; For urban industrial and commercial applications: semi-solid-state batteries have an S=0.88 (thermal runaway temperature + floor space matching), while sodium-ion batteries have an S=0.82 (safety matching but low energy density). The final choice was semi-solid-state batteries. For power supply scenarios in remote areas: Compressed air energy storage S=0.91 (cost + environmental adaptability matching), sodium-ion battery S=0.83 (cost matching but short energy storage time), the final selection is compressed air energy storage; For data center backup power scenarios: superconducting energy storage S=0.89 (interruption time matching), flywheel energy storage S=0.84 (interruption time slightly higher), and the final selection is superconducting energy storage.

[0031] In a preferred embodiment of the present invention, a power grid frequency regulation scenario is used as an example for illustration. Scenario requirements: response delay ≤ 1.2s, annual regulation frequency ≥ 1.3 × 10⁻⁶. 5 The matching degree of the candidate technologies was ≥0.8.

[0032] The relevant parameters are shown in Table 2: Table 2 The calculation period is 10 years, and the local electricity price is 0.6 yuan / kWh.

[0033] Flywheel energy storage: C d =1500÷15×10=1000;C o =15×10=150;C e=10×0.6×10=60; E0=10×500×85%=4250; C=(1000+150+60)÷4250=0.285.

[0034] Superconducting energy storage: C d =6000÷15×10=4000;C o =60×10=600;C e =50×0.6×10=300; E0=10×500×90%=4500; C=(4000+600+300)÷4500=1.089. In summary, flywheel energy storage has a lower overall cost per kilowatt-hour.

[0035] This real-time implementation allows for differentiated economic calculations for different application levels, aligning with the actual operating conditions of each level and improving the accuracy of economic assessments. The calculation of the comprehensive cost per kilowatt-hour covers all cost items throughout the entire lifecycle, avoiding the limitations of focusing only on the initial investment cost.

[0036] S3. Based on the power grid topology data, power grid energy storage data, real-time sensor data, and operating cost data, construct a power allocation model and corresponding operating power constraints, equipment state safety constraints, energy storage operating environment constraints, and application scenario constraints based on the target energy storage type. Specifically, the power allocation model is a mathematical model used to calculate the charging and discharging power allocation of energy storage units at each application level of the hybrid energy storage system. It aims to minimize overall operating cost and solves for the optimal power allocation by combining multi-dimensional constraints. Operating power constraints are technical constraints to ensure that the power output of the hybrid energy storage system matches the load demand and that the power at each level does not exceed the rated threshold. These constraints include power balance constraints, rated power constraints for the instantaneous response layer, rated power constraints for the short-term peak-shaving layer, rated power constraints for the medium- and long-term energy storage layer, and energy storage charging and discharging efficiency constraints. Equipment state safety constraints are safety constraints to ensure the safety of the energy storage equipment itself and prevent overcharging, over-discharging, and accelerated aging. These constraints include the energy storage equipment's state of charge (SOC) constraints and state of health (SOH) constraints. Energy storage operating environment constraints are environmental parameter constraints to ensure that the energy storage equipment operates under reasonable conditions, including energy storage operating temperature constraints. Application scenario constraints are specific adaptation constraints set for the core requirements of specific application scenarios, including response speed constraints for grid frequency regulation scenarios, endurance constraints for new energy cross-day peak-shaving scenarios, and electrolyte recovery constraints for flow batteries.

[0037] To illustrate, the access point, converter configuration, and bus architecture of the energy storage system are determined based on grid topology data; the rated parameters and constraint thresholds of each energy storage technology are determined based on grid energy storage data; the load fluctuation characteristics and real-time operating conditions are determined based on real-time sensor data; and the cost calculation parameters are determined based on operating cost data. A power allocation model with the goal of minimizing the overall operating cost is constructed, and four categories of constraint conditions are constructed as boundary conditions for solving the model.

[0038] Preferably, the power allocation model is as follows: ; in, For overall operating costs; The equipment depreciation cost for the j-th target energy storage type; The operation and maintenance cost for the j-th target energy storage type; The energy consumption cost of the j-th target energy storage type; This represents the total power generation of the hybrid energy storage system.

[0039] Specifically, equipment depreciation cost is the annual amortized cost of the initial purchase cost of energy storage equipment over its designed lifespan, and is a core component of the total lifecycle cost. Operation and maintenance costs include daily inspections, consumable replacements, labor, and repair costs throughout the energy storage system's lifecycle. Energy consumption costs are the electricity costs incurred during the operation of the energy storage system, including self-consumption by the equipment and power consumption by auxiliary systems. The total power generation of the hybrid energy storage system is the total electrical energy output by the hybrid energy storage system throughout its entire lifecycle.

[0040] Specifically, the operational constraints include: power balance constraints, rated power constraints of the instantaneous response layer, rated power constraints of the short-term peak shaving layer, rated power constraints of the medium- and long-term energy storage layer, and energy storage charge and discharge efficiency constraints; the equipment state safety constraints include: energy storage equipment state of charge constraints and energy storage equipment health state constraints; the energy storage operating environment constraints include: energy storage operating temperature constraints; and the application scenario constraints include: grid frequency regulation scenario response speed constraints, new energy cross-day peak shaving scenario endurance time constraints, and energy storage electrolyte recovery constraints.

[0041] Specifically, the power balance constraint is: ,in, This indicates the power allocated to the instantaneous response layer, in megawatts (MW). This indicates the power allocated by the short-term peak-shaving layer, in megawatts (MW). This indicates the power allocated to the medium- and long-term energy storage layer, in megawatts (MW). This indicates real-time load power, in megawatts (MW). The rated power constraint of the instantaneous response layer is: ,in, This indicates the rated power of the instantaneous response layer, in megawatts (MW). The rated power constraint of the short-term peak-shaving layer is: ,in, This indicates the rated power of the short-term peak-shaving layer, in megawatts (MW). The rated power constraint of the medium- and long-term energy storage layer is: ,in, This indicates the rated power of the medium- and long-term energy storage layer, in megawatts (MW). The energy storage charge and discharge efficiency constraints include flywheel energy storage charge and discharge efficiency constraints, flow battery charge and discharge efficiency constraints, and lithium-ion battery charge and discharge efficiency constraints. The flywheel energy storage charging and discharging efficiency constraint is: , The flywheel energy storage charging and discharging efficiency is represented; the charging and discharging efficiency constraint of the flow battery is: , This indicates the charge / discharge efficiency of the flow battery; the charge / discharge efficiency constraint of the lithium-ion battery is: , Indicates the charge / discharge efficiency of a lithium-ion battery; The state of charge constraints of the energy storage devices include flywheel energy storage state of charge (SOC) constraints, lithium / sodium-ion battery state of charge (SOC) constraints, and flow battery state of charge (SOC) constraints. The flywheel energy storage state of charge (SOC) constraint is as follows: , This indicates the flywheel energy storage state of charge; the state of charge (SOC) constraint of the lithium / sodium ion battery is: , This indicates the state of charge (SOC) of a lithium-ion or sodium-ion battery; the SOC constraint for the flow battery is: , Indicates the state of charge of the flow battery; The energy storage device health status constraints include lithium / sodium ion battery health status (SOH) constraints, flow battery health status (SOH) constraints, and flywheel energy storage health status (SOH) constraints. The state of health (SOH) constraint for the lithium / sodium ion battery is as follows: In the formula, This indicates the state of health of a lithium-ion or sodium-ion battery; the state of health (SOH) constraint for the flow battery is: In the formula, This indicates the state of health of the flow battery; the state of health (SOH) of the flywheel energy storage is constrained as follows: In the formula, Indicates the health status of flywheel energy storage; The energy storage operating temperature constraints include lithium-ion battery operating temperature constraints, flow battery operating temperature constraints, and flywheel energy storage operating temperature constraints. The operating temperature constraint for the lithium-ion battery is: , This indicates the operating temperature of the lithium-ion battery, in degrees Celsius (°C); the operating temperature constraint for the flow battery is: , The operating temperature of the flow battery is expressed in degrees Celsius (°C); the operating temperature constraint for the flywheel energy storage is: , This indicates the operating temperature of the flywheel energy storage system, in degrees Celsius (°C). The response speed constraint for the power grid frequency regulation scenario is: In the formula, This indicates the instantaneous response delay, measured in seconds (s). The driving time constraint for the new energy cross-day peak shaving scenario is as follows: In the formula, This indicates the single-charge endurance time of the medium- and long-term energy storage layer, in hours (h). The constraint for the recovery of the energy storage electrolyte is: In the formula, This indicates the electrolyte recovery rate of the flow battery.

[0042] S4. Under the constraints of operating power, equipment state safety, energy storage operating environment, and application scenario, with the goal of minimizing the overall operating cost, the power allocation model is solved to obtain the current power allocation of each application level of the hybrid energy storage system. Specifically, the comprehensive operating cost is the total cost over the entire lifecycle of the hybrid energy storage system, including equipment depreciation costs, operation and maintenance costs, and energy consumption costs for various energy storage technologies. The application levels of the hybrid energy storage system are system architecture levels divided according to energy storage functions and response characteristics, including an instantaneous response layer, a short-term peak-shaving layer, and a medium- to long-term energy storage layer. The current power allocation is the charge / discharge power allocation value for each round of charging and discharging for each application level energy storage unit, obtained through a power allocation model.

[0043] Indicatively, a nonlinear programming algorithm is used to solve the power allocation model, with four major categories of constraints as the solution boundary and the minimum overall operating cost as the optimization objective. The current power allocation of the instantaneous response layer, short-term peak-shaving layer, and medium-to-long-term energy storage layer is obtained through iterative solution. At the same time, the state variables such as SOC, SOH, and operating temperature of each energy storage device are obtained.

[0044] Preferably, the application layers of the hybrid energy storage system include: an instantaneous response layer, a short-term peak-shaving layer, and a medium- to long-term energy storage layer; Under constraints of operating power, equipment state safety, energy storage operating environment, and application scenario, with the objective of minimizing overall operating cost, the power allocation model is solved to obtain the current power allocation at each application level of the hybrid energy storage system, including: The first initial power allocation of the instantaneous response layer is calculated based on the real-time load fluctuation value in the real-time data of the sensor, the initial state of charge of the target energy storage type in the instantaneous response layer, and the preset instantaneous response weight. The second initial allocation power of the short-term peak shaving layer is calculated based on the load average value in the real-time data of the sensor, the first initial allocation power, the initial health status of the target energy storage type of the short-term peak shaving layer, and the preset short-term peak shaving weight. The third initial allocation power of the medium- and long-term energy storage layer is calculated based on the total daily load demand, the first initial allocation power, the second initial allocation power, and the preset medium- and long-term energy storage weight in the real-time data of the sensor. With the goal of minimizing overall operating costs, the constraints of operating power, equipment state safety, energy storage operating environment, and application scenario are used as the solution boundary conditions. The solution is iteratively obtained based on the first initial power allocation, the second initial power allocation, and the third initial power allocation to obtain the current power allocation of each application level of the hybrid energy storage system. Specifically, the instantaneous response layer is the architectural layer in the hybrid energy storage system responsible for high-frequency, millisecond-second instantaneous power regulation. It is primarily adapted to scenarios such as grid frequency regulation and transient stability control. The core energy storage types are flywheel energy storage and superconducting energy storage, and it is equipped with a high-frequency energy storage converter (PCS). The response delay is controlled within 0.5 seconds. The short-term peak shaving layer is the architectural layer in the hybrid energy storage system responsible for minute-hour-level power regulation and smoothing short-term load fluctuations. It is primarily adapted to scenarios such as intraday peak shaving and load tracking. The core energy storage types are high-rate lithium-ion batteries, sodium-ion batteries, and semi-solid-state batteries, and it is equipped with a medium-frequency energy storage converter (PCS). The medium-to-long-term energy storage layer is the architectural layer in a hybrid energy storage system responsible for hourly-to-daily long-term energy dispatch and inter-day peak shaving. It is primarily adapted to scenarios such as inter-day peak shaving and seasonal peak shaving for new energy sources. The core energy storage types are flow batteries and compressed air energy storage, paired with low-frequency energy storage converters (PCS). For example, flow batteries are paired with integrated electrolyte recycling devices, and compressed air energy storage is paired with insulated thermal storage tanks. Real-time load fluctuation is the difference between the current load power and the previous load power; a positive value represents an increase in load, and a negative value represents a decrease. Initial state of charge (SBC) is the proportion of remaining charge of the energy storage device to its rated capacity at the initial moment of power allocation calculation, representing the remaining energy level of the energy storage device. Preset instantaneous response weight is a weighting coefficient representing the proportion of load fluctuation borne by the instantaneous response layer, with a value of 0.7-0.9. Average load represents the average load power over a preset period, in MW. Initial health state is the health state of the energy storage device at the initial moment of power allocation calculation, representing the capacity retention rate and performance degradation of the energy storage device, expressed as a percentage of rated capacity. The preset short-term peak-shaving weight is a weighting coefficient representing the proportion of remaining load fluctuations borne by the short-term peak-shaving layer, with a value of 0.6-0.8. The total daily load demand is the total load electricity demand of the target scenario within one day, in MWh. The preset medium- and long-term energy storage weight is a weighting coefficient representing the proportion of the daily load base value borne by the medium- and long-term energy storage layer, with a value of 0.9-1.0.

[0045] Indicative instantaneous power distribution (applicable to flywheel energy storage or superconducting energy storage): In the formula, This represents the power allocated by the instantaneous response layer, initially the first initial power allocation, in megawatts (MW). This represents the real-time load fluctuation value, with the unit being megawatts (MW). Positive values ​​indicate an increase in load, while negative values ​​indicate a decrease in load. This represents the instantaneous response weight, with a value between 0.7 and 0.9. This represents the state of charge (SOC) correction factor; when SOC ≥ 80%, =1, when SOC < 20% =0.3, to avoid overcharging and over-discharging of energy storage devices.

[0046] Indicative of short-term power distribution (applicable to high-rate lithium-ion or sodium-ion batteries): In the formula, This represents the power allocated to the short-term peak-shaving layer, initially the second initial power allocation, in megawatts (MW). This represents the average load over 10 minutes, in megawatts (MW). This represents the first initial power allocated to the instantaneous response layer, in megawatts (MW). This represents the short-term peak-shaving weight, with a value between 0.6 and 0.8. This represents the correction factor for state of health (SOH). When SOH ≥ 80%, =1, when SOH < 60% =0.5, adapting to the performance degradation of aging equipment; SOH indicates the health status of energy storage equipment.

[0047] Indicative of medium- to long-term power distribution (applicable to flow batteries or compressed air energy storage): In the formula, This represents the power allocated to the medium- and long-term energy storage layer. Initially, it is the third initial power allocation, and the unit is megawatts (MW). This represents the total daily load demand, expressed in megawatt-hours (MWh). This represents the first initial power allocated to the instantaneous response layer, in megawatts (MW). This represents the second initial power allocated by the short-term peak-shaving layer, in megawatts (MW). This represents the weight of medium- and long-term energy storage, with a value between 0.9 and 1.0.

[0048] In a preferred embodiment of the present invention, three initial power allocations are used as initial values ​​for iteration, the objective function is to minimize the overall operating cost, and four major categories of constraints are used as boundary conditions. An equal nonlinear optimization algorithm is employed for iterative solution. When the iteration results satisfy all constraints and the objective function converges, the iteration stops, and the current power allocation for each application layer is output. Simultaneously, state variables such as SOC, SOH, and operating temperature of each energy storage device are output, including: the final power allocation for the instantaneous response layer, the final power allocation for the short-term peak-shaving layer, the final power allocation for the medium- and long-term energy storage layer, and the flywheel energy storage state of charge. State of charge of lithium-ion or sodium-ion batteries State of charge of flow battery Health status of lithium-ion or sodium-ion batteries Flow battery health status Flywheel energy storage health status Lithium-ion battery operating temperature Operating temperature of flow battery Flywheel energy storage operating temperature .

[0049] By implementing this embodiment, the linkage between power allocation and the real-time status of energy storage devices is realized, avoiding problems such as overcharging and over-discharging, accelerated aging, and extending the service life of the devices. Iterative solutions are performed with multi-dimensional constraints as boundaries, taking into account technical, safety, and economic goals, and achieving global optimization of power allocation.

[0050] S5. Calculate the selection suitability based on the current power allocation, the real-time data from the sensor, the rated power corresponding to the target energy storage type, the current comprehensive operating cost, and the average cost per kilowatt-hour in the operating cost data. Specifically, the selection compatibility is a quantitative indicator that characterizes the overall compatibility performance between the final selection scheme and the integrated system. The value ranges from 0 to 1, and the closer the value is to 1, the better the overall performance.

[0051] To illustrate, based on the current power allocation and real-time data, the parameter compliance rate and equipment utilization rate are calculated; the comprehensive cost per kilowatt-hour is calculated based on the current comprehensive operating cost, and the cost optimization coefficient is calculated by combining the average cost per kilowatt-hour of a single energy storage unit; the three indicators are weighted and summed to obtain the selection suitability, where the parameter compliance rate has a weight of 0.4, the equipment utilization rate has a weight of 0.3, and the cost optimization coefficient has a weight of 0.3.

[0052] Preferably, the selection suitability is calculated based on the current power allocation, the real-time data from the sensors, the rated power corresponding to the target energy storage type, the current comprehensive operating cost, and the average cost per kilowatt-hour in the operating cost data, including: Based on the current power allocation, the actual operating data of each application level target energy storage type in the real-time data of the sensors, the rated power corresponding to the target energy storage type, and the demand threshold of the energy storage application scenario to be integrated, calculate the parameter compliance rate of each application level target energy storage type. The equipment utilization rate is calculated based on the current power allocation, the rated power corresponding to the target energy storage type, and the actual output power of each application level in the real-time data of the sensors. The current comprehensive cost per kilowatt-hour is determined based on the current comprehensive operating cost. The cost optimization coefficient is calculated based on the current comprehensive cost per kilowatt-hour and the average cost per kilowatt-hour of a single energy storage unit in the operating cost data. The selection suitability is obtained by weighting and summing the parameter compliance rate, equipment utilization rate, and cost optimization coefficient.

[0053] Specifically, the parameter compliance rate is a quantitative indicator representing whether the core parameters of the energy storage equipment meet the requirements of the scenario, expressed as a percentage (%). It is calculated as the percentage of the actual test value to the scenario requirement threshold, with a commissioning requirement of a parameter compliance rate ≥ 95%. The equipment utilization rate is a quantitative indicator representing the degree of utilization of the rated capacity / power of the energy storage equipment, expressed as a percentage (%). It is calculated as the time average of the ratio of actual output power to rated power. The cost optimization coefficient is a quantitative indicator representing the degree of cost optimization of the hybrid energy storage solution relative to a single energy storage solution, ranging from 0 to 1. A higher value indicates a better cost optimization effect.

[0054] To illustrate, the parameter compliance rate is calculated for the selected energy storage type, using the following formula: In the formula, This indicates the parameter compliance rate, expressed as a percentage (%). This represents the actual test values ​​of the core parameters of the energy storage device, that is, the actual operating data of the target energy storage type at each application level in the real-time data of the sensors; This represents the quantitative requirement value of the core parameters of the scenario, that is, the rated power corresponding to the target energy storage type; This represents the percentage conversion factor, with a value of 100, used to convert ratios into percentage form. The commissioning requirement is a parameter compliance rate ≥ 95% (demand threshold). Specific test items include: for superconducting energy storage, power interruption time must be tested to ensure it meets the requirement of ≤ 0.055s; for compressed air energy storage, heat loss rate must be tested to ensure it meets the requirement of ≤ 5% / day; for flow batteries, electrolyte recovery rate must be tested to ensure it meets the requirement of ≥ 99.75%.

[0055] In a preferred embodiment of the present invention, a selection suitability calculation formula is used to assign preset weights to three indicators: parameter compliance rate weight 0.4, equipment utilization rate weight 0.3, and cost optimization coefficient weight 0.3. The selection suitability is obtained by weighted summation. in, This indicates the compatibility of the selection, with a value ranging from 0 to 1. The closer the value is to 1, the better the compatibility. This represents the weight of the parameter compliance rate, with a value of 0.4. This indicates the parameter compliance rate, expressed as a percentage (%). This represents the weight of equipment utilization, with a value of 0.3. The equipment utilization rate is calculated by combining the real-time operating parameters of the energy storage equipment collected by the SCADA system with the rated power parameters of the hierarchical topology, and is expressed as a percentage (%). This represents the cost optimization weight, with a value of 0.3. This represents a fixed coefficient, with a value of 1, used to ensure the baseline range of cost optimization items; This indicates the overall cost per kilowatt-hour of the current selected option, expressed in yuan per kilowatt-hour. This represents the average cost per kilowatt-hour for a single energy storage technology solution, expressed in yuan per kilowatt-hour.

[0056] By implementing this embodiment, a comprehensive evaluation system was constructed from three core dimensions: technical performance, resource utilization, and economy. This system fully covers the core requirements of hybrid energy storage systems and avoids the limitations of evaluation based on a single indicator. Through cost optimization coefficients, the hybrid energy storage solution was benchmarked against traditional single energy storage solutions, quantifying the technical and economic advantages of the present invention.

[0057] S6. If the selected compatibility is greater than or equal to the preset compatibility threshold, the final power allocation is obtained; otherwise, the target energy storage type is re-selected and the current power allocation is updated.

[0058] Specifically, the preset adaptability threshold is a critical value used to determine whether the power allocation scheme and the selection scheme are qualified, and is used to verify whether the scheme meets the comprehensive requirements of the scenario. The final power allocation is the optimal charging and discharging power allocation value of each application level energy storage unit after the selection adaptability verification is qualified.

[0059] To illustrate, a preset adaptation threshold (0.8) is set in advance, and the calculated selection adaptation is verified. If the selection adaptation is greater than or equal to the adaptation threshold, it means that the current solution meets the comprehensive requirements, and the current power allocation is determined as the final power allocation. If the selection adaptation is less than the adaptation threshold, it means that the comprehensive performance of the current solution does not meet the standard. The selection process is then returned to the selection step to re-select the target energy storage type, and the power allocation model is rebuilt and solved. The current power allocation is updated, and the selection adaptation is calculated again until the selection adaptation is greater than or equal to the preset adaptation threshold, thus forming a closed-loop iterative optimization.

[0060] S7. Schedule the integrated operation of the corresponding energy storage to be integrated according to the final power allocation.

[0061] Preferably, scheduling the integrated operation of the corresponding energy storage system to be integrated according to the final power allocation includes: Based on the final power allocation, charge and discharge control commands for energy storage units in each application level are generated. The system schedules energy storage units at each application level according to the charge and discharge control commands, and obtains real-time operating data after the energy storage units are integrated and running. The system calculates the response deviation based on the real-time operating data and the final power allocation. The constraints of the power allocation model are modified based on the response deviation.

[0062] Specifically, response deviation is a quantitative indicator characterizing the degree of deviation between the actual output power and the final power allocation of a hybrid energy storage system. The core indicator is the coordinated response deviation rate, expressed in %. The commissioning requirement is that the coordinated response deviation rate is ≤5%.

[0063] In a preferred embodiment of the present invention, the final power allocation is converted into control commands for the corresponding level of energy storage converter (PCS). The instantaneous response layer corresponds to the high-frequency PCS, with a control command update frequency ≤10ms; the short-term peak-shaving layer corresponds to the medium-frequency PCS, with a control command update frequency ≤100ms; and the medium-to-long-term energy storage layer corresponds to the low-frequency PCS, with a control command update frequency ≤1s. The control commands include charge / discharge power setpoints, start / stop signals, protection thresholds, etc., to ensure that the energy storage units perform charge / discharge actions according to the final power allocation. Before generating control commands, phased debugging is carried out: Single-unit debugging: For each energy storage unit, the compliance rate of core parameters is tested, requiring ≥95%, such as electrolyte recovery rate for flow batteries, heat loss rate for compressed air energy storage, and power interruption time for superconducting energy storage; System joint debugging: Simulate the scenario load curve and test the coordinated response deviation rate, requiring ≤5%. After successful debugging, the formal grid-connected control commands are generated.

[0064] The charging and discharging control commands are sent to the corresponding PCS to schedule each energy storage unit to perform charging and discharging actions, achieving coordinated response of multiple types of energy storage. After the system is connected to the grid, real-time operating data is collected through the SCADA system, including the real-time output power, SOC, SOH, operating temperature, grid frequency, and grid voltage of each energy storage unit. The data acquisition frequency is matched with the scenario requirements: the acquisition frequency is 1 second / time for grid frequency regulation scenarios and 1 hour / time for cross-day peak shaving scenarios.

[0065] Calculation of response deviation: in, This indicates the response deviation, expressed as a percentage (%). This represents the actual coordinated response value of the system, i.e., the actual output power; This represents the quantified requirement for scenario-based collaborative response, i.e., the final power allocation amount; This represents the percentage conversion factor. When the response deviation > 5%, the cause of the deviation is analyzed. If it is due to equipment performance degradation, the threshold of the equipment state safety constraint is adjusted; if it is due to changes in grid operating conditions, the boundary of the operating power constraint is adjusted; if it is due to changes in ambient temperature, the threshold of the energy storage operating environment constraint is adjusted. After adjusting the constraints, the power allocation model is resolved, the power allocation is updated, and new charging and discharging control commands are generated to achieve closed-loop dynamic optimization operation of the hybrid energy storage system.

[0066] By implementing this embodiment, adaptive optimization of the hybrid energy storage system is achieved through the calculation of response deviation and dynamic correction of constraints, adapting to dynamic changes in operating conditions, equipment status, and environment. A closed-loop optimization mechanism is constructed for the operation phase, continuously improving the system's operating performance and economy, extending the system's service life, and solving the problem of lack of dynamic optimization during the operation phase in existing technologies.

[0067] This invention quantifies scenario matching based on real-time sensor data, sensor simulation data, grid energy storage data, grid topology data, and operating cost data. It selects target energy storage types by combining levelized cost per kilowatt-hour (LCOE) data, eliminating the need for fixed charge / discharge power ratios and the subjectivity of manual experience-based selection. Based on the target energy storage type, it constructs a power allocation model by integrating grid topology data, real-time sensor data, and operating cost data. This model is further constrained by multiple constraints, including operating power constraints, equipment safety constraints, energy storage operating environment constraints, and application scenario constraints. The model is solved with the goal of minimizing overall operating cost, yielding real-time power allocation at each application level. This allocation can dynamically adjust to real-time fluctuations in renewable energy output and grid load, rather than being statically fixed. This overcomes the limitations of traditional fixed-ratio allocations, which cannot adapt to dynamic grid changes, achieving dynamic optimization and adaptation. A selection matching index is introduced for closed-loop verification. If the matching degree is insufficient, the energy storage type is re-selected and the power allocation is updated, ensuring safe, collaborative, and adaptable operation of the energy storage system.

[0068] Currently, energy storage technology selection lacks a quantitative adaptation mechanism, resulting in poor scenario adaptability and strong decision-making subjectivity. Traditional hybrid energy storage technologies often rely on experience to select energy storage types, failing to establish a quantitative matching system between scenario requirements and energy storage technology parameters. On the one hand, quantitative indicators are not extracted and weighted according to the core needs of the scenario, leading to a disconnect between selection results and actual needs. On the other hand, the lack of multi-technology matching degree evaluation algorithms makes it impossible to accurately select the optimal energy storage combination, easily leading to problems such as a single technology being unable to meet multiple needs and redundant waste in hybrid combinations, affecting the overall system performance and economy.

[0069] The system lacks a comprehensive consideration of various constraints, resulting in a disconnect between safety, technical, and economic objectives. First, safety and technical constraints are not quantitatively integrated, making it prone to shortening equipment lifespan due to overcharging / over-discharging and exceeding temperature limits, or causing malfunctions due to power distribution exceeding rated ranges. Second, optimization targets are limited to local indicators, failing to establish a global optimization model centered on the overall cost per kilowatt-hour, focusing only on equipment investment costs while ignoring the entire lifecycle costs such as depreciation, maintenance, and energy consumption, leading to low long-term system efficiency. Third, there is insufficient adaptation to the characteristics of different energy storage technologies, with a lack of differentiated topologies and auxiliary devices. For example, flow batteries lack dedicated recovery devices, and compressed air energy storage suffers from poor heat loss control, hindering system performance.

[0070] Power allocation and operation optimization lack a systematic approach, resulting in low coordination efficiency and insufficient commissioning and verification. Existing hybrid energy storage power allocation mostly adopts a fixed ratio mode without a dynamic adaptation mechanism: First, a hierarchical power allocation model has not been established according to energy storage functions (instantaneous response, short-term peak shaving, and medium- to long-term energy storage), making it impossible to dynamically adjust the output of each layer according to load fluctuations, leading to poor coordination between energy storage systems and low resource utilization; Second, there is a lack of a full-process commissioning and iterative optimization system, with single-unit testing and system-wide commissioning disconnected, only verifying the compliance of basic parameters, without iteratively optimizing the selection and integration scheme through the adaptability model, resulting in problems such as response deviation and insufficient adaptability after the system is connected to the grid, making it difficult to meet the long-term operation requirements of the scenario.

[0071] This invention first constructs a quantitative selection system, establishing a three-level selection algorithm consisting of demand parameter quantification, weight allocation, and matching degree calculation. Then, it establishes a multi-dimensional constraint-based collaborative integration system, balancing safety, technology, and economic objectives. A layered topology architecture is designed to adapt to the characteristics of different energy storage technologies, with dedicated auxiliary devices to enhance efficiency. A quantitative constraint system covering technology (power balance, charge / discharge efficiency), safety (SOC, SOH, temperature), and operation (recovery rate, response speed) is constructed, aiming to minimize overall operating costs and achieve synergistic optimization of lifecycle costs and system performance. Finally, optimized power allocation and full-process debugging are performed to improve system synergy and reliability. A full-process mechanism is designed, encompassing single-unit compliance testing, system integration deviation verification, and iterative optimization of adaptability. System performance is verified through quantitative models, continuously optimizing selection and integration schemes to improve long-term system reliability and economy.

[0072] See Figure 2 This is a schematic diagram of a hybrid energy storage integrated device based on energy storage selection according to an embodiment of the present invention, comprising: The power grid data acquisition module is used to acquire real-time sensor data, sensor simulation data, power grid energy storage data, power grid topology data, and operating cost data based on the energy storage application scenarios to be integrated. The target energy storage type screening module is used to calculate the scenario matching degree between the energy storage to be integrated and the application scenario to be integrated based on the real-time data of the sensor, the simulation data of the sensor, and the grid energy storage data, and to screen out the target energy storage type based on the scenario matching degree, the preset matching degree threshold, and the cost per kilowatt-hour in the operating cost data. The model and constraint construction module is used to construct a power allocation model and corresponding operating power constraints, equipment state safety constraints, energy storage operating environment constraints and application scenario constraints based on the target energy storage type, according to grid topology data, grid energy storage data, real-time sensor data and operating cost data. The current power allocation solution module is used to solve the power allocation model under the constraints of operating power, equipment state safety, energy storage operating environment and application scenario, with the goal of minimizing the overall operating cost, to obtain the current power allocation of each application level of the hybrid energy storage system. The selection compatibility calculation module is used to calculate the selection compatibility based on the current power allocation, the real-time data of the sensor, the rated power corresponding to the target energy storage type, the current comprehensive operating cost, and the average cost per kilowatt-hour in the operating cost data. The final power allocation determination module is used to obtain the final power allocation if the selection adaptability is greater than or equal to a preset adaptability threshold; otherwise, the target energy storage type is re-selected and the current power allocation is updated. The integrated operation module is used to schedule the integrated operation of the corresponding energy storage to be integrated based on the final power allocation.

[0073] This invention provides a hybrid energy storage integration device based on energy storage selection. In the grid data acquisition module, real-time sensor data, sensor simulation data, grid energy storage data, grid topology data, and operating cost data are acquired based on the application scenario of the energy storage to be integrated. In the target energy storage type selection module, the scenario matching degree between the energy storage to be integrated and the application scenario is calculated based on the real-time sensor data, sensor simulation data, and grid energy storage data. Based on the scenario matching degree, a preset matching degree threshold, and the cost per kilowatt-hour in the operating cost data, the target energy storage type is selected. In the model and constraint construction module, a power allocation model is constructed based on the grid topology data, grid energy storage data, real-time sensor data, and operating cost data, along with corresponding operating power constraints, equipment state safety constraints, energy storage operating environment constraints, and other constraints. The system employs scenario constraints. Based on the current power allocation calculation module, under constraints of operating power, equipment safety status, energy storage operating environment, and application scenario, the power allocation model is solved with the objective of minimizing overall operating cost, yielding the current power allocation for each application level of the hybrid energy storage system. In the selection and suitability calculation module, the selection and suitability are calculated based on the current power allocation, real-time sensor data, the rated power corresponding to the target energy storage type, the current overall operating cost, and the average cost per kilowatt-hour in the operating cost data. Then, according to the final power allocation determination module, if the selection and suitability is greater than or equal to a preset suitability threshold, the final power allocation is obtained; otherwise, the target energy storage type is re-selected and the current power allocation is updated. Finally, in the integrated operation module, the corresponding energy storage to be integrated is scheduled for integrated operation based on the final power allocation.

[0074] This embodiment quantifies the scenario matching degree based on real-time sensor data, sensor simulation data, grid energy storage data, grid topology data, and operating cost data. It selects target energy storage types by combining the cost per kilowatt-hour, eliminating the use of fixed charge / discharge power ratios and the subjectivity of manual experience-based selection. Based on the target energy storage type, it constructs a power allocation model by integrating grid topology data, real-time sensor data, and operating cost data, and overlays multiple constraints including operating power constraints, equipment state safety constraints, energy storage operating environment constraints, and application scenario constraints. The model is solved with the goal of minimizing the overall operating cost, yielding real-time power allocation at each application level. This allocation can be dynamically adjusted according to real-time fluctuations in renewable energy output and grid load, rather than a static fixed allocation. This addresses the shortcomings of traditional fixed-ratio allocation, which cannot adapt to dynamic grid changes, achieving dynamic optimization and adaptation. A selection matching degree index is introduced for closed-loop verification. If the matching degree is insufficient, the energy storage type is re-selected and the power allocation is updated, ensuring safe, coordinated, and adaptable operation of the energy storage system.

[0075] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0076] Those skilled in the art will understand that, for convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0077] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a hybrid energy storage integration method based on energy storage selection as described in the above embodiments. The terminal device may be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0078] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0079] The memory can be used to store the computer program. The processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device or other volatile solid-state storage device.

[0080] Another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the hybrid energy storage integration method based on energy storage selection described in the above embodiment.

[0081] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0082] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A hybrid energy storage integration method based on energy storage selection, characterized in that, include: Based on the energy storage application scenarios to be integrated, real-time sensor data, sensor simulation data, grid energy storage data, grid topology data, and operating cost data are acquired. Based on the real-time data from the sensors, the sensor simulation data, and the grid energy storage data, the scenario matching degree between the energy storage to be integrated and the application scenario to be integrated is calculated. Based on the scenario matching degree, the preset matching degree threshold, and the cost per kilowatt-hour in the operating cost data, the target energy storage type is selected. Based on grid topology data, grid energy storage data, real-time sensor data, and operating cost data, a power allocation model is constructed based on the target energy storage type, along with corresponding operating power constraints, equipment state safety constraints, energy storage operating environment constraints, and application scenario constraints. Under constraints of operating power, equipment state safety, energy storage operating environment, and application scenario, with the goal of minimizing overall operating cost, the power allocation model is solved to obtain the current power allocation of each application level of the hybrid energy storage system. The selection suitability is calculated based on the current power allocation, the real-time data from the sensors, the rated power corresponding to the target energy storage type, the current comprehensive operating cost, and the average cost per kilowatt-hour in the operating cost data. If the selection compatibility is greater than or equal to the preset compatibility threshold, the final power allocation is obtained; otherwise, the target energy storage type is re-selected and the current power allocation is updated. The corresponding integrated energy storage will be scheduled for operation based on the final power allocation.

2. The hybrid energy storage integration method based on energy storage selection as described in claim 1, characterized in that, Based on the real-time data from the sensors, the simulation data from the sensors, and the grid energy storage data, the scenario matching degree between the energy storage to be integrated and the application scenario to be integrated is calculated, including: Based on the real-time data from the sensors, the simulation data from the sensors, and the grid energy storage data, extract the required parameters for the energy storage application scenarios to be integrated. The expert scoring method is used to compare and score the requirement parameters pairwise, generating a judgment matrix; The normalized eigenvector is calculated based on the judgment matrix. The normalized eigenvector is used as the initial importance score for each requirement parameter. The initial importance score is then normalized to obtain the final importance score. The scenario matching degree between the energy storage to be integrated and its application scenario is obtained by weighting the final importance score, the real-time sensor data corresponding to the demand parameters, and the corresponding sensor simulation data.

3. The hybrid energy storage integration method based on energy storage selection as described in claim 1, characterized in that, Based on the scenario matching degree, the preset matching degree threshold, and the cost per kilowatt-hour in the operating cost data, target energy storage types are selected, including: Based on the scene matching degree and the preset matching degree threshold, the energy storage to be integrated with a scene matching degree greater than or equal to the preset matching degree threshold is selected as the first integrated energy storage. For each application level, the comprehensive cost per kilowatt-hour of each first integrated energy storage in the application level of the energy storage application scenario to be integrated is calculated based on the cost per kilowatt-hour in the operating cost data. The first integrated energy storage with the lowest comprehensive cost per kilowatt-hour is taken as the target energy storage type of the application level of the energy storage application scenario to be integrated.

4. The hybrid energy storage integration method based on energy storage selection as described in claim 1, characterized in that, The application layers of hybrid energy storage systems include: instantaneous response layer, short-term peak shaving layer, and medium- and long-term energy storage layer; Under constraints of operating power, equipment state safety, energy storage operating environment, and application scenario, with the objective of minimizing overall operating cost, the power allocation model is solved to obtain the current power allocation at each application level of the hybrid energy storage system, including: The first initial power allocation of the instantaneous response layer is calculated based on the real-time load fluctuation value in the real-time data of the sensor, the initial state of charge of the target energy storage type in the instantaneous response layer, and the preset instantaneous response weight. The second initial allocation power of the short-term peak shaving layer is calculated based on the load average value in the real-time data of the sensor, the first initial allocation power, the initial health status of the target energy storage type of the short-term peak shaving layer, and the preset short-term peak shaving weight. The third initial allocation power of the medium- and long-term energy storage layer is calculated based on the total daily load demand, the first initial allocation power, the second initial allocation power, and the preset medium- and long-term energy storage weight in the real-time data of the sensor. With the goal of minimizing overall operating costs, the constraints of operating power, equipment state safety, energy storage operating environment, and application scenario are used as the solution boundary conditions. The solution is iteratively obtained based on the first initial power allocation, the second initial power allocation, and the third initial power allocation to obtain the current power allocation of each application level of the hybrid energy storage system.

5. The hybrid energy storage integration method based on energy storage selection as described in claim 1, characterized in that, Based on the current power allocation, real-time sensor data, rated power corresponding to the target energy storage type, current comprehensive operating cost, and average cost per kilowatt-hour in the operating cost data, the selection suitability is calculated, including: Based on the current power allocation, the actual operating data of each application level target energy storage type in the real-time data of the sensors, the rated power corresponding to the target energy storage type, and the demand threshold of the energy storage application scenario to be integrated, calculate the parameter compliance rate of each application level target energy storage type. The equipment utilization rate is calculated based on the current power allocation, the rated power corresponding to the target energy storage type, and the actual output power of each application level in the real-time data of the sensors. The current comprehensive cost per kilowatt-hour is determined based on the current comprehensive operating cost. The cost optimization coefficient is calculated based on the current comprehensive cost per kilowatt-hour and the average cost per kilowatt-hour of a single energy storage unit in the operating cost data. The selection suitability is obtained by weighting and summing the parameter compliance rate, equipment utilization rate, and cost optimization coefficient.

6. The hybrid energy storage integration method based on energy storage selection as described in claim 1, characterized in that, The power allocation model is as follows: ； in, For overall operating costs; The equipment depreciation cost for the j-th target energy storage type; The operation and maintenance cost for the j-th target energy storage type; The energy consumption cost of the j-th target energy storage type; This represents the total power generation of the hybrid energy storage system.

7. The hybrid energy storage integration method based on energy storage selection as described in claim 1, characterized in that, The integrated operation of the corresponding energy storage system is scheduled according to the final power allocation, including: Based on the final power allocation, charge and discharge control commands for energy storage units in each application level are generated. The system schedules energy storage units at each application level according to the charge and discharge control commands, and obtains real-time operating data after the energy storage units are integrated and running. The system calculates the response deviation based on the real-time operating data and the final power allocation. The constraints of the power allocation model are modified based on the response deviation.

8. A hybrid energy storage integrated device based on energy storage selection, characterized in that, include: The power grid data acquisition module is used to acquire real-time sensor data, sensor simulation data, power grid energy storage data, power grid topology data, and operating cost data based on the energy storage application scenarios to be integrated. The target energy storage type screening module is used to calculate the scenario matching degree between the energy storage to be integrated and the application scenario to be integrated based on the real-time data of the sensor, the simulation data of the sensor, and the grid energy storage data, and to screen out the target energy storage type based on the scenario matching degree, the preset matching degree threshold, and the cost per kilowatt-hour in the operating cost data. The model and constraint construction module is used to construct a power allocation model and corresponding operating power constraints, equipment state safety constraints, energy storage operating environment constraints and application scenario constraints based on the target energy storage type, according to grid topology data, grid energy storage data, real-time sensor data and operating cost data. The current power allocation solution module is used to solve the power allocation model under the constraints of operating power, equipment state safety, energy storage operating environment and application scenario, with the goal of minimizing the overall operating cost, to obtain the current power allocation of each application level of the hybrid energy storage system. The selection compatibility calculation module is used to calculate the selection compatibility based on the current power allocation, the real-time data of the sensor, the rated power corresponding to the target energy storage type, the current comprehensive operating cost, and the average cost per kilowatt-hour in the operating cost data. The final power allocation determination module is used to obtain the final power allocation if the selection adaptability is greater than or equal to a preset adaptability threshold; otherwise, the target energy storage type is re-selected and the current power allocation is updated. The integrated operation module is used to schedule the integrated operation of the corresponding energy storage to be integrated based on the final power allocation.

9. A terminal device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a hybrid energy storage integration method based on energy storage selection as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform a hybrid energy storage integration method based on energy storage selection as described in any one of claims 1 to 7.

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