Inertia optimization control method, device, medium and equipment for energy storage-containing photovoltaic power station

Abstract

This disclosure relates to the field of power system technology and provides a method, device, medium, and equipment for inertia optimization control of a photovoltaic power station with energy storage. The method includes: real-time monitoring of the grid voltage and frequency at the grid connection point of the photovoltaic power station; real-time calculation of frequency deviation and frequency change rate based on the monitoring results; initiating inertia optimization control when the frequency deviation exceeds a frequency dead zone threshold and the frequency change rate exceeds a frequency change rate dead zone threshold, including: calculating the photovoltaic virtual inertia power component and the energy storage virtual inertia power component based on the frequency deviation, frequency change rate, and the real-time state of charge of the energy storage system; adjusting the output power of the photovoltaic system based on the photovoltaic virtual inertia power component; and controlling the charging and discharging power of the energy storage system based on the energy storage virtual inertia power component. This disclosure effectively improves the inertia support capability of the photovoltaic power station, enabling rapid response and effective inertia compensation when grid frequency fluctuates, enhancing grid stability, and ensuring the safe and stable operation of the power system.

Description

Technical Field

[0001] This disclosure relates to the field of power system technology, and more specifically, to a method, apparatus, medium, and equipment for optimizing the inertia control of a photovoltaic power station with energy storage. Background Technology

[0002] Photovoltaic power generation has emerged as one of the most promising energy forms in the renewable energy field due to its significant advantages of low cost and flexible deployment. With a high proportion of renewable energy being integrated into microgrids, photovoltaic systems, connected to the grid via power electronic converters, undertake crucial functions of frequency and voltage regulation, which is essential for maintaining the stability of the entire microgrid system. However, when photovoltaic power generation is operated in conjunction with traditional synchronous generators, the significant differences in their characteristics pose greater challenges to system reliability. Ensuring their stable and coordinated operation has become a critical issue that urgently needs to be addressed.

[0003] In related technologies, to improve the stability of high-penetration photovoltaic (PV) power generation systems, some studies have proposed virtual synchronous generator (VRG) control methods. These methods provide virtual inertia support to the grid by simulating the rotor dynamic characteristics of a synchronous generator, effectively suppressing system frequency fluctuations. However, in high-proportion PV-connected microgrids, PV power generation units often operate at reduced rates to ensure regulation margins. Existing studies have used master-slave or sensor-based methods to reserve regulation capacity, but this easily leads to power waste and reduces energy utilization efficiency and decarbonization effects. Some studies maintain the output power at the rated proportion of the maximum power point to ensure reserve capacity, but do not endow PV with frequency and voltage regulation capabilities. Other methods rely on energy storage devices to compensate for insufficient dynamic regulation, but this increases investment and operation and maintenance costs, and PV and energy storage devices are independent, making coordinated control of PV and energy storage impossible. Summary of the Invention

[0004] This disclosure provides at least one method, device, medium, and equipment for optimizing the inertia control of a photovoltaic power station with energy storage. This effectively improves the inertia support capability of the photovoltaic power station, enables it to respond quickly and provide effective inertia compensation when the grid frequency fluctuates, enhances the stability of the grid, avoids grid faults caused by excessive frequency fluctuations, and ensures the safe and stable operation of the power system.

[0005] This disclosure provides an embodiment of a method for optimizing the inertia control of a photovoltaic power station with energy storage, including:

[0006] Real-time monitoring of grid voltage and frequency at the grid connection point of photovoltaic power plants, and real-time calculation of frequency deviation and frequency change rate based on monitoring results;

[0007] When the frequency deviation is greater than the frequency dead zone threshold and the frequency change rate is greater than the frequency change rate dead zone threshold, inertia optimization control is initiated, including:

[0008] Based on the frequency deviation, frequency change rate, and real-time state of charge of the energy storage system, the photovoltaic virtual inertia power component of the photovoltaic system and the energy storage virtual inertia power component of the energy storage system are calculated.

[0009] The output power of the photovoltaic system is adjusted based on the photovoltaic virtual inertia power component; and the charging and discharging power of the energy storage system is controlled based on the energy storage virtual inertia power component.

[0010] In some possible embodiments, calculating the photovoltaic virtual inertia power component of the photovoltaic system and the energy storage virtual inertia power component of the energy storage system includes:

[0011] Based on the frequency deviation and the frequency change rate, the total virtual inertia power is calculated using the virtual inertia coefficient and the active power droop coefficient.

[0012] The inertial power demand dominance is calculated based on the ratio of the frequency change rate to a preset maximum frequency change rate threshold; and the power demand dominance is calculated based on the ratio of the frequency deviation to a preset maximum frequency deviation threshold.

[0013] Based on the inertial power demand dominance and power demand dominance, an adaptive allocation coefficient is determined;

[0014] The photovoltaic virtual inertia power components are determined based on the total virtual inertia power and the adaptive allocation coefficient.

[0015] Based on the real-time state of charge of the energy storage system and the preset state of charge threshold, the energy storage power correction coefficient is calculated.

[0016] The energy storage virtual inertia power component is determined based on the total virtual inertia power, the adaptive allocation coefficient, and the energy storage power correction coefficient.

[0017] In some possible embodiments, the calculation of the energy storage power correction factor includes:

[0018] When the real-time state of charge is lower than the first power threshold or the real-time state of charge is higher than the second power threshold, the energy storage power correction coefficient is set to 1; wherein the second power threshold is greater than the first power threshold.

[0019] In some possible embodiments, the calculation of the energy storage power correction factor includes:

[0020] When the real-time state of charge is between the first energy threshold and the second energy threshold, the energy storage power correction coefficient is determined based on the real-time state of charge, the preset maximum allowable state of charge, and the preset minimum allowable state of charge.

[0021] In some possible embodiments, determining the energy storage power correction coefficient based on the real-time state of charge, the preset maximum allowable state of charge, and the preset minimum allowable state of charge includes:

[0022] The energy storage power correction coefficient is calculated based on the energy storage power correction coefficient calculation formula, the real-time state of charge, the first energy threshold, and the second energy threshold.

[0023] The formula for calculating the energy storage power correction coefficient is as follows:

[0024] ;

[0025] Wherein, SOC represents the real-time state of charge; This is represented as the preset maximum permissible state of charge; This is represented as the preset minimum permissible state of charge; This is represented as the first battery threshold. This is represented as the second power threshold.

[0026] In some possible embodiments, adjusting the output power of the photovoltaic system based on the photovoltaic virtual inertia power component includes:

[0027] Based on the photovoltaic virtual inertia power components, the reduced reserve power of the photovoltaic system is determined;

[0028] Based on the current maximum output power of the photovoltaic system and the reduced reserve power of the photovoltaic system, determine the output power of the photovoltaic system to be adjusted.

[0029] The output power of the photovoltaic system is adjusted according to the output power to be adjusted.

[0030] In some possible embodiments, after controlling the charging and discharging power of the energy storage system based on the energy storage virtual inertia power component, the method further includes:

[0031] Based on the real-time state of charge of the energy storage system, the state of charge of the energy storage system is regulated by the variable power point tracking control strategy of the photovoltaic system, so as to keep the energy storage system in the bidirectional charging and discharging region and optimize the inertia frequency regulation capability.

[0032] This disclosure provides an inertia optimization control device for a photovoltaic power station with energy storage, comprising:

[0033] The data calculation module is used to monitor the grid voltage and frequency at the grid connection point of the photovoltaic power station in real time, and calculate the frequency deviation and frequency change rate in real time based on the monitoring results.

[0034] The inertia optimization module is used to initiate inertia optimization control when the frequency deviation is greater than the frequency dead zone threshold and the frequency change rate is greater than the frequency change rate dead zone threshold, including:

[0035] Based on the frequency deviation, frequency change rate, and real-time state of charge of the energy storage system, the photovoltaic virtual inertia power component of the photovoltaic system and the energy storage virtual inertia power component of the energy storage system are calculated.

[0036] The output power of the photovoltaic system is adjusted based on the photovoltaic virtual inertia power component; and the charging and discharging power of the energy storage system is controlled based on the energy storage virtual inertia power component.

[0037] In some possible embodiments, the inertia optimization module is specifically used for:

[0038] Based on the frequency deviation and the frequency change rate, the total virtual inertia power is calculated using the virtual inertia coefficient and the active power droop coefficient.

[0039] The inertial power demand dominance is calculated based on the ratio of the frequency change rate to a preset maximum frequency change rate threshold; and the power demand dominance is calculated based on the ratio of the frequency deviation to a preset maximum frequency deviation threshold.

[0040] Based on the inertial power demand dominance and power demand dominance, an adaptive allocation coefficient is determined;

[0041] The photovoltaic virtual inertia power components are determined based on the total virtual inertia power and the adaptive allocation coefficient.

[0042] Based on the real-time state of charge of the energy storage system and the preset state of charge threshold, the energy storage power correction coefficient is calculated.

[0043] The energy storage virtual inertia power component is determined based on the total virtual inertia power, the adaptive allocation coefficient, and the energy storage power correction coefficient.

[0044] In some possible embodiments, the inertia optimization module is specifically used for:

[0045] When the real-time state of charge is lower than the first power threshold or the real-time state of charge is higher than the second power threshold, the energy storage power correction coefficient is set to 1; wherein the second power threshold is greater than the first power threshold.

[0046] In some possible embodiments, the inertia optimization module is specifically used for:

[0047] When the real-time state of charge is between the first energy threshold and the second energy threshold, the energy storage power correction coefficient is determined based on the real-time state of charge, the preset maximum allowable state of charge, and the preset minimum allowable state of charge.

[0048] In some possible embodiments, the inertia optimization module is specifically used for:

[0049] The energy storage power correction coefficient is calculated based on the energy storage power correction coefficient calculation formula, the real-time state of charge, the first energy threshold, and the second energy threshold.

[0050] The formula for calculating the energy storage power correction coefficient is as follows:

[0051] ;

[0052] Wherein, SOC represents the real-time state of charge; This is represented as the preset maximum permissible state of charge; This is represented as the preset minimum permissible state of charge; This is represented as the first battery threshold. This is represented as the second power threshold.

[0053] In some possible embodiments, the inertia optimization module is specifically used for:

[0054] Based on the photovoltaic virtual inertia power components, the reduced reserve power of the photovoltaic system is determined;

[0055] Based on the current maximum output power of the photovoltaic system and the reduced reserve power of the photovoltaic system, determine the output power of the photovoltaic system to be adjusted.

[0056] The output power of the photovoltaic system is adjusted according to the output power to be adjusted.

[0057] In some possible embodiments, the inertia optimization module is further configured to:

[0058] Based on the real-time state of charge of the energy storage system, the state of charge of the energy storage system is regulated by the variable power point tracking control strategy of the photovoltaic system, so as to keep the energy storage system in the bidirectional charging and discharging region and optimize the inertia frequency regulation capability.

[0059] This disclosure provides a computer device, including a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the inertia optimization method for photovoltaic power plants with energy storage as described in any of the above possible embodiments.

[0060] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the inertia optimization method for a photovoltaic power station with energy storage as described in any of the above possible embodiments.

[0061] The inertia optimization control method, device, medium, and equipment for photovoltaic power plants with energy storage provided in this disclosure respond to frequency fluctuations through photovoltaic-energy storage synergy. Utilizing the rapid characteristics of energy storage to provide core inertia support, and allowing photovoltaic systems to adjust as needed, this not only improves the adaptability of photovoltaic power plants to grid frequency fluctuations but also fully leverages the role of the energy storage system in inertia support, effectively enhancing the inertia support capability of photovoltaic power plants and achieving synergistic optimization operation between photovoltaic power plants and energy storage systems. When grid frequency fluctuates, it can respond quickly and provide effective inertia compensation, enhancing grid stability, avoiding grid faults caused by excessive frequency fluctuations, and ensuring the safe and stable operation of the power system.

[0062] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0063] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings referenced in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0064] Figure 1 A flowchart of an inertia optimization control method for a photovoltaic power station with energy storage provided in an embodiment of this disclosure is shown;

[0065] Figure 2 A flowchart of a method for calculating virtual inertia power components provided in an embodiment of this disclosure is shown;

[0066] Figure 3 A flowchart of a photovoltaic system adjustment method provided in an embodiment of this disclosure is shown;

[0067] Figure 4 A schematic diagram of the structure of a photovoltaic power station inertia optimization control device with energy storage provided in an embodiment of this disclosure is shown.

[0068] Figure 5A schematic diagram of the structure of a computer device provided in an embodiment of this disclosure is shown. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0070] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0071] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0072] Photovoltaic (PV) power generation, with its advantages of low cost and flexible deployment, has become one of the most promising renewable energy sources. In microgrids with a high proportion of renewable energy integration, PV systems are connected to the grid via power electronic converters, undertaking frequency and voltage regulation functions to maintain system stability. However, the coordinated operation of PV power generation with traditional synchronous generators poses a greater challenge to its reliability. In recent years, researchers have proposed the Virtual Synchronous Generator (VSG) control method, which is widely regarded as an important means to improve the stability of high-penetration PV power generation systems. The Virtual Synchronous Generator (VSG) provides virtual inertia support to the grid by simulating the rotor dynamic characteristics of a synchronous generator, effectively suppressing system frequency fluctuations.

[0073] Research has found that in microgrids with a high proportion of photovoltaic (PV) grid connection, PV power generation units typically operate in derating mode rather than continuously maintaining maximum power output to ensure sufficient system regulation margin. Existing research has attempted to reserve regulation capacity using master-slave or sensor-based methods, but this forced deviation from the maximum power point (MPP) leads to power waste, reduced energy efficiency, and decreased decarbonization effectiveness. Some studies have addressed PV volatility by maintaining the PV system's output power at a rated proportion of its maximum power point to ensure reserve capacity. However, these methods do not endow the PV system with frequency and voltage regulation capabilities. Furthermore, some methods utilize energy storage devices to compensate for the insufficient dynamic regulation capabilities of the PV system; however, relying solely on energy storage increases investment and maintenance costs, and the relatively independent nature of PV power generation and energy storage devices fails to achieve coordinated control between PV and energy storage.

[0074] Based on the above research, this disclosure provides a method, device, medium, and equipment for inertia optimization control of a photovoltaic power station with energy storage. By real-time monitoring of the grid voltage and frequency at the grid connection point and accurate calculation of frequency deviation and rate of change, the dynamic changes in grid frequency can be quickly and accurately perceived, providing a reliable basis for subsequent inertia optimization control. When the frequency deviation and rate of change exceed the corresponding dead zone threshold, inertia optimization control is initiated. Based on the frequency deviation, rate of change, and real-time state of charge of the energy storage system, the virtual inertia power components of the photovoltaic system and the energy storage system are calculated respectively, thereby making targeted adjustments to the output power of the photovoltaic system and the charging and discharging power of the energy storage system.

[0075] In this embodiment, by coordinating photovoltaic (PV) and energy storage responses to frequency fluctuations, the rapid characteristics of energy storage provide core inertia support, while allowing PV to adjust as needed. This not only improves the adaptability of the PV power plant to grid frequency fluctuations but also fully leverages the role of the energy storage system in inertia support, effectively enhancing the inertia support capability of the PV power plant and achieving coordinated and optimized operation between the PV power plant and the energy storage system. When grid frequency fluctuations occur, it can respond quickly and provide effective inertia compensation, enhancing grid stability, avoiding grid faults caused by excessive frequency fluctuations, and ensuring the safe and stable operation of the power system.

[0076] To facilitate understanding of this embodiment, the executing entity of the inertia optimization control method for photovoltaic power plants with energy storage provided in this disclosure embodiment will first be described in detail. The executing entity of the inertia optimization control method for photovoltaic power plants with energy storage provided in this disclosure embodiment is a computer device. This computer device can be a terminal device or a server. The terminal device can also be a mobile device, a user terminal, a terminal, a handheld device, a computing device, an in-vehicle device, a wearable device, etc. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data, and artificial intelligence platforms. Optionally, this method can also be applied to an implementation environment composed of computer devices and servers.

[0077] The inertia optimization control method for photovoltaic power plants with energy storage provided in this application will be described in detail below with reference to the accompanying drawings. See also Figure 1 The diagram shows a flowchart of an inertia optimization control method for a photovoltaic power station with energy storage provided in an embodiment of this disclosure. The method includes the following steps S101-S102:

[0078] S101 monitors the grid voltage and frequency at the photovoltaic power station's grid connection point in real time, and calculates the frequency deviation and frequency change rate in real time based on the monitoring results.

[0079] As we understand it, grid voltage frequency refers to the number of times the alternating current in the power grid changes periodically per second, measured in Hertz (Hz), and can be used to measure the operational stability of the power system. Continuous acquisition of grid voltage frequency can be achieved using specialized power quality monitoring equipment. This equipment typically consists of high-precision sensors, a data processing module, and a communication module. The sensors convert the voltage signal in the grid into a measurable electrical signal. The data processing module analyzes and processes the collected electrical signal to calculate the current grid voltage frequency. The communication module transmits the calculation results to the monitoring system in real time, allowing staff to promptly grasp the grid's operational status. Through such power quality monitoring equipment, the grid voltage frequency can be continuously acquired, providing reliable data support for subsequent analysis and control.

[0080] Furthermore, based on the monitored grid voltage and frequency data, frequency deviation and frequency change rate can be calculated in real time. Frequency deviation refers to the difference between the actual monitored grid voltage and frequency and the standard frequency (e.g., the standard grid frequency for a certain region is 50Hz). In actual operation, due to various factors, such as sudden changes in electricity load and power generation equipment failures, the grid voltage and frequency may deviate from the standard value. By calculating the frequency deviation, the degree of deviation of the grid voltage and frequency can be intuitively understood. The frequency change rate represents the change in frequency deviation per unit time, reflecting the speed of frequency change. Calculating the frequency change rate can help us predict the trend of grid frequency changes and take corresponding control measures in advance. The calculation method is usually to calculate the ratio of the change in frequency deviation to the time interval within a certain time interval.

[0081] Here, the formula for calculating frequency deviation can be expressed as:

[0082] ;

[0083] in, This is expressed as frequency deviation; This is expressed as the rated frequency; This refers to the grid voltage frequency at the grid connection point of the photovoltaic power station. This is represented as the frequency dead zone, which can be set to ±0.01Hz~±0.05Hz.

[0084] Here, the formula for calculating the rate of change of frequency can be expressed as:

[0085] ;

[0086] in, Expressed as the rate of change of frequency; This is represented as the dead zone of frequency change rate, which can be set to ±0.01 Hz / s.

[0087] S102, when the frequency deviation is greater than the frequency dead zone threshold and the frequency change rate is greater than the frequency change rate dead zone threshold, inertia optimization control is initiated.

[0088] Here, inertia optimization control is activated when the calculated frequency deviation exceeds a preset frequency dead zone threshold and the frequency change rate exceeds a preset frequency change rate dead zone threshold. The frequency dead zone threshold and frequency change rate dead zone threshold are set to avoid frequent control actions triggered by small frequency fluctuations, thereby ensuring system stability and reliability. Inertia optimization control refers to simulating the inertia characteristics of traditional generator sets by rationally adjusting the output power of the photovoltaic system and energy storage system, providing necessary inertia support to the power grid, thereby enhancing the grid's ability to cope with frequency mutations and maintaining grid frequency stability.

[0089] Understandably, in traditional power systems, generator sets, relying on the inertia of their rotating components, can automatically adjust their output power to stabilize the frequency when the grid frequency changes. However, in photovoltaic power plants with energy storage, the photovoltaic system itself does not possess inertial characteristics. Although the energy storage system can charge and discharge rapidly, it still requires a reasonable control strategy to exert a similar inertial effect. The inertial optimization control proposed in this disclosure is a solution to this problem. It can accurately adjust the power output of the photovoltaic system and the energy storage system according to the real-time changes in the grid frequency, enabling the entire photovoltaic power plant to provide effective inertial support like a traditional generator set when the grid frequency fluctuates, thus ensuring the safe and stable operation of the grid.

[0090] Specifically, after inertia optimization control is initiated, the photovoltaic virtual inertia power component and the energy storage virtual inertia power component of the energy storage system can be calculated and determined based on frequency deviation, frequency change rate, and the real-time state of charge of the energy storage system. The real-time state of charge of the energy storage system refers to the ratio of the current stored electricity to the rated capacity, usually expressed as a percentage, reflecting the remaining amount of electricity in the energy storage device. The photovoltaic virtual inertia power component and the energy storage virtual inertia power component are used to guide the operational adjustments of the photovoltaic system and the energy storage system, respectively. The photovoltaic virtual inertia power component determines the output power that the photovoltaic system needs to increase or decrease during inertia optimization control, while the energy storage virtual inertia power component determines the magnitude and direction of the charging and discharging power of the energy storage system.

[0091] For example, refer to Figure 2 As shown, the calculation of the photovoltaic virtual inertia power component and the energy storage virtual inertia power component may include the following steps S201~S206:

[0092] S201, based on the frequency deviation and the frequency change rate, calculate the total virtual inertia power using the virtual inertia coefficient and the active power droop coefficient.

[0093] Here, the virtual inertia coefficient reflects the power system's ability to simulate inertia characteristics; it is related to the power system's structure and parameters (and can be set to 50, 80, etc.). The active power droop coefficient is used to adjust the relationship between the system's active power output and frequency (and can be set to 20, 30, etc.). For example, by substituting the frequency deviation and frequency change rate into the formula containing the virtual inertia coefficient and the active power droop coefficient, the total virtual inertia power can be calculated. This power represents the total power support required by the power system to stabilize the frequency.

[0094] The formula for calculating the total virtual inertia power is as follows:

[0095] ;

[0096] In the formula, Represented as total virtual inertia power; Represented as virtual inertia coefficient; It is expressed as the active power droop factor.

[0097] S202, calculate the inertial power demand dominance based on the ratio of the frequency change rate to a preset maximum frequency change rate threshold; and calculate the power demand dominance based on the ratio of the frequency deviation to a preset maximum frequency deviation threshold.

[0098] It is understandable that the maximum frequency change rate threshold and the maximum frequency deviation threshold are preset based on the power grid's safe operation standards and the equipment's capacity. In actual power system planning and operation, different regions and types of power grids will set different specific values ​​based on factors such as equipment characteristics, load structure, and safety and stability requirements. For example, in some regional power grids with extremely high requirements for power supply reliability, advanced equipment, and relatively small load fluctuations, the maximum frequency change rate threshold may be set at 0.5 Hz / s, and the maximum frequency deviation threshold at 0.8 Hz. In contrast, in some power grids with a large proportion of industrial load, frequent load fluctuations, and relatively old equipment, considering the equipment's safe operation boundaries and the system's stability margin, the maximum frequency change rate threshold may be set at 0.3 Hz / s, and the maximum frequency deviation threshold at 0.5 Hz. No specific limits are set here.

[0099] Specifically, the inertial power demand dominance reflects the degree of influence of the frequency change rate on the total virtual inertial power demand, while the power demand dominance reflects the degree of influence of the frequency deviation on the total virtual inertial power demand. For example, when the frequency change rate is large, the inertial power demand dominance will increase, indicating that more power is needed to cope with the rapid frequency change.

[0100] Here, the formula for calculating the dominance of inertial power demand can be expressed as:

[0101] ;

[0102] in, This is expressed as the dominance of inertial power demand; This represents the maximum frequency change rate threshold corresponding to the maximum inertial power that the photovoltaic system and energy storage system can provide, and is a power system attribute value.

[0103] Here, the formula for calculating the power demand dominance can be expressed as:

[0104] ;

[0105] in, This is expressed as the degree of dominance of power demand; This represents the maximum frequency deviation threshold that the system can maintain at the network.

[0106] S203, based on the inertial power demand dominance and power demand dominance, determine the adaptive allocation coefficient.

[0107] Specifically, the adaptive allocation factor aims to rationally allocate total virtual inertia power to photovoltaic and energy storage systems based on the relative importance of the rate of frequency change and frequency deviation. For example, if the inertia power demand is more dominant, indicating a more significant impact from the rate of frequency change, the adaptive allocation factor may tend to allocate more total virtual inertia power to energy storage systems that can respond quickly to frequency changes.

[0108] Here, the expression for the adaptive allocation coefficient can be expressed as:

[0109] ;

[0110] in, Time weighting coefficient.

[0111] S204, Based on the total virtual inertia power and the adaptive allocation coefficient, determine the photovoltaic virtual inertia power component.

[0112] Here, by multiplying the total virtual inertia power by the adaptive allocation coefficient assigned to the photovoltaic system, the photovoltaic virtual inertia power component can be obtained. This component determines the amount of power that the photovoltaic system needs to adjust in inertia optimization control. For example, if the total virtual inertia power is 100kW and the adaptive allocation coefficient assigned to the photovoltaic system is 0.4, then the photovoltaic virtual inertia power component is 40kW.

[0113] The calculation expression for the photovoltaic virtual inertia power component is as follows:

[0114] ;

[0115] In the formula, It is represented as the photovoltaic virtual inertia power component.

[0116] S205, calculate the energy storage power correction coefficient based on the real-time state of charge of the energy storage system and the preset state of charge threshold.

[0117] Specifically, the preset state of charge (SOC) threshold is designed to ensure that the energy storage system operates within a safe and reliable range, avoiding overcharging or over-discharging. It can include a first SOC threshold (low SOC threshold) and a second SOC threshold (high SOC threshold). Overcharging intensifies the internal chemical reactions of the energy storage device, potentially leading to increased internal pressure and abnormal temperature rise, thereby accelerating battery aging, reducing battery capacity and performance, shortening device lifespan, and even causing safety issues such as battery swelling and leakage, posing a potential threat to the stable operation of the power system. Over-discharging damages the internal structure of the battery, reduces active materials, and causes irreversible capacity decay, affecting the energy storage system's ability to provide continuous and stable power support to the grid. The energy storage power correction coefficient is adjusted based on the comparison between the real-time SOC of the energy storage system and the preset threshold. For example, when the real-time SOC of the energy storage system approaches the upper limit threshold, the energy storage power correction coefficient will decrease to limit the charging power of the energy storage system and prevent overcharging.

[0118] For example, the calculation expression for the energy storage power correction coefficient can be expressed as:

[0119] ;

[0120] in, It is represented by the energy storage power correction factor; SOC represents the real-time state of charge. This is represented as the preset maximum permissible state of charge; This is represented as the preset minimum permissible state of charge; This is represented as the first battery threshold. This is represented as the second power threshold.

[0121] On the one hand, when the real-time state of charge is below the first energy threshold, it means that the energy storage system's energy level is low. To enable it to charge quickly and restore its regulation capability, the energy storage power correction coefficient is set to 1, allowing the energy storage system to perform high-power charging. Similarly, when the real-time state of charge is above the second energy threshold, it indicates that the energy storage system is close to full charge. To prevent overcharging, the energy storage power correction coefficient is also set to 1, limiting its continued charging.

[0122] On the other hand, when the real-time state of charge (SOC) is between the first and second energy thresholds, the energy storage power correction coefficient needs to be determined by comprehensively considering the real-time SOC, the preset maximum allowable SOC, and the preset minimum allowable SOC. The energy storage power correction coefficient can be adjusted proportionally based on the relative position of the real-time SOC between the maximum and minimum allowable SOC. If the real-time SOC is close to the minimum allowable SOC, the energy storage power correction coefficient is relatively large to encourage the energy storage system to absorb more energy; if the real-time SOC is close to the maximum allowable SOC, the energy storage power correction coefficient is relatively small to limit the charging power of the energy storage system and ensure that the energy storage system always operates within a safe and reasonable parameter range.

[0123] S206, Based on the total virtual inertia power, the adaptive allocation coefficient, and the energy storage power correction coefficient, determine the energy storage virtual inertia power component.

[0124] Understandably, when determining the virtual inertia power component of energy storage, the total virtual inertia power can be first multiplied by the adaptive allocation coefficient assigned to the energy storage system, and then further adjusted in conjunction with the energy storage power correction coefficient. This results in the final virtual inertia power component, which determines the charging and discharging power of the energy storage system in inertia optimization control. For example, if the total virtual inertia power is 100kW, the adaptive allocation coefficient assigned to the energy storage system is 0.6, and the energy storage power correction coefficient is 0.8, then the virtual inertia power component is 100kW × 0.6 × 0.8 = 48kW. A negative sign indicates charging, and a positive sign indicates discharging. Through these steps, the virtual inertia power components of photovoltaic and energy storage systems can be accurately calculated, providing a precise basis for the operation and adjustment of both systems.

[0125] Understandably, after calculating the photovoltaic (PV) virtual inertia power components and the energy storage virtual inertia power components of the energy storage system, the output power of the PV system can be adjusted based on the calculated PV virtual inertia power components, and the charging and discharging power of the energy storage system can be controlled based on the energy storage virtual inertia power components. A PV system is a device that converts solar energy into electrical energy, and its output power is affected by various factors such as light intensity and temperature. By adjusting the output power of the PV system, it can better adapt to changes in grid frequency, thus stabilizing the grid frequency. An energy storage system can store electrical energy when there is excess power and release it when there is insufficient power, thus balancing grid supply and demand. After obtaining the energy storage virtual inertia power components, inputting them into the energy storage controller enables the regulation of the energy storage system: when the energy storage virtual inertia power component is positive, it indicates that the energy storage system needs to discharge to provide additional power to the grid; when the energy storage virtual inertia power component is negative, it indicates that the energy storage system needs to charge to absorb excess electrical energy from the grid.

[0126] Specifically, when adjusting the output power of a photovoltaic system based on the photovoltaic virtual inertia power component, refer to Figure 3 As shown, the steps S301 to S303 may be included:

[0127] S301, Based on the photovoltaic virtual inertia power component, determine the reduced reserve power of the photovoltaic system.

[0128] Here, the photovoltaic virtual inertia power component reflects the power adjustment required by the photovoltaic system to cope with grid frequency changes. Based on this component, combined with the operating characteristics of the photovoltaic system and the grid demand, the required reserve power for grid frequency reduction can be calculated. Determining the reserve power is to ensure that the photovoltaic system can quickly reduce its output power when the grid frequency drops, providing necessary frequency support to the grid.

[0129] S302, Based on the current maximum output power of the photovoltaic system and the reduced reserve power of the photovoltaic system, determine the output power of the photovoltaic system to be adjusted.

[0130] It is understandable that the current maximum output power of a photovoltaic (PV) system is the maximum electrical energy it can output under current conditions of sunlight and temperature. By comprehensively analyzing the current maximum output power and the reduced reserve power, the adjusted output power of the PV system after considering frequency regulation requirements can be accurately determined. Here, the actual output capacity of the PV system and the frequency regulation requirements of the power grid are fully considered to ensure that the adjusted output power can meet part of the grid's needs without placing an excessive burden on the PV system.

[0131] S303, Adjust the output power of the photovoltaic system according to the output power to be adjusted.

[0132] Furthermore, after determining the output power to be adjusted, the output power of the photovoltaic system can be precisely adjusted by controlling key equipment such as the inverter, so that it reaches the level of the output power to be adjusted.

[0133] Here, the formula for calculating the output power of a photovoltaic system can be expressed as:

[0134] ;

[0135] in, This is expressed as the output power of the photovoltaic system; This represents the current maximum output power; This indicates a reduction in reserve power.

[0136] In some possible embodiments, after controlling the charging and discharging power of the energy storage system based on the virtual inertia power component, the real-time state of charge (SOC) of the energy storage system can be used as a reference to precisely regulate the SOC of the energy storage system using the variable power point tracking (VPT) control strategy of the photovoltaic (PV) system. The real-time SOC of the energy storage system directly affects its available energy reserves and charging and discharging efficiency in grid frequency regulation; excessively high or low SOC will limit its inertia-based frequency regulation function. The VPT control strategy of the PV system can dynamically adjust the PV output power according to environmental factors and grid demand. Using this strategy, the PV output can be flexibly changed according to the real-time SOC of the energy storage system. When the SOC is high, the PV output can be reduced to promote energy storage discharge; when the SOC is low, the PV output can be increased to increase energy storage charging, keeping the energy storage system in the bidirectional charging and discharging region.

[0137] Here, within the bidirectional charging and discharging region, the energy storage system can charge and discharge rapidly and flexibly according to changes in grid frequency, providing timely inertia support to the grid, enhancing the power system's ability to cope with frequency fluctuations and other issues, ensuring the safe and stable operation of the power system, and optimizing its inertia frequency regulation capability.

[0138] The inertia optimization control method, device, medium, and equipment for photovoltaic power plants with energy storage provided in this disclosure respond to frequency fluctuations through photovoltaic-energy storage synergy. Utilizing the rapid characteristics of energy storage to provide core inertia support, and allowing photovoltaic systems to adjust as needed, this not only improves the adaptability of photovoltaic power plants to grid frequency fluctuations but also fully leverages the role of the energy storage system in inertia support, effectively enhancing the inertia support capability of photovoltaic power plants and achieving synergistic optimization operation between photovoltaic power plants and energy storage systems. When grid frequency fluctuates, it can respond quickly and provide effective inertia compensation, enhancing grid stability, avoiding grid faults caused by excessive frequency fluctuations, and ensuring the safe and stable operation of the power system.

[0139] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0140] Based on the same inventive concept, this disclosure also provides an inertia optimization control device for a photovoltaic power station with energy storage, which corresponds to the inertia optimization control method for photovoltaic power stations with energy storage. Since the principle of the device in this disclosure for solving the problem is similar to the inertia optimization control method for photovoltaic power stations with energy storage described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0141] Reference Figure 4The diagram shown is a schematic of an inertia optimization control device 400 for a photovoltaic power station with energy storage provided in an embodiment of this disclosure. The device includes:

[0142] The data calculation module 401 is used to monitor the grid voltage frequency at the grid connection point of the photovoltaic power station in real time, and calculate the frequency deviation and frequency change rate in real time based on the monitoring results.

[0143] Inertia optimization module 402 is used to initiate inertia optimization control when the frequency deviation is greater than the frequency dead zone threshold and the frequency change rate is greater than the frequency change rate dead zone threshold, including:

[0144] Based on the frequency deviation, frequency change rate, and real-time state of charge of the energy storage system, the photovoltaic virtual inertia power component of the photovoltaic system and the energy storage virtual inertia power component of the energy storage system are calculated.

[0145] The output power of the photovoltaic system is adjusted based on the photovoltaic virtual inertia power component; and the charging and discharging power of the energy storage system is controlled based on the energy storage virtual inertia power component.

[0146] In some possible embodiments, the inertia optimization module 402 is specifically used for:

[0147] Based on the frequency deviation and the frequency change rate, the total virtual inertia power is calculated using the virtual inertia coefficient and the active power droop coefficient.

[0148] The inertial power demand dominance is calculated based on the ratio of the frequency change rate to a preset maximum frequency change rate threshold; and the power demand dominance is calculated based on the ratio of the frequency deviation to a preset maximum frequency deviation threshold.

[0149] Based on the inertial power demand dominance and power demand dominance, an adaptive allocation coefficient is determined;

[0150] The photovoltaic virtual inertia power components are determined based on the total virtual inertia power and the adaptive allocation coefficient.

[0151] Based on the real-time state of charge of the energy storage system and the preset state of charge threshold, the energy storage power correction coefficient is calculated.

[0152] The energy storage virtual inertia power component is determined based on the total virtual inertia power, the adaptive allocation coefficient, and the energy storage power correction coefficient.

[0153] In some possible embodiments, the inertia optimization module 402 is specifically used for:

[0154] When the real-time state of charge is lower than the first power threshold or the real-time state of charge is higher than the second power threshold, the energy storage power correction coefficient is set to 1; wherein the second power threshold is greater than the first power threshold.

[0155] In some possible embodiments, the inertia optimization module 402 is specifically used for:

[0156] When the real-time state of charge is between the first energy threshold and the second energy threshold, the energy storage power correction coefficient is determined based on the real-time state of charge, the preset maximum allowable state of charge, and the preset minimum allowable state of charge.

[0157] In some possible embodiments, the inertia optimization module 402 is specifically used for:

[0158] The energy storage power correction coefficient is calculated based on the energy storage power correction coefficient calculation formula, the real-time state of charge, the first energy threshold, and the second energy threshold.

[0159] The formula for calculating the energy storage power correction coefficient is as follows:

[0160] ;

[0161] Wherein, SOC represents the real-time state of charge; This is represented as the preset maximum permissible state of charge; This is represented as the preset minimum permissible state of charge; This is represented as the first battery threshold. This is represented as the second power threshold.

[0162] In some possible embodiments, the inertia optimization module 402 is specifically used for:

[0163] Based on the photovoltaic virtual inertia power components, the reduced reserve power of the photovoltaic system is determined;

[0164] Based on the current maximum output power of the photovoltaic system and the reduced reserve power of the photovoltaic system, determine the output power of the photovoltaic system to be adjusted.

[0165] The output power of the photovoltaic system is adjusted according to the output power to be adjusted.

[0166] In some possible embodiments, the inertia optimization module 402 is further configured to:

[0167] Based on the real-time state of charge of the energy storage system, the state of charge of the energy storage system is regulated by the variable power point tracking control strategy of the photovoltaic system, so as to keep the energy storage system in the bidirectional charging and discharging region and optimize the inertia frequency regulation capability.

[0168] Based on the same technical concept, this disclosure also provides a computer device. (See also...) Figure 5 The diagram shows the structure of a computer device 500 provided in this embodiment of the present disclosure, including a processor 501, a memory 502, and a bus 503. The memory 502 is used to store execution instructions and includes a main memory 5021 and an external memory 5022. The main memory 5021, also called internal memory, is used to temporarily store computational data in the processor 501, as well as data exchanged with external memory 5022 such as a hard disk. The processor 501 exchanges data with the external memory 5022 through the main memory 5021.

[0169] In this embodiment, the memory 502 is specifically used to store application code that executes the solution of this application, and its execution is controlled by the processor 501. That is, when the computer device 500 is running, the processor 501 communicates with the memory 502 through the bus 503, so that the processor 501 executes the application code stored in the memory 502, and then executes the method described in any of the foregoing embodiments.

[0170] The memory 502 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0171] Processor 501 may be an integrated circuit chip with signal processing capabilities. The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0172] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the computer device 500. In other embodiments of this application, the computer device 500 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0173] This disclosure also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program performs the steps of the inertia optimization control method for a photovoltaic power station with energy storage described in the above-described method embodiments. The storage medium can be volatile or non-volatile computer-readable storage.

[0174] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the inertia optimization control method for photovoltaic power plants with energy storage described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0175] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0176] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0177] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0178] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0179] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0180] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A method for optimizing the inertia control of a photovoltaic power station with energy storage, characterized in that, include: Real-time monitoring of grid voltage and frequency at the grid connection point of photovoltaic power plants, and real-time calculation of frequency deviation and frequency change rate based on monitoring results; When the frequency deviation is greater than the frequency dead zone threshold and the frequency change rate is greater than the frequency change rate dead zone threshold, inertia optimization control is initiated, including: Based on the frequency deviation, frequency change rate, and real-time state of charge of the energy storage system, the photovoltaic virtual inertia power component of the photovoltaic system and the energy storage virtual inertia power component of the energy storage system are calculated. The output power of the photovoltaic system is adjusted based on the photovoltaic virtual inertia power component; and the charging and discharging power of the energy storage system is controlled based on the energy storage virtual inertia power component. The calculation of the photovoltaic virtual inertia power component of the photovoltaic system and the energy storage virtual inertia power component of the energy storage system includes: Based on the frequency deviation and the frequency change rate, the total virtual inertia power is calculated using the virtual inertia coefficient and the active power droop coefficient. The inertial power demand dominance is calculated based on the ratio of the frequency change rate to a preset maximum frequency change rate threshold; and the power demand dominance is calculated based on the ratio of the frequency deviation to a preset maximum frequency deviation threshold. Based on the inertial power demand dominance and power demand dominance, an adaptive allocation coefficient is determined; The photovoltaic virtual inertia power components are determined based on the total virtual inertia power and the adaptive allocation coefficient. Based on the real-time state of charge of the energy storage system and the preset state of charge threshold, the energy storage power correction coefficient is calculated. The energy storage virtual inertia power component is determined based on the total virtual inertia power, the adaptive allocation coefficient, and the energy storage power correction coefficient.

2. The method according to claim 1, characterized in that, The preset state of charge threshold includes a first energy threshold and a second energy threshold; the calculation of the energy storage power correction coefficient includes: When the real-time state of charge is lower than the first energy threshold or the real-time state of charge is higher than the second energy threshold, the energy storage power correction coefficient is set to 1; wherein the second energy threshold is greater than the first energy threshold.

3. The method according to claim 2, characterized in that, The calculation of the energy storage power correction coefficient includes: When the real-time state of charge is between the first energy threshold and the second energy threshold, the energy storage power correction coefficient is determined based on the real-time state of charge, the preset maximum allowable state of charge, and the preset minimum allowable state of charge.

4. The method according to claim 3, characterized in that, The step of determining the energy storage power correction coefficient based on the real-time state of charge, the preset maximum allowable state of charge, and the preset minimum allowable state of charge includes: The energy storage power correction coefficient is calculated based on the energy storage power correction coefficient calculation formula, the real-time state of charge, the first energy threshold, and the second energy threshold. The formula for calculating the energy storage power correction coefficient is as follows: ； Wherein, SOC represents the real-time state of charge; This is represented as the preset maximum permissible state of charge; This is represented as the preset minimum permissible state of charge; This is represented as the first battery threshold. This is represented as the second power threshold.

5. The method according to claim 1, characterized in that, Adjusting the output power of the photovoltaic system based on the photovoltaic virtual inertia power component includes: Based on the photovoltaic virtual inertia power components, the reduced reserve power of the photovoltaic system is determined; Based on the current maximum output power of the photovoltaic system and the reduced reserve power of the photovoltaic system, determine the output power of the photovoltaic system to be adjusted. The output power of the photovoltaic system is adjusted according to the output power to be adjusted.

6. The method according to claim 1, characterized in that, After controlling the charging and discharging power of the energy storage system based on the energy storage virtual inertia power component, the method further includes: Based on the real-time state of charge of the energy storage system, the state of charge of the energy storage system is regulated by the variable power point tracking control strategy of the photovoltaic system, so as to keep the energy storage system in the bidirectional charging and discharging region and optimize the inertia frequency regulation capability.

7. A photovoltaic power station inertia optimization control device with energy storage, characterized in that, include: The data calculation module is used to monitor the grid voltage and frequency at the grid connection point of the photovoltaic power station in real time, and calculate the frequency deviation and frequency change rate in real time based on the monitoring results. The inertia optimization module is used to initiate inertia optimization control when the frequency deviation is greater than the frequency dead zone threshold and the frequency change rate is greater than the frequency change rate dead zone threshold, including: Based on the frequency deviation, frequency change rate, and real-time state of charge of the energy storage system, the photovoltaic virtual inertia power component of the photovoltaic system and the energy storage virtual inertia power component of the energy storage system are calculated. The output power of the photovoltaic system is adjusted based on the photovoltaic virtual inertia power component; and the charging and discharging power of the energy storage system is controlled based on the energy storage virtual inertia power component. Specifically, the inertia optimization module is used for: Based on the frequency deviation and the frequency change rate, the total virtual inertia power is calculated using the virtual inertia coefficient and the active power droop coefficient. The inertial power demand dominance is calculated based on the ratio of the frequency change rate to a preset maximum frequency change rate threshold; and the power demand dominance is calculated based on the ratio of the frequency deviation to a preset maximum frequency deviation threshold. Based on the inertial power demand dominance and power demand dominance, an adaptive allocation coefficient is determined; The photovoltaic virtual inertia power components are determined based on the total virtual inertia power and the adaptive allocation coefficient. Based on the real-time state of charge of the energy storage system and the preset state of charge threshold, the energy storage power correction coefficient is calculated. The energy storage virtual inertia power component is determined based on the total virtual inertia power, the adaptive allocation coefficient, and the energy storage power correction coefficient.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

9. A computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

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