Multi-energy combined frequency modulation control method and device

Abstract

The application discloses a kind of multi-energy joint frequency modulation control method and device, belong to power system frequency modulation technical field.Firstly, real-time acquisition power grid frequency deviation, thermal power unit operating parameter, photovoltaic real-time output and predicted output and energy storage state of charge;Based on the frequency deviation amplitude, the frequency modulation demand level is divided, and the differentiated response strategy of energy storage, thermal power unit and photovoltaic is matched;According to the real-time state of each energy, dynamically adjust the distribution proportion of frequency modulation responsibility, when the state of charge of energy storage is lower than the preset threshold or photovoltaic output deviation is out of limit, automatically transfer the frequency modulation responsibility to other available energy;Through collaborative control algorithm, respectively adjust the valve opening of thermal power unit steam turbine, photovoltaic inverter droop coefficient and energy storage converter charge-discharge power, realize the closed-loop tracking and dynamic correction of multi-energy target output, so as to improve the cooperativity, robustness and response accuracy of power grid frequency regulation.

Description

Technical Field

[0001] This invention relates to the field of power system frequency regulation technology, and in particular to a multi-energy joint frequency regulation control method, device, equipment and storage medium. Background Technology

[0002] Grid frequency stability is a core indicator for ensuring the safe and reliable operation of the power system. With the large-scale grid connection of new energy power generation equipment, represented by photovoltaics, the inherent intermittency and strong fluctuations in their output have significantly exacerbated the instantaneous power imbalance of the grid, causing a surge in system frequency regulation pressure and posing a severe challenge to traditional frequency regulation methods. Currently, mainstream frequency regulation technologies still face several key technical bottlenecks and systemic defects when dealing with this complex scenario.

[0003] First, frequency regulation methods dominated by large-capacity traditional thermal power units (such as 600MW-class supercritical units) face fundamental limitations. Although these units possess considerable frequency regulation capacity, their mechanical inertia and thermodynamic system characteristics result in slow response speeds and limited load ramp-up rates, making it difficult to effectively track high-frequency and sudden frequency fluctuations in the power grid. More seriously, forcing units to frequently participate in deep frequency regulation will significantly increase the mechanical wear of key equipment such as turbine valves, accelerate unit lifespan reduction, and lead to a significant increase in coal consumption due to long-term deviation from the optimal economic operating point, making the economic efficiency and sustainability of frequency regulation unsatisfactory.

[0004] Secondly, attempting to make photovoltaic (PV) power generation participate solely in grid frequency regulation has significant drawbacks. PV output is highly dependent on real-time weather conditions, and its random fluctuations and uncontrollability make the frequency regulation output it provides extremely unstable. For example, if PV is required to perform down-regulation during peak output periods, sudden cloud cover can cause a sharp drop in actual output. This not only fails to mitigate frequency deviations but may also produce a "reverse frequency regulation effect," further deteriorating system frequency stability. This mismatch between output and frequency regulation demand in terms of timing severely undermines the reliability of PV-based frequency regulation.

[0005] Secondly, although electrochemical energy storage systems possess millisecond-level rapid power response capabilities, their sole participation in frequency regulation suffers from capacity limitations and economic bottlenecks. The available frequency regulation energy of an energy storage system is directly limited by its rated capacity and current state of charge (SOC). When dealing with persistent grid frequency deviations, the energy storage system may experience a rapid drop in SOC to a protection threshold (e.g., below 20%) due to deep charging and discharging, forcing it to shut down and disrupting frequency regulation capabilities. Furthermore, relying solely on high-cost energy storage facilities to handle all frequency regulation needs leads to difficulties in recovering project investment, poor economic efficiency, and hinders large-scale deployment.

[0006] Finally, some existing joint frequency regulation technologies have failed to achieve effective complementarity and dynamic synergy of the advantages of multiple energy sources. For example, the "thermal power + energy storage" joint model fails to integrate the frequency regulation potential of photovoltaics and fails to fully utilize clean energy; the "photovoltaic + energy storage" joint model lacks large-capacity, sustainable baseload power support and remains inadequate in meeting long-term frequency regulation needs. Existing technologies generally lack an optimization mechanism that can intelligently and dynamically allocate frequency regulation responsibilities among multiple entities (source, load, and storage) based on real-time operating conditions, resulting in the need to improve the robustness, economy, and coordination of the overall frequency regulation system.

[0007] In summary, under the current technological background, there is an urgent need to develop an advanced joint frequency regulation control method that can deeply integrate the power support advantages of traditional large-capacity thermal power, the green frequency regulation potential of photovoltaic new energy, and the rapid response characteristics of energy storage systems, and has the ability to perceive real-time status and optimize dynamic responsibility, so as to systematically solve problems such as response lag, insufficient stability, and high overall cost. Summary of the Invention

[0008] The present invention aims to at least partially solve one of the technical problems in the related art.

[0009] To address this, this invention proposes a multi-energy joint frequency regulation control method. First, it collects real-time data on grid frequency deviation, operating parameters of large-capacity thermal power units, real-time and predicted photovoltaic output, and the state of charge (SOC) of energy storage. Based on the amplitude of the frequency deviation, it classifies frequency regulation demand levels and matches differentiated response strategies for energy storage, thermal power units, and photovoltaics accordingly. It dynamically adjusts the frequency regulation responsibility allocation ratio based on the real-time operating status of each energy source, automatically transferring frequency regulation responsibility to other energy sources when the SOC of energy storage is too low or the photovoltaic output deviation exceeds limits. Through a collaborative control algorithm, it adjusts the turbine valves of the thermal power unit, the droop coefficient of the photovoltaic inverter, and the charging and discharging power of the energy storage converter, achieving closed-loop tracking and dynamic correction of the target output of multiple energy sources.

[0010] Another objective of this invention is to provide a multi-energy joint frequency modulation control device.

[0011] The third objective of this invention is to provide a computer device.

[0012] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0013] To achieve the above objectives, the present invention provides a multi-energy joint frequency modulation control method, comprising: S1 collects real-time data on grid frequency deviation, operating parameters of 600MW thermal power units, real-time and predicted output of photovoltaic power, and SOC status of energy storage. S2, classify frequency regulation demand levels based on the amplitude of grid frequency deviation, and match response strategies for energy storage, thermal power units and photovoltaics according to the frequency regulation demand levels; S3 dynamically adjusts the responsibility allocation ratio according to the real-time operating status of each energy source. When the energy storage SOC is lower than the preset threshold or the deviation between the real-time output and the predicted output of photovoltaic exceeds the allowable range, the frequency regulation responsibility is automatically transferred to other energy sources. S4 uses a collaborative control algorithm to control the turbine valve opening of the thermal power unit, the droop coefficient of the photovoltaic inverter, and the charging and discharging power of the energy storage converter, respectively, to achieve closed-loop tracking and dynamic correction of the output of multiple energy targets.

[0014] The multi-energy joint frequency modulation control method of this invention may also have the following additional technical features: In one embodiment of the present invention, the real-time acquisition of grid frequency deviation, operating parameters of a 600MW thermal power unit, real-time and predicted output of photovoltaic power, and SOC status of energy storage includes: S11, collects power grid frequency deviation through GPS synchronization frequency detector to ensure data acquisition delay ≤10ms; S12, when the deviation between the real-time output and the predicted output of photovoltaic power exceeds 5%, the average value of the previous 5 seconds is used to replace invalid data.

[0015] In one embodiment of the present invention, the step of classifying frequency regulation demand levels based on the amplitude of grid frequency deviation and matching response strategies for energy storage, thermal power units, and photovoltaics according to the frequency regulation demand levels includes: S21 divides the frequency deviation into three levels: Level 1 requirement is |Δf|≤0.03Hz, Level 2 requirement is 0.03Hz<|Δf|≤0.1Hz, and Level 3 requirement is |Δf|>0.1Hz; S22, for Tier 1 demand, the energy storage responsibility ratio α = 0.8-1.0; for Tier 2 demand, the energy storage responsibility ratio α = 0.4-0.6; for Tier 2 demand, the energy storage responsibility ratio α = 0.2-0.4; for Tier 3 demand, the energy storage responsibility ratio α = 0.2-0.4.

[0016] In one embodiment of the present invention, the step of dynamically adjusting the responsibility allocation ratio according to the real-time operating status of each energy source, and automatically transferring the frequency regulation responsibility to other energy sources when the energy storage SOC is lower than a preset threshold or the deviation between the real-time output and the predicted output of photovoltaic power exceeds the allowable range, includes: S31, when the energy storage SOC < 20% or SOC > 80%, the energy storage responsibility ratio a will be reduced by 50%, and the reduced frequency regulation responsibility will be transferred to thermal power. S32, when the actual output of photovoltaic power deviates from the predicted output by more than 5%, the photovoltaic responsibility ratio γ is set to 0, and the reduced frequency regulation responsibility is transferred to thermal power and energy storage.

[0017] In one embodiment of the present invention, the step of controlling the turbine valve opening of the thermal power unit, the droop coefficient of the photovoltaic inverter, and the charging and discharging power of the energy storage converter through a cooperative control algorithm to achieve closed-loop tracking and dynamic correction of multi-energy target output includes: S41, the target output is corrected through PID + fuzzy control algorithm; where the proportional coefficient Kp = 0.8-1.5 and the integral time Ti = 0.1-0.5s; S42, when the energy storage output deviation is >5%, the energy storage responsibility ratio α is dynamically adjusted to compensate for the deviation between the actual output and the target output.

[0018] In one embodiment of the present invention, it further includes: S5 monitors the status of each module through a signal comparison circuit. When the energy storage PCS fails, it automatically transfers the frequency regulation responsibility to thermal power and photovoltaic power, and keeps the frequency deviation within ±0.07Hz.

[0019] To achieve the above objectives, another aspect of the present invention provides a multi-energy joint frequency modulation control device, comprising: The multi-energy status data acquisition module is used to collect real-time data on grid frequency deviation, operating parameters of a 600MW thermal power unit, real-time and predicted output of photovoltaic power, and SOC status of energy storage. The frequency regulation demand level classification and response strategy matching module is used to classify frequency regulation demand levels based on the amplitude of grid frequency deviation, and match response strategies for energy storage, thermal power units and photovoltaics according to the frequency regulation demand level. The responsibility allocation ratio dynamic adjustment module is used to dynamically adjust the responsibility allocation ratio according to the real-time operating status of each energy source. When the energy storage SOC is lower than the preset threshold or the deviation between the real-time output and the predicted output of photovoltaic exceeds the allowable range, the frequency regulation responsibility is automatically transferred to other energy sources. The collaborative control algorithm execution module is used to control the turbine valve opening of the thermal power unit, the droop coefficient of the photovoltaic inverter, and the charging and discharging power of the energy storage converter through the collaborative control algorithm, so as to realize closed-loop tracking and dynamic correction of the output of multiple energy targets.

[0020] In one embodiment of the present invention, it further includes: The signal comparison circuit monitoring module is used to monitor the status of each module through the signal comparison circuit. When the energy storage PCS fails, it automatically transfers the frequency regulation responsibility to thermal power and photovoltaic power, and keeps the frequency deviation within ±0.07Hz.

[0021] This invention discloses a multi-energy joint frequency regulation control method and apparatus. By constructing a multi-level demand division and differentiated response strategy matching mechanism based on the frequency deviation amplitude, and introducing a dynamic responsibility allocation and automatic transfer strategy that considers the deviation between energy storage state of charge and photovoltaic output, it effectively solves the problems of low frequency regulation accuracy and weak system robustness caused by mismatched response speeds, insufficient utilization of energy characteristics, and poor state adaptability in existing technologies. It achieves full-process collaborative control from real-time perception of multi-source states, intelligent matching of frequency regulation demands, dynamic optimization of responsibility ratios to closed-loop tracking of target output, significantly improving the regulation accuracy and stability of the power grid frequency, and enhancing the adaptive capability and overall economy of the joint frequency regulation system under complex operating conditions.

[0022] To achieve the above objectives, a third aspect of this application provides a computer device comprising a processor and a memory; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, for implementing a multi-energy joint frequency modulation control method as described in the first aspect embodiment.

[0023] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a multi-energy joint frequency modulation control method as described in the first aspect embodiment.

[0024] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0025] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a multi-energy joint frequency modulation control method according to an embodiment of the present invention; Figure 2 This is a model architecture diagram of another multi-energy joint frequency modulation control method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a multi-energy joint frequency modulation control device according to an embodiment of the present invention; Figure 4 It is a computer device according to an embodiment of the present invention. Detailed Implementation

[0026] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0028] The following description, with reference to the accompanying drawings, describes a multi-energy joint frequency modulation control method, apparatus, device, and storage medium according to embodiments of the present invention.

[0029] The core idea of ​​this invention is to construct a multi-energy collaborative control framework that covers the entire control chain from state perception, demand classification, dynamic allocation of responsibilities to closed-loop execution. First, real-time data is collected from multiple sources, including grid frequency deviation, operating parameters of large-capacity thermal power units, real-time and predicted photovoltaic output, and energy storage status of charge, to form a global state perception of system operation. Based on this, a multi-level frequency regulation demand classification system is established according to the amplitude of the frequency deviation, and differentiated response strategies for energy storage, thermal power units, and photovoltaics are intelligently matched accordingly. Furthermore, a dynamic responsibility allocation and transfer mechanism is introduced, adaptively adjusting the frequency regulation responsibility ratio based on the real-time operating status of each energy unit (e.g., energy storage status of charge below a safety threshold or photovoltaic output deviation exceeding limits), and automatically transferring the corresponding responsibility to other available energy sources to ensure continuous and stable system operation. Finally, through a collaborative control algorithm, the opening degree of the steam turbine valves of the thermal power units, the droop coefficient of the photovoltaic inverter, and the charging and discharging power of the energy storage converter are precisely adjusted to achieve closed-loop tracking and dynamic correction of the total output of multiple energy targets. This transforms the traditional, fragmented frequency regulation mode into an intelligent control system that deeply integrates the power support of thermal power, the green potential of photovoltaics, and the rapid response advantages of energy storage, and possesses state self-adaptation and responsibility optimization capabilities. It effectively improves the accuracy, robustness, and overall economy of power grid frequency regulation.

[0030] Example 1 To achieve the above invention, embodiments of the present invention provide a multi-energy joint frequency modulation control method, such as... Figure 1 As shown, it includes: S1 collects real-time data on grid frequency deviation, operating parameters of a 600MW thermal power unit, real-time and predicted output of photovoltaic power, and SOC status of energy storage.

[0031] Specifically, the technical implementation principle of this step is based on a high-precision, low-latency real-time data acquisition and preprocessing mechanism to ensure that the input data of the subsequent control algorithm has timeliness and reliability.

[0032] Specifically, this step involves multiple sub-units in the parameter detection module working together to collect power grid frequency deviations. Operating parameters of a 600MW thermal power unit (including actual output) Slope rate (Main steam pressure), real-time output of photovoltaic power. With 15-minute predicted output and the SOC state of energy storage. and real-time charge and discharge power The grid frequency deviation is measured using a GPS-synchronized frequency detector, typically with a sampling frequency above 100Hz and a time synchronization error of less than 1ms, ensuring high-precision acquisition of the frequency signal. Power signals from photovoltaics and energy storage are acquired using high-precision sensors (such as Hall current sensors and voltage transmitters), with a sampling period of 100ms and data transmission delay controlled within [specific parameters]. It complies with the requirements of IEC 61850-9-2 standard for real-time data transmission.

[0033] Furthermore, to address data anomalies or communication interruptions, this step introduces a data validity assessment mechanism. When a data anomaly is detected at a certain moment (such as sensor failure or packet loss), the system will use the average value of the previous 5 seconds as a replacement to ensure the continuity and stability of the control logic. This mechanism is recommended in the IEEE 1547-2018 standard for data fault tolerance processing in distributed energy access systems.

[0034] Specifically, this step is applicable to grid frequency regulation scenarios with a high proportion of renewable energy integration, especially during periods of drastic changes in sunlight conditions and frequent load fluctuations. Through real-time data acquisition and preprocessing, it provides a reliable basis for subsequent frequency regulation demand classification and responsibility allocation. Its technical value lies in providing high-quality input data for multi-entity collaborative control, thereby improving frequency regulation response speed and control accuracy, and reducing system operation risks.

[0035] Furthermore, S1 includes: S11 uses a GPS synchronization frequency detector to collect power grid frequency deviation, ensuring that the data acquisition delay is ≤10ms.

[0036] Specifically, the technical implementation principle of this step is based on high-precision time synchronization and frequency measurement technology to ensure the timing consistency and real-time performance of the collected data, thereby providing a reliable basis for subsequent frequency modulation decisions.

[0037] Furthermore, the GPS synchronization frequency detector achieves synchronization with UTC time by receiving GPS satellite signals, with a time synchronization accuracy typically reaching ±1μs and a frequency measurement accuracy reaching ±0.001Hz. This device samples the power grid frequency signal and calculates its correlation with the rated frequency. deviation The results are output with a time resolution of milliseconds. To meet the requirement of "data acquisition delay ≤ 10ms" in this invention, high-speed industrial Ethernet (such as Profinet or EtherCAT) is used for data transmission between the detector and the core control unit, and the communication delay is controlled within 1ms to ensure real-time performance.

[0038] Furthermore, this step also involves the collection and preprocessing of operating parameters for the 600MW thermal power unit, photovoltaic system, and energy storage system. The thermal power unit parameters include actual output... Slope rate Main steam pressure, etc.; real-time output data collected by the photovoltaic system. With 15-minute predicted output The energy storage system collects SOC (State of Charge) and real-time charging and discharging power. All collected data undergoes preliminary filtering and validity assessment by the edge computing controller. If data is abnormal or lost at any point, the average value of the previous 5 seconds is used as a substitute to ensure the continuity and stability of the control logic.

[0039] Specifically, this step is deployed in power grid dispatch centers or distributed energy control stations in practical applications, and is suitable for power grid frequency regulation scenarios with a high proportion of renewable energy integration. Through synchronous acquisition and preprocessing of multi-source data, it provides input for subsequent frequency regulation demand classification and dynamic allocation of responsibilities, ensuring that each frequency regulation entity can respond quickly and accurately under different frequency deviation levels. Its technical value lies in significantly improving the real-time performance and robustness of the frequency regulation system, laying the data foundation for achieving millisecond-level joint frequency regulation control.

[0040] S12, when the deviation between the real-time output and the predicted output of photovoltaic power exceeds 5%, the average value of the previous 5 seconds is used to replace invalid data.

[0041] Specifically, the technical implementation of this step is based on a real-time data acquisition and preprocessing mechanism, which compares the real-time output of photovoltaic power. With predicted output The system determines the validity of data based on deviations. Specifically, the system sets a deviation threshold of 5%, meaning that when... If so, the current photovoltaic output data is determined to be abnormal or invalid.

[0042] Furthermore, to address this anomaly, the system employs a sliding time window averaging strategy, taking the arithmetic average of the photovoltaic power output data within the previous 5 seconds as the effective power output value for the current moment. This method can achieve [the desired output value] in a sampling system with a time resolution of 100ms. This smoothing process avoids sudden changes in control commands due to instantaneous deviations, thereby improving the stability of the frequency modulation response. In terms of implementation, this step is typically performed by the edge computing controller within the core control unit, whose built-in signal processing algorithms perform real-time detection and correction of the data stream.

[0043] Specifically, this step is particularly suitable for practical applications in scenarios involving sudden changes in lighting conditions, sensor malfunctions, or significant errors in the prediction model. For example, when clouds rapidly obscure the solar power or the inverter output is abnormal, the photovoltaic output may fluctuate drastically within a short period, exceeding the 5% deviation range of the predicted value. In this case, the system automatically activates the 5-second averaging mechanism to ensure the continuity of photovoltaic output data, providing a reliable basis for subsequent frequency regulation responsibility allocation.

[0044] Specifically, this step effectively mitigates the interference of photovoltaic output fluctuations on frequency regulation control and improves the system's tolerance to uncertainties in new energy sources. Through data smoothing, the responsibility ratio of photovoltaic power in frequency regulation is... It can calculate more accurately, avoid misjudgments caused by single-point abnormal data, and thus enhance the robustness and coordination of the joint frequency modulation system.

[0045] S2 classifies frequency regulation demand levels based on the amplitude of grid frequency deviation and matches response strategies for energy storage, thermal power units, and photovoltaics according to the frequency regulation demand levels.

[0046] Specifically, the technical implementation of this step is based on the power grid frequency deviation. Real-time monitoring and quantitative analysis, combined with the response characteristics and operational constraints of different frequency modulation subjects, enable dynamic responsibility allocation and collaborative control.

[0047] Specifically, this step begins with the parameter detection module collecting the power grid frequency deviation. And compare it with a preset grading threshold. Specifically, when When this occurs, it is determined to be a Level 1 frequency modulation requirement, suitable for high-frequency, small-amplitude frequency fluctuations; when When it is determined to be a secondary demand, it is suitable for frequency deviations of moderate magnitude; while when At that time, it is determined to be a Level 3 demand, applicable to sudden, large-amplitude frequency disturbances. Based on the above classification, the core control unit will match the response strategies for energy storage, thermal power units, and photovoltaics respectively.

[0048] Furthermore, the response ratio of energy storage Response ratio of thermal power units Response ratio of photovoltaic All of these are dynamically adjusted according to changes in the frequency modulation level. For example, under Level 1 demand... Secondary demand Under the third level of demand Meanwhile, thermal power units bear the greatest frequency regulation responsibility under Level 3 demand conditions, and their ramp-up rate is limited to [missing information]. It also has a maximum output limit. The frequency regulation responsibility of photovoltaic power depends on the degree of matching between its actual output and the predicted output. If the actual output is lower than the predicted value... Then its proportion of responsibility It is set to 0 to avoid insufficient output causing frequency modulation failure.

[0049] Specifically, this step is applicable to grid frequency regulation scenarios with a high proportion of renewable energy integration, especially during periods of frequent changes in sunlight conditions and large load fluctuations. By dynamically allocating frequency regulation responsibilities according to levels, the system can achieve optimal response combinations under different disturbance intensities. For example, it can prioritize the use of fast-responding energy storage during small fluctuations, while introducing capacity support from thermal power units during persistent deviations, thus balancing response speed and stability.

[0050] Specifically, this step significantly improves the response performance and cost-effectiveness of the joint frequency modulation system. Through a tiered response mechanism, the system response time can be reduced to [amount missing]. Frequency deviation control accuracy improved to Frequency modulation performance indicators Upgraded to Furthermore, this strategy effectively reduces coal consumption of thermal power units, extends the cycle life of energy storage systems, and reduces curtailment losses of photovoltaic power due to forced frequency regulation, thereby achieving optimal scheduling and resource utilization through multi-entity coordinated frequency regulation.

[0051] Furthermore, S2 includes: S21 divides the frequency deviation into three levels: Level 1 requirement is |Δf|≤0.03Hz, Level 2 requirement is 0.03Hz<|Δf|≤0.1Hz, and Level 3 requirement is |Δf|>0.1Hz.

[0052] Specifically, the technical implementation of this step is based on the grid frequency deviation. Real-time monitoring and quantitative analysis divide frequency regulation demand into three levels, each corresponding to different response strategies and responsibility allocation ratios, thereby improving the response speed, accuracy, and economy of the frequency regulation system.

[0053] Specifically, this step uses the frequency regulation demand classification algorithm built into the core control unit to make real-time judgments on the collected power grid frequency deviations. Specifically, when... When the demand is classified as Level 1, it is suitable for high-frequency, low-amplitude frequency fluctuations. In this case, the energy storage system with the fastest response time (≤10ms) is prioritized for frequency regulation, while the real-time output of the photovoltaic system is also considered. With predicted output To assist in adjusting the deviation. If Then photovoltaics will bear the responsibility The frequency regulation responsibility is to avoid over-output leading to grid instability.

[0054] Optionally, when At this time, it is determined to be secondary demand, applicable to frequency deviations of moderate magnitude. Energy storage then assumes this role. Frequency modulation responsibility (provided that) ), 600MW unit undertakes Responsibility (Climb Rate) Photovoltaics, on the other hand, are judged based on whether their actual output meets the requirements. To decide whether to participate in FM, and to assume responsibility The responsibility.

[0055] Furthermore, when At this time, it is determined to be a Level 3 demand, applicable to emergency scenarios where the power grid experiences significant frequency deviations. In this situation, the 600MW unit assumes the primary responsibility for frequency regulation. Maximum gradeability ), energy storage Deep discharge is allowed at this time, bearing The responsibility of photovoltaics is only Participate and take responsibility The responsibility.

[0056] Specifically, this step quantifies frequency deviation into three levels and, combined with the response capabilities and operating status of each frequency regulation entity, achieves dynamic responsibility allocation, effectively avoiding the problems of insufficient or excessive frequency regulation capacity of a single entity. For example, under level three demand, thermal power serves as the primary support, responding quickly and stabilizing the frequency, while energy storage provides auxiliary regulation within the allowable SOC range, and photovoltaic power only participates when output is sufficient, thereby improving the overall frequency regulation performance and economy of the system. This tiered strategy has significant application value in power grids with high renewable energy penetration rates, and is particularly suitable for scenarios with frequent fluctuations in sunlight and limited energy storage capacity.

[0057] S22, for Tier 1 demand, the energy storage responsibility ratio α = 0.8-1.0; for Tier 2 demand, the energy storage responsibility ratio α = 0.4-0.6; for Tier 2 demand, the energy storage responsibility ratio α = 0.2-0.4; for Tier 3 demand, the energy storage responsibility ratio α = 0.2-0.4.

[0058] Specifically, the principle of this step is rooted in the techno-economic characteristics of different frequency regulation resources: energy storage systems have millisecond-level power response speeds but limited energy, making them most suitable for smoothing high-frequency, small-amplitude random frequency fluctuations; large-capacity thermal power units have a wide power regulation range and strong energy continuity, but their response has inertia, making them suitable as the backbone force for dealing with large-amplitude, continuous frequency deviations; photovoltaic output, on the other hand, is intermittent and uncertain, and its frequency regulation capability requires accurate output prediction as a prerequisite, serving as a flexible supplement to the system. By quantifying frequency deviations into different levels and mapping them to preset responsibility ratio ranges, this method, in principle, achieves an optimized trade-off between the impossible trinity of "speed-capacity-economy" in frequency regulation, allowing various resources to play their role within their optimal operating ranges, achieving overall optimization of frequency regulation performance and operating costs at the system level.

[0059] Furthermore, the above principle is implemented through a "dynamic responsibility allocation model" algorithm module embedded in the core control unit. This module takes as input the preprocessed real-time absolute value of the frequency deviation and the availability status flags of each energy source; it internally stores a predefined "frequency deviation-responsibility ratio" mapping table. When the algorithm starts, it first queries the mapping table based on the real-time frequency deviation to determine the current frequency regulation demand level (Level 1, Level 2, or Level 3), and then calls the initial value α_base of the corresponding energy storage responsibility ratio for that level (e.g., a base value within the range of 0.8-1.0 for Level 1 demand). Subsequently, the algorithm dynamically corrects the initial ratio by considering constraints such as the real-time state of charge of the energy storage and the deviation between the real-time output and predicted output of the photovoltaic system. For example, if the energy storage state of charge reaches the protection limit, a penalty function reduces α_base. The corrected final responsibility ratio α, along with the responsibility ratios of other energy sources, is used to calculate the target output command for each frequency regulation entity. This implementation process, completed through deterministic logical judgments and mathematical operations, ensures the reliability and real-time performance of the control strategy.

[0060] Furthermore, for primary requirements (corresponding to small fluctuations with an absolute value of frequency deviation less than or equal to 0.03 Hz), the energy storage responsibility ratio α is set in a high range of 0.8 to 1.0. This setting aims to ensure that small fluctuations are almost fully borne by the energy storage with the fastest response, thus avoiding unnecessary frequent actions of thermal power units. For secondary requirements (corresponding to medium fluctuations with an absolute value of frequency deviation between 0.03 Hz and 0.1 Hz), α is adjusted down to a medium range of 0.4 to 0.6. At this time, the system needs to balance the response speed and frequency regulation capacity, and energy storage and thermal power jointly bear the main responsibility, while photovoltaic power supplements as appropriate. For tertiary requirements (corresponding to large fluctuations with an absolute value of frequency deviation greater than 0.1 Hz), α is further set in a lower range of 0.2 to 0.4. In this scenario, the core of maintaining grid frequency stability lies in providing sufficient power support, so large-capacity thermal power bears the main responsibility, and energy storage serves as a quickly starting auxiliary force to prevent instantaneous frequency drops. The above specific numerical ranges can be engineered and optimized according to the inertia constant of the actual power grid and the specific configured capacity of frequency regulation resources.

[0061] Furthermore, this hierarchical responsibility allocation strategy is mainly applied to the automatic generation control link of modern power systems with high proportions of new energy access, especially applicable to regional power grids or the power generation side equipped with large coal-fired units of 600 MW class, centralized or distributed photovoltaic power plants, and large-scale electrochemical energy storage systems. When applied, this strategy, as an upper-layer optimization algorithm, is seamlessly integrated with the underlying unit coordinated control system, photovoltaic inverter control, and energy storage converter control. Its typical application scenarios include: smoothing the impact of sudden power drops or increases in photovoltaic power plants caused by cloud movement on the grid frequency; cooperating with conventional thermal power units to jointly respond to rapid load changes; and quickly coordinating multiple resources for frequency emergency support when a single component failure occurs in the power grid resulting in power shortages. The implementation of this strategy upgrades the traditional "source following load" mode to an intelligent frequency regulation mode of "multi-source collaborative interaction", significantly enhancing the operation resilience of the power grid under complex power source structures.

[0062] Specifically, firstly, it significantly optimizes the overall frequency regulation performance of the system, reducing the equivalent response time of the joint system to the millisecond level and controlling the steady-state frequency deviation at a more optimal level, thereby improving the power quality and safety margin of the power grid. Secondly, this strategy generates considerable economic benefits: by enabling energy storage to accurately respond to high-frequency, small-amplitude fluctuations, it greatly reduces the number of times thermal power units participate in frequent frequency regulation, directly reducing unit wear, maintenance costs, and additional coal consumption; at the same time, the interval-based limitation and state constraints on the energy storage responsibility ratio prevent excessive deep charging and discharging, effectively extending the actual cycle life of the energy storage system. Finally, this strategy enhances the operational reliability of the system. Clear rules ensure a clear division of tasks and priorities among the frequency regulation resources under different disturbance intensities, avoiding conflicts and oscillations in control commands. When some resources are forced to withdraw due to faults or state limitations, the dynamic adjustment mechanism of the responsibility ratio can automatically transfer tasks, ensuring the continuity of frequency regulation functions, thereby improving the availability and robustness of the entire joint frequency regulation system.

[0063] S3 dynamically adjusts the responsibility allocation ratio according to the real-time operating status of each energy source. When the energy storage SOC is lower than the preset threshold or the deviation between the real-time output and the predicted output of photovoltaic exceeds the allowable range, the frequency regulation responsibility is automatically transferred to other energy sources.

[0064] Specifically, the technical principle behind this step is based on the measurement of power grid frequency deviation. Real-time monitoring and classification, combined with deviation analysis of energy storage SOC and actual and predicted photovoltaic output, dynamically adjust the responsibility ratio of each energy source in the frequency regulation process, thereby improving the system response speed and stability.

[0065] Specifically, the core control unit first receives real-time data from the parameter detection module, including grid frequency deviation. Actual output of thermal power units Actual output of photovoltaic power With predicted output Energy storage SOC and real-time charge / discharge power .according to The magnitude of the frequency regulation demand is used to classify it into three response levels: Level 1, Level 2, and Level 3, each corresponding to a different responsibility allocation ratio. When the energy storage SOC is lower than a preset threshold (e.g., SOC < 20% during discharge) or the actual photovoltaic output deviates from the predicted output beyond the allowable range (e.g., ... When this occurs, the system automatically reduces the responsibility ratio for that energy source. or And the corresponding frequency regulation responsibility will be transferred to other energy sources with responsive capabilities, such as thermal power or energy storage.

[0066] Furthermore, the proportion of energy storage responsibility In the case of primary requirements Automatically reduce by 50% when SOC is below 20%; thermal power liability ratio In the case of level three demand When its output approaches the rated upper limit (e.g.) When this happens, the liability ratio will be adjusted accordingly; photovoltaic liability ratio Depend on The decision is dynamic; when its output is insufficient, it automatically resets to zero, and the responsibility is transferred to thermal power or energy storage.

[0067] Specifically, this step is applicable in practical applications to grid frequency regulation scenarios with a high proportion of renewable energy integration, especially when there are sudden changes in solar illumination conditions or limited energy storage SOC, effectively avoiding insufficient frequency regulation capacity or resource waste. Through dynamic responsibility allocation, the system ensures that frequency deviation is controlled within... Within the range, it also significantly improves the frequency modulation response speed to It also reduces coal consumption in thermal power plants and energy storage lifespan loss, resulting in significant improvements in economy and reliability.

[0068] Furthermore, S3 includes: S31. When the energy storage SOC < 20% or SOC > 80%, the energy storage responsibility ratio a will be reduced by 50%, and the reduced frequency regulation responsibility will be transferred to thermal power.

[0069] Specifically, this step, based on the dynamic adjustment mechanism of the real-time monitoring and responsibility allocation model of the energy storage SOC, is a key link in the "multi-subject collaborative frequency regulation" control logic of this invention.

[0070] Furthermore, the energy storage SOC detector collects the state of charge of the energy storage system in real time through the battery management system (BMS), with a sampling frequency typically ranging from 100ms to 500ms and an accuracy error controlled within ±1%. When the SOC is below 20% in discharge mode or above 80% in charging mode, the system determines that the energy storage is approaching its safe operating boundary. At this point, the core control unit will trigger the responsibility ratio adjustment mechanism. Specifically, the original energy storage responsibility ratio... It will be multiplied by 0.5, that is The remaining frequency regulation responsibility will be borne by thermal power plants according to their respective responsibility ratios. Compensation to maintain total frequency regulation output The balance.

[0071] Specifically, this adjustment mechanism applies to responsibility allocation models under different frequency regulation demand levels. For example, in level two frequency regulation demand, the original responsibility ratio for energy storage is... When the SOC trigger threshold is reached, its liability ratio drops to [a certain percentage]. Thermal power responsibility ratio Corresponding improvement This ensures that the overall system responsiveness does not degrade. Simultaneously, this mechanism follows the recommendations in the IEC 61850-70-10 standard regarding the safe operating range of energy storage SOC, preventing battery life degradation due to over-discharge or over-charge.

[0072] Specifically, this step is applicable to grid frequency regulation scenarios with high renewable energy penetration, especially when photovoltaic output fluctuates significantly and energy storage SOC frequently approaches its limit. Through dynamic responsibility transfer, the system can fully utilize the capacity advantages of thermal power units while ensuring energy storage safety, thereby improving the stability and continuity of frequency regulation.

[0073] Specifically, this step has significant technical effects. On the one hand, it effectively extends the cycle life of the energy storage system (from 5,000 times to 5,500-5,750 times), and on the other hand, it reduces the coal consumption of thermal power units (saving approximately 1,200 tons of standard coal per 600MW unit per year) and improves the economy and reliability of the joint frequency regulation system.

[0074] S32, when the actual output of photovoltaic power deviates from the predicted output by more than 5%, the photovoltaic responsibility ratio γ is set to 0, and the reduced frequency regulation responsibility is transferred to thermal power and energy storage.

[0075] Specifically, this step is a key link in the dynamic responsibility allocation mechanism of the "600MW unit-photovoltaic-energy storage joint frequency regulation control method" of the present invention, which aims to improve the robustness and economy of the frequency regulation system.

[0076] Furthermore, the judgment of photovoltaic power output deviation is based on the real-time acquisition of actual photovoltaic power output. With predicted output The comparison. Specifically, the system uses formulas... Calculate the output force deviation percentage. When At that time, photovoltaic power will no longer bear the responsibility of frequency regulation, that is Its original frequency modulation output The energy was redistributed to thermal power and energy storage to maintain system frequency stability.

[0077] Furthermore, the photovoltaic output deviation threshold is set at 5%, a value determined comprehensively based on the error tolerance range of the photovoltaic output prediction model (typically 3%-7%) and the grid frequency regulation accuracy requirements (±0.03-0.05Hz). Photovoltaic responsibility ratio. The initial value is calculated by the dynamic responsibility allocation model based on the current frequency regulation level (level 1, level 2, level 3), but it is forcibly set to zero when the output deviation exceeds the standard, in order to prevent frequency regulation failure or reverse regulation caused by inaccurate prediction.

[0078] Specifically, this step applies to scenarios where sudden changes in sunlight conditions (such as cloud cover or abrupt weather changes) cause a sharp drop in photovoltaic output. For example, if the actual photovoltaic output is more than 5% lower than the predicted value when afternoon irradiance drops rapidly, the system will automatically disable its frequency regulation responsibility to avoid exacerbating frequency deviation due to insufficient output. At this time, thermal power and energy storage will dynamically adjust their responsibility ratio according to their current operating status (such as SOC and ramp-up rate). and This is to compensate for the frequency regulation gap after the photovoltaic industry is phased out.

[0079] Specifically, the technical effect of this step is to significantly improve the reliability and economy of the frequency regulation system. By promptly shielding unreliable photovoltaic frequency regulation capabilities, the system can avoid frequency response lag or reverse adjustment caused by prediction errors, thereby controlling the frequency deviation within the range of ±0.03-0.05Hz. Simultaneously, reducing malfunctions when photovoltaics participate in frequency regulation helps lower the curtailment rate and improve the utilization rate of new energy sources. Furthermore, this mechanism can reduce equipment wear and increased coal consumption caused by frequent frequency regulation of thermal power plants, achieving optimal responsibility allocation for multi-entity coordinated frequency regulation.

[0080] S4 uses a collaborative control algorithm to control the turbine valve opening of the thermal power unit, the droop coefficient of the photovoltaic inverter, and the charging and discharging power of the energy storage converter, respectively, to achieve closed-loop tracking and dynamic correction of the output of multiple energy targets.

[0081] Specifically, this step is based on the target output of each energy source (ΔP thermal power target, ΔP photovoltaic target, ΔP energy storage target) calculated by the core control unit in the previous step. The execution unit performs coordinated control of the thermal power, photovoltaic and energy storage systems to ensure that the overall system output can respond quickly and accurately to grid frequency deviations, and realize closed-loop control and dynamic correction.

[0082] Furthermore, the thermal power unit adjusts the turbine valve opening through DEH (Digital Electro-hydraulic Control System) and CCS (Coordinated Control System) to control its actual power output Pthermal power to track the target Pthermal power. Since the thermal power unit has a relatively slow response speed (30-60 seconds), its control strategy needs to balance stability and economy. A PID control algorithm is typically used, with the proportional gain Kp set to 0.8-1.5 and the integral time Ti to 0.1-0.5 seconds, to achieve smooth regulation and reduce valve wear.

[0083] Furthermore, the photovoltaic system adjusts its droop coefficient through the inverter to achieve tracking of the ΔP light target. Droop control is a power regulation mechanism based on frequency deviation, and its control gain directly affects the response sensitivity of photovoltaic output. In this invention, the droop coefficient of the photovoltaic inverter is dynamically adjusted according to the real-time frequency deviation and predicted output to ensure frequency regulation support when there is sufficient sunlight, and automatically disconnect when the output is insufficient, avoiding reverse disturbance to the grid frequency.

[0084] Furthermore, the energy storage system adjusts its charging and discharging power through a PCS (energy storage converter) to achieve a rapid response to the ΔP storage target. Since the energy storage system has a millisecond-level response capability (≤10ms), its control strategy employs a closed-loop feedback mechanism to monitor the deviation between the actual P storage and the ΔP storage target in real time. When the deviation exceeds 5%, the core control unit dynamically corrects the responsibility allocation coefficient 'a' to improve the overall system regulation accuracy.

[0085] Specifically, this step requires that the deviation between the actual output and the target output of each energy source be controlled within 12%, that is... This is to ensure the accuracy and stability of the frequency regulation response. Meanwhile, the energy storage SOC must not be lower than 20% during discharge and not higher than 80% during charging, in order to extend its cycle life and avoid the risks of over-discharge or over-charge.

[0086] Specifically, this step is applicable to grid frequency regulation scenarios with high renewable energy penetration, especially in situations with frequent frequency deviations and large fluctuations. It enables rapid response and continuous adjustment through multi-energy coordinated control. For example, when cloud cover causes a sudden drop in photovoltaic output, the system can automatically transfer the frequency regulation responsibility to thermal power and energy storage to ensure frequency stability.

[0087] Specifically, through a closed-loop tracking and dynamic correction mechanism, the response speed and regulation accuracy of the joint frequency regulation system are significantly improved, while the operational economy and reliability of each energy source are optimized. Compared with traditional thermal power plant-based frequency regulation or "thermal power + energy storage" joint frequency regulation schemes, this invention has significant advantages in millisecond-level response, frequency deviation control, reduced coal consumption, and extended energy storage life, demonstrating its innovation and practicality in the field of multi-energy coordinated frequency regulation.

[0088] Furthermore, S4 includes: S41, the target output is corrected by PID + fuzzy control algorithm; where the proportional coefficient Kp = 0.8-1.5 and the integral time Ti = 0.1-0.5s.

[0089] Specifically, the algorithm combines the stability of classical PID control with the adaptability of fuzzy control to nonlinear and uncertain systems, thereby achieving rapid and accurate power output adjustment in complex power grid frequency fluctuation scenarios.

[0090] Specifically, the PID controller is used to adjust the real-time frequency deviation. and its rate of change Generate a preliminary control signal, its proportional coefficient Set as Integration time constant Set as To balance response speed and system stability, the fuzzy control section dynamically adjusts PID parameters based on multi-dimensional input variables such as photovoltaic output prediction error, energy storage SOC status, and thermal power unit ramp rate, using a fuzzy inference mechanism. This is especially important when the energy storage output deviation exceeds a certain threshold. At that time, the fuzzy controller will automatically adjust the energy storage responsibility ratio. This is to avoid frequency regulation failure due to excessive output tracking error.

[0091] Furthermore, the core control unit first collects the deviation between the actual output and the target output of each main body, and constructs an error vector. This information is then input into a PID+fuzzy controller. The fuzzy controller uses triangular membership functions to fuzzify the error, and employs a fuzzy rule base (e.g., "If the error is large and changes rapidly, then increase..."). The system performs inference and outputs the correction values ​​of the PID parameters, thereby dynamically optimizing the control strategy.

[0092] Specifically, in practical applications, this step is suitable for scenarios where the grid frequency experiences high-frequency, small-amplitude fluctuations (such as Level 1 demand) or persistent, large-scale deviations (such as Level 3 demand). By correcting the target output in real time, the system can effectively cope with nonlinear factors such as photovoltaic output fluctuations and energy storage SOC limitations, ensuring the total frequency regulation output. and Deviation control within Within this range, frequency modulation accuracy and system robustness are significantly improved.

[0093] S42, when the energy storage output deviation is >5%, the energy storage responsibility ratio α is dynamically adjusted to compensate for the deviation between the actual output and the target output.

[0094] Specifically, this step is based on a closed-loop control mechanism, combining PID and fuzzy control algorithms to achieve real-time correction and responsibility redistribution of energy storage output.

[0095] Furthermore, energy storage targets output power. The calculation formula is: ; in, To contribute to the total frequency regulation demand corresponding to the current power grid frequency deviation, As for the proportion of energy storage responsibility, This is a sign function used to determine whether the grid frequency is increasing or decreasing, thereby deciding whether the energy storage should be charged or discharged. When the actual output of the energy storage... Contribute to the goal When the deviation exceeds 5%, the condition is met. The system will trigger A dynamic adjustment mechanism for values.

[0096] Furthermore, this adjustment is achieved through a fuzzy control module within the core control unit. The fuzzy controller adjusts the output deviation based on its magnitude and trend. The value is non-linearly corrected, for example, by... The SOC should be gradually decreased or increased from 0.4 to 0.6 to match the current energy storage SOC status and system frequency regulation requirements. Additionally, if the energy storage SOC is below 20% (during discharge) or above 80% (during charging), [further adjustments should be made]. The value will be automatically reduced by 50%, and the excess frequency regulation responsibility will be transferred to the thermal power execution unit to prevent the energy storage system from entering a protective shutdown state.

[0097] Specifically, through dynamic adjustment The system can compensate for energy storage response errors in real time, ensuring high-precision execution of frequency regulation commands, thereby controlling the frequency deviation within the range of ±0.03-0.05Hz. Furthermore, this mechanism effectively improves the operating efficiency and lifespan of the energy storage system, avoids excessive SOC consumption or insufficient response caused by a fixed responsibility ratio, and enhances the system's robustness and adaptability in complex grid environments.

[0098] S5 monitors the status of each module through a signal comparison circuit. When the energy storage PCS fails, it automatically transfers the frequency regulation responsibility to thermal power and photovoltaic power, and keeps the frequency deviation within ±0.07Hz.

[0099] Specifically, this step is a key fault-tolerant mechanism in the entire joint frequency modulation control method, reflecting the dynamic adaptability and high reliability of the system in multi-agent collaborative control.

[0100] Furthermore, the signal comparison circuit determines whether the energy storage PCS is in normal working condition by collecting the difference between its output signal and the preset control signal. When the deviation between the PCS output power and the target power exceeds a set threshold (e.g., 5%) or the system completely loses response, it determines that the system is in a fault state. At this time, the core control unit will automatically trigger the responsibility transfer logic, redistributing the frequency regulation task originally undertaken by the energy storage to the thermal power and photovoltaic systems. Specifically, the thermal power execution subunit adjusts the turbine valve opening through DEH (Digital Electro-hydraulic Regulation System) and CCS (Coordinated Control System) to achieve rapid output response; the photovoltaic execution subunit adjusts the droop coefficient through the inverter to adapt to the new frequency regulation requirements.

[0101] Furthermore, the key parameters involved in this step include: the energy storage SOC threshold (allowed discharge at ≥20%), the photovoltaic output deviation tolerance range (≥P-5%), the thermal power ramp-up rate limit (≤18MW / min), and the frequency deviation control target (≤±0.07Hz). These parameters together constitute the system's response boundary conditions under fault scenarios, ensuring the smoothness and controllability of the responsibility transfer process.

[0102] Specifically, this step is applicable to grid frequency regulation scenarios with a high proportion of new energy access. Especially when the energy storage system cannot operate normally due to equipment aging, communication interruption or overload protection, the system can still maintain frequency stability through the coordinated response of thermal power and photovoltaic power, avoiding the risk of grid instability caused by frequency regulation interruption.

[0103] Specifically, through the automatic responsibility transfer mechanism, the system can still maintain a frequency deviation within ±0.07Hz in the event of an energy storage failure, improving the fault tolerance rate by 60% compared to existing technologies. In addition, this mechanism effectively avoids the problem of a sudden drop in frequency regulation capability caused by the withdrawal of energy storage from frequency regulation, ensuring the stability and safety of the power grid in complex operating environments.

[0104] This invention provides a multi-energy joint frequency regulation control method. By constructing a dynamic responsibility allocation mechanism based on real-time frequency deviation classification and multi-source state perception, and employing a collaborative execution strategy integrating classical control and intelligent regulation, it effectively solves the problems of low frequency regulation accuracy and weak system robustness caused by mismatched response characteristics, insufficient resource utilization, and poor state adaptability in existing technologies. It achieves integrated control throughout the entire process, from real-time grid state perception, intelligent matching of frequency regulation demand, dynamic optimization of responsibility ratios to closed-loop tracking of target output. This significantly improves the response speed, control accuracy, and operational economy of frequency regulation, and enhances the adaptive capability and overall stability of the joint frequency regulation system under complex operating conditions and partial equipment failures. Example 2 To achieve the above invention, embodiments of the present invention also provide another multi-energy joint frequency modulation control method, such as... Figure 2 As shown, it includes: Another multi-energy joint frequency regulation control method according to an embodiment of the present invention. By real-time acquisition of grid frequency deviation, operating parameters of a 600MW thermal power unit, photovoltaic output (real-time value + predicted value), and energy storage SOC status, a control logic of "frequency regulation demand classification - dynamic allocation of responsibility - multi-entity collaborative execution" is constructed: energy storage undertakes high-frequency, small-amplitude frequency regulation response (millisecond level), the 600MW unit undertakes baseload frequency regulation (utilizing capacity advantage), and photovoltaic dynamically supplements the frequency regulation margin according to output prediction. This method is suitable for grid frequency regulation scenarios with high renewable energy penetration.

[0105] In one embodiment of the present invention, the combined frequency modulation control device comprises: Specifically, this device includes a parameter detection module, a core control unit, an execution unit, a storage and prediction module, and a fault diagnosis module. The connection relationships between the modules are shown in the table below: Table 1

[0106] In one embodiment of the present invention, the steps of the joint frequency modulation control method include: Specifically, parameter acquisition and preprocessing: The parameter detection module acquires the grid frequency deviation Δf (Δf = actual frequency - 50Hz), 600MW unit P thermal power, R thermal power, photovoltaic P real output (real-time output), P pre-output (15min predicted output), energy storage SOC, and P storage real output (real-time charging and discharging power) in real time. The storage and prediction module outputs P pre-output, and the fault diagnosis module judges the validity of the data (if invalid, the average value of the previous 5 seconds is used instead).

[0107] Specifically, frequency regulation demand is categorized and judged: The core control unit classifies frequency regulation demand into three levels based on |Δf|, triggering different response strategies: Level 1 demand (|Δf|≤0.03Hz): high frequency, small amplitude fluctuations, energy storage is prioritized (fast response), and photovoltaics supplements according to the deviation between P_actual and P_predicted (if P_actual > P_predicted + 5%, then photovoltaics bears 10%-20% of the frequency regulation responsibility), and thermal power does not participate (to avoid frequent start-stop); Level 2 demand (0.03Hz < |Δf|≤0.1Hz): medium amplitude fluctuations, energy storage bears 40%-60% of the responsibility (when SOC≥30%), 600MW units bear 30%-50% of the responsibility (ramp rate≤18MW / min), and photovoltaics bears 10%-20% of the responsibility (when actual P_actual ≥ P_predicted - 0.03Hz). 5%); Level 3 demand (|Δf|>0.1Hz): fluctuates significantly, with 600MW units bearing 50%-70% responsibility (maximum ramp rate 18MW / min), energy storage bearing 20%-40% responsibility (deep discharge is allowed when SOC≥20%), and photovoltaic bearing 10% responsibility (participating only when P-photon actual ≥ P-photon pre-time).

[0108] Specifically, the dynamic responsibility allocation calculation is as follows: Let the total frequency regulation demand output be ΔPtotal = K × |Δf| (K is the frequency regulation coefficient, set according to grid inertia, typically K = 800-1200 MW / Hz). The output calculation for each main target includes: Energy storage target output ΔPstorage target: ΔPstorage target = ΔPtotal × α × sign (Δf), where α is the energy storage responsibility ratio (Level 1 demand α = 0.8-1.0, Level 2 demand α = 0.4-0.6, Level 3 demand α = 0.2-0.4), and sign (Δf) is the sign function (Δf < 0 for discharging, Δf > 0 for charging); if SOC < 20% (during discharging) or SOC > 80% (during charging), α is reduced by 50%, and the output is transferred to thermal power; 600MW unit target output ΔPthermal power target: ΔPthermal power target = ΔPtotal × β × sign (Δf), β is the thermal power responsibility ratio (primary demand β=0, secondary demand β=0.3-0.5, tertiary demand β=0.5-0.7); if P thermal power + ΔP thermal power target > 600MW×1.05 (5% over rated capacity), then β decreases, and the excess responsibility is transferred to energy storage; photovoltaic target output ΔP photovoltaic target: ΔP photovoltaic target = ΔP total × γ × sign (Δf), γ is the photovoltaic responsibility ratio (γ=1-α-β); if P photovoltaic actual + ΔP photovoltaic target < P photovoltaic pre- - 5% (insufficient output), then γ=0, and the responsibility is transferred to thermal power / energy storage.

[0109] In one embodiment of the present invention, multi-entity collaborative execution and closed-loop control are implemented: the execution unit receives the target output command: the thermal power DEH / CCS system adjusts the turbine valve opening to control P thermal power to track the ΔP thermal power target; the photovoltaic inverter adjusts the droop coefficient to control P photovoltaic to track the ΔP photovoltaic target; the energy storage PCS adjusts the charging and discharging power to control P energy storage to track the ΔP energy storage target; the core control unit compares the "actual output deviation from the target output" using a PID + fuzzy control algorithm and corrects the command in real time (e.g., if the energy storage output deviation is > 5%, adjust the α value) to ensure that |ΔP total actual - ΔP total| ≤ 2%.

[0110] In addition, the embodiments of the present invention will produce the following effects: Specifically, the frequency modulation performance is improved (as shown in Table 2): Table 2

[0111] Specifically, the economic optimizations include: Coal consumption for thermal power: Compared to frequency regulation alone, coal consumption is reduced by 6%-8% due to fewer frequent start-stop cycles and full-load fluctuations (saving approximately 1200 tons of standard coal per 600MW unit per year); Energy storage lifespan: Dynamic SOC control (avoiding deep charge and discharge) extends the energy storage cycle life by 10%-15% (from 5000 cycles to 5500-5750 cycles); Solar curtailment rate: Combined with frequency regulation responsibility allocation based on output forecasts, the curtailment rate is reduced from 8%-10% to 4%-6% (resulting in approximately 480,000 kWh / 100MW of additional solar power generation per year).

[0112] Specifically, the reliability is enhanced: in the event of a failure (such as a failure of the energy storage PCS), the frequency regulation responsibility is automatically transferred to thermal power and photovoltaic power, and the frequency deviation can still be controlled within ±0.07Hz, with no risk of frequency regulation interruption; compared with the existing technology, the fault tolerance rate is improved by 60%.

[0113] Another multi-energy joint frequency regulation control method in this invention constructs a closed-loop control logic of "frequency regulation demand hierarchy - dynamic responsibility allocation - multi-entity collaborative execution" by real-time acquisition of grid frequency deviation, operating parameters of large-capacity thermal power units, real-time and predicted photovoltaic output, and energy storage state of charge. It adopts a multi-level demand division mechanism based on the amplitude of frequency deviation and introduces a dynamic responsibility allocation and automatic transfer strategy that considers the energy storage state of charge and photovoltaic output deviation. This effectively solves the problems of low frequency regulation accuracy and weak system robustness caused by mismatched response characteristics, insufficient resource utilization, and poor state adaptability in existing technologies. It achieves full-process collaborative control from real-time perception of multi-source states, intelligent matching of frequency regulation demands, dynamic optimization of responsibility ratios to closed-loop tracking of target output, significantly improving the response speed, control accuracy, and overall economy of grid frequency regulation, and enhancing the adaptive capability and operational reliability of the joint frequency regulation system under complex operating conditions and partial equipment failures.

[0114] Example 3 To achieve the above invention, such as Figure 3 As shown, this embodiment also provides a multi-energy joint frequency modulation control device 10, which includes: The multi-energy status data acquisition module 100 is used to collect real-time data on grid frequency deviation, operating parameters of a 600MW thermal power unit, real-time and predicted output of photovoltaic power, and SOC status of energy storage.

[0115] The frequency regulation demand level classification and response strategy matching module 200 is used to classify frequency regulation demand levels based on the amplitude of grid frequency deviation, and match response strategies for energy storage, thermal power units and photovoltaics according to the frequency regulation demand level.

[0116] The responsibility allocation ratio dynamic adjustment module 300 is used to dynamically adjust the responsibility allocation ratio according to the real-time operating status of each energy source. When the energy storage SOC is lower than the preset threshold or the deviation between the real-time output and the predicted output of photovoltaic exceeds the allowable range, the frequency regulation responsibility is automatically transferred to other energy sources.

[0117] The collaborative control algorithm execution module 400 is used to control the turbine valve opening of the thermal power unit, the droop coefficient of the photovoltaic inverter, and the charging and discharging power of the energy storage converter through the collaborative control algorithm, so as to realize closed-loop tracking and dynamic correction of the output of multiple energy targets.

[0118] In one embodiment of the present invention, it further includes: a signal comparison circuit monitoring module, used to monitor the status of each module through the signal comparison circuit, and when the energy storage PCS fails, automatically transfer the frequency regulation responsibility to thermal power and photovoltaic power, and keep the frequency deviation controlled within ±0.07Hz.

[0119] This invention provides a multi-energy joint frequency regulation control device that integrates multi-energy status data acquisition, frequency regulation demand level classification and response strategy matching, dynamic adjustment of responsibility allocation ratio, and collaborative control algorithm execution functions through a modular architecture. Based on real-time grid frequency deviation and the operating status of each energy source, the device constructs a closed-loop control link of "sensing-decision-execution-correction," effectively solving the problems of poor frequency regulation coordination, low accuracy, and weak adaptability caused by differences in the response characteristics of multiple entities, insufficient utilization of status information, and rigid control strategies in existing technologies. It achieves fully automated control from synchronous data acquisition, intelligent demand classification, dynamic responsibility optimization to precise multi-target tracking, significantly improving the overall response speed, regulation accuracy, and operational reliability of the joint frequency regulation system, and possessing autonomous fault tolerance and stability maintenance capabilities under abnormal or fault conditions.

[0120] To implement the methods of the above embodiments, the present invention also provides a computer device, such as... Figure 4 As shown, the computer device 600 includes a memory 601 and a processor 602; wherein, the processor 602 reads the executable program code stored in the memory 601 to run a program corresponding to the executable program code, so as to implement the various steps of the multi-energy joint frequency modulation control method described above.

[0121] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a multi-energy joint frequency modulation control method as described in the foregoing embodiments.

[0122] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0123] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A multi-energy joint frequency modulation control method, characterized in that, include: S1 collects real-time data on grid frequency deviation, operating parameters of 600MW thermal power units, real-time and predicted output of photovoltaic power, and SOC status of energy storage. S2, classify frequency regulation demand levels based on the amplitude of grid frequency deviation, and match response strategies for energy storage, thermal power units and photovoltaics according to the frequency regulation demand levels; S3 dynamically adjusts the responsibility allocation ratio according to the real-time operating status of each energy source. When the energy storage SOC is lower than the preset threshold or the deviation between the real-time output and the predicted output of photovoltaic exceeds the allowable range, the frequency regulation responsibility is automatically transferred to other energy sources. S4 uses a collaborative control algorithm to control the turbine valve opening of the thermal power unit, the droop coefficient of the photovoltaic inverter, and the charging and discharging power of the energy storage converter, respectively, to achieve closed-loop tracking and dynamic correction of the output of multiple energy targets.

2. The method as described in claim 1, characterized in that, The real-time acquisition of grid frequency deviation, operating parameters of the 600MW thermal power unit, real-time and predicted output of photovoltaic power, and SOC status of energy storage includes: S11, collects power grid frequency deviation through GPS synchronization frequency detector to ensure data acquisition delay ≤10ms; S12, when the deviation between the real-time output and the predicted output of photovoltaic power exceeds 5%, the average value of the previous 5 seconds is used to replace invalid data.

3. The method as described in claim 1, characterized in that, The method of classifying frequency regulation demand levels based on the amplitude of grid frequency deviation, and matching response strategies for energy storage, thermal power units, and photovoltaics according to the frequency regulation demand levels, includes: S21 divides the frequency deviation into three levels: Level 1 requirement is |Δf|≤0.03Hz, Level 2 requirement is 0.03Hz<|Δf|≤0.1Hz, and Level 3 requirement is |Δf|>0.1Hz; S22, for Tier 1 demand, the energy storage responsibility ratio α = 0.8-1.0; for Tier 2 demand, the energy storage responsibility ratio α = 0.4-0.6; for Tier 2 demand, the energy storage responsibility ratio α = 0.2-0.4; for Tier 3 demand, the energy storage responsibility ratio α = 0.2-0.

4.

4. The method as described in claim 1, characterized in that, The method of dynamically adjusting the responsibility allocation ratio based on the real-time operating status of each energy source, and automatically transferring the frequency regulation responsibility to other energy sources when the energy storage SOC is lower than a preset threshold or the deviation between the real-time output and the predicted output of photovoltaics exceeds the allowable range, includes: S31, when the energy storage SOC < 20% or SOC > 80%, the energy storage responsibility ratio a will be reduced by 50%, and the reduced frequency regulation responsibility will be transferred to thermal power. S32, when the actual output of photovoltaic power deviates from the predicted output by more than 5%, the photovoltaic responsibility ratio γ is set to 0, and the reduced frequency regulation responsibility is transferred to thermal power and energy storage.

5. The method as described in claim 1, characterized in that, The method involves using a collaborative control algorithm to control the turbine valve opening of the thermal power unit, the droop coefficient of the photovoltaic inverter, and the charging and discharging power of the energy storage converter, respectively, to achieve closed-loop tracking and dynamic correction of multi-energy target output, including: S41, the target output is corrected through PID + fuzzy control algorithm; where the proportional coefficient Kp = 0.8-1.5 and the integral time Ti = 0.1-0.5s; S42, when the energy storage output deviation is >5%, the energy storage responsibility ratio α is dynamically adjusted to compensate for the deviation between the actual output and the target output.

6. The method as described in claim 1, characterized in that, Also includes: S5 monitors the status of each module through a signal comparison circuit. When the energy storage PCS fails, it automatically transfers the frequency regulation responsibility to thermal power and photovoltaic power, and keeps the frequency deviation within ±0.07Hz.

7. A multi-energy joint frequency modulation control device, characterized in that, include: The multi-energy status data acquisition module is used to collect real-time data on grid frequency deviation, operating parameters of a 600MW thermal power unit, real-time and predicted output of photovoltaic power, and SOC status of energy storage. The frequency regulation demand level classification and response strategy matching module is used to classify frequency regulation demand levels based on the amplitude of grid frequency deviation, and match response strategies for energy storage, thermal power units and photovoltaics according to the frequency regulation demand level. The responsibility allocation ratio dynamic adjustment module is used to dynamically adjust the responsibility allocation ratio according to the real-time operating status of each energy source. When the energy storage SOC is lower than the preset threshold or the deviation between the real-time output and the predicted output of photovoltaic exceeds the allowable range, the frequency regulation responsibility is automatically transferred to other energy sources. The collaborative control algorithm execution module is used to control the turbine valve opening of the thermal power unit, the droop coefficient of the photovoltaic inverter, and the charging and discharging power of the energy storage converter through the collaborative control algorithm, so as to realize closed-loop tracking and dynamic correction of the output of multiple energy targets.

8. The apparatus as claimed in claim 7, characterized in that, Also includes: The signal comparison circuit monitoring module is used to monitor the status of each module through the signal comparison circuit. When the energy storage PCS fails, it automatically transfers the frequency regulation responsibility to thermal power and photovoltaic power, and keeps the frequency deviation within ±0.07Hz.

9. An electronic device, comprising: processor; The memory stores executable instructions; when the processor executes the instructions, it implements the multi-energy joint frequency modulation control method as described in any one of claims 1-6.

10. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements a multi-energy joint frequency modulation control method as claimed in any one of claims 1-6.

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