Automatic generation control (AGC) operation method of gradient homodromous distribution strategy

Abstract

The invention discloses an automatic generation control (AGC) operation method based on a gradient equidirectional distribution strategy. The method comprises the steps of receiving a set value of a control center, measuring actual generation power, judging a distribution mode, executing gradient equidirectional distribution, issuing a distribution instruction, monitoring an operation state and the like. The set value needs verification and format conversion, and the actual generated power is obtained by collecting electrical parameters and calculating. And determining to execute small load distribution or gradient equidirectional distribution according to a comparison result of the difference value between the set value and the actual generated power and a preset threshold value. The gradient same-direction distribution algorithm comprises the steps of determining participating units, calculating distribution units, calculating a proportionality coefficient and distributing a load value. The distribution instruction needs to be coded and is transmitted to the generator set control system through a special communication line. The operation state monitoring comprises the steps of collecting operation parameters and judging a parameter range, and triggering an alarm and adjusting if the range is exceeded. According to the invention, the response speed and stability of the power system can be improved, and the reliability and safety of power supply are ensured.

Description

Technical Field

[0001] The present invention relates to the field of automatic control of power systems, and particularly to an automatic generation control (AGC) operation method with a gradient same-direction distribution strategy. Background Art

[0002] The automatic generation control (AGC) system is a key component of modern power systems. It is not only used to adjust the grid frequency, but more importantly, to achieve economic load distribution when controlling the power generation output of each unit. The operation efficiency and security of power systems are the core concerns of the power industry. Intelligent load scheduling strategies use artificial intelligence, big data, and Internet of Things technologies to predict and schedule the load in power systems, aiming to reduce the energy consumption of power systems, improve the reliability and stability of power supply. Data integrity verification and format conversion are important steps to ensure the accuracy of the data received by the AGC system, which is crucial for the stable operation of the system. Real-time monitoring of generator sets and dynamic adjustment of threshold values can make the power system more flexible in responding to changes in grid load, improving the adaptability and stability of the system. The application of data communication encryption algorithms ensures the security and confidentiality of data transmission between the control center and the AGC system, preventing data from being illegally intercepted or tampered with during transmission. The timely triggering of power system anomaly responses and alarm signals, as well as intelligent load distribution scheduling, are crucial for quickly restoring the stable operation of the power system and ensuring the continuity and reliability of power supply.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: Although the prior art has made certain progress in power system load scheduling and monitoring, there is still room for improvement in real-time data processing, load prediction accuracy, and system response speed. In addition, existing encryption algorithms may not fully consider the special requirements of power systems, resulting in potential risks in data transmission security and confidentiality. At the same time, existing load scheduling strategies may not fully utilize advanced data analysis and artificial intelligence technologies, thus unable to achieve optimal load distribution and scheduling. The existence of these problems limits the further improvement of the operation efficiency and security of power systems. Summary of the Invention

[0004] Embodiments of the present invention aim to provide an automatic generation control (AGC) operation method with a gradient same-direction distribution strategy to solve the technical problems proposed in the prior art.

[0005] Embodiments of the present invention solve their technical problems by adopting the following technical solutions:

[0006] Provide an automatic generation control (AGC) operation method with a gradient same-direction distribution strategy, including:

[0007] Step 1: Receive the set value from the control center and obtain the power system set value sent by the control center.

[0008] Step 2: Measure the actual power generation. Measure the actual power generation of the generator set.

[0009] Step 3: Determine the distribution method. Based on the comparison result between the difference between the set value and the actual power generation and the preset small load threshold value, determine to execute small load distribution or gradient same-direction distribution.

[0010] Step 4: Execute gradient same-direction distribution. If gradient same-direction distribution is executed, perform load distribution according to the gradient same-direction distribution algorithm.

[0011] Step 5: Send the distribution instruction. Send the load distribution result to the control system of the generator set in the form of an instruction.

[0012] Step 6: Monitor the operation status. Continuously monitor the operation status of the generator set after adjusting the load to ensure that the requirements of the power system are met.

[0013] Furthermore, the set value in Step 1 needs to undergo data integrity verification and format conversion, and be converted into a data format that can be processed within the AGC system. And the set value includes the active power set value P set and the reactive power set value Q set .

[0014] Furthermore, Step 2 includes:

[0015] Step 2.1: Collect electrical parameters. Collect the voltage value U and current value I at the output end of the generator set through a voltage transformer and a current transformer.

[0016] Step 2.2: Calculate the power factor. Obtain the power factor using a power factor measuring device

[0017] Step 2.3: Calculate the actual power generation. Calculate the actual power generation according to the formula where P real represents the actual power generation.

[0018] Furthermore, Step 3 includes:

[0019] Step 3.1: Calculate the power difference. Calculate the difference ΔP = P set - P real ;

[0020] Step 3.2: Compare with the threshold value. Compare the difference ΔP with the preset small load threshold value T. If ΔP > T, execute gradient same-direction distribution. If ΔP < T, execute small load distribution.

[0021] Further, the gradient same - direction distribution algorithm in step 4 includes the following steps:

[0022] Step 4.1: Determine the participating units. Determine the generating units whose adjustable ranges are not fully utilized as the units participating in the distribution, and set the total number of units participating in the distribution as n;

[0023] Step 4.2: Calculate the allocation times. Calculate the number of units to be allocated represents rounding down;

[0024] Step 4.3: Calculate the adjustable range ratio of each unit. For the i - th unit participating in the distribution, its adjustable range is R i , calculate its proportionality coefficient

[0025] Step 4.4: Calculate the allocated load of each unit. The allocated load value Pi of the i - th unit i,alloc = k i ×ΔP.

[0026] Further, step 4.1 includes:

[0027] Step 4.11: Obtain the real - time load of the units. Real - time obtain the current load value Pi of each generating unit i,current ;

[0028] Step 4.12: Compare with the maximum adjustable load. Compare the current load value of each unit with its maximum adjustable load value P i,max for comparison. If P i,current < Pi i,max , then this unit participates in the distribution.

[0029] Further, step 5 includes:

[0030] Step 5.1: Instruction encoding. Encode the allocated load value of each unit into a control instruction according to a predetermined communication protocol;

[0031] Step 5.2: Transmit the instruction. Transmit the control instruction to the control system of the generating unit through a dedicated communication line, and the communication line needs to meet the requirements of high reliability and low latency.

[0032] Further, step 6 includes:

[0033] Step 6.1: Collect operation parameters. Continuously collect operation parameters such as the output power P output , frequency f, voltage U, etc. of the generating unit;

[0034] Step 6.2: Judge the parameter range. Judge whether the collected parameters are within the preset normal operation parameter range. If they exceed the range, trigger an alarm signal and make adjustments according to the preset strategy.

[0035] Further, it also includes:

[0036] Step 7: Dynamically adjust the threshold value. According to the historical load change data and the current operating state of the power system, dynamically adjust the small load threshold value T. The adjustment formula is T new = T old + a×ΔL, where T new is the new threshold value, T old is the old threshold value, a is the adjustment coefficient, and ΔL is the recent load change amount.

[0037] Further, in Step 8: Remote communication encryption. All data communications between the control center and the AGC system are encrypted using an encryption algorithm to ensure the security and confidentiality of data transmission. The encryption algorithm needs to meet certain encryption strength and key management requirements.

[0038] The above embodiments of the present invention have at least the following beneficial effects: By implementing the method for improving the automatic generation control (AGC) system described in the present invention, the operating efficiency and security of the power system can be improved. Through precise data integrity verification and format conversion, the accuracy and reliability of data transmission between the control center and the AGC system can be ensured, thereby reducing the operating risks of the power system caused by data errors. At the same time, by real-time monitoring the operating state of the generator set and dynamically adjusting the small load threshold value according to the historical load change data, the power system can respond more flexibly to the changes in the grid load, improving the adaptability and stability of the system.

[0039] In addition, by using an encryption algorithm to encrypt all data communications between the control center and the AGC system, the present invention can effectively prevent data from being illegally intercepted or tampered with during transmission, enhancing the security of the system. This method can also ensure that when an abnormality occurs in the power system, an alarm signal can be triggered in a timely manner and adjusted according to the preset strategy, so as to quickly restore the stable operation of the power system and ensure the continuity and reliability of power supply. Description of the Drawings

[0040] One or more embodiments are exemplarily illustrated by the figures in the corresponding drawings. These exemplary illustrations do not limit the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements, unless otherwise stated, and the figures in the drawings do not constitute a proportional limitation.

[0041] Figure 1 It is a schematic flowchart of the automatic generation control AGC operation method with a gradient same-direction distribution strategy provided by an embodiment of the present invention. Detailed Embodiments

[0042] To facilitate the understanding of the present invention, the present invention will be described in more detail below with reference to the accompanying drawings and specific embodiments. It should be noted that when an element is expressed as "connected" to another element, it can be directly on the other element, or there may be one or more intermediate elements therebetween. The terms "upper", "lower", "left", "right", "upper end", "lower end", "top", and "bottom" used in this specification indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for facilitating the description of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0043] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not used to limit the present invention.

[0044] The following combines Figure 1 , and through specific embodiments, the automatic generation control AGC operation method 100 of the gradient co-directional distribution strategy provided by the embodiments of the present application will be described in detail.

[0045] Figure 1 is a schematic flowchart of the automatic generation control AGC operation method of the gradient co-directional distribution strategy provided by the present invention. The automatic generation control AGC operation method of the gradient co-directional distribution strategy provided by one embodiment of the present invention includes:

[0046] Step 1, receiving the set value of the control center and obtaining the set value of the power system issued by the control center;

[0047] Step 2, measuring the actual power generation, and measuring the actual power generation of the generator set;

[0048] Step 3, judging the distribution method, and determining to execute small load distribution or gradient co-directional distribution according to the comparison result of the difference between the set value and the actual power generation and the preset small load threshold value;

[0049] Step 4, executing gradient co-directional distribution. If gradient co-directional distribution is executed, load distribution is performed according to the gradient co-directional distribution algorithm;

[0050] Step 5, issuing a distribution instruction, and issuing the load distribution result to the control system of the generator set in the form of an instruction;

[0051] Step 6, monitoring the operation state, continuously monitoring the operation state of the generator set after adjusting the load to ensure that the requirements of the power system are met.

[0052] It should be noted that this embodiment relates to an operation method of an improved automatic generation control (AGC) system. This method first receives the set values from the control center, that is, the power system set values issued by the control center. These set values include the active power set value and the reactive power set value. Active power refers to the electric energy actually output by the generator set, while reactive power is related to the voltage support of the power system. After receiving these set values, the system needs to perform data integrity verification on them to ensure that the data has not been tampered with during transmission, and convert the data format into a format that can be processed inside the AGC system.

[0053] Specifically, the reception and processing of the set values involve a series of technical parameters and steps. For example, the active power set value and the reactive power set value can be transmitted from the control center to the AGC system through a specific communication protocol. Data integrity verification may involve using techniques such as checksum and digital signature to verify the integrity of the data. Format conversion may involve converting the data from one encoding format to another to ensure that the AGC system can correctly parse and process this data. The reception and processing of these set values are the basis for the operation of the AGC system and provide the necessary information for subsequent load distribution and monitoring.

[0054] Preferably, for the processing of the set values, more advanced data encryption and decryption technologies can be adopted to improve the security of data transmission. For example, the AES (Advanced Encryption Standard) algorithm can be used to encrypt the set values to ensure that only authorized AGC systems can decrypt and use this data.

[0055] Furthermore, format conversion can adopt dynamic data exchange (DDX) or similar technologies to adapt to the data exchange requirements between different control centers and AGC systems. The adoption of these technologies can further improve the reliability and security of the AGC system and ensure the stable operation of the power system.

[0056] In some embodiments, the set values in step 1 need to undergo data integrity verification and format conversion, and be converted into a data format that can be processed inside the AGC system, and the set values include the active power set value P set and the reactive power set value Q set .

[0057] It should be noted that this embodiment details the processing flow of the set values, including data integrity verification and format conversion. Data integrity verification refers to the process of verifying whether the data remains complete during transmission and has not been illegally tampered with. Format conversion refers to converting the data from one format to another to meet the requirements of internal system processing. In the AGC system, these steps are the key links to ensure that the received set values are accurate and can be correctly understood and executed by the system.

[0058] Specifically, the data integrity verification of the set value can be achieved in various ways. For example, a hash function can be used to generate a digest of the data, and then the digest is transmitted together with the set value. The receiving end verifies the data integrity by comparing the digest values. Format conversion may involve converting the data sent by the control center from one encoding format (such as XML or JSON) to the format used internally by the AGC system (such as binary or a specific protocol format). The active power set value and the reactive power set value in the set value need to be organized according to a specific data structure so that the system can parse and process them. The specific settings of these parameters depend on the system design and the specific requirements of the power system.

[0059] Preferably, in order to improve the security and reliability of data transmission, more complex encryption algorithms such as RSA or ECC (Elliptic Curve Cryptography) can be used to encrypt the set value. In this way, even if the data is intercepted during transmission, it cannot be decrypted without the correct key. In terms of format conversion, automated format conversion tools can be used, which can automatically convert the received data into the internal format of the system according to predefined rules.

[0060] Furthermore, a buffering mechanism can be set up to handle possible delays or packet losses during data transmission, ensuring the integrity and accuracy of the data. These measures can further improve the robustness and security of the AGC system.

[0061] In some embodiments, step 2 includes:

[0062] Step 2.1, collect electrical parameters, and collect the voltage value U and current value I at the output end of the generator set through a voltage transformer and a current transformer;

[0063] Step 2.2, calculate the power factor, and obtain the power factor using a power factor measuring device

[0064] Step 2.3, calculate the actual power generation, according to the formula Calculate the actual power generation, where P real represents the actual power generation.

[0065] It should be noted that this embodiment details the process of measuring the actual power generation, including three sub-steps: collecting electrical parameters, calculating the power factor, and calculating the actual power generation. Electrical parameters refer to the voltage value and current value at the output end of the generator set, and these parameters are crucial for calculating the actual power generation. The power factor is a parameter that measures the power quality, and it represents the ratio of the actual power to the apparent power.

[0066] Specifically, the process of collecting electrical parameters involves using voltage transformers and current transformers to obtain the voltage and current values at the output end of the generator set. These devices can convert high voltages and large currents into low voltages and small currents suitable for measurement, ensuring the safety and accuracy of the measurement. The calculation of the power factor requires the use of a power factor measurement device, which can provide real-time power factor values. The formula for calculating the actual power generation is P = U * I * cosφ, where P represents the actual power generation, U represents the voltage value, I represents the current value, and cosφ represents the power factor.

[0067] Preferably, to improve the measurement accuracy and reliability, high-precision sensors and measurement devices can be used. For example, voltage and current transformers with high resolution and fast response time can be selected, as well as devices that can provide high-precision power factor measurements.

[0068] Furthermore, the collected data can be filtered and calibrated through software algorithms to eliminate possible measurement errors. When calculating the actual power generation, temperature compensation and non-linear correction can be considered to adapt to different working environments and conditions. These measures can further improve the performance and adaptability of the system.

[0069] In some embodiments, step 3 includes:

[0070] Step 3.1, calculate the power difference, calculate the difference ΔP between the set value and the actual power generation, ΔP = P set -P real ;

[0071] Step 3.2, compare with the threshold value, compare the difference ΔP with the preset small load threshold value T. If ΔP > T, then perform gradient same-direction distribution. If ΔP < T, then perform small load distribution.

[0072] It should be noted that this embodiment describes the process of determining the distribution method, which includes two key steps: calculating the power difference and comparing with the threshold value. The power difference refers to the difference between the set value and the actual power generation, and the threshold value is a preset standard used to determine whether to perform gradient same-direction distribution or small load distribution. This comparison result will directly affect the subsequent load distribution strategy.

[0073] Specifically, the process of calculating the power difference involves comparing the set value with the actual power generation, that is, P d iff = P s et - P a ctual, where P d iff represents the power difference, P set represents the set value, P actual represents the actual power generation. This difference will be used to determine whether to adjust the load of the generator set. In the step of comparing with the threshold value, the threshold value (P t hreshold) is a preset value used to distinguish different load distribution situations. If the power difference is greater than the threshold value, gradient same-direction distribution is selected; if it is less than or equal to the threshold value, small-load distribution is selected.

[0074] Preferably, in order to make the judgment of the distribution method more flexible and accurate, the concept of an adaptive threshold value can be introduced. This means that the threshold value can be dynamically adjusted according to the real-time load changes and historical data of the power grid, rather than being fixed. For example, an algorithm can be set to automatically adjust the threshold value according to the trend and amplitude of load changes over a certain period of time.

[0075] Furthermore, machine learning algorithms can also be considered to be introduced to predict the optimal threshold value setting by analyzing historical data, so as to optimize the load distribution strategy. These improvements can make the system more intelligent and better adapt to the actual operation of the power grid.

[0076] In some embodiments, the gradient same-direction distribution algorithm in step 4 includes the following steps:

[0077] Step 4.1, determine the participating units. Determine the generator sets whose adjustable ranges are not fully utilized as the participating units for distribution, and let the total number of participating units be n;

[0078] Step 4.2, calculate the number of allocated units, calculate the number of allocated generator set units represents rounding down;

[0079] Step 4.3, calculate the adjustable range ratio of each unit. For the i-th participating unit, its adjustable range is R i , calculate its proportional coefficient

[0080] Step 4.4, calculate the allocated load of each unit. The allocated load value P of the i-th unit i,alloc = k i ×ΔP.

[0081] It should be noted that this embodiment details the specific steps of the gradient same-direction distribution algorithm, which includes determining the participating units, calculating the number of allocated units, calculating the adjustable range ratio of each unit, and calculating the allocated load of each unit. The gradient same-direction distribution algorithm is a method for optimizing the load distribution of the power system, which reasonably distributes the load by considering the adjustable ranges and proportional coefficients of each generator set.

[0082] Specifically, the steps of determining the participating units involve identifying which generating units have not fully utilized their adjustable ranges, and these units will participate in subsequent load distribution. The adjustable range refers to the range of generating power that a unit can adjust without exceeding safety and efficiency limits. In the step of calculating the number of units for distribution, the number of units for distribution refers to the total number of units participating in the distribution, and this number will affect subsequent load distribution calculations. The step of calculating the ratio of the adjustable ranges of each unit involves calculating a ratio coefficient for each unit participating in the distribution based on the sum of its adjustable range and the adjustable ranges of other units, and this ratio coefficient will be used to determine the load amount to be allocated to each unit. The step of calculating the allocated load for each unit is to determine the specific allocated load value for each unit based on the ratio coefficient and the total load adjustment amount.

[0083] Preferably, to improve the efficiency and accuracy of the gradient co-directional distribution algorithm, a more refined unit status monitoring and prediction model can be introduced. For example, the operating parameters of each unit, such as temperature, pressure, and fuel consumption rate, can be monitored in real time to more accurately determine its adjustable range.

[0084] Furthermore, optimization algorithms, such as linear programming or dynamic programming, can be used to optimize the calculation of the number of units for distribution and the ratio coefficient, ensuring that the load distribution is both fair and efficient. The response time and historical performance of the units can also be considered to predict their response capabilities in future load changes, thus making more reasonable decisions when allocating loads. These refinements and alternative solutions can make the gradient co-directional distribution algorithm more adaptable to the complex and changing power grid operation environment.

[0085] In some embodiments, step 4.1 includes:

[0086] Step 4.11, obtaining the real-time load of the units, and obtaining the current load value P of each generating unit in real time i,current ;

[0087] Step 4.12, comparing with the maximum adjustable load, comparing the current load value of each unit with its maximum adjustable load value P o,max for comparison. If P i,current < P o,max , then this unit participates in the distribution.

[0088] It should be noted that this embodiment details the specific steps of determining the units participating in the gradient co-directional distribution, including two key links: obtaining the real-time load of the units and comparing with the maximum adjustable load. The real-time load refers to the load amount actually borne by the generating unit currently, and the maximum adjustable load refers to the maximum load that the unit can adjust without affecting the safety and efficiency of the unit. The comparison result of these two parameters will determine which units will participate in the load distribution.

[0089] Specifically, the step of obtaining the real-time load of the unit involves real-time monitoring and collecting the current load values of each generating unit. This is usually achieved through sensors and data acquisition systems connected to the unit, which can provide real-time operation data of the unit.

[0090] More specifically, the step of comparing with the maximum adjustable load is to compare the real-time load of each unit with its maximum adjustable load to determine whether the unit still has remaining adjustment capacity. If the real-time load of the unit is less than its maximum adjustable load, it is considered that the unit has the potential to participate in the distribution.

[0091] Preferably, in order to more accurately determine the units participating in the distribution, advanced data analysis and prediction technologies can be introduced. For example, historical operation data and machine learning algorithms can be used to predict the load change trend of the unit, so as to more accurately evaluate the adjustable range of the unit.

[0092] Furthermore, the maintenance plan and expected operating status of the unit can also be considered to ensure that the long-term health and efficiency of the unit will not be affected during load distribution. These refinements and alternative solutions can make the unit load distribution more accurate and improve the operating efficiency and reliability of the power system.

[0093] In some embodiments, step 5 includes:

[0094] Step 5.1, instruction encoding, encoding the allocated load values of each unit into control instructions according to a predetermined communication protocol;

[0095] Step 5.2, transmitting the instruction, transmitting the control instruction to the control system of the generating unit through a dedicated communication line, and the communication line needs to meet the requirements of high reliability and low latency.

[0096] It should be noted that this embodiment involves the process of issuing the load distribution result to the control system of the generating unit in the form of an instruction, including two main steps: instruction encoding and transmitting the instruction. Instruction encoding refers to converting the allocated load value into a control instruction that can be recognized and executed by the control system of the generating unit. Transmitting the instruction refers to sending these control instructions to the generating unit through a communication line.

[0097] Specifically, the instruction encoding step needs to encode the allocated load value of each unit according to a predetermined communication protocol. The communication protocol defines the format and rules of data transmission to ensure that information can be correctly parsed and executed in the control system. For example, industrial communication protocols such as MODBUS or DNP3 can be used, and these protocols are widely used for communication between devices in the power system.

[0098] More specifically, during the encoding process, the allocated load value is converted into binary code or a specific data packet format for transmission over a communication line. The transmission instruction step involves sending the encoded control instruction to the control system of the generator set via a dedicated communication line. These communication lines need to meet the requirements of high reliability and low latency to ensure that the control instruction can reach the target unit in a timely and accurate manner.

[0099] Preferably, to improve the security and efficiency of instruction transmission, encryption technology and compression algorithms can be used to process the control instruction. Encryption technology can ensure that the data during transmission is not intercepted or tampered with by unauthorized third parties, while the compression algorithm can reduce the size of the transmitted data and improve the transmission speed.

[0100] Furthermore, a redundant transmission mechanism can be introduced, that is, the same control instruction is sent through multiple communication paths to improve the transmission reliability. In terms of instruction encoding, more advanced encoding algorithms such as CRC check can be considered to ensure the integrity and correctness of the transmitted data. These refinement and alternative solutions can further enhance the stability and security of the system.

[0101] In some embodiments, step 6 includes:

[0102] Step 6.1, collect operating parameters, continuously collect operating parameters such as the output power P output , frequency f, voltage U, etc. of the generator set;

[0103] Step 6.2, judge the parameter range, judge whether the collected parameters are within the preset normal operating parameter range. If they exceed the range, an alarm signal is triggered and adjusted according to the preset strategy.

[0104] It should be noted that this embodiment describes the process of monitoring the operating state of the generator set, including two key links: collecting operating parameters and judging the parameter range. Operating parameters refer to the key performance indicators of the generator set during operation, such as output power, frequency, and voltage. These parameters are crucial for ensuring the stability and safety of the power system.

[0105] Specifically, the step of collecting operating parameters involves continuously collecting data such as the output power, frequency, and voltage of the generator set. These data can be obtained in real time through sensors and monitoring systems connected to the generator set. For example, the output power can be calculated by measuring the voltage and current at the output end of the unit, the frequency can be monitored by a frequency measuring device, and the voltage can be directly measured by a voltage sensor. The step of judging the parameter range is to compare the collected parameters with the preset normal operating parameter range to determine whether the unit is in a normal operating state. These preset ranges are usually determined based on the design parameters and historical operating data of the unit.

[0106] Preferably, in order to improve the accuracy and response speed of monitoring, advanced data analysis techniques can be introduced, such as real-time data stream processing and predictive analysis. Real-time data stream processing can quickly analyze and respond to a large amount of operating parameter data, while predictive analysis can predict potential anomalies based on historical data and give early warnings.

[0107] Furthermore, a multi-level alarm system can be set up to trigger different levels of alarms according to the degree of parameter deviation from the normal range, so as to take corresponding countermeasures. These refined and alternative solutions can make the operation status monitoring more efficient and reliable.

[0108] In some embodiments, it further includes:

[0109] Step 7, dynamically adjust the threshold value. According to the historical load change data and the current operating state of the power system, dynamically adjust the small load threshold value T, and the adjustment formula is T new = T old + a × ΔL, where T new is the new threshold value, T old is the old threshold value, a is the adjustment coefficient, and ΔL is the recent load change amount.

[0110] It should be noted that this embodiment relates to a method for dynamically adjusting the small load threshold value to adapt to the historical load change data and the current operating state of the power system. The small load threshold value is a key parameter used to determine when to execute the small load distribution strategy. Adjusting this value can make the system more flexible to adapt to the actual needs of the power grid.

[0111] Specifically, the process of dynamically adjusting the threshold value includes calculating the new threshold value, which involves three parameters: the old threshold value, the adjustment coefficient, and the recent load change amount. The old threshold value is the previously set reference value for judging whether to execute the small load distribution. The adjustment coefficient is a proportional parameter used to adjust the size of the threshold value according to the load change amount. The recent load change amount refers to the load change situation of the power system within a certain time window, and this quantity can reflect the dynamic characteristics of the power grid load. The new threshold value is calculated by the formula new threshold value = old threshold value + adjustment coefficient × recent load change amount.

[0112] Preferably, in order to make the adjustment of the threshold value more accurate and adaptable, an adaptive control algorithm can be introduced. For example, a method based on model predictive control (MPC) can be used to dynamically adjust the threshold value according to the historical data and prediction model of the power grid load.

[0113] Furthermore, the peak-valley load characteristics and seasonal variations of the power grid can also be considered, and different adjustment coefficients can be set to adapt to these changes. An expert system or artificial intelligence algorithm can also be introduced to provide more intelligent adjustment strategies according to the complexity of the power grid operation. These refinements and alternative solutions can make the adjustment of the threshold value more flexible and precise, improving the response ability and stability of the power system.

[0114] In some embodiments, in step 8, remote communication encryption, all data communications between the control center and the AGC system are encrypted using an encryption algorithm to ensure the security and confidentiality of data transmission. The encryption algorithm needs to meet certain encryption strength and key management requirements.

[0115] It should be noted that this embodiment describes the data communication encryption process between the control center and the Automatic Generation Control (AGC) system. Data communication encryption refers to encrypting the transmitted data using a specific algorithm to ensure the security and confidentiality of data transmission. Encryption strength and key management are two key concepts in the encryption process, referring to the security level of the encryption algorithm and the management method of the encryption key, respectively.

[0116] Specifically, all data communications between the control center and the AGC system are encrypted using an encryption algorithm. This includes the issuance of set values, the reporting of actual power generation, and the transmission of load distribution instructions, etc. The selection of the encryption algorithm should meet certain encryption strength requirements, that is, it can resist currently known cryptographic attack means. At the same time, key management requirements involve the generation, storage, distribution, and update of keys to ensure the security and effectiveness of the keys. Specific parameter settings include selecting appropriate encryption algorithms (such as AES, RSA, etc.), key lengths (such as 128 bits, 256 bits, etc.), and key update periods, etc. The setting of these parameters needs to be determined according to the security requirements and performance requirements of the system.

[0117] Preferably, in order to further improve the security of data communication, a multi-level encryption strategy can be adopted. For example, symmetric encryption and asymmetric encryption algorithms can be used simultaneously to utilize their respective advantages. Symmetric encryption (such as AES) is usually used to encrypt a large amount of data, while asymmetric encryption (such as RSA) is used to encrypt a small amount of data, such as key exchange.

[0118] Furthermore, a digital signature mechanism can also be introduced to ensure the integrity of data and the authenticity of the source. Digital signatures can be achieved by encrypting the hash value of the data using an asymmetric encryption algorithm. These refinements and alternative solutions can provide more comprehensive protection for the communication security of the power system.

[0119] The above embodiments of the present invention have the following beneficial effects: Implementing the method for improving the automatic generation control (AGC) system of the present invention can optimize the load distribution and operation monitoring of the power system, and improve the accuracy and response speed of power dispatching. By receiving the set value of the control center, measuring the actual power generation, intelligently judging the distribution method, executing the gradient same-direction distribution algorithm, issuing distribution instructions, and monitoring the operation status, etc., it can ensure that the power system can quickly adjust when facing load changes, and maintain the stability and reliability of the power grid. In addition, the method also includes measures such as data integrity verification, format conversion, dynamic adjustment of threshold values, and remote communication encryption, which can further enhance the security of the system and the confidentiality of data transmission.

[0120] In addition, the method for improving the AGC system of the present invention can reduce energy waste and improve power generation efficiency. By accurately measuring and calculating the actual power generation, and making intelligent distribution according to the difference between the set value and the actual power generation, the load of the generator sets can be more reasonably dispatched, and the waste of power generation resources caused by uneven load distribution can be reduced. At the same time, the continuous monitoring and dynamic adjustment capabilities of the system can make the power system more adaptable to the actual operation status of the power grid, and respond to the changes of the power grid load in a timely manner, thereby improving the overall power generation efficiency and the economy of the power grid operation.

[0121] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods. Among them, the program instructions can be stored in the above storage medium in the form of a software product, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. And the foregoing storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, or terminal devices such as computers, servers, mobile phones, and tablets.

[0122] Finally, it should be noted that: The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; under the idea of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and the steps can be implemented in any order, and there are many other changes in different aspects of the present invention as above. For the sake of simplicity, they are not provided in detail; Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: They can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An automatic generation control (AGC) operation method with a gradient co-directional distribution strategy, characterized in that It includes the following steps: Step 1: Receive the set value from the control center and obtain the set value of the power system sent by the control center; Step 2: Measure the actual power generation. Measure the actual power generation of the generator set; Step 3: Determine the distribution method. According to the comparison result between the difference between the set value and the actual power generation and the preset small load threshold value, determine to execute small load distribution or gradient same-direction distribution; Step 4: If gradient same-direction distribution is executed, perform load distribution according to the gradient same-direction distribution algorithm; Step 5: Send the distribution instruction. Send the load distribution result to the control system of the generator set in the form of an instruction; Step 6: Monitor the operation status. Continuously monitor the operation status of the generator set after adjusting the load to ensure that the requirements of the power system are met.

2. The operating method according to claim 1, characterized in that The set value in Step 1 needs to undergo data integrity verification and format conversion to be converted into a data format that can be processed within the AGC system, and the set value includes the active power set value P set and the reactive power set value Q set .

3. The operating method according to claim 2, characterized in that, Step 2 includes: Step 2.1: Collect electrical parameters. Collect the voltage value U and current value I at the output end of the generator set through a voltage transformer and a current transformer; Step 2.2, calculate the power factor, and obtain the power factor by using a power factor measuring device Step 2.3, calculate the actual power generation, according to the formula Calculate the actual power generation, where P real represents the actual power generation.

4. The operating method according to claim 3, characterized in that Step 3 includes: Step 3.1, calculate the power difference, calculate the difference ΔP between the set value and the actual power generation power, where ΔP = P set - P real ; Step 3.2: Compare with the threshold value. Compare the difference ΔP with the preset small load threshold value T. If ΔP>T, execute gradient same-direction distribution. If ΔP<T, execute small load distribution.

5. The operating method according to claim 4, characterized in that, The gradient same-direction distribution algorithm in Step 4 includes the following steps: Step 4.1: Determine the participating units. Determine the generator sets whose adjustable range is not fully utilized as the participating units for distribution. Let the total number of participating units for distribution be n; Step 4.2: Calculate the distribution times. Calculate the distribution unit times. The formula is: where N is the number of allocated unit shifts, indicating rounding down; Step 4.3, calculate the adjustable range ratio of each unit. For the i-th unit participating in the allocation, its adjustable range is R i , calculate its proportionality coefficient. The formula is: Step 4.4: Calculate the load distributed to each unit. The load value distributed to the i-th unit is: P i,alloc = k i × ΔP.

6. The operating method according to claim 5, wherein Step 4.1 includes: Step 4.11, obtain the real-time load of the unit and obtain the current load value P of each generating unit in real time i,current ; Step 4.12, compare the maximum adjustable load, and compare the current load value of each unit with its maximum adjustable load value P i,max for comparison. If P i,current < P i,max , then this unit participates in the distribution.

7. The operating method according to claim 6, characterized in that, Step 5 includes: Step 5.1: Instruction encoding. Encode the load value distributed to each unit into a control instruction according to a predetermined communication protocol; Step 5.2: Transmit the instruction. Transmit the control instruction to the control system of the generator set through a dedicated communication line. The communication line needs to meet the requirements of high reliability and low latency.

8. The operating method according to claim 7, characterized in that Step 6 includes: Step 6.1, collect operating parameters, continuously collect operating parameters such as the output power P, output frequency f, and voltage U of the generator set; Step 6.2: Judge the parameter range. Judge whether the collected parameters are within the preset normal operation parameter range. If it exceeds the range, trigger an alarm signal and make adjustments according to the preset strategy.

9. The operating method according to claim 8, characterized in that It also includes: Step 7: Dynamically adjust the threshold value. Dynamically adjust the small load threshold value T according to the historical load change data and the current operation status of the power system. The adjustment formula is: T new = T old + a × ΔL, where T new is the new threshold value, T old is the old threshold value, a is the adjustment coefficient, and ΔL is the recent load change amount.

10. The operation method according to claim 9, characterized in that: Step 8: Remote communication encryption. All data communications between the control center and the AGC system are encrypted using an encryption algorithm to ensure the security and confidentiality of data transmission. The encryption algorithm needs to meet certain encryption strength and key management requirements.

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