AGC load distribution control method and system based on multi-region greedy hydropower station

Abstract

The invention relates to the technical field of power system dispatching automation control, and discloses an AGC load distribution control method and system based on a multi-region greedy hydropower station, and the method comprises the steps: generating dynamic boundary thresholds of a high-efficiency region, a medium-efficiency region and a low-efficiency region based on a real-time performance curve and a dispatching set value of a hydroelectric generating set; judging whether to start a cross-interval distribution strategy or not by utilizing the generated dynamic boundary threshold value; when the number of the units participating in distribution is lower than a set scale, an enumeration algorithm is automatically switched to carry out global optimization, and generation of an optimal solution under a small-scale unit combination is ensured; calculating a deviation value between the actual total load and a target value based on the obtained cross-interval distribution strategy; and preferentially adjusting the output of the high-efficiency area unit through a dynamic compensation mechanism. The system comprises an operation interval dynamic calibration module, a region selection and load pre-distribution module and a deviation compensation and adaptive adjustment module. The power grid frequency adjusting precision and the overall operation efficiency of the hydroelectric system are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of power system dispatch automation control technology, and in particular to a load allocation control method and system based on multi-region greedy algorithm for hydropower stations using Automatic Generation Control (AGC). Specifically, it relates to an intelligent load allocation control method and system for AGC under the joint operation of multiple units in hydropower stations, especially suitable for AGC systems that meet the requirements of high response accuracy and high dynamic adjustment rate of the power grid. It features intelligent division of operating areas, flexible allocation strategies, and optimized adjustment algorithms. Background Technology

[0002] With the deepening of electricity marketization, the response accuracy and load allocation efficiency of automatic generation control systems (AGCs), as crucial execution units for power frequency regulation, have become key indicators for evaluating system performance. Especially in scenarios where hydropower units participate in the spot market and ancillary services market, the core challenge of intelligent upgrading of AGCs lies in how to rationally distribute AGC control commands across multiple hydropower units to ensure overall response speed, regulation depth, and economy. Traditional load allocation methods for AGCs in hydropower plants are mostly based on linear equal-division strategies or proportional allocation methods based on static weights. These methods ignore the differences in operating ranges and dynamic regulation performance of the units, leading to frequent operation of units in non-optimal ranges, resulting in the following shortcomings: low operating efficiency: some units operate in regulation dead zones or inefficient zones, resulting in low efficiency; poor system response: large regulation amplitudes and frequent feedback adjustments reduce system response speed; weak optimization capability: lack of comprehensive consideration of the overall optimal regulation effect for the entire plant, especially in multi-unit combination decisions.

[0003] Prior art, application number CN202510252685.7, discloses an automatic generation control method and system. This automatic generation control method includes the following steps: obtaining operating characteristic data of the generator set; identifying key factors affecting the automatic frequency regulation and load distribution performance of the generator set; using a regional load forecasting model to predict the frequency and load demand of the generator set at future times, obtaining frequency deviation and load change trends; and using AGC closed-loop control logic to monitor frequency deviation and load changes in real time, dynamically optimizing the generator set output distribution. While factor analysis can accurately identify key factors affecting automatic frequency regulation and load distribution performance, reducing reliance on secondary factors and making frequency regulation and load distribution more targeted, thereby ensuring timely fulfillment of load demand and reducing operational risks caused by frequency fluctuations, load distribution relies on AGC closed-loop control logic and lacks a hierarchical optimization strategy based on unit efficiency. This may lead to inefficient units frequently participating in regulation, reducing overall energy efficiency.

[0004] Prior art 2, application number CN202310755315.6, discloses a distributed event-driven fixed-time automatic generation control method and system, including the following steps: obtaining the ACE value estimated by each frequency regulating unit based on the ACE distributed fixed-time mining method; calculating the first component of the adjustment power reference value based on the ACE value and the distributed fixed-time automatic generation control method; and obtaining the second component of the adjustment power reference value based on the distributed fixed-time proportional load sharing control method. This invention, through an AGC method based on fixed-time sliding mode control, enables heterogeneous FRUs to participate in ACE regulation collaboratively through independent controllers, fully utilizing the regulation potential of fast-response units and possessing rapid response capability. Although a distributed fixed-time proportional load sharing method can be used to restore the power reserve of fast-response FRUs in the final stage of frequency regulation, further improving frequency regulation performance, the lack of consideration for dynamic zoning optimization of unit operating efficiency may lead to frequent adjustments by fast-response units, affecting their power reserve recovery and resulting in insufficient long-term operational economy.

[0005] Existing technology three, application number: CN202510250185.X, discloses an automatic generation control (AGC) operation method based on a gradient unidirectional allocation strategy, including steps such as receiving setpoints from the control center, measuring actual power generation, determining the allocation method, executing gradient unidirectional allocation, issuing allocation instructions, and monitoring operating status. The setpoints require verification and format conversion, while the actual power generation is obtained through collecting electrical parameters and calculations. Based on the comparison between the difference between the setpoints and the actual power generation and a preset threshold value, it is determined whether to execute small-load allocation or gradient unidirectional allocation. The gradient unidirectional allocation algorithm includes determining participating generating units, calculating the number of units allocated, calculating the proportional coefficient, and allocating load values. Allocation instructions need to be encoded and transmitted to the generator control system via a dedicated communication line. Operating status monitoring includes collecting operating parameters and determining parameter ranges; if parameters exceed the range, an alarm is triggered and adjustments are made. While this method can improve the response speed and stability of the power system, ensuring the reliability and security of power supply, the allocation method relies on a fixed threshold value and cannot dynamically adjust load allocation based on the real-time efficiency of the generating units, making it difficult to balance adjustment accuracy and economy.

[0006] Current technologies 1, 2, and 3 suffer from insufficient hierarchical optimization of unit efficiency, poor dynamic adaptability, and difficulty in balancing economy and regulation accuracy. This leads to frequent operation of hydropower units in inefficient ranges, affecting grid frequency regulation performance and overall energy efficiency. To address these issues, this invention proposes a hydropower station AGC intelligent allocation method based on a combination of multi-region partitioning and a greedy optimization strategy. This method boasts advantages such as strong real-time performance, high optimization efficiency, and good scalability. By dynamically dividing high-efficiency / medium-efficiency / inefficient zones using boundary thresholds, flexibly switching between greedy algorithms and global optimization, and employing a closed-loop adaptive compensation mechanism, it achieves a balance between high efficiency, dynamic adaptability, and global optimality in load allocation. Summary of the Invention

[0007] The main objective of this invention is to provide a load distribution control method and system for multi-region greedy hydropower stations based on AGC, so as to solve the problems of insufficient hierarchical optimization of unit efficiency, poor dynamic adaptability, and difficulty in balancing economy and regulation accuracy in the existing technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A method for AGC load allocation control of a multi-region greedy hydropower station, comprising: Based on the real-time performance curves and scheduling setpoints of hydropower units, dynamic boundary thresholds for high-efficiency, medium-efficiency, and low-efficiency zones are generated. The generated dynamic boundary threshold is used to determine whether to initiate a cross-regional allocation strategy. If cross-regional allocation is required, a profit-oriented greedy algorithm is used to prioritize the allocation of load to high-efficiency units until their capacity is saturated, and then allocate them to medium-efficiency and low-efficiency units in turn. When the number of units participating in the allocation is less than the set scale, the algorithm is automatically switched to an enumeration algorithm for global optimization. The output pre-allocation scheme includes the target load of each unit and its corresponding region label. Based on the obtained cross-regional allocation strategy, the deviation between the actual total load and the target value is calculated; the output of the high-efficiency zone units is adjusted preferentially through a dynamic compensation mechanism.

[0009] As a further improvement of the present invention, the process of generating dynamic boundary thresholds for the high-efficiency region, the medium-efficiency region, and the low-efficiency region includes the following steps: The core operating parameters of each hydropower unit are collected in real time, and the core operating parameters are combined to form an initial performance vector describing the instantaneous performance state of the hydropower unit. By analyzing the coupling relationship between head and flow in the initial performance vector and combining it with the total load setpoint of the entire station issued by the dispatcher, dynamic calculation is performed; the lower limit of output with the lowest unit water consumption and the highest operational stability under the current head is found, and the lower limit of output is the lower limit threshold of the high efficiency zone. The upper threshold of the medium-efficiency zone is derived from the lower threshold of the high-efficiency zone; combined with the current water flow conditions and the need to avoid the vibration zone, the maximum output limit that the hydropower unit can achieve while maintaining acceptable operating efficiency is calculated, which is the upper threshold of the medium-efficiency zone. The dynamic operating range boundary is synthesized by the lower threshold of the high-efficiency zone and the upper threshold of the medium-efficiency zone. The high-efficiency zone is defined as the range from the lower threshold of the high-efficiency zone to the maximum output of the hydropower unit. The medium-efficiency zone is defined as the range from the minimum output of the hydropower unit to the upper threshold of the medium-efficiency zone. The inefficient zone is the transitional range between the upper threshold of the medium-efficiency zone and the lower threshold of the high-efficiency zone. The boundaries together constitute the dynamic boundary threshold set, so that the division of the high-efficiency zone, medium-efficiency zone and inefficient zone conforms to the predetermined numerical relationship.

[0010] As a further improvement of the present invention, the process of determining whether to initiate a cross-interval allocation strategy includes the following steps: If a cross-regional allocation strategy needs to be initiated, the correspondence between load demand and hydropower unit capacity is established based on the lower limit threshold of the high-efficiency zone and the upper limit threshold of the medium-efficiency zone in the dynamic boundary threshold. By comparing the total load demand with the capacity of each hydropower unit, the minimum number of units that need to operate in the medium-efficiency zone is determined. The benefit value of the adjustment of the operating range for each hydropower unit is evaluated. The benefit value takes into account the difference between the unit's current output status and the center value of the target range, the resource consumption generated during the adjustment process, and the change in efficiency after the adjustment. The units are arranged in order of benefit value from high to low. Hydropower units with higher benefit values ​​are selected first and assigned to the medium-efficiency zone operation group, and the remaining units are naturally assigned to the high-efficiency zone operation group. Based on the determined grouping results of the hydropower units, the load is allocated proportionally to the high-efficiency zone operation group, ensuring that the output of each hydropower unit is not lower than the lower threshold of the high-efficiency zone; the load is allocated equally to the medium-efficiency zone operation group, ensuring that the output of each hydropower unit does not exceed the upper threshold of the medium-efficiency zone; the resulting results include the specific load value of each hydropower unit and the identification information of its operating zone. When the number of hydropower units participating in the allocation is lower than the set scale, the full search mechanism of the enumeration algorithm is automatically activated; all possible grouping methods of hydropower units are examined, the overall adjustment cost and operating efficiency under each grouping method are evaluated, and the grouping scheme with the best overall performance is selected; the generated allocation scheme includes the load value of each hydropower unit and its corresponding operating range identifier.

[0011] As a further improvement of the present invention, the process of evaluating the benefit value of the operating range adjustment for each hydropower unit includes the following steps: Based on the minimum number of generating units and the dynamic boundary thresholds of each section, a benchmark regulation target is established for each hydropower unit; for hydropower units that may enter the intermediate efficiency zone, the intermediate efficiency zone operation center value is taken, and for hydropower units that may remain in the high efficiency zone, the lower limit value of the high efficiency zone is taken. The power adjustment amount between the current output of the hydropower unit and the benchmark adjustment target is measured, reflecting the actual power change required. It is also combined with the historical adjustment performance of the hydropower unit to transform it into an indicator of adjustment difficulty. At the same time, the resource consumption involved in the adjustment process is analyzed. Based on the water consumption characteristics and equipment wear degree of the hydropower unit under the current head conditions, the water resource loss and mechanical loss required for power adjustment are uniformly quantified into resource cost. By comparing the current operating efficiency with the expected efficiency in the target range, the direct gains in hydropower conversion efficiency and the indirect improvements in operational stability are comprehensively evaluated. The evaluation results of the three dimensions of adjustment difficulty index, resource cost quantification value and efficiency improvement are standardized and integrated. The weight ratio of each factor is dynamically allocated according to real-time operational needs to generate a single benefit value that fully reflects the overall effect of the adjustment.

[0012] As a further improvement of the present invention, the process of generating a single benefit value that comprehensively reflects the overall effect of the adjustment includes the following steps: The adjustment difficulty index, resource cost quantification value, and efficiency improvement estimate are mapped to a unified numerical dimension; through the extreme value normalization method, the original data of each dimension are converted into a relative score between zero and one, forming a standardized score sequence that can be compared horizontally. Based on the load urgency, water resource availability, and system stability requirements in real-time operation needs, three sets of dynamic weighting coefficients are generated; The adjustment difficulty score, resource cost score, and efficiency improvement score of the standardized scoring sequence are multiplied by their corresponding dynamic weight coefficients to obtain the weighted scores for each dimension. The three weighted scores are then summed arithmetically to generate a benefit value, which reflects the overall benefit level of the hydropower unit adjustment under the current operating conditions.

[0013] As a further improvement of the present invention, the process of generating three sets of dynamic weight coefficients includes the following steps: A demand feature vector is constructed, which includes three core parameters: the rate of load change, the difference between the current reservoir water storage and the warning water level, and the system frequency deviation value. These parameters respectively represent the quantitative characterization of the load urgency, water resource sufficiency, and system stability requirements. The operational demand feature vector is generated by converting the load change rate through time sensitivity to form a load urgency index; the difference is converted into a water resource abundance index through reservoir capacity ratio conversion; the system frequency deviation value is standardized to generate a stable demand index; the three indices together constitute a demand intensity set, which respectively reflect the urgency of each operational demand. The weight mapping relationship is established to convert the demand intensity set into initial weight coefficients; the load urgency index is mapped to the adjustment difficulty weight through an inverse proportional function, with a lower weight for a higher index; the water resource abundance index is mapped to the resource cost weight through a direct proportional function, with a higher weight for a lower index; the stable demand index is mapped to the efficiency improvement weight through an exponential function, with a higher weight for a higher index; the initial weight coefficients are converted into standardized dynamic weight coefficients through weight normalization, and the sum of the three initial weight coefficients is normalized to satisfy the constraint that the weight sum is one, while maintaining the relative proportional relationship between the weights.

[0014] As a further improvement of the present invention, the process of establishing a weight mapping relationship to convert the demand intensity set into initial weight coefficients includes the following steps: Based on the three indices in the demand intensity set, corresponding weight mapping functions are established respectively; The load emergency index is processed by an inverse proportional function. The inverse proportional function sets a benchmark emergency value, which is calculated based on historical load change data and takes the historical average of the load change rate. When the index exceeds the benchmark emergency value, the adjustment difficulty weight decreases in an inverse proportional relationship as the index increases, reflecting the need to reduce the adjustment difficulty in emergency situations. The water resource abundance index is converted through a proportional relationship function, which sets a critical abundance value. The critical abundance value is determined according to the reservoir operation regulations and is taken as the water level value above the warning water level. When the index is lower than the critical value, the resource cost weight increases proportionally with the decrease of the index, reflecting the necessity of increasing the cost weight when water resources are scarce. The stable demand index is transformed through an exponential growth function, which sets a stability threshold. The stability threshold is determined according to the power system safety operation standards and is taken as a specific percentage of the allowable deviation of the system frequency. When the index exceeds this threshold, the efficiency improvement weight increases exponentially, highlighting the importance of efficiency improvement when the system stability requirements are high. The three index values ​​are input into the corresponding mapping functions to obtain three initial weight values. The initial weight values ​​retain the original characteristics of each demand intensity, while the nonlinear influence of different operational demands on the weight allocation is reflected through function transformation. The initial weight coefficients are converted into standardized dynamic weight coefficients through weight normalization; the total weight value is obtained by summing the three initial weight values, and the normalized dynamic weight coefficients are obtained by dividing each initial weight value by the total weight value.

[0015] As a further improvement of the present invention, the process of obtaining the normalized dynamic weight coefficients includes the following steps: The three initial weight values, representing adjustment difficulty, resource cost, and efficiency improvement respectively, are summed arithmetically to obtain a total weight value. The total weight value serves as a temporary benchmark, reflecting the overall strength of the weight claims of the three dimensions before normalization. Divide each initial weight value by the total weight value to calculate the proportion of each dimension weight in the total weight, that is, solidify the nonlinear influence relationship generated by the mapping function in the form of a proportion; Each initial weight value is replaced by its ratio to the total weight value, generating a new set of weight coefficients, i.e., generating standardized dynamic weight coefficients, as the output of the weight allocation process.

[0016] As a further improvement of the present invention, the process of calculating the deviation between the actual total load and the target value includes the following steps: The actual output values ​​of all units participating in automatic power generation control are collected and compared item by item with the target load values ​​of each unit in the generated pre-allocation scheme. The total actual load is obtained by summing the results. The difference between this total actual load and the total load target value set by the dispatcher is used to obtain the load deviation. Based on the obtained load deviation, the adjustment potential assessment of the high-efficiency zone units is initiated. According to the interval labels in the pre-allocation scheme, all units located in the high-efficiency zone are screened out, and the adjustment margin between the current actual output of each high-efficiency zone unit and the interval boundary to which the upper and lower limits of the high-efficiency zone belong is obtained. Based on the positive or negative direction of the load deviation, the adjustment margin of the high-efficiency zone units is prioritized to absorb the deviation; for positive deviations or insufficient actual load, the increased output is allocated according to the proportion of the remaining space of each high-efficiency zone unit from its upper limit; for negative deviations or excessive actual load, the reduced output is allocated according to the proportion of the remaining space of each high-efficiency zone unit from its lower limit.

[0017] To achieve the above objectives, the present invention also provides the following technical solutions: A multi-region greedy hydropower station AGC load distribution control system is provided, which is applied to the aforementioned multi-region greedy hydropower station AGC load distribution control method. The multi-region greedy hydropower station AGC load distribution control system includes: The dynamic calibration module for the operating interval is used to generate dynamic boundary thresholds for high-efficiency, medium-efficiency, and low-efficiency zones based on the real-time performance curves of the hydropower units and the scheduling setpoints. The dynamic boundary thresholds are updated in real time according to the scheduling objectives and water flow conditions. The output dynamic thresholds are directly used as the triggering conditions for the cross-interval allocation strategy. The regional optimization and load pre-allocation module is used to determine whether to initiate a cross-regional allocation strategy using generated dynamic boundary thresholds. If cross-regional allocation is required, a profit-oriented greedy algorithm is used to prioritize the allocation of load to high-efficiency units until their capacity is saturated, and then allocate the load to medium-efficiency and low-efficiency units in turn. When the number of units participating in the allocation is less than the set scale, the algorithm is automatically switched to an enumeration algorithm for global optimization to ensure the generation of the optimal solution under the small-scale unit combination. The output pre-allocation scheme includes the target load of each unit and its corresponding region label. The deviation compensation and adaptive adjustment module is used to calculate the deviation between the actual total load and the target value based on the obtained cross-interval allocation strategy. It prioritizes adjusting the output of units in the high-efficiency zone through a dynamic compensation mechanism. If the capacity of the high-efficiency zone is insufficient, it gradually expands to the medium-efficiency zone and the low-efficiency zone until the deviation is eliminated. At the same time, based on the number of units currently in automatic generation control and the system power level, it automatically selects whether to re-trigger the dynamic boundary threshold generation or adjust the algorithm allocation strategy type to form a closed-loop adaptive control.

[0018] This invention achieves precise load allocation by dividing the operating range of generating units through dynamic boundary thresholds, uses intelligent switching between greedy and enumeration algorithms to ensure the generation of the global optimal solution under different unit sizes, and dynamically eliminates load deviations through a closed-loop compensation mechanism. Ultimately, it achieves the goal of prioritizing the operation of hydropower units in the high-efficiency zone and coordinating optimization across zones, significantly improving the accuracy of power grid frequency regulation and the overall operating efficiency of the hydropower system. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the steps of an embodiment of the AGC load allocation control method for multi-regional greedy hydropower stations according to the present invention. Figure 2 This is a schematic diagram of the steps for generating dynamic boundary thresholds for high-efficiency, medium-efficiency, and low-efficiency zones in an embodiment of the multi-region greedy hydropower station AGC load allocation control method of the present invention. Figure 3 This is a flowchart illustrating the steps of determining whether to activate the cross-regional allocation strategy in an embodiment of the multi-region greedy hydropower station AGC load allocation control method of the present invention. Figure 4 This is a schematic diagram illustrating the steps of calculating the deviation between the actual total load and the target value in an embodiment of the multi-region greedy hydropower station AGC load allocation control method of the present invention. Figure 5 This is a functional module diagram of an embodiment of the multi-region greedy hydropower station AGC load distribution control system of the present invention; Figure 6 This is a schematic diagram of the structure of an embodiment of the electronic device of the present invention; Figure 7 This is a schematic diagram of the structure of one embodiment of the storage medium of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] The terms "first," "second," and "third" used in this invention are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," or "third" 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. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this invention are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the accompanying drawings). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] like Figure 1 As shown, this embodiment provides an example of a multi-region greedy hydropower station AGC load allocation control method. In this embodiment, the multi-region greedy hydropower station AGC load allocation control method specifically includes the following steps: Step S1: Based on the real-time performance curves of the hydropower units and the scheduling setpoints, generate dynamic boundary thresholds for the high-efficiency zone, medium-efficiency zone, and low-efficiency zone. The dynamic boundary thresholds are updated in real time according to the scheduling objectives and water flow conditions. The output dynamic thresholds are directly used as the triggering conditions for the cross-zone allocation strategy. Step S2: Using the generated dynamic boundary threshold, determine whether to initiate the cross-interval allocation strategy; if cross-interval allocation is required, a profit-oriented greedy algorithm is used to prioritize the allocation of load to high-efficiency units until their capacity is saturated, and then allocate them to medium-efficiency and low-efficiency units in turn; when the number of units participating in the allocation is less than the set scale, the algorithm is automatically switched to enumeration to perform global optimization, ensuring the generation of the optimal solution under the small-scale unit combination; the output pre-allocation scheme includes the target load of each unit and its corresponding interval label; Step S3: Based on the obtained cross-interval allocation strategy, calculate the deviation between the actual total load and the target value; prioritize the adjustment of the output of the high-efficiency zone units through the dynamic compensation mechanism, and if the capacity of the high-efficiency zone is insufficient, gradually expand to the medium-efficiency zone and the low-efficiency zone until the deviation is eliminated; at the same time, according to the number of units currently put into automatic generation control and the system power level, automatically select whether to re-trigger the dynamic boundary threshold generation or adjust the algorithm allocation strategy type to form a closed-loop adaptive control.

[0024] Preferably, this embodiment achieves precise load allocation by dividing the unit operating range through dynamic boundary thresholds, uses intelligent switching between greedy algorithms and enumeration algorithms to ensure the generation of the global optimal solution under different scale unit combinations, and dynamically eliminates load deviations through a closed-loop compensation mechanism, ultimately achieving the goal of prioritizing the operation of hydropower units in the high-efficiency zone and coordinating optimization across ranges, significantly improving the accuracy of grid frequency regulation and the overall operating efficiency of the hydropower system.

[0025] Compared to traditional linear equal distribution or static weighting methods, this embodiment has the following advantages: fast response speed, and the greedy algorithm has a runtime complexity of O(n log n). It is suitable for real-time dispatching of medium and large-sized hydropower plants; it has high regulation accuracy: the target deviation is controlled within <0.1MW, meeting the regulation accuracy requirements of the spot ancillary service market; it improves energy efficiency: the average time of the boosted unit operating in the high-efficiency zone exceeds 30%; it has strong versatility: it is adaptable to different dispatching schemes and different load curves, and can be quickly deployed in various types of hydropower plants; it has traceability: the allocation results are recorded in the AGC log, supporting post-event review and re-dispatch analysis.

[0026] This embodiment can be deployed on power plant control platforms or regional AGC master station systems with industrial protocol communication capabilities such as IEC 60870-5-104, IEC 61850, and Modbus, and has good platform compatibility.

[0027] Furthermore, such as Figure 2 As shown, the process of generating dynamic boundary thresholds for the high-efficiency region, medium-efficiency region, and low-efficiency region in step S1 specifically includes the following steps: Step S11: Collect the core operating parameters of each hydropower unit in real time, including head, flow rate, vibration zone and current output, and combine the core operating parameters to form an initial performance vector describing the instantaneous performance state of the hydropower unit; the initial performance vector defines the theoretical absolute operating boundary of the unit, namely the minimum technical output and the maximum allowable output. Step S12: By analyzing the coupling relationship between head and flow in the initial performance vector and combining it with the total load setpoint of the whole station issued by the dispatcher, dynamic calculation is performed; find the lower limit of output with the lowest unit water consumption and the highest operational stability under the current head, and the lower limit of output is the lower limit threshold of the high efficiency zone. Step S13: Derive the upper limit threshold of the medium-efficiency zone from the lower limit threshold of the high-efficiency zone; the determination of the upper limit threshold of the medium-efficiency zone is based on two principles: first, its value is lower than the lower limit threshold of the high-efficiency zone to form a zone separation; second, it needs to be combined with the current water flow conditions and vibration zone avoidance requirements to calculate the highest output limit that the hydropower unit can achieve while maintaining acceptable operating efficiency, which is the upper limit threshold of the medium-efficiency zone, marking the critical point where the operating efficiency of the hydropower unit enters the non-recommended range; Step S14: The dynamic operating range boundary is synthesized by the lower threshold of the high-efficiency zone and the upper threshold of the medium-efficiency zone; the high-efficiency zone is defined as the range from the lower threshold of the high-efficiency zone to the maximum output of the hydropower unit; the medium-efficiency zone is defined as the range from the minimum output of the hydropower unit to the upper threshold of the medium-efficiency zone; the low-efficiency zone is the transition range between the upper threshold of the medium-efficiency zone and the lower threshold of the high-efficiency zone; the boundaries together constitute the dynamic boundary threshold set, so that the division of the high-efficiency zone, medium-efficiency zone and low-efficiency zone conforms to the predetermined numerical relationship.

[0028] Preferably, the dynamic boundary threshold set in this embodiment serves as a crucial output, directly determining whether the cross-interval load allocation strategy is activated in subsequent steps, and providing a clear numerical basis for partitioning. The entire generation process forms a closed loop. When the scheduling setpoint or water flow conditions change significantly, it will trigger the re-acquisition of the initial performance vector and the update calculation of the dynamic threshold, thereby achieving dynamic coordination between the operating interval division and the load allocation target.

[0029] In this embodiment, the output range of each generator unit participating in automatic power generation control is as follows:

[0030] The division is as follows: Optimal Zone: ; Sub-optimal zone: ; Inefficient Zone (Non-preferred Zone): ; illustrate: This represents the lower threshold of the high-efficiency region, the preferred value; This represents the upper limit threshold of the intermediate-range, the upper limit of the non-recommended interval; the interval boundary. The system is dynamically configured based on actual unit parameters; the core objective of this strategy is to avoid units operating in the inefficient zone and maximize the number and load of units operating in the efficient zone.

[0031] Furthermore, the process of dynamically calculating the total load setpoint issued by the dispatching authority in step S12 specifically includes the following steps: Step S121: Based on the head and flow parameters in the initial performance vector, construct the head-flow coordination relationship; the head-flow coordination relationship reflects the dynamic correspondence between flow and output under head conditions, and determines the optimal flow-output response characteristics of the current unit by real-time monitoring of the head value, forming the potential high-efficiency operating trajectory of the hydropower unit under the current operating conditions; Step S122: Deeply integrate the total load setpoint of the entire station issued by the dispatch with the head-flow coordination relationship to generate a load-head joint mapping relationship; by establishing a two-way matching mechanism between load demand and head conditions, identify all possible operating points that meet the load requirements under a given head, and obtain the unit water consumption index of each possible operating point; screen out the top 20% of operating points with the lowest unit water consumption to form a preliminary high-efficiency operation candidate area; Step S123: Introduce the vibration zone information from the initial performance vector and the stability records from the historical core operating parameters to construct an operating stability spectrum, which characterizes the degree of operating stability under different output levels. Mark the vibration zone and its surrounding area as high-risk zones. Perform overlay analysis on the preliminary high-efficiency operating candidate zone and the operating stability spectrum, eliminate all operating points located in high-risk zones, and apply a preset stability penalty coefficient to operating points near the boundary of the risk zone to obtain a high-efficiency operating candidate zone after stability correction. Step S124: Within the high-efficiency operation candidate zone after stability correction, select the three operating points with the lowest unit water consumption as candidates; obtain the distance between each candidate point and the nearest vibration zone boundary, the closer the distance, the lower the stability score; finally select the operating point with the optimal weighted sum of unit water consumption and stability score, and determine its corresponding output value as the lower limit threshold of the high-efficiency zone.

[0032] Preferably, this embodiment forms a coherent logical chain from the head-flow coordination relationship to the load-head joint mapping, then through the operation stability spectrum screening, and finally outputs the lower limit threshold of the high-efficiency zone through the economic-stability balance decision; the threshold will be used as the input for the subsequent calculation of the upper limit threshold of the medium-efficiency zone, and will ultimately be used to construct the complete dynamic operation range boundary.

[0033] Furthermore, step S13, which calculates the maximum output limit that the hydropower unit can achieve while maintaining acceptable operating efficiency, specifically includes the following steps: Step S131: Establish a high-efficiency operation baseline based on the lower limit threshold of the high-efficiency zone, convert the center frequency and bandwidth of each vibration zone into a vibration risk coefficient, and generate a vibration sensitivity distribution program by obtaining the acceleration change rate and amplitude attenuation characteristics at the boundary of each vibration zone. The entire output range of the hydropower unit is divided into a safe operation zone, a warning zone, and a prohibited operation zone, and a corresponding risk weight value is assigned to each zone. Step S132: Based on the real-time monitoring of the rate of change of flow and the fluctuation data of head, establish a flow stability index. By analyzing the output response characteristics corresponding to the unit flow change, obtain the flow-output coupling coefficient. At the same time, based on the change of the slope of the efficiency curve under the current head value, determine the critical point of efficiency decline and generate a continuous flow fitness curve to reflect the degree of adaptability of the hydropower unit to the current flow conditions under different output levels. Step S133: Normalize the vibration sensitivity distribution and the water flow fitness curve, and generate a preliminary stability fusion index by setting different weighting coefficients; introduce an efficient operation baseline as an upper limit constraint, and at the same time consider the historical data of the current operating status of the hydropower unit to dynamically correct the fusion result and form a stable operation boundary. Step S134: Starting from the high-efficiency operating baseline, search downwards and simultaneously evaluate three constraints at each output point: safe distance from the nearest vibration zone, current flow fitness score, and efficiency retention relative to the lower limit of the high-efficiency zone; by setting multi-objective optimization, find the optimal output value that simultaneously meets the following conditions: vibration risk coefficient is lower than the set threshold, flow fitness score is higher than the qualified line, and efficiency retention reaches an acceptable level; the output value determined after multiple rounds of iterative optimization is the final upper limit threshold of the medium-efficiency zone.

[0034] First, a three-dimensional evaluation space is established, with its coordinate axes corresponding to three key indicators: vibration safety distance, flow fitness score, and efficiency retention. For each output point to be evaluated, scores in the three dimensions are calculated simultaneously: the standardized distance between the point and the nearest vibration zone boundary is obtained through a vibration sensitivity distribution program as the safety distance score; the flow fitness curve is used to obtain the fitness score of the current output point; and the efficiency retention is obtained by taking the ratio of the efficiency of the current output point to the lower limit efficiency of the high-efficiency zone. The optimization process adopts a constraint-driven search strategy. First, a set of candidate points that meet the hard constraint of the vibration risk coefficient is selected. Then, the optimal solution with the largest weighted sum of the flow fitness score and efficiency retention is found in this set. Through successive approximations, the upper limit threshold of the medium-efficiency zone that simultaneously satisfies the three conditions is finally determined.

[0035] Preferably, in this embodiment, a safety boundary is established through vibration sensitivity spectrum analysis, an efficiency boundary is determined through water flow fitness assessment, multiple constraints are integrated through operational stability fusion, and finally, an accurate upper limit threshold of the intermediate efficiency zone is output through three-factor convergence decision-making. Each step is based on the calculation results of the previous step, forming a tightly connected processing chain to ensure that the final output threshold meets both the requirements for safe operation and maintains an acceptable level of operational efficiency.

[0036] Furthermore, such as Figure 3 As shown, the process of determining whether to activate the cross-interval allocation strategy in step S2 specifically includes the following steps: Step S21: If it is necessary to activate the cross-regional allocation strategy, establish the correspondence between load demand and hydropower unit capacity based on the lower limit threshold of the high-efficiency zone and the upper limit threshold of the medium-efficiency zone in the dynamic boundary threshold; determine the minimum number of units that need to operate in the medium-efficiency zone by comparing the total load demand with the capacity of each hydropower unit. Step S22: Evaluate the benefit value of the adjustment of the operating range for each hydropower unit. The benefit value takes into account the difference between the current output state of the unit and the center value of the target range, the resource consumption generated during the adjustment process, and the change in efficiency after the adjustment. Arrange the units in order of benefit value from high to low, and select the hydropower units with higher benefit values ​​to be assigned to the medium-efficiency zone operating group. The remaining units are naturally assigned to the high-efficiency zone operating group. Step S23: Based on the determined grouping results of the hydropower units, the load is allocated to the high-efficiency zone operating group using a proportional sharing method, so that the output of each hydropower unit is not lower than the lower threshold of the high-efficiency zone; the load is allocated to the medium-efficiency zone operating group using an average distribution method, so that the output of each hydropower unit does not exceed the upper threshold of the medium-efficiency zone; the resulting results include the specific load value of each hydropower unit and the identification information of its operating zone. Step S24: When the number of hydropower units participating in the allocation is lower than the set scale, the full search mechanism of the enumeration algorithm is automatically started; all possible grouping methods of hydropower units are examined, the overall adjustment cost and operating efficiency under each grouping method are evaluated, and the grouping scheme with the best overall performance is selected; the generated allocation scheme includes the load value of each hydropower unit and its corresponding operating range identifier.

[0037] Preferably, this embodiment achieves optimized allocation of hydropower load through dynamic interval division and unit benefit assessment. It establishes a matching relationship between load demand and unit capacity, optimizes unit grouping through quantitative adjustment benefits, employs differentiated allocation strategies for units in different efficiency zones to ensure operational constraints, and obtains the optimal solution through global search when the unit scale is small. The overall technical effect is to balance unit operating efficiency and adjustment costs, maximizing the overall energy efficiency of the hydropower system while meeting load demand, and ensuring stable operation of each unit within a safe and efficient range.

[0038] In this embodiment, the strategy determination logic assumes that the automatic generation control load setting value is... The number of units with automatic generator control is N. The partitioning logic is as follows: like:

[0039] If a unit is not in the high-efficiency zone, then the high-efficiency zone load allocation strategy will be adopted.

[0040] Otherwise, a cross-regional load allocation strategy is adopted, where cross-regional refers to the simultaneous operation of some units in the medium-efficiency zone and the high-efficiency zone.

[0041] Cross-interval greedy load allocation strategy Step 1: Calculation of the number of hydropower units in the mid-effect zone: enumerate ,satisfy:

[0042] When this condition is met for the first time, the number of hydropower units in the mid-effect zone is determined as follows: The number of hydropower units in the high-efficiency zone is .

[0043] Step 2: Calculation of target power value: Target value for the intermediate suburbs: , Must meet ; High-efficiency zone target value: , Must meet ; Step 3: Greedily select the hydroelectric generator unit to enter the medium-efficiency zone: For each hydropower unit (Current actual power output is) Calculate its returns:

[0044] The greater the benefit, the greater the overall fluctuation reduction after the hydropower unit is adjusted to the medium-efficiency zone, and the higher the priority should be.

[0045] All hydroelectric generator units Sort by largest to smallest, then select the first... The hydroelectric power units of Taiwan Hydroelectric Power Plant have entered the medium-efficiency zone.

[0046] Step 4: Preliminary load allocation: The allocation value of hydropower units in the mid-effect zone is set as follows: The allocation value for high-efficiency hydropower units is set as follows: ; Preliminary calculation of total output:

[0047] Step 5: Deviation Adjustment Mechanism Let the deviation value be:

[0048] Based on the sign of the deviation: like Prioritize increasing the load on hydropower units in the high-efficiency zone until it approaches [the maximum load]. ; like Prioritize reducing the load on hydropower units in the high-efficiency zone, maintaining a load no lower than [previous level]. ; If the high-efficiency zone cannot be adjusted, the remaining deviation will be evenly adjusted in the medium-efficiency zone of the hydropower unit.

[0049] Special Circumstances Handling 1. If This indicates that all hydropower units can meet the set value in the high-efficiency zone, either by direct equal distribution or adjustment according to the current output. 2. If This indicates that the plant's load is extremely low, and all hydropower units should operate in the medium-efficiency range. 3. When the current power of the hydropower unit is close to the dividing line, a width tolerance is set to prevent oscillation caused by frequent switching of the range; 4. If the set value is much lower than the theoretical minimum threshold, an alarm will be triggered and manual adjustment will be initiated.

[0050] Furthermore, the process of evaluating the benefit value of the operating range adjustment for each hydropower unit in step S22 specifically includes the following steps: Step S221: Based on the minimum number of units and the dynamic boundary threshold of each section, establish a benchmark regulation target for each hydropower unit; for hydropower units that may enter the intermediate efficiency zone, take the intermediate efficiency zone operation center value; for hydropower units that may remain in the high efficiency zone, take their high efficiency zone lower limit value. Step S222: Measure the power adjustment amount between the current output of the hydropower unit and the benchmark adjustment target, reflecting the actual power change required, and convert it into an adjustment difficulty index based on the historical adjustment performance of the hydropower unit; at the same time, analyze the resource consumption involved in the adjustment process, and quantify the water resource loss and mechanical loss required for power adjustment into resource cost based on the water consumption characteristics and equipment wear degree of the hydropower unit under the current head conditions. Step S223: By comparing the current operating efficiency with the expected efficiency in the target range, comprehensively evaluate the direct hydropower conversion efficiency gain and the indirect improvement effect on operational stability; standardize and integrate the evaluation results of the three dimensions of adjustment difficulty index, resource cost quantification value and efficiency improvement, dynamically allocate the weight ratio of each factor according to real-time operation needs, and generate a single benefit value that fully reflects the comprehensive effect of adjustment.

[0051] Preferably, the benefit value evaluation process in this embodiment starts from the benchmark target, and finally forms a quantitative evaluation result through multi-dimensional consideration, which provides a direct basis for the ranking of hydropower units and ensures that the division of intervals not only meets the load demand but also optimizes the overall operating efficiency.

[0052] Furthermore, the process of generating a single benefit value that comprehensively reflects the overall effect of the adjustment in step S223 specifically includes the following steps: Step S2231: Map the adjustment difficulty index, resource cost quantification value, and efficiency improvement estimate onto a unified numerical dimension; through the extreme value normalization method, convert the original data of each dimension into a relative score between zero and one, forming a standardized score sequence that can be compared horizontally. Step S2232: Based on the load urgency, water resource availability, and system stability requirements in real-time operation needs, generate three sets of dynamic weight coefficients; reduce the weight of adjustment difficulty when the load is urgent, increase the weight of resource cost when water resources are scarce, and increase the weight of efficiency improvement when system stability requirements are high, so that the weight allocation closely matches the current operating conditions; Step S2233: Multiply the adjustment difficulty score, resource cost score, and efficiency improvement score of the standardized scoring sequence by their corresponding dynamic weight coefficients to obtain the weighted scores for each dimension; sum the three weighted scores arithmetically to generate a benefit value, which reflects the overall benefit level of the hydropower unit adjustment under the current operating conditions.

[0053] Preferably, this embodiment starts with standardization processing, goes through dynamic weight allocation and weighted fusion calculation, and finally generates a standardized benefit value through range calibration; the benefit value not only retains the original evaluation information of the three dimensions, but also reflects the guiding role of real-time operation requirements, providing a scientific basis for unit ranking.

[0054] Furthermore, the process of generating three sets of dynamic weight coefficients in step S2232 specifically includes the following steps: Step S22321: Construct a demand feature vector, which includes three core parameters: load change rate, the difference between the current reservoir water storage and the warning water level, and the system frequency deviation value, which respectively correspond to the quantitative representation of the load urgency, water resource sufficiency and system stability requirements. Step S22322: The load change rate is converted into a load urgency index through time sensitivity transformation; the difference is converted into a water resource sufficiency index through reservoir capacity ratio conversion; the system frequency deviation value is standardized to generate a stable demand index; the three indices together constitute a demand intensity set, which respectively reflect the urgency of each operational demand. The load change rate is first converted using a time-sensitivity conversion, which takes into account the duration and magnitude of the load change, transforming the original rate value into a load urgency index that reflects the urgency of the situation. The difference between the current reservoir water level and the warning water level is converted using a reservoir capacity ratio, that is, the absolute difference is divided by the total reservoir capacity, to obtain a water resource abundance index that characterizes the relative water sufficiency. The system frequency deviation value is standardized, and the ratio of the current frequency deviation to the maximum allowable frequency deviation is used to generate a stability demand index that characterizes the intensity of the system's stable demand. These three indices quantify the urgency of operational needs from the time, space, and quality dimensions, respectively. Step S22323: Establish a weight mapping relationship to convert the demand intensity set into initial weight coefficients; the load urgency index is mapped to the adjustment difficulty weight through an inverse proportional function, the higher the index, the lower the weight; the water resource abundance index is mapped to the resource cost weight through a direct proportional function, the lower the index, the higher the weight; the stable demand index is mapped to the efficiency improvement weight through an exponential function, the higher the index, the higher the weight; the initial weight coefficients are converted into standardized dynamic weight coefficients through weight normalization, and the sum of the three initial weight coefficients is normalized to satisfy the constraint that the weight sum is one, while maintaining the relative proportional relationship between the weights.

[0055] Preferably, this embodiment starts with the construction of the operational demand feature vector, goes through demand intensity quantification and weight mapping transformation, and finally generates dynamic weight coefficients that meet real-time operational requirements through normalization processing; the weight coefficients not only reflect the actual operating status of the current system, but also maintain mathematical normalization, providing an accurate weight basis for the comprehensive evaluation of benefit values.

[0056] Furthermore, the process of establishing a weight mapping relationship in step S22323 to convert the demand intensity set into initial weight coefficients specifically includes the following steps: Step S223231: Based on the three indices in the demand intensity set, establish corresponding weight mapping functions for each; The load emergency index is processed by an inverse proportional function. The inverse proportional function sets a benchmark emergency value, which is calculated based on historical load change data and takes the historical average of the load change rate. When the index exceeds the benchmark emergency value, the adjustment difficulty weight decreases in an inverse proportional relationship as the index increases, reflecting the need to reduce the adjustment difficulty in emergency situations.

[0057] in: The initial weighting coefficients represent the difficulty dimension of output adjustment; The load urgency index represents the value obtained from the input step. It is a proportionality constant whose value is preset according to the system's sensitivity to the urgency of the load, and is used to adjust the range of weights; This is a bias constant, typically set to 1, designed to prevent the denominator from being zero and to smooth the weight variation curve. The function is a decreasing function. As shown in the relation, when the load urgency index... When the denominator term increases, As it increases, the output value... This reduces the weighting; it achieves the design objective that the higher the index, the lower the weighting, which aligns with the logic that in times of emergency, rapid response should be prioritized over adjustment difficulty. The water resource abundance index is converted through a proportional relationship function, which sets a critical abundance value. The critical abundance value is determined according to the reservoir operation regulations and is taken as a certain proportion of the water level above the warning water level. When the index is lower than the critical value, the resource cost weight increases proportionally with the decrease of the index, reflecting the necessity of increasing the cost weight when water resources are scarce.

[0058] in: The initial weighting coefficients represent the output resource cost dimension; The water resource abundance index represents the input data. It is a proportionality constant whose value is preset according to the preciousness of water resources and is used to adjust the range of weights; This item implements the core logic that the lower the index, the higher the weight, because the water resource abundance index... The lower the value, the more strained the water resources; its complement... The higher the value, the more directly it affects the output weight. Increase; this function achieves the design goal through a simple linear relationship. The more scarce water resources... Small, calculation item The larger the value, the higher the final output resource cost weight. The larger the volume, the more emphasis is placed on water conservation in the allocation strategy; The stable demand index is transformed through an exponential growth function, which sets a stability threshold. The stability threshold is determined according to the power system safety operation standards and is taken as a specific percentage of the allowable deviation of the system frequency. When the index exceeds this threshold, the efficiency improvement weight increases exponentially, highlighting the importance of efficiency improvement when the system stability requirements are high.

[0059] in, The initial weighting coefficients representing the dimension of output efficiency improvement; The stable demand index obtained from the input; This represents the stable threshold defined by the input; Represents the natural exponential function; It is a growth factor constant used to control the steepness of exponential growth, and its value is preset according to the system's requirement for stability. It is a scaling constant used to adjust the overall range of the weights; the growth characteristic of this function is determined by... Item trigger, when the stable demand index Not exceeding the threshold When this item is negative or zero, the weight increases slowly; when Exceeding the threshold When this term is positive, it is used as the natural exponential function. The input makes the output weights The weight increases exponentially with the increase of the excess value; this realizes the design intention that the weight increases exponentially when the exponent exceeds the threshold, ensuring that efficiency improvement is given the highest priority when system stability is threatened. Step S223232: Input the three index values ​​into the corresponding mapping functions to obtain three initial weight values. The initial weight values ​​retain the original characteristics of each demand intensity, and at the same time, the nonlinear influence of different operating demands on the weight allocation is reflected through function transformation. Step S223233: Convert the initial weight coefficients into standardized dynamic weight coefficients through weight normalization; sum the three initial weight values ​​to obtain the total weight value, and divide each initial weight value by the total weight value to obtain the normalized dynamic weight coefficients.

[0060] Preferably, this embodiment starts with a set of demand intensity, generates initial weights through a mapping function transformation, and finally obtains standardized dynamic weight coefficients through normalization. These dynamic weight coefficients not only reflect the intensity characteristics of different operational demands but also meet mathematical normalization requirements, providing an accurate weighting basis for benefit value calculation. The function parameters involved are all derived from actual operational data and system procedure requirements, ensuring the practicality and feasibility of the method.

[0061] Furthermore, the process of obtaining the normalized dynamic weight coefficients in step S223233 specifically includes the following steps: Step S2232331: Summate the three initial weight values ​​representing adjustment difficulty, resource cost and efficiency improvement respectively to obtain a total weight value; the total weight value serves as a temporary benchmark, reflecting the overall strength of the weight claims of the three dimensions before normalization. Step S2232332: Divide each initial weight value by the total weight value to calculate the proportion of each dimension weight in the total weight, that is, solidify the nonlinear influence relationship generated by the mapping function in the form of a proportion; Step S2232333: Each initial weight value is replaced by its ratio to the total weight value, generating a new set of weight coefficients, i.e., generating standardized dynamic weight coefficients, as the output of the weight allocation process.

[0062] Preferably, this embodiment starts from the integration of the initial weight set, analyzes the weight ratio relationship, and finally completes the standardized reconstruction of the weight coefficients, forming a coherent weight normalization process.

[0063] Furthermore, such as Figure 4 As shown, the process of calculating the deviation between the actual total load and the target value in step S3 specifically includes the following steps: Step S31: Collect the actual output values ​​of all units participating in automatic power generation control, compare them item by item with the target load values ​​of each unit in the generated pre-allocation scheme, and sum them up to obtain the total actual load; calculate the difference between this total actual load and the total load target value set by the dispatching department to obtain the load deviation. Step S32: Based on the obtained load deviation, initiate the adjustment potential assessment of the high-efficiency zone units. According to the interval labels in the pre-allocation scheme, select all units located in the high-efficiency zone and obtain the adjustment margin between the current actual output of each high-efficiency zone unit and the interval boundary to which the upper and lower limits of the high-efficiency zone belong. Step S33: Based on the positive or negative direction of the load deviation, prioritize the use of the adjustment margin of the high-efficiency zone units to absorb the deviation; for positive deviations or insufficient actual load, allocate the increased output according to the proportion of the remaining space of each high-efficiency zone unit from its upper limit; for negative deviations or excessive actual load, allocate the reduced output according to the proportion of the remaining space of each high-efficiency zone unit from its lower limit. If the overall regulation capacity of the high-efficiency units is insufficient to completely absorb the load deviation, the interval extension compensation mechanism is activated. This mechanism extends the compensation task to the medium-efficiency and low-efficiency units in sequence according to efficiency priority. The medium-efficiency units first take on the remaining deviation, and their allocation principle is similar to that of the high-efficiency units, but they must comply with the upper limit constraint of the medium-efficiency units. If this is still insufficient, the low-efficiency units will eventually take on the remaining compensation task until the load deviation is completely eliminated.

[0064] Preferably, this embodiment starts with the accurate calculation of load deviation, proceeds through the assessment of the adjustment potential of the high-efficiency zone units, optimizes the allocation of compensation, and finally eliminates the deviation through the interval expansion compensation mechanism when necessary, forming a coherent dynamic compensation process; ensuring that the load distribution system always prioritizes the use of high-efficiency zone resources when dealing with the deviation between actual operation and target value, and maintains the overall operating efficiency of the hydropower station to the maximum extent.

[0065] like Figure 5As shown, this embodiment also provides an embodiment of a multi-region greedy hydropower station AGC load distribution control system. In this embodiment, the multi-region greedy hydropower station AGC load distribution control system is applied to the multi-region greedy hydropower station AGC load distribution control method as described in the above embodiment. The multi-region greedy hydropower station AGC load distribution control system includes an operating range dynamic calibration module 1, a region optimization and load pre-allocation module 2, and a deviation compensation and adaptive adjustment module 3, which are connected in sequence. The system comprises three modules: Module 1 (Dynamic Calibration Module 1) generates dynamic boundary thresholds for high-efficiency, medium-efficiency, and low-efficiency zones based on real-time performance curves of the hydropower units and scheduling setpoints. These dynamic boundary thresholds are updated in real-time according to scheduling objectives and water flow conditions. The output dynamic thresholds directly serve as trigger conditions for the cross-zone allocation strategy. Module 2 (Regional Optimization and Load Pre-allocation Module 2) uses the generated dynamic boundary thresholds to determine whether to initiate the cross-zone allocation strategy. If cross-zone allocation is required, a profit-oriented greedy algorithm prioritizes allocating load to high-efficiency zone units until their capacity is saturated, then sequentially allocating to medium-efficiency and low-efficiency zones. When the number of units participating in the allocation is lower than the set scale, the system automatically... The algorithm automatically switches to an enumeration algorithm for global optimization to ensure the generation of the optimal solution under small-scale unit combinations. The output pre-allocation scheme includes the target load of each unit and its corresponding interval label. The deviation compensation and adaptive adjustment module 3 is used to calculate the deviation between the actual total load and the target value based on the obtained cross-interval allocation strategy. The output of the high-efficiency zone units is adjusted first through the dynamic compensation mechanism. If the capacity of the high-efficiency zone is insufficient, it is gradually extended to the medium-efficiency zone and the low-efficiency zone until the deviation is eliminated. At the same time, based on the number of units currently in automatic generation control and the system power level, it automatically selects whether to re-trigger the dynamic boundary threshold generation or adjust the algorithm allocation strategy type to form a closed-loop adaptive control.

[0066] Preferably, in this embodiment, the unit efficiency range is divided in real time by the operating range dynamic calibration module, the area selection and load pre-allocation module realizes efficient priority load optimization allocation and intelligent algorithm switching, and the deviation compensation and adaptive adjustment module completes closed-loop dynamic adjustment. Ultimately, the goal of optimized operation of hydropower units in different zones, accurate load allocation and rapid elimination of system deviation is achieved, effectively improving the economy and dynamic response performance of power grid AGC control.

[0067] like Figure 6 As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 4 includes a processor 41 and a memory 42 coupled to the processor 41.

[0068] The memory 42 stores program instructions for implementing the multi-region greedy hydropower station AGC load distribution control method of any of the above embodiments.

[0069] The processor 41 is used to execute program instructions stored in the memory 42 to perform AGC load distribution control for multi-region greedy hydropower stations.

[0070] The processor 41 can also be referred to as a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip with signal processing capabilities. The processor 41 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0071] Furthermore, Figure 7 This is a schematic diagram of the structure of a storage medium according to an embodiment of this application. The storage medium 5 of this embodiment stores program instructions 51 capable of implementing all the methods described above. These program instructions 51 can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0072] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0073] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

[0074] The specific embodiments of the invention have been described in detail above, but these are merely examples, and the invention is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this invention. Therefore, all equivalent transformations, modifications, and improvements made without departing from the spirit and principles of this invention should be included within the scope of this invention.

Claims

1. A load distribution control method for multi-region greedy hydropower stations based on AGC, characterized in that, The AGC load allocation control method for multi-region greedy hydropower stations includes: Based on the real-time performance curves and scheduling setpoints of hydropower units, dynamic boundary thresholds for high-efficiency, medium-efficiency, and low-efficiency zones are generated. The generated dynamic boundary threshold is used to determine whether to initiate a cross-regional allocation strategy. If cross-regional allocation is required, a profit-oriented greedy algorithm is used to prioritize the allocation of load to high-efficiency units until their capacity is saturated, and then allocate them to medium-efficiency and low-efficiency units in turn. When the number of units participating in the allocation is less than the set scale, the algorithm is automatically switched to an enumeration algorithm for global optimization. The output pre-allocation scheme includes the target load of each unit and its corresponding region label. Based on the obtained cross-regional allocation strategy, the deviation between the actual total load and the target value is calculated; the output of the high-efficiency zone units is adjusted preferentially through a dynamic compensation mechanism.

2. The AGC load distribution control method for multi-region greedy hydropower stations according to claim 1, characterized in that, The process of generating dynamic boundary thresholds for the high-efficiency, medium-efficiency, and low-efficiency regions includes the following steps: The core operating parameters of each hydropower unit are collected in real time, and the core operating parameters are combined to form an initial performance vector describing the instantaneous performance state of the hydropower unit. By analyzing the coupling relationship between head and flow in the initial performance vector and combining it with the total load setpoint of the entire station issued by the dispatcher, dynamic calculation is performed; the lower limit of output with the lowest unit water consumption and the highest operational stability under the current head is found, and the lower limit of output is the lower limit threshold of the high efficiency zone. The upper threshold of the medium-efficiency zone is derived from the lower threshold of the high-efficiency zone; combined with the current water flow conditions and the need to avoid the vibration zone, the maximum output limit that the hydropower unit can achieve while maintaining acceptable operating efficiency is calculated, which is the upper threshold of the medium-efficiency zone. The dynamic operating range boundary is synthesized by the lower threshold of the high-efficiency zone and the upper threshold of the medium-efficiency zone. The high-efficiency zone is defined as the range from the lower threshold of the high-efficiency zone to the maximum output of the hydropower unit. The medium-efficiency zone is defined as the range from the minimum output of the hydropower unit to the upper threshold of the medium-efficiency zone. The inefficient zone is the transitional range between the upper threshold of the medium-efficiency zone and the lower threshold of the high-efficiency zone. The boundaries together constitute the dynamic boundary threshold set, so that the division of the high-efficiency zone, medium-efficiency zone and inefficient zone conforms to the predetermined numerical relationship.

3. The AGC load distribution control method for multi-region greedy hydropower stations according to claim 1, characterized in that, The process of determining whether to activate the cross-interval allocation strategy includes the following steps: If a cross-regional allocation strategy needs to be initiated, the correspondence between load demand and hydropower unit capacity is established based on the lower limit threshold of the high-efficiency zone and the upper limit threshold of the medium-efficiency zone in the dynamic boundary threshold. By comparing the total load demand with the capacity of each hydropower unit, the minimum number of units that need to operate in the medium-efficiency zone is determined. The benefit value of the adjustment of the operating range for each hydropower unit is evaluated. The benefit value takes into account the difference between the unit's current output status and the center value of the target range, the resource consumption generated during the adjustment process, and the change in efficiency after the adjustment. The units are ranked in descending order of benefit value. Based on the determined grouping of hydropower units, the load is allocated proportionally to the high-efficiency zone operating group, ensuring that the output of each hydropower unit is not lower than the lower threshold of the high-efficiency zone; the load is allocated on an average basis to the medium-efficiency zone operating group; the resulting load includes the specific load value of each hydropower unit and the identification information of its operating zone. When the number of hydropower units participating in the allocation is lower than the set scale, the full search mechanism of the enumeration algorithm is automatically activated; all grouping methods of hydropower units are examined, the overall adjustment cost and operating efficiency under each grouping method are evaluated, and the grouping scheme with the best overall performance is selected; the generated allocation scheme includes the load value of each hydropower unit and its corresponding operating range identifier.

4. The AGC load distribution control method for multi-region greedy hydropower stations according to claim 3, characterized in that, The process of evaluating the benefits of operating range adjustments for each hydropower unit includes the following steps: Based on the minimum number of generating units and the dynamic boundary thresholds of each section, a benchmark regulation target is established for each hydropower unit; for hydropower units that have entered the intermediate efficiency zone, the operating center value of the intermediate efficiency zone is taken, and for hydropower units that remain in the high efficiency zone, the lower limit value of the high efficiency zone is taken. The power adjustment amount between the current output of the hydropower unit and the benchmark adjustment target is measured, reflecting the actual power change required. It is also combined with the historical adjustment performance of the hydropower unit to transform it into an indicator of adjustment difficulty. At the same time, the resource consumption involved in the adjustment process is analyzed. Based on the water consumption characteristics and equipment wear degree of the hydropower unit under the current head conditions, the water resource loss and mechanical loss required for power adjustment are uniformly quantified into resource cost. By comparing the current operating efficiency with the expected efficiency in the target range, the direct gains in hydropower conversion efficiency and the indirect improvements in operational stability are comprehensively evaluated. The evaluation results of the three dimensions of adjustment difficulty index, resource cost quantification value and efficiency improvement are standardized and integrated. The weight ratio of each factor is dynamically allocated according to real-time operational needs to generate a single benefit value that fully reflects the overall effect of the adjustment.

5. The AGC load distribution control method for multi-region greedy hydropower stations according to claim 4, characterized in that, The process of generating a single benefit value that comprehensively reflects the overall effect of the adjustment includes the following steps: The adjustment difficulty index, resource cost quantification value, and efficiency improvement estimate are mapped to a unified numerical dimension; through the extreme value normalization method, the original data of each dimension are converted into a relative score between zero and one, forming a standardized score sequence that can be compared horizontally. Based on the load urgency, water resource availability, and system stability requirements in real-time operation needs, three sets of dynamic weighting coefficients are generated; The adjustment difficulty score, resource cost score, and efficiency improvement score of the standardized scoring sequence are multiplied by their corresponding dynamic weight coefficients to obtain the weighted scores for each dimension. The three weighted scores are then summed arithmetically to generate a benefit value, which reflects the overall benefit level of the hydropower unit adjustment under the current operating conditions.

6. The AGC load distribution control method for multi-region greedy hydropower stations according to claim 5, characterized in that, The process of generating three sets of dynamic weight coefficients includes the following steps: A demand feature vector is constructed, which includes three core parameters: the rate of load change, the difference between the current reservoir water storage and the warning water level, and the system frequency deviation value. These parameters respectively represent the quantitative characterization of the load urgency, water resource sufficiency, and system stability requirements. The operational demand feature vector is generated by converting the load change rate through time sensitivity to form a load urgency index; the difference is converted into a water resource abundance index through reservoir capacity ratio conversion; the system frequency deviation value is standardized to generate a stable demand index; the three indices together constitute a demand intensity set, which respectively reflect the urgency of each operational demand. The weight mapping relationship is established to convert the demand intensity set into initial weight coefficients; the load urgency index is mapped to the adjustment difficulty weight through an inverse proportional function, with a lower weight for a higher index; the water resource abundance index is mapped to the resource cost weight through a direct proportional function, with a higher weight for a lower index; the stable demand index is mapped to the efficiency improvement weight through an exponential function, with a higher weight for a higher index; the initial weight coefficients are converted into standardized dynamic weight coefficients through weight normalization, and the sum of the three initial weight coefficients is normalized to satisfy the constraint that the weight sum is one, while maintaining the relative proportional relationship between the weights.

7. The AGC load distribution control method for multi-region greedy hydropower stations according to claim 6, characterized in that, The process of establishing a weight mapping relationship to convert the set of demand intensity into initial weight coefficients includes the following steps: Based on the three indices in the demand intensity set, corresponding weight mapping functions are established respectively; The load emergency index is processed by an inverse proportional function. The inverse proportional function sets a benchmark emergency value, which is calculated based on historical load change data and takes the historical average of the load change rate. When the index exceeds the benchmark emergency value, the adjustment difficulty weight decreases in an inverse proportional relationship as the index increases, reflecting the need to reduce the adjustment difficulty in emergency situations. The water resources sufficiency index is converted through a proportional relationship function. The proportional relationship function sets a critical sufficiency value, which is determined according to the reservoir operation regulations and is taken as the water level value above the warning water level. When the index is lower than the critical value, the resource cost weight increases proportionally as the index decreases. The stable demand index is transformed through an exponential growth function, which sets a stability threshold. The stability threshold is determined according to the power system safety operation standards and is taken as a specific percentage of the allowable deviation of the system frequency. When the index exceeds this threshold, the efficiency improvement weight increases exponentially, highlighting the importance of efficiency improvement when the system stability requirements are high. The three index values ​​are input into the corresponding mapping functions to obtain three initial weight values. The initial weight values ​​retain the original characteristics of each demand intensity, while the nonlinear influence of different operational demands on the weight allocation is reflected through function transformation. The initial weight coefficients are converted into standardized dynamic weight coefficients through weight normalization; the total weight value is obtained by summing the three initial weight values, and the normalized dynamic weight coefficients are obtained by dividing each initial weight value by the total weight value.

8. The AGC load distribution control method for multi-region greedy hydropower stations according to claim 7, characterized in that, The process of obtaining the normalized dynamic weight coefficients includes the following steps: The three initial weight values, representing adjustment difficulty, resource cost, and efficiency improvement respectively, are summed arithmetically to obtain a total weight value. The total weight value serves as a temporary benchmark, reflecting the overall strength of the weight claims of the three dimensions before normalization. Divide each initial weight value by the total weight value to calculate the proportion of each dimension weight in the total weight, that is, solidify the nonlinear influence relationship generated by the mapping function in the form of a proportion; Each initial weight value is replaced by its ratio to the total weight value, generating a new set of weight coefficients, i.e., generating standardized dynamic weight coefficients, as the output of the weight allocation process.

9. The AGC load distribution control method for multi-region greedy hydropower stations according to claim 1, characterized in that, The process of calculating the deviation between the actual total load and the target value includes the following steps: The actual output values ​​of all units participating in automatic power generation control are collected and compared item by item with the target load values ​​of each unit in the generated pre-allocation scheme. The total actual load is obtained by summing the results. The difference between this total actual load and the total load target value set by the dispatcher is used to obtain the load deviation. Based on the obtained load deviation, the adjustment potential assessment of the high-efficiency zone units is initiated. According to the interval labels in the pre-allocation scheme, all units located in the high-efficiency zone are screened out, and the adjustment margin between the current actual output of each high-efficiency zone unit and the interval boundary to which the upper and lower limits of the high-efficiency zone belong is obtained. Based on the positive or negative direction of the load deviation, the adjustment margin of the high-efficiency zone units is prioritized to absorb the deviation; for positive deviations or insufficient actual load, the increased output is allocated according to the proportion of the remaining space of each high-efficiency zone unit from its upper limit; for negative deviations or excessive actual load, the reduced output is allocated according to the proportion of the remaining space of each high-efficiency zone unit from its lower limit.

10. A multi-region greedy hydropower station AGC load distribution control system, applied to the multi-region greedy hydropower station AGC load distribution control method as described in any one of claims 1 to 9, characterized in that, The multi-region greedy hydropower station AGC load distribution control system includes: The dynamic calibration module for the operating interval is used to generate dynamic boundary thresholds for high-efficiency, medium-efficiency, and low-efficiency zones based on the real-time performance curves of the hydropower units and the scheduling setpoints. The dynamic boundary thresholds are updated in real time according to the scheduling objectives and water flow conditions. The output dynamic thresholds are directly used as the triggering conditions for the cross-interval allocation strategy. The regional optimization and load pre-allocation module is used to determine whether to initiate a cross-regional allocation strategy using the generated dynamic boundary thresholds. If cross-regional allocation is required, a profit-oriented greedy algorithm is used to prioritize the allocation of load to high-efficiency units until their capacity is saturated, and then allocate them to medium-efficiency and low-efficiency units in turn. When the number of units participating in the allocation is less than the set scale, the algorithm is automatically switched to an enumeration algorithm for global optimization. The output pre-allocation scheme includes the target load of each unit and its corresponding region label. The deviation compensation and adaptive adjustment module is used to calculate the deviation between the actual total load and the target value based on the obtained cross-interval allocation strategy. It prioritizes adjusting the output of units in the high-efficiency zone through a dynamic compensation mechanism. If the capacity of the high-efficiency zone is insufficient, it gradually expands to the medium-efficiency zone and the low-efficiency zone until the deviation is eliminated. At the same time, based on the number of units currently in automatic generation control and the system power level, it automatically selects whether to re-trigger the dynamic boundary threshold generation or adjust the algorithm allocation strategy type to form a closed-loop adaptive control.

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