A power grid unit intelligent scheduling method, server, medium and product

Abstract

A power grid unit intelligent scheduling method, server, medium and product, relate to the field of data processing for power grid operation supervision and management. According to the application, the server calculates the equipment health degree of each unit, and distributes the load according to the equipment health degree, avoiding the long-term overload operation of some units and accelerating the aging. When the actual load value exceeds the predicted load value, the main regulating unit with the highest equipment health degree is preferred to bear the additional load to ensure safety. If the main regulating unit capacity is insufficient, the remaining load shortage is allocated according to the equipment health degree of other units, realizing the dynamic optimization of load distribution. This method can effectively balance the power grid load distribution and equipment health, ensuring the power supply reliability of the power grid, prolonging the equipment life, reducing the failure risk, and improving the economy and safety of the power grid operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing for power grid operation supervision and management, and particularly relates to a power grid unit intelligent scheduling method, a server, a medium and a product. BACKGROUND

[0002] With the continuous improvement of people's living standards, the demand for electricity is showing a rapid growth trend, and the complexity of power grid operation continues to increase. In order to ensure the safe and stable operation of the power grid and meet the power demand of users, it is necessary to reasonably schedule each generator unit in the power grid.

[0003] At present, the power grid unit scheduling method is to obtain the total system load value, and then distribute the total system load value to different units according to the rated capacity proportion of each unit. In the actual operation process, the power grid unit scheduling system collects load change information and adjusts the output of each unit accordingly.

[0004] However, since the actual operating state of the unit is not considered, some units may bear a large load for a long time, which may cause the equipment of these units to wear out and the risk of equipment failure to increase, thereby affecting the safe and stable operation of the power grid. SUMMARY

[0005] The present application provides a power grid unit intelligent scheduling method, a server, a medium and a product, which can effectively balance the power grid load distribution and equipment health.

[0006] In a first aspect, the application provides a method for intelligent scheduling of power grid units, applied to a server, which comprises: calculating the equipment health degree of each unit based on a unit characteristic database, the unit characteristic database comprising basic equipment parameters, operating state data and performance constraint conditions of each unit; determining a unit group load distribution scheme within a preset time period in the future according to the equipment health degree and a predicted load curve of the power grid, the predicted load curve of the power grid comprising predicted load values at each time point, and the unit group load distribution scheme comprising load bearing values of each unit corresponding to each time point respectively, the equipment health degree being greater, the load bearing value being greater; calculating the difference between an actual load curve of the power grid and the predicted load curve of the power grid to obtain a load shortage at each time point, the actual load curve of the power grid comprising actual load values at each time point; taking the unit with the highest equipment health degree as a main regulating unit, and calculating an adjustable load space of the main regulating unit at each time point, the adjustable load space being used to represent the difference between the maximum allowable load value and the load bearing value of the main regulating unit; if the adjustable load space is greater than or equal to the load shortage, determining the sum of the main regulating load bearing value and the load shortage as the target load value of the main regulating unit, the main regulating load bearing value being used to represent the load bearing value corresponding to the main regulating unit; if the adjustable load space is less than the load shortage, calculating a remaining load shortage according to the load shortage and the maximum allowable load value, determining the maximum allowable load value as the target load value of the main regulating unit, and distributing the remaining load shortage to other units according to the equipment health degree.

[0007] By adopting the above technical solution, the server calculates the equipment health degree of each unit, and distributes the load according to the equipment health degree, thereby avoiding the acceleration of aging caused by the long-term overloading operation of some units. When the actual load value exceeds the predicted load value, the main regulating unit with the highest equipment health degree is preferred to bear the additional load to ensure safety, and if the capacity of the main regulating unit is insufficient, the remaining load shortage is distributed to other units according to the equipment health degree, thereby realizing the dynamic optimization of load distribution. This method can effectively balance the load distribution and equipment health of the power grid, thereby ensuring the power supply reliability of the power grid, prolonging the service life of the equipment, reducing the risk of failure, and improving the economy and safety of the power grid operation.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the unit group load distribution scheme within the preset time period in the future is determined according to the equipment health degree and the predicted load curve of the power grid, specifically comprising: dividing the equipment health degree of each unit by the sum of the equipment health degrees of all units to obtain the operating weight corresponding to each unit respectively, the equipment health degree being greater, the operating weight being greater; multiplying the predicted load value at each time point by the operating weight of each unit to obtain the load bearing value corresponding to each unit at each time point respectively.

[0009] By adopting the technical solution, the server converts the equipment health degree into a running weight to allocate the load, so as to establish a scientific and reasonable load allocation mechanism. The unit group with a higher equipment health degree obtains a greater running weight, thereby bearing more load, so that the load allocation is more accurate and flexible, and the load bearing value of each unit strictly corresponds to the actual health condition of the unit. This method avoids the imbalance of equipment wear caused by simple equal division or allocation according to the rated capacity.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the unit group load allocation scheme in the future preset time period according to the equipment health degree and the grid predicted load curve, the method further comprises: determining the maximum allowable load value and the maximum ramp rate of each unit according to the performance constraint condition, the maximum ramp rate being used to represent the maximum load change amount allowed by the unit in a unit time; judging whether the unit group load allocation scheme meets the constraint condition according to the maximum allowable load value and the maximum ramp rate, the constraint condition including that the load bearing value of each unit at each time point is less than or equal to the corresponding maximum allowable load value, and the absolute value of the difference between the load bearing values of adjacent time points of each unit is less than or equal to the corresponding maximum ramp rate; if the unit group load allocation scheme does not meet the constraint condition, adjusting the load bearing value of each unit in the unit group load allocation scheme, so as to obtain the unit group load allocation scheme meeting the constraint condition.

[0011] By adopting the technical solution, the consideration of the unit performance constraint condition is increased, including the maximum allowable load value and the maximum ramp rate. The server automatically judges whether the unit group load allocation scheme meets the constraint condition, and if not, adjusts and optimizes it until a feasible scheme meeting the constraint condition is obtained. This mechanism ensures that the load allocation considers the equipment health degree and does not exceed the physical limit and safety boundary of the equipment, effectively prevents the overloading and severe load fluctuation of the equipment, and improves the safety and reliability of the grid operation.

[0012] In combination with some embodiments of the first aspect, in some embodiments, if the unit group load allocation scheme does not meet the constraint condition, the load bearing value of each unit in the unit group load allocation scheme is adjusted, so as to obtain the unit group load allocation scheme meeting the constraint condition, specifically including: adjusting the load bearing value of the unit not meeting the constraint condition to a constraint boundary value to obtain a plurality of to-be-allocated load values, the constraint boundary value including the maximum allowable load value or the ramp allowable load value; arranging the remaining units in descending order according to the size of the equipment health degree to obtain a candidate unit sequence; sequentially traversing each candidate unit in the candidate unit sequence, and determining a target candidate unit that can bear the to-be-allocated load value according to the adjustable load space of each candidate unit; allocating the to-be-allocated load value to the target candidate unit, and updating the load bearing value of the target candidate unit, so as to obtain the unit group load allocation scheme meeting the constraint condition.

[0013] By adopting the technical solution, when the unit group load distribution scheme does not satisfy the constraint condition, the server adjusts the load bearing value of the unit exceeding the constraint condition to the constraint boundary value, obtains a plurality of to-be-distributed load values, and then sorts the remaining units according to the equipment health degree, and preferentially distributes the to-be-distributed load to the remaining units in a good health condition. This hierarchical and step-by-step adjustment method not only ensures the satisfaction of the constraint condition, but also maintains the principle of preferential distribution based on the equipment health degree. By establishing a candidate unit sequence and evaluating the adjustable load space one by one, the server can quickly find the target candidate unit most suitable for bearing the additional load, and realize precise adjustment and dynamic optimization of load distribution.

[0014] In combination with some embodiments of the first aspect, in some embodiments, adjusting the load bearing value of the unit not satisfying the constraint condition to the constraint boundary value specifically includes: if the load bearing value of the unit not satisfying the constraint condition is greater than the maximum allowed load value, adjusting the load bearing value to the maximum allowed load value as the constraint boundary value; and if the absolute value of the difference between the load bearing values of the unit at adjacent time points is greater than the maximum ramp rate, calculating the ramp allowed load value at the current time according to the load bearing value at the last time and the maximum ramp rate as the constraint boundary value.

[0015] By adopting the technical solution, the server respectively handles the violation of the maximum allowed load value constraint and the maximum ramp rate constraint. For the case of exceeding the maximum allowed load value, the server directly reduces the load bearing value to the maximum allowed load value; for the case of exceeding the maximum ramp rate, the server calculates the ramp allowed load value at the current time based on the load bearing value at the last time and the maximum ramp rate. This refined constraint processing mechanism ensures the stability and safety of load adjustment, avoids the dramatic fluctuation of unit load, and also provides accurate boundary conditions for subsequent load redistribution.

[0016] In some embodiments of the first aspect, in some embodiments, after the step of calculating the remaining load shortage according to the load shortage and the maximum allowed load value, determining the target load value of the main regulating unit as the maximum allowed load value, and distributing the remaining load shortage to other units according to the equipment health degree, the method further comprises: when all other units reach their respective maximum allowed load values and there is remaining load shortage that has not been distributed, collecting thermal state indicators, mechanical state indicators, and combustion state indicators of each unit; determining a safety margin of each unit according to the thermal state indicators, the mechanical state indicators, and the combustion state indicators; determining a temporarily overloaded unit as a unit whose safety margin exceeds a preset safety margin threshold, calculating a maximum allowed overloaded load value and a maximum allowed overloaded duration of the temporarily overloaded unit, the maximum allowed overloaded load value and the maximum allowed overloaded duration being determined based on the safety margin, and the maximum allowed overloaded duration being used to limit the duration of overloaded operation; and distributing the remaining load shortage that has not been distributed to the temporarily overloaded unit according to the safety margin.

[0017] By adopting the above technical solution, the server establishes an overloaded operation mechanism in an emergency state, that is, the thermal state indicators, the mechanical state indicators, and the combustion state indicators of each unit are analyzed, and the safety margin of each unit is calculated. For a unit with sufficient safety margin, the server sets it as a temporarily overloaded unit, and determines its maximum allowed overloaded load value and maximum allowed overloaded duration according to the safety margin. This overloaded management mechanism based on multi-dimensional safety evaluation not only guarantees the power supply reliability in an emergency, but also ensures the safety of equipment by strictly controlling the overload range and the overload duration.

[0018] In some embodiments of the first aspect, in some embodiments, after the step of distributing the remaining load shortage that has not been distributed to the temporarily overloaded unit according to the safety margin, the method further comprises: monitoring the operating parameters of the temporarily overloaded unit in real time; and immediately removing the overloaded operation state when the operating parameters show an abnormality.

[0019] By adopting the above technical solution, the server monitors the temporarily overloaded unit in real time, and immediately removes the overloaded operation state and restores the unit to a normal operation mode as soon as an abnormality is found. This timely response safety protection mechanism provides reliable safety protection for overloaded operation, effectively prevents damage to equipment caused by overloaded operation, guarantees the reliability of power grid power supply, and maximally protects the safety of equipment.

[0020] In a second aspect, the embodiments of the present application provide a server, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is configured to store computer program codes, the computer program codes comprising computer instructions, and the one or more processors invoke the computer instructions to cause the server to perform the method described in the first aspect and any possible implementation manner of the first aspect.

[0021] In a third aspect, the embodiments of the present application provide a computer program product comprising instructions which, when executed on a server, cause the server to perform the method described in the first aspect and any possible implementation manner of the first aspect.

[0022] In a fourth aspect, the embodiments of the present application provide a computer-readable storage medium comprising instructions which, when executed on a server, cause the server to perform the method described in the first aspect and any possible implementation manner of the first aspect.

[0023] It can be understood that the server provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved thereby can refer to the beneficial effects in the corresponding method, which will not be described here again.

[0024] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0025] 1. By adopting the above technical solution, the server calculates the equipment health degree of each machine group, and distributes the load according to the equipment health degree, thereby avoiding that some machine groups are long-term overloaded and accelerated aging. When the actual load value exceeds the predicted load value, the additional load is preferentially borne by the main regulating machine group with the highest equipment health degree, to ensure safety, and if the main regulating machine group capacity is insufficient, the remaining load shortage is distributed according to the equipment health degrees of other machine groups, thereby realizing dynamic optimization of load distribution. This method can effectively balance the load distribution and equipment health of the power grid, ensuring the power supply reliability of the power grid, prolonging the service life of the equipment, reducing the risk of failure, and improving the economy and safety of power grid operation.

[0026] 2. By adopting the above technical solution, the consideration of the performance constraint conditions of the machine group is increased, including the maximum allowed load value and the maximum ramp rate. The server automatically judges whether the load distribution scheme of the machine group meets these constraint conditions, and if not, adjusts and optimizes until a feasible scheme that meets the constraint conditions is obtained. This mechanism ensures that the load distribution considers the equipment health degree and does not exceed the physical limit and safety boundary of the equipment, effectively prevents equipment overload and severe load fluctuations, and improves the safety and reliability of power grid operation.

[0027] 3. By adopting the technical scheme, when the unit group load distribution scheme does not satisfy the constraint condition, the server adjusts the load bearing value of the unit exceeding the constraint condition to the constraint boundary value, obtains a plurality of to-be-distributed load values, and then sorts the remaining units according to the equipment health degree, and preferentially distributes the to-be-distributed load to the remaining units in a better health condition. This step-by-step adjustment method not only ensures the satisfaction of the constraint condition, but also maintains the principle of preferential distribution based on the equipment health degree. By establishing a candidate unit sequence and evaluating the adjustable load space one by one, the server can quickly find the target candidate unit most suitable for bearing the additional load, and realize precise adjustment and dynamic optimization of load distribution. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 is a flowchart of the power grid unit intelligent scheduling method in the embodiment of the application.

[0029] Figure 2 is another flowchart of the power grid unit intelligent scheduling method in the embodiment of the application.

[0030] Figure 3 is an entity device structure schematic diagram of the server in the embodiment of the application. DETAILED DESCRIPTION

[0031] The terms used in the following embodiments of the present application are only for the purpose of describing the specific embodiments and are not intended to be limiting to the present application. As used in the specification, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "and / or" as used herein refer to any or all possible combinations of one or more of the associated listed items.

[0032] Hereinafter, the terms "first", "second", are only for the purpose of description, and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0033] The method provided by the present embodiment is described in the flow. Please refer to Figure 1 is a flowchart of the power grid unit intelligent scheduling method in the embodiment of the application.

[0034] S101、Based on the unit feature database, calculate the equipment health degree of each unit, the unit feature database includes the basic equipment parameters, running state data and performance constraint conditions of each unit;

[0035] Wherein, the unit feature database refers to the data set storing the feature information of each unit, which is used for feature description and state recording of the unit. The basic equipment parameters refer to the basic operation parameters of the unit, such as rated capacity, start-up time, minimum technical output, etc. The running state data refers to the real-time running data of the unit, including output, vibration, temperature and other dynamic monitoring data. The performance constraint condition refers to the constraints limiting the operation of the unit, including maximum allowable load value, maximum climbing rate, etc. The equipment health degree is used to represent the overall operation state and reliability of the unit, and the larger the value is, the better the state of the unit is.

[0036] Specifically, the server accesses the unit feature database to obtain the basic equipment parameters, running state data and performance constraint conditions of each unit. Then, the server calculates the equipment health degree of each unit by using a comprehensive evaluation algorithm according to these data. The algorithm considers factors such as equipment wear degree, fault frequency, running stability, etc., and calculates a health degree score between 0 and 1 through weighted calculation. This health degree score will be an important basis for subsequent load distribution.

[0037] The following lists a specific example, assuming that there are 3 generator units (numbered #1, #2, #3) in the power grid, and the server obtains the following information from the unit feature database:

[0038] 1、Basic equipment parameters:

[0039] #1 unit: rated capacity 300MW, start-up time 40 minutes, minimum technical output 90MW;

[0040] #2 unit: rated capacity 500MW, start-up time 30 minutes, minimum technical output 150MW;

[0041] #3 unit: rated capacity 400MW, start-up time 35 minutes, minimum technical output 120MW;

[0042] 2、Running state data (statistics in the last 30 days):

[0043] #1 unit: average output 250MW, vibration average 2.1mm / s, temperature average 538℃, fault frequency 1 time;

[0044] #2 unit: average output 420MW, vibration average 1.8mm / s, temperature average 542℃, fault frequency 0 times;

[0045] #3 unit: average output 350MW, vibration average 2.3mm / s, temperature average 545℃, fault frequency 2 times;

[0046] 3. Performance constraints:

[0047] #1 unit: maximum allowable load 290MW, maximum ramp rate 30MW / min;

[0048] #2 unit: maximum allowable load 480MW, maximum ramp rate 40MW / min;

[0049] #3 unit: maximum allowable load 380MW, maximum ramp rate 35MW / min;

[0050] The server uses a comprehensive evaluation algorithm to calculate the health degree according to the following weights:

[0051] (1) Degree of equipment wear (30%): consider the length of operation, average load rate;

[0052] (2) Running stability (40%): consider the stability of vibration, temperature and other parameters;

[0053] (3) Fault condition (30%): consider the fault frequency and severity;

[0054] The final calculation of the equipment health degree:

[0055] #1 unit: 0.85;

[0056] #2 unit: 0.92;

[0057] #3 unit: 0.78;

[0058] As can be seen, the equipment health degree of #2 unit is the highest, and will get more load allocation in subsequent load allocation.

[0059] S102, according to the equipment health degree and the grid predicted load curve, determine the unit group load allocation scheme in the future preset time, the grid predicted load curve includes the predicted load value of each time, the unit group load allocation scheme includes the load bearing value of each unit corresponding to each time, the greater the equipment health degree, the greater the load bearing value;

[0060] Wherein, the future preset time refers to the time span of the unit group load allocation scheme, usually 24 hours or 48 hours. The grid predicted load curve refers to the predicted load change of the grid in the future preset time, including the predicted electricity demand of each time. The unit group load allocation scheme is used to represent the power generation task arrangement of each unit at different times. The load bearing value is used to represent the power generation load value that a unit needs to bear at a certain time.

[0061] Specifically, the server obtains a power grid predicted load curve, which is generated by historical data analysis and load prediction algorithm. Then, the server calculates the operation weight of each unit according to the equipment health degree, and the unit with higher equipment health degree obtains greater operation weight. Next, the server allocates the predicted load value at each time point to each unit according to the operation weight, and obtains the load bearing value of each unit at different time points. The whole allocation process follows the principle that the higher the equipment health degree, the more the load bearing, and at the same time ensures that the sum of the load bearing values at each time point is equal to the predicted load value.

[0062] Optionally, generally, the determination of the unit group load distribution scheme in the future preset time period according to the equipment health degree and the power grid predicted load curve can be realized by the following manner, which is not limited herein: dividing the equipment health degree of each unit by the sum of the equipment health degrees of all units to obtain the operation weight corresponding to each unit respectively, and the greater the equipment health degree, the greater the operation weight; multiplying the predicted load value at each time point by the operation weight of each unit to obtain the load bearing value corresponding to each unit at each time point respectively.

[0063] Next, the example in step S101 is continued to illustrate the load distribution process.

[0064] Firstly, the server obtains the power grid predicted load curve in the next 24 hours (with an interval of 4 hours):

[0065] Time points: 0:00, 4:00, 8:00, 12:00, 16:00, 20:00, 24:00;

[0066] Predicted load values (MW): 800, 700, 900, 1100, 1000, 1200, 900;

[0067] Secondly, the server calculates the operation weight of each unit:

[0068] Total equipment health degree = 0.85 + 0.92 + 0.78 = 2.55;

[0069] Operation weight of #1 unit = 0.85 / 2.55 = 0.333;

[0070] Operation weight of #2 unit = 0.92 / 2.55 = 0.361;

[0071] Operation weight of #3 unit = 0.78 / 2.55 = 0.306;

[0072] Then, the server calculates the load distribution scheme at each time point:

[0073] 0:00 time point (total load 800MW):

[0074] The load bearing value of the #1 unit = 800 x 0.333 = 266 MW;

[0075] The load bearing value of the #2 unit = 800 x 0.361 = 289 MW;

[0076] The load bearing value of the #3 unit = 800 x 0.306 = 245 MW;

[0077] 4:00 (total load 700 MW):

[0078] The load bearing value of the #1 unit = 700 x 0.333 = 233 MW;

[0079] The load bearing value of the #2 unit = 700 x 0.361 = 253 MW;

[0080] The load bearing value of the #3 unit = 700 x 0.306 = 214 MW;

[0081] Similarly, the complete 24-hour load distribution scheme is obtained:

[0082] Table 1 Load distribution scheme table of unit group (1)

[0083]

[0084] S103, calculate the difference between the actual load curve of the power grid and the predicted load curve of the power grid, to obtain the load shortage at each time, the actual load curve of the power grid includes the actual load value at each time;

[0085] The actual load curve of the power grid refers to the actual measured load data in the power grid, reflecting the real electricity demand. The load shortage refers to the difference between the actual load value and the predicted load value, which can be positive or negative. Time refers to a specific sampling time point on the load curve, usually with an interval of 15 minutes or 30 minutes. The actual load value is used to represent the actual electricity load at a certain time.

[0086] Specifically, the server obtains real-time load data through a power grid monitoring system to generate an actual load curve of the power grid. Then, the server compares the actual load curve of the power grid with the previous predicted load curve of the power grid, and calculates the load difference at each time. If the actual load value is greater than the predicted load value, a positive load shortage is obtained, indicating that the power generation capacity needs to be increased.

[0087] S104, taking the unit with the highest health degree of the equipment as the main regulating unit, calculating the adjustable load space of the main regulating unit at each time, the adjustable load space being used to represent the difference between the maximum allowed load value and the load bearing value of the main regulating unit;

[0088] The main regulating unit refers to a unit that undertakes the main regulating task, and is usually selected from a unit with the best equipment state. The adjustable load space refers to the load that can be increased by the unit, and is used to measure the regulating capacity of the unit. The maximum allowable load value refers to the maximum load that can be undertaken by the unit under the premise of ensuring safety.

[0089] Specifically, the server selects a unit with the highest equipment health degree from all units as the main regulating unit. Then, the server obtains the maximum allowable load value and the current load undertaking value of the main regulating unit. Next, the server calculates the difference between the maximum allowable load value and the load undertaking value at each time point, to obtain the adjustable load space of the main regulating unit at different time points. These adjustable load space data will be used to determine whether the main regulating unit can independently undertake the additional load regulating task.

[0090] S105, if the adjustable load space is greater than or equal to the load shortage, determining the sum of the main regulating load undertaking value and the load shortage as the target load value of the main regulating unit, and the main regulating load undertaking value is used to represent the load undertaking value corresponding to the main regulating unit;

[0091] The main regulating load undertaking value refers to the load undertaking value originally planned for the main regulating unit in the unit group load distribution scheme. The target load value refers to the load value that the main regulating unit needs to reach after adjustment. The load shortage refers to the difference between the actual load value and the predicted load value.

[0092] Specifically, the server compares the adjustable load space of the main regulating unit with the load shortage. When the adjustable load space is greater than or equal to the load shortage, it indicates that the main regulating unit has sufficient regulating capacity, and the server will calculate the target load value of the main regulating unit. The calculation method is to add the original load undertaking value of the main regulating unit to the load shortage to obtain a new target load value. In this case, the load regulating task will be completely undertaken by the main regulating unit, and other units do not need to participate in the regulation.

[0093] S106, if the adjustable load space is less than the load shortage, calculating a remaining load shortage according to the load shortage and the maximum allowable load value, determining the maximum allowable load value as the target load value of the main regulating unit, and distributing the remaining load shortage to other units according to the equipment health degree.

[0094] The remaining load shortage refers to the load difference that still exists after the main regulating unit is regulated. The other units refer to all units except the main regulating unit. The load distribution refers to the distribution of the load to different units according to a specific rule. The equipment health degree ratio refers to the distribution ratio determined according to the equipment health degree of each unit.

[0095] Specifically, the server adjusts the target load value of the master group to its maximum allowed load value, and then calculates the remaining load shortage. Next, the server calculates the distribution coefficient according to the equipment health of other groups, and the group with higher equipment health obtains a larger distribution coefficient. Then, the server distributes the remaining load shortage to other groups according to the distribution coefficient, and obtains the load value that each group needs to increase. Finally, the server generates a regulation scheme containing the new load value of all groups, and sends it to each group for execution. This multi-group coordinated regulation method not only ensures load balancing, but also avoids over-regulation of a single group.

[0096] By adopting the above technical solution, the server calculates the equipment health of each group, and distributes the load according to the equipment health, avoiding the acceleration of aging caused by the long-term overloading of some groups. When the actual load value exceeds the predicted load value, the highest equipment health of the master group is preferred to bear the additional load to ensure safety, and if the master group capacity is insufficient, the remaining load shortage is distributed according to the equipment health of other groups, realizing dynamic optimization of load distribution. This method can effectively balance the load distribution and equipment health of the power grid, ensuring the reliability of power supply, prolonging the service life of equipment, reducing the risk of failure, and improving the economy and safety of power grid operation.

[0097] The method provided by the embodiment will be described in further detail below. Please refer to Figure 2 , which is another flowchart of the power grid group intelligent scheduling method in the embodiment of the present application.

[0098] After step S102, the following steps can also be executed, or not, which is not limited here:

[0099] S201, determining the maximum allowed load value and the maximum ramp rate of each group according to the performance constraint condition, the maximum ramp rate being used to represent the maximum load change allowed by the group in unit time;

[0100] The performance constraint condition refers to a set of technical parameters and rules that limit the operation of the group. The maximum allowed load value refers to the maximum power generation load that the group can bear under the premise of ensuring safe operation. The maximum ramp rate is used to represent the maximum load change allowed by the group in unit time. The unit time usually refers to a time interval of 15 minutes or 30 minutes.

[0101] Specifically, the server reads the performance constraints of each unit from the unit feature database, including rated capacity, minimum technical output, equipment state and other parameters. Then, the server considers the operating environment, equipment state and safety margin of the unit to calculate the maximum allowable load value of each unit. At the same time, the server determines the maximum ramp rate of each unit according to the dynamic response characteristics, regulation capacity and operating procedure requirements of the unit. These constraints will be the basis for subsequent load distribution and adjustment.

[0102] S202, according to the maximum allowable load value and the maximum ramp rate, it is judged whether the unit group load distribution scheme meets the constraint condition, and the constraint condition includes that the load bearing value of each unit at each time point is less than or equal to the corresponding maximum allowable load value, and the absolute value of the difference between the load bearing values of adjacent time points of each unit is less than or equal to the corresponding maximum ramp rate;

[0103] Among them, the constraint condition refers to the operating limit rule that must be met. The load bearing value refers to the power generation load allocated to the unit at a certain time. Adjacent time points refer to two consecutive time points on the load curve.

[0104] Specifically, the server checks the maximum allowable load constraint of each unit at each time point to verify whether the allocated load bearing value exceeds the maximum allowable load value. Then, the server checks the maximum ramp rate constraint of each unit at adjacent time points to calculate the absolute value of the load change and verify whether it exceeds the maximum ramp rate. During the checking process, the server records the units, time points and specific values that do not meet the constraint conditions, which will be used for subsequent scheme adjustment. Only when all units at all time points meet all constraints, the unit group load distribution scheme is considered to be feasible.

[0105] S203, if the unit group load distribution scheme does not meet the constraint condition, adjust the load bearing value of each unit in the unit group load distribution scheme to obtain a unit group load distribution scheme that meets the constraint condition;

[0106] Specifically, the server identifies the specific circumstances that violate the constraint conditions, including which units at which time points violate which constraints. For the case of exceeding the maximum allowable load value, the server reduces the load bearing value of the unit to the maximum allowable load value; for the case of exceeding the maximum ramp rate, the server adjusts the load bearing value of the adjacent time points to make the load change meet the requirements. During the adjustment process, the server will redistribute the excess load to other units with regulation capacity according to the equipment health degree. If the constraint is still not met after single adjustment, the server will continue to iterate and optimize until a feasible scheme that meets all constraints is obtained.

[0107] Optionally, generally, if the unit group load distribution scheme does not satisfy the constraint condition, the load bearing value of each unit in the unit group load distribution scheme is adjusted so as to obtain a unit group load distribution scheme satisfying the constraint condition, which can be realized in the following manner, without limitation: the load bearing value of the unit not satisfying the constraint condition is adjusted to a constraint boundary value, and the constraint boundary value includes a maximum allowed load value or a ramp allowed load value; the remaining units are arranged in descending order according to the size of the equipment health degree, and a candidate unit sequence is obtained; each candidate unit in the candidate unit sequence is traversed in turn, and a target candidate unit that can bear the to-be-distributed load value is determined according to the adjustable load space of each candidate unit; the to-be-distributed load value is distributed to the target candidate unit, and the load bearing value of the target candidate unit is updated, so as to obtain a unit group load distribution scheme satisfying the constraint condition.

[0108] Optionally, generally, the load bearing value of the unit not satisfying the constraint condition is adjusted to a constraint boundary value, which can be realized in the following manner, without limitation: if the load bearing value of the unit not satisfying the constraint condition is greater than the maximum allowed load value, the load bearing value is adjusted to the maximum allowed load value as the constraint boundary value; if the absolute value of the difference between the load bearing values of the unit not satisfying the constraint condition at adjacent time intervals is greater than the maximum ramp rate, the ramp allowed load value at the current time interval is calculated according to the load bearing value at the previous time interval and the maximum ramp rate as the constraint boundary value.

[0109] Suppose there are three units A, B and C, the time interval is 15 minutes, and the related parameters are as follows:

[0110] The equipment health degree is 0.9 for A, 0.7 for B and 0.5 for C;

[0111] The maximum allowed load value is 600 MW for A, 400 MW for B and 300 MW for C;

[0112] The maximum ramp rate is 60 MW / 15 min for A, 40 MW / 15 min for B and 30 MW / 15 min for C;

[0113] The initial unit group load distribution scheme (unit: MW) is as follows:

[0114] Table 2 Unit group load distribution scheme table (2)

[0115]

[0116] Analysis shows that there are two places that violate the constraint condition:

[0117] The load of unit A at t3 is 650 MW, which exceeds the maximum allowed load of 600 MW;

[0118] The load variation of unit B at t2 to t3 is 70 MW (420-350) which exceeds the maximum ramp rate 40 MW;

[0119] Adjustment process:

[0120] (1) Handle the maximum allowed load constraint violation: reduce the load of unit A at t3 from 650 MW to 600 MW, which requires 50 MW of re-allocated load;

[0121] (2) Handle the ramp rate constraint violation: the load of unit B at t2 is 350 MW, and the maximum ramp rate is 40 MW, so the load of unit B at t3 should be adjusted to 390 MW (350+40), and 30 MW of re-allocated load is required;

[0122] Re-allocate excess load: the total load to be allocated is 80 MW (50+30), and the candidate units sorted by device health are C (because A has reached the maximum load, and B is limited by the ramp rate), check the adjustable space of unit C: 300-280=20 MW, allocate 20 MW to unit C, and the remaining 60 MW cannot be allocated temporarily, which needs to be solved by other measures;

[0123] Final adjusted unit group load allocation scheme (unit: MW):

[0124] Table 3 Unit group load allocation scheme table (3)

[0125]

[0126] This adjusted unit group load allocation scheme meets all the maximum allowed load and maximum ramp rate constraints of the units. For the remaining 60 MW of load that cannot be solved by regular adjustment, starting a standby unit or taking other measures such as temporary overload needs to be considered.

[0127] S204, calculate the difference between the actual load curve of the power grid and the predicted load curve of the power grid, and obtain the load shortage at each time, wherein the actual load curve of the power grid includes the actual load value at each time;

[0128] Specifically, refer to step S103, which will not be repeated here.

[0129] S205, take the unit with the highest device health as the main adjusting unit, and calculate the adjustable load space of the main adjusting unit at each time, wherein the adjustable load space is used to represent the difference between the maximum allowed load value and the load bearing value of the main adjusting unit;

[0130] Specifically, refer to step S104, which will not be repeated here.

[0131] S206, if the adjustable load space is greater than or equal to the load shortage, determining the sum of the main load bearing value and the load shortage as the target load value of the main regulating unit, and the main load bearing value is used to represent the load bearing value corresponding to the main regulating unit;

[0132] Specifically, please refer to step S105, which will not be repeated here.

[0133] S207, if the adjustable load space is less than the load shortage, calculating the remaining load shortage according to the load shortage and the maximum allowable load value, determining the maximum allowable load value as the target load value of the main regulating unit, and distributing the remaining load shortage to other units according to the equipment health degree;

[0134] Specifically, please refer to step S106, which will not be repeated here.

[0135] S208, when all other units reach their respective maximum allowable load values and there is a remaining load shortage that has not been allocated, collecting the thermal state indicators, mechanical state indicators and combustion state indicators of each unit;

[0136] The thermal state indicators are used to represent the temperature, pressure and other thermal parameters of the unit. The mechanical state indicators are used to represent the bearing vibration, speed fluctuation and other mechanical operation parameters. The combustion state indicators are used to represent the fuel combustion efficiency, flame characteristics and other combustion system parameters. The remaining load shortage refers to the load that has not been allocated after all units reach the maximum allowable load value.

[0137] Specifically, first, the server confirms whether all units have reached the maximum allowable load value and calculates the remaining load shortage that has not been allocated. Then, the server obtains the operating parameters of each unit in real time through a distributed data acquisition system. These operating parameters include multi-dimensional data such as turbine temperature distribution, boiler steam parameters, shaft vibration values, bearing temperature, combustion chamber temperature, oxygen distribution, etc. The server stores these data according to the thermal system, mechanical system and combustion system, providing a data basis for subsequent safety margin assessment.

[0138] S209, determining the safety margin of each unit according to the thermal state indicators, mechanical state indicators and combustion state indicators;

[0139] Specifically, the server standardizes the collected state indicators so that indicators of different dimensions can be compared. Then, the server calculates the safety scores of the thermal state, mechanical state and combustion state according to the preset evaluation model. The preset evaluation model takes into account the distance of each indicator from the safety threshold, the parameter fluctuation trend and historical operation experience. Finally, the server calculates the comprehensive safety margin through weighted calculation, and the weight setting reflects the influence degree of each type of state indicator on equipment safety. The safety margin is an indicator of the ability of the unit to withstand additional load.

[0140] S210, determine the unit whose safety margin exceeds the preset safety margin threshold as a temporary overload unit, calculate the maximum allowable overload load value and the maximum allowable overload duration of the temporary overload unit, the maximum allowable overload load value and the maximum allowable overload duration are determined based on the safety margin, and the maximum allowable overload duration is used to limit the duration of overload operation;

[0141] Among them, the temporary overload unit refers to the unit that allows short-time overload operation. The maximum allowable overload load value refers to the maximum overload operation load allowed by the temporary overload unit. The maximum allowable overload duration refers to the longest time allowed by the temporary overload unit for overload operation. The preset safety margin threshold is used to represent the standard value for judging whether overload operation is allowed.

[0142] Specifically, the server compares the safety margin of each unit with the preset safety margin threshold, and determines the unit whose safety margin exceeds the preset safety margin threshold as a temporary overload unit. Then, the server calculates the maximum allowable overload load value of each temporary overload unit according to its safety margin, which usually increases with the increase of safety margin. At the same time, the server also determines the maximum allowable overload duration according to the safety margin, the higher the safety margin, the longer the maximum allowable overload duration. These overload operation parameters will be used to control the intensity and time of overload operation.

[0143] S211, distribute the remaining load shortage that has not been allocated to the temporary overload unit according to the safety margin;

[0144] Specifically, the server calculates the safety margin ratio of each temporary overload unit, which is used as the weight coefficient of load distribution. Then, the server distributes the remaining load shortage that has not been allocated according to these weight coefficients, and the temporary overload unit with higher safety margin bears more overload load. During the distribution process, the server needs to ensure that the overload load of each temporary overload unit does not exceed its maximum allowable overload load value.

[0145] S212, real-time monitoring of the operation parameters of the temporary overload unit;

[0146] Among them, the operation parameter refers to various real-time data describing the operation state of the unit. Real-time monitoring refers to the process of continuously collecting and analyzing operation data.

[0147] Specifically, the server collects running parameters every several seconds, including but not limited to: turbine temperature, bearing temperature and vibration, generator stator temperature, rotor current, boiler temperature, feed water flow, combustion conditions and other key parameters. At the same time, the server calculates the rate of change and trend of these running parameters in real time, and performs dynamic analysis. When a parameter approaches the warning threshold, the server will increase the sampling frequency of the parameter and monitor it more closely. The server will also compare real-time data with historical data to detect abnormal changes in a timely manner.

[0148] S213, when the running parameter shows an abnormality, immediately release the overload running state.

[0149] Wherein, the running parameter shows an abnormality refers to the case that the running parameter exceeds the safe range. The overload running is released refers to the process of reducing the load of the temporarily overloaded unit to the normal running level.

[0150] Specifically, the server quickly confirms the abnormal parameter and excludes the possibility of false abnormality such as sensor failure. After confirming the authenticity, the server immediately generates an emergency control instruction to require the relevant unit to reduce the load. According to the severity of the abnormality, the server will select different load reduction strategies: for slight abnormalities, gradual load reduction is adopted to slowly reduce the load to the normal level; for serious abnormalities, a fast load reduction program is started to release the overload state at the fastest speed under the premise of safety. After the overload is released, the server continues to monitor the unit parameters for a period of time to ensure that the running state completely returns to normal.

[0151] The server in the embodiments of the present application will be described from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic diagram of an entity device structure of the server in the embodiments of the present application.

[0152] It should be noted that, Figure 3 The structure of the server shown is only an example and should not impose any limitation on the functions and use range of the embodiments of the present application.

[0153] As Figure 3 shown, the server includes a CPU 301, which can perform various appropriate actions and processes according to programs stored in a read-only memory ROM 302 or loaded from a storage portion 308 to a random access memory RAM 303, such as performing the methods described in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302 and the RAM 303 are connected to each other through a bus 304. An I / O interface 305 is also connected to the bus 304.

[0154] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a push button switch, and the like; an output section 307 including a Liquid Crystal Display (LCD), and an audio output device, a lamp, and the like; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, and the like. The communication section 309 performs a communication process via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as necessary. A removable recording medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like, is attached to the drive 310 as necessary so that a computer program read therefrom is installed into the storage section 308 as necessary.

[0155] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present application. For example, an embodiment of the present application includes a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing a computer program for executing the method shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network by the communication section 309, and / or installed from the removable recording medium 311. When the computer program is executed by the CPU 301, various functions defined in the present application are performed.

[0156] Note that specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any appropriate combination thereof. In the present application, the computer-readable storage medium can be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0157] The flowcharts and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved.

[0158] In particular, the server of the embodiment includes a processor and a memory, and the memory stores a computer program. When the computer program is executed by the processor, the power grid unit intelligent scheduling method provided by the above embodiment is implemented.

[0159] As another aspect, the present application also provides a computer-readable storage medium. The storage medium can be included in the server described in the above embodiments, or can exist separately and not be assembled into the server. The storage medium carries one or more computer programs. When the one or more computer programs are executed by a processor of the server, the server implements the power grid unit intelligent scheduling method provided in the above embodiments.

[0160] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

[0161] In the above embodiments, according to the context, the term "when" can be interpreted as meaning "if" or "after" or "in response to determining" or "in response to detecting". Similarly, according to the context, the phrase "on determining" or "if detecting (the stated condition or event)" can be interpreted as meaning "if determining" or "in response to determining" or "on detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)".

[0162] Those skilled in the art can understand that all or part of the processes in the above method embodiments can be implemented by a computer program instructing relevant hardware to complete, and the program can be stored in a computer-readable storage medium. The program can include the processes of the above method embodiments when executed. The foregoing storage medium includes ROM, random access memory (RAM), magnetic disk or optical disk, and various program code storage media.

Claims

1. A method for intelligent dispatching of power grid units, characterized in that, Applied to a server, the method includes: Based on the unit characteristic database, the equipment health of each unit is calculated. The unit characteristic database includes the basic equipment parameters, operating status data and performance constraints of each unit. Based on the equipment health status and the power grid predicted load curve, a unit group load allocation scheme is determined for a future preset time period. The power grid predicted load curve includes the predicted load value at each moment, and the unit group load allocation scheme includes the load borne value of each unit at each moment. The higher the equipment health status, the higher the load borne value. The difference between the actual load curve of the power grid and the predicted load curve of the power grid is calculated to obtain the load deficit at each time point. The actual load curve of the power grid includes the actual load value at each time point. The unit with the highest equipment health is designated as the main dispatch unit. The adjustable load space of the main dispatch unit at each moment is calculated. The adjustable load space is used to represent the difference between the maximum allowable load value and the load capacity of the main dispatch unit. If the adjustable load space is greater than or equal to the load deficit, the sum of the main dispatch load undertaking value and the load deficit is determined as the target load value of the main dispatch unit, and the main dispatch load undertaking value is used to represent the load undertaking value corresponding to the main dispatch unit; If the adjustable load space is less than the load deficit, the remaining load deficit is calculated based on the load deficit and the maximum allowable load value. The maximum allowable load value is determined as the target load value of the main dispatch unit, and the remaining load deficit is allocated to other units according to the equipment health status. After the step of determining the unit group load allocation scheme within a preset time period based on the equipment health status and the power grid predicted load curve, the method further includes: determining the maximum allowable load value and maximum ramp rate of each unit according to the performance constraints, wherein the maximum ramp rate is used to represent the maximum allowable load change of the unit within a unit time; determining whether the unit group load allocation scheme meets the constraints based on the maximum allowable load value and the maximum ramp rate, wherein the constraints include that the load-bearing value of each unit at each moment is less than or equal to the corresponding maximum allowable load value, and the absolute value of the difference between the load-bearing values ​​of each unit at adjacent moments is less than or equal to the corresponding maximum ramp rate; if the unit group load allocation scheme does not meet the constraints, adjusting the load-bearing value of each unit in the unit group load allocation scheme to obtain a unit group load allocation scheme that meets the constraints. If the unit group load allocation scheme does not meet the constraints, the load-bearing value of each unit in the unit group load allocation scheme is adjusted to obtain a unit group load allocation scheme that meets the constraints. Specifically, this includes: adjusting the load-bearing value of the units that do not meet the constraints to the constraint boundary value to obtain multiple load values ​​to be allocated, wherein the constraint boundary value includes the maximum allowable load value or the ramp allowable load value; sorting the remaining units in descending order according to the equipment health status to obtain a candidate unit sequence; sequentially traversing each candidate unit in the candidate unit sequence, and determining the target candidate unit that can bear the load value to be allocated based on the adjustable load space of each candidate unit; allocating the load value to be allocated to the target candidate unit, and updating the load-bearing value of the target candidate unit to obtain a unit group load allocation scheme that meets the constraints. The step of adjusting the load-bearing value of units that do not meet the constraints to the constraint boundary value specifically includes: if the load-bearing value of a unit that does not meet the constraints is greater than the maximum allowable load value, then the load-bearing value is adjusted to the maximum allowable load value as the constraint boundary value; if the absolute value of the difference between the load-bearing values ​​of units that do not meet the constraints at adjacent times is greater than the maximum ramp rate, then the ramp allowable load value at the current time is calculated based on the load-bearing value at the previous time and the maximum ramp rate as the constraint boundary value.

2. The method according to claim 1, characterized in that, The step of determining the unit group load allocation scheme within a preset time period based on the equipment health status and the power grid predicted load curve specifically includes: Divide the equipment health of each unit by the sum of the equipment health of all units to obtain the operating weight of each unit. The higher the equipment health, the higher the operating weight. The predicted load value at each time point is multiplied by the operating weight of each unit to obtain the load borne value of each unit at each time point.

3. The method according to claim 1, characterized in that, After the steps of calculating the remaining load deficit based on the load deficit and the maximum allowable load value if the adjustable load space is less than the load deficit, determining the maximum allowable load value as the target load value of the main dispatching unit, and allocating the remaining load deficit to other units according to the equipment health status, the method further includes: When all other units have reached their respective maximum allowable load values ​​and there is a remaining load shortfall that has not been allocated, the thermal status index, mechanical status index and combustion status index of each unit are collected. The safety margin of each unit is determined based on the thermal state index, the mechanical state index, and the combustion state index. Units whose safety margin exceeds a preset safety margin threshold are identified as temporary overload units. The maximum allowable overload load value and the maximum allowable overload duration of the temporary overload unit are calculated. The maximum allowable overload load value and the maximum allowable overload duration are determined based on the safety margin. The maximum allowable overload duration is used to limit the duration of overload operation. The unallocated remaining load deficit will be allocated to the temporary overload units according to the safety margin.

4. The method according to claim 3, characterized in that, After the step of allocating the unallocated remaining load deficit to the temporary overload units according to the safety margin, the method further includes: Real-time monitoring of the operating parameters of the temporary overload unit; When the operating parameters show abnormalities, immediately remove the overload operation status.

5. A server, characterized in that, The server includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, and the one or more processors invoking the computer instructions to cause the server to perform the method as described in any one of claims 1 to 4.

6. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the server, it causes the server to perform the method as described in any one of claims 1 to 4.

7. A computer program product, characterized in that, When the computer program product is run on the server, the server performs the method as described in any one of claims 1 to 4.

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