A heat network scheduling processing method and system considering a unit waste heat utilization system

Abstract

The application provides a kind of heat supply network scheduling processing method and system considering unit waste heat utilization system, belongs to heat supply network scheduling technical field, specifically includes: according to the predicted data of heat demand load in future preset period and the matching heat supply interval data under different prediction deviation types as a basis, determine the real-time intervention processing of steam heat network heater is not needed, in the predicted data of heat demand load after future preset period and the matching condition of matching heat supply interval and prediction deviation type as a basis, determine the intervention control strategy of steam heat network heater in future preset period, improve the operation stability of steam heat network heater.

Description

Technical Field

[0001] This invention belongs to the field of heat network dispatching technology, and in particular relates to a heat network dispatching method and system that considers the utilization of waste heat from generating units. Background Technology

[0002] During operation, the flue gas from thermal power units is generally discharged directly without treatment, resulting in significant heat loss. Therefore, how to recover the waste heat from the flue gas to improve energy utilization efficiency and reduce unit energy consumption during the heating process has become an urgent technical problem to be solved.

[0003] Specifically, the invention patent application CN202310844935.7, "A High-Efficiency Hydrogen Energy Cogeneration System and Its Dispatch and Control Method," utilizes hydrogen energy cogeneration to transfer and convert energy from the power system, flexibly outputting electrical energy, heat energy, and other energy forms. It also flexibly releases waste heat from the system according to external heat load demands, effectively supplementing the heat source gap in the heating network and improving the overall energy utilization rate of the system. However, the following technical problems exist: When waste heat recovery systems provide heating, they heavily rely on flue gas temperature. Therefore, the frequency of flue gas temperature fluctuations affects the control requirements for the operation of steam heating network heaters. If the control strategy for the operation of steam heating network heaters is not determined in conjunction with the flue gas temperature changes, it will lead to both excessive energy consumption and reduced service life due to frequent operation of the steam heating network heaters.

[0004] Specifically, this application provides a heat network scheduling and processing method and system that considers the utilization of waste heat from generating units. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a heat network dispatching method considering a unit waste heat utilization system, which includes: S1 uses the flue gas temperature monitoring data as a basis to determine the change data of the flue gas temperature monitoring data. Based on the change data, it determines that when there is no need to intervene in the steam heating network heater in real time, the matching status of the flue gas temperature and the heating network scheduling demand is determined, and combined with the prediction deviation type of the heating demand load under different weather types, the matching heating range under different prediction deviation types is determined. Based on the predicted heating demand load data for the future preset period and the matching heating range data under different prediction deviation types, S2 determines that real-time intervention of the steam heating network heater is not required, and then proceeds to the next step. S3 determines the intervention control strategy for the steam heating network heaters in the future preset period based on the predicted heating demand load data after the future preset period, the matching status of the matching heating range, and the type of prediction deviation.

[0006] The beneficial effects of this invention are as follows: Based on the changing data, it is determined whether the steam heating network heater needs to be intervened in real time. This enables the determination of a control strategy for real-time intervention of the steam heating network heater based on the changes in flue gas temperature. This avoids the technical problem of poor heating reliability caused by the inability to intervene in real time when the flue gas temperature changes drastically.

[0007] Based on the predicted heating demand load data after a preset future period and the matching status of the corresponding heating range, as well as the type of prediction deviation, the intervention control strategy for the steam heating network heaters in the preset future period is determined. This enables the determination of the intervention control strategy for the steam heating network heaters in the preset future period based on the expected demand for their use. While ensuring the operational stability of the steam heating network heaters, the system can respond and process them in a timely manner when heating deviations are detected, further improving the reliability of heating treatment.

[0008] Furthermore, the flue gas temperature monitoring data is determined based on the monitoring results of the flue gas temperature monitoring device.

[0009] Furthermore, the variation data of the flue gas temperature monitoring data is determined based on the distribution data of monitoring times within different flue gas temperature ranges.

[0010] It is understood that the flue gas temperature range is divided into a preset number of intervals based on historical monitoring data of the flue gas temperature in an equally spaced manner.

[0011] Furthermore, it was determined that there was no need for real-time intervention of the steam heating network heater, specifically including: Based on the variation data of the flue gas temperature monitoring data, the distribution data of monitoring time in different flue gas temperature ranges are determined; Based on the distribution data of monitoring times in different flue gas temperature ranges, the proportion of monitoring times in different flue gas temperature ranges is determined. Based on the proportion of monitoring times within different flue gas temperature ranges, it is determined whether real-time intervention of the steam heating network heater is necessary.

[0012] Furthermore, the process that eliminates the need for steam network heaters specifically includes: Based on the predicted heating demand load data for the future preset period and the matching heating range data under different prediction deviation types, the intervention of steam network heaters is determined. Based on the predicted heating demand load data for a future preset period, the time period that falls within the matching heating range is determined and used as the reliable matching time period; Based on the distribution data of the reliable matching time period under different prediction deviation types, determine the reliable matching time period under different prediction deviation types; Based on the reliable matching time period data and the reliable matching time period data under different prediction deviation types, it is determined whether real-time intervention of the steam heating network heater is required.

[0013] In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for heat network scheduling and processing considering a unit waste heat utilization system when running the computer program.

[0014] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0016] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart of a heat network scheduling and processing method that considers the waste heat utilization system of the generating unit; Figure 2 This is a flowchart illustrating a method for determining that real-time intervention of the steam heating network heater is unnecessary. Figure 3 This is a flowchart illustrating the method for determining the type of prediction bias. Detailed Implementation

[0018] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0019] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that other elements / components / etc. may exist in addition to the listed elements / components / etc.

[0020] Example 1 like Figure 1 As shown, this application provides a heat network dispatching method considering a unit waste heat utilization system, specifically including: S1 uses the flue gas temperature monitoring data as a basis to determine the change data of the flue gas temperature monitoring data. Based on the change data, it determines that when there is no need to intervene in the steam heating network heater in real time, the matching status of the flue gas temperature and the heating network scheduling demand is determined, and combined with the prediction deviation type of the heating demand load under different weather types, the matching heating range under different prediction deviation types is determined. Based on the predicted heating demand load data for the future preset period and the matching heating range data under different prediction deviation types, S2 determines that real-time intervention of the steam heating network heater is not required, and then proceeds to the next step. S3 determines the intervention control strategy for the steam heating network heaters in the future preset period based on the predicted heating demand load data after the future preset period, the matching status of the matching heating range, and the type of prediction deviation.

[0021] Furthermore, the flue gas temperature monitoring data is determined based on the monitoring results of the flue gas temperature monitoring device.

[0022] Furthermore, the variation data of the flue gas temperature monitoring data is determined based on the distribution data of monitoring times within different flue gas temperature ranges.

[0023] It is understood that the flue gas temperature range is divided into a preset number of intervals based on historical monitoring data of the flue gas temperature in an equally spaced manner.

[0024] Specifically, such as Figure 2 As shown, it is determined that there is no need for real-time intervention of the steam heating network heater, specifically including: Based on the variation data of the flue gas temperature monitoring data, the distribution data of monitoring time in different flue gas temperature ranges are determined; Based on the distribution data of monitoring times in different flue gas temperature ranges, the proportion of monitoring times in different flue gas temperature ranges is determined. Based on the proportion of monitoring times within different flue gas temperature ranges, it is determined whether real-time intervention of the steam heating network heater is necessary.

[0025] It should be noted that the proportion of monitoring times in different flue gas temperature ranges is determined based on the proportion of monitoring times in the flue gas temperature ranges among all monitoring times.

[0026] Understandably, the need for real-time intervention of the steam heating network heater is determined based on the proportion of monitoring times within different flue gas temperature ranges, specifically including: When there is no flue gas temperature range where the proportion of monitoring times is greater than the preset monitoring time proportion threshold, it indicates that the flue gas temperature is changing rapidly. Therefore, it is possible that the flue gas temperature is changing so that the waste heat recovery system cannot reliably provide heat energy. Therefore, it is determined that the steam heating network heater needs to be intervened in real time. When the heat energy of the waste heat recovery system cannot meet the demand, heat energy should be supplemented in a timely manner.

[0027] Additionally, it should be noted that when there is a flue gas temperature range where the proportion of monitoring times exceeds the preset threshold, the proportion of monitoring times in different flue gas temperature ranges on different dates is determined based on the distribution data of different monitoring times. The date where the proportion of monitoring times exceeds the preset threshold is taken as the temperature stabilization date. When there is no temperature stabilization date, it is determined that the steam heating network heater needs to be intervened in real time. When the heat energy of the waste heat recovery system cannot meet the demand, heat energy is supplemented in a timely manner.

[0028] Furthermore, when there are temperature stable dates, the date stability coefficient of the flue gas temperature is determined based on the proportion of the number of temperature stable dates. When the date stability coefficient of the flue gas temperature is greater than a preset stability coefficient threshold, it is determined that the steam heating network heater does not need to be intervened in real time. However, if the date stability coefficient of the flue gas temperature is not greater than the preset stability coefficient threshold, it is determined that the steam heating network heater needs to be intervened in real time.

[0029] The core idea of ​​this logic is: if the flue gas temperature of the waste heat boiler is stable, it means that the waste heat supply is reliable and no backup is needed; if the flue gas temperature fluctuates greatly, it means that the waste heat supply may be unreliable and a backup heat source needs to be intervened at any time.

[0030] Example: Steam heating network heater intervention judgment system based on flue gas temperature stability Step 1: Data Preparation and Parameter Setting. Data Source: Temperature sensors installed at the waste heat flue gas outlet of the waste heat boiler collect continuous temperature data. Assuming a data collection frequency of once per minute, the flue gas temperature range is divided into several consecutive intervals. Key Thresholds are Set: Preset monitoring time percentage threshold (T_time): For example, set to 60%. This means that if a temperature interval occupies more than 60% of the monitoring time, it is considered a "dominant interval." Preset percentage threshold (T_date): Used to determine whether a single day is stable; it can be the same as T_time, for example, also set to 70%. Preset stability coefficient threshold (T_stable): For example, set to 70%. This means that the entire system is considered reliable only if it is stable for more than 70% of the days.

[0031] Step 2: Short-term fluctuation analysis (determining intraday stability). Objective: Analyze data from the most recent time period (e.g., the past 3 months) to see if flue gas temperature is stable. Statistical distribution: Count how many monitoring times each flue gas temperature range occupied in the past 3 months, and check if any range has a percentage greater than T_time (70%). Example result: Range 2 has a percentage of 76.4%, which is greater than 70%. Therefore, a dominant range exists. Preliminary conclusion: The flue gas temperature has been relatively stable in the past 24 hours. Proceed to the next step of analysis.

[0032] Step 3: Long-term stability analysis (judging multi-day trends) Objective: To analyze the frequency of such "stable days" over a longer period (e.g., the past year) and identify "temperature stable dates": Repeat step two of the analysis for each day of the past year. If the proportion of monitoring times within a certain flue gas temperature range is greater than T_date (70%), that day is marked as a "temperature stable date." Assuming that after analyzing data from the past 365 days, 100 days were marked as "temperature stable dates," the date stability coefficient is calculated as follows: Stability coefficient = (Number of temperature stable dates / Total number of analyzed days) × 100%. Example calculation: Stability coefficient = (100 days / 365 days) × 100% = 27%. Step 4: Final Decision Objective: Based on long-term stability analysis results, determine whether real-time standby intervention of the steam heating network heater is necessary. Decision rule: Compare the calculated stability coefficient with the preset stability coefficient threshold T_stable (70%). If the stability coefficient > 70%, it indicates that the system is very stable in most cases and the waste heat supply is reliable. If it is determined that real-time intervention of the steam heating network heater is not necessary, and if the stability coefficient ≤ 70%, it indicates that the system has many unstable conditions and there is a risk of waste heat supply interruption.

[0033] Therefore, it was ultimately determined that the steam heating network heaters need to be activated in real time. When the heat energy from the waste heat recovery system cannot meet the demand, the control system should be able to promptly activate the heaters to supplement the heat energy.

[0034] Furthermore, the weather types are classified based on temperature, or perceived temperature, or comprehensive meteorological index, and specifically, time periods within the same interval will be classified into the same weather type.

[0035] Method 1: Simple temperature-based classification (most basic); Severe cold type: Periods where the average or minimum temperature is below a certain threshold (e.g., -10℃). Load characteristics: Load reaches its peak and remains high; Cold type: Periods where the average temperature is within a certain range (e.g., -10℃ to 5℃); Transitional (mild) type: Periods where the average temperature is within a certain range (e.g., 5℃ to 15℃). Load characteristics: Moderate load, very sensitive to temperature changes, small temperature fluctuations can lead to large load changes; Off-season type: Daily average temperature is above a certain threshold (e.g., 15℃). Load characteristics: Load is mainly determined by production processes, heating load is basically zero, and the overall load level is low.

[0036] Method 2: Classification based on perceived temperature or comprehensive meteorological index (recommended) Use "Apparent Temperature" or "Felt Temperature". This is an index that comprehensively considers temperature and wind speed. For example, at the same low temperature, a windy day will feel much colder and have a higher heat load than a windless day. The calculation formula (example) is: Actual temperature = 13.12 + 0.6215*T - 11.37*V^0.16 + 0.3965*T*V^0.16 (where T is the air temperature in degrees Celsius and V is the wind speed in km / h). Classification: Based on the calculated actual temperature, intervals can be divided using a method similar to Method 1. A custom load-related meteorological index can be constructed using the "Comprehensive Meteorological Index," for example: Meteorological load index = a * temperature + b * humidity + c * wind speed + d * solar radiation intensity, where the coefficients a, b, c, and d can be determined through historical data and regression analysis. Then, weather types are classified according to the numerical range of this index.

[0037] Specifically, such as Figure 3 As shown, the method for determining the type of prediction deviation is as follows: Based on the deviation between the predicted heating demand load and the actual heating load in the weather type, determine the average absolute value of the deviation rate between different time periods in the weather type. The forecast deviation type of the weather type is determined based on the average of the absolute values ​​of the deviation rates.

[0038] It is understood that when the average absolute value of the deviation rate of the weather type is greater than the preset deviation rate threshold, the prediction deviation type of the weather type is determined to be a type I deviation type, and if the average absolute value of the deviation rate of the weather type is not greater than the preset deviation rate threshold, the prediction deviation type of the weather type is determined to be a type II deviation type.

[0039] Specifically: Data preparation and parameter setting. Known conditions: We have defined several weather types through cluster analysis (e.g., typical winter days, windy and cold winter days, rainy, snowy and freezing days, spring / autumn transition days, sunny spring days, and summer non-heating days). Data: For each weather type, we have historical data for multiple dates of that type, including: predicted heat load values ​​and actual heat load values. Setting key thresholds: Preset deviation rate threshold (T_deviation): For example, set to 10%. This threshold means that we consider an average prediction deviation exceeding 10% unacceptable, belonging to the type of inaccurate prediction. If the average value > 10%, the prediction deviation type for this weather type is determined to be a type I deviation (inaccurate prediction). If the average value ≤ 10%, the prediction deviation type for this weather type is determined to be a type II deviation (accurate prediction).

[0040] Furthermore, the method for determining the matching heating range under the prediction deviation type is as follows: The flue gas temperature range with the largest proportion of monitoring times is taken as the target temperature range. Based on the matching between the target temperature range and the heating network scheduling requirements, the matching between the heat supply of the waste heat recovery system and the heating network scheduling requirements within the target temperature range is determined. Based on the matching situation, determine the load matching time periods in different heating supply zones; Based on load matching time period data in different heating supply intervals, the matching heating supply interval under the prediction deviation type is determined.

[0041] It is understood that the load matching period is the period during which the heat provided by the waste heat recovery system is the same as the heat required by the heating network dispatching command.

[0042] Specifically, based on load matching time period data within different heating supply intervals, the matching heating supply interval under the prediction deviation type is determined, including: In a type of weather with two deviations, if the total duration of the load matching period within the heating supply range exceeds a preset duration threshold, then the heating supply range is determined to be a matching heating supply range in the weather type. When determining the number of matching heating intervals in the second type of weather type within a first-class deviation weather condition, if the number of matching heating intervals in the second-class deviation weather condition meets the requirement (i.e., exceeds the preset threshold for the number of heating intervals), then to ensure the reliability of heating, it is determined that there are no matching heating intervals in the first-class deviation weather condition.

[0043] When the number of matching heating intervals in the second type of deviation does not meet the requirements, the response matching factor in the second type of deviation is determined based on the number of matching heating intervals in the second type of deviation and the proportion of the number of days in the weather type of the second type of deviation. When the response matching factor is greater than the preset matching factor threshold, it is determined that there is no matching heating interval in the weather type of the first type of deviation.

[0044] If the response matching factor is greater than the preset matching factor threshold, then in a certain type of deviation, the heating interval in which all time periods belong to the load matching period is determined as the matching heating interval in the weather type.

[0045] In one possible embodiment, the target temperature range is: based on analysis, the target temperature range where flue gas most frequently occurs is 120°C ~ 140°C, corresponding to a stable heat supply of 90 tons / hour for the waste heat system. The heat supply range is divided (unit: tons / hour): A: 0-70; B: 70-85; C: 85-95; D: 95-110; E: above 110. Preset duration threshold (T_duration): 4 hours, preset number of heating intervals threshold (T_interval_count): 3, preset matching factor threshold (T_factor): 0.5.

[0046] Time-based deviation types: The period from 06:00 to 10:00 every day (morning peak load period) is classified as type II deviation (accurate prediction), while the period from 14:00 to 18:00 every day (afternoon transition period) is classified as type I deviation (inaccurate prediction).

[0047] Step 2: Determine the matching interval for the Type II deviation period (accurate prediction). Target period: 06:00-10:00 (Type II deviation) Analysis process: The system retrieves all historical data from 06:00 to 10:00, filtering out times when the flue gas temperature is between 120°C and 140°C. The total duration of the load matching period (demand = 90 tons / hour) for each heating network demand within these timeframes is calculated. Assuming the following results: Interval A: 0.5 hours, Interval B: 1 hour, Interval C: 15 hours (far exceeding the 4-hour threshold), Interval D: 12 hours (far exceeding the 4-hour threshold), Interval E: 0 hours; Decision: Since the total matching time for intervals C and D both far exceeds the 4-hour threshold, the system determines that for the second type of deviation period from 06:00 to 10:00, the matching heating intervals are C and D.

[0048] Step 3: Determine the matching intervals for the first type of deviation period (inaccurate prediction). Process the inaccurate prediction period, such as 14:00-18:00 (first type of deviation). The first-level judgment is: Is the number of matching intervals for the second type of deviation period sufficient? Question: Is the total number of reliable matching intervals established in all second-type deviation period periods (B, C, D, a total of 3) greater than the preset threshold (3)? Judgment: 3 is not greater than 3 ("greater than" the threshold). Therefore, the number "does not meet the requirement". The logic proceeds to the next level.

[0049] Second-level judgment: Calculate the response matching factor. Formula: Response matching factor = Number of Type II matching intervals × Percentage of Type II time period duration. Data: Number of Type II matching intervals = 3. Assuming that the total duration of all Type II deviation types of time periods in a day is 10 hours, accounting for 10 / 24 ≈ 41.7% of the day, calculate: Response matching factor = 3 × 0.417 = 1.25; Third-level judgment: Decision based on matching factors Question: Is the response matching factor (1.25) greater than the preset threshold (0.5)? Judgment: Yes, 1.25 > 0.5. Final Decision: Since the response matching factor is sufficiently large, it indicates that the system is highly reliable for a considerable portion of the day. To ensure absolute safety throughout the entire daily cycle, a "zero-trust" strategy must be adopted for periods with inaccurate predictions, such as 14:00-18:00.

[0050] The ruling states that there is no matching heating range for the 14:00-18:00 period, which falls under the category of Type 1 deviation. This means that every afternoon, regardless of the predicted demand, the system requires the steam heating network heaters to be ready to intervene at any time.

[0051] Dividing the system into time periods fundamentally improves decision-making accuracy and enables precise control: the system recognizes that the reliability of predictions varies at different times of the day. Morning peak demand (06:00-10:00) is stable and predictable, allowing for reliable use of waste heat; however, afternoon demand (14:00-18:00) is unpredictable due to weather, activities, and other factors, necessitating backup heat sources. Dynamic strategy: the intervention strategy for steam heating network heaters is no longer simply "on all day" or "off all day," but rather generates a "daily intervention schedule." For example: "No intervention from 00:00 to 06:00, no intervention from 06:00 to 10:00, pending from 10:00 to 14:00, real-time intervention from 14:00 to 18:00, no intervention from 18:00 to 24:00". Economic optimization: This mode can maximize the potential of waste heat resources while ensuring safety, and compress the use of backup energy to the truly necessary and short periods of time, thereby achieving the economic optimization of system operation.

[0052] Specifically, the preset future time period is between 6 hours and 48 hours, which determines whether the real-time intervention of the steam heating network heater is needed during the preset future time period, thereby ensuring the operational stability of the steam heating network heater.

[0053] Furthermore, the process that eliminates the need for steam network heaters specifically includes: Based on the predicted heating demand load data for the future preset period and the matching heating range data under different prediction deviation types, the intervention of steam network heaters is determined. Based on the predicted heating demand load data for a future preset period, the time period that falls within the matching heating range is determined and used as the reliable matching time period; Based on the distribution data of the reliable matching time period under different prediction deviation types, determine the reliable matching time period under different prediction deviation types; Based on the reliable matching time period data and the reliable matching time period data under different prediction deviation types, it is determined whether real-time intervention of the steam heating network heater is required.

[0054] It is understood that, based on the reliable matching time period data and the reliable matching time period data under different prediction deviation types, it is determined whether real-time intervention of the steam heating network heater is required, specifically including: The time periods excluding the reliable matching time periods in the future preset time period are taken as other time periods. It is determined whether the total duration of the other time periods is greater than the preset duration threshold. If so, it is determined that the real-time intervention of the steam heating network heater is required. If not, proceed to the next step. Based on the reliable matching time period data, determine the total duration of the reliable matching time period under the two types of deviation, the proportion of the duration in the reliable matching time period, and use it as the predicted deviation duration proportion. Determine whether the predicted deviation duration proportion is greater than the preset deviation duration proportion threshold. If so, it is determined that real-time intervention of the steam heating network heater is required. If not, proceed to the next step. Determine whether all reliable matching time periods are under the same type of deviation. If so, it is determined that real-time intervention of the steam heating network heater is not required. If not, proceed to the next step. Based on the average of the predicted deviation duration ratio and the total duration of other time periods in the future preset time period, an intervention risk coefficient is determined. When the intervention risk coefficient is greater than the preset risk coefficient threshold, it is determined that real-time intervention of the steam heating network heater is required. When the intervention risk coefficient is not greater than the preset risk coefficient threshold, it is determined that real-time intervention of the steam heating network heater is not required.

[0055] Specifically, the basic scenario and parameter settings are as follows: the future preset time period is 18 hours; the preset duration threshold (T_other) is 4 hours (referring to the upper limit of tolerance for "other time periods"); the preset deviation duration ratio threshold (T_reliable_ratio) is 80% (referring to the required proportion of reliable time periods of type II deviation in the total reliable time period); and the preset risk coefficient threshold (T_risk) is 0.4.

[0056] Step 2: Obtain the predicted data and matching intervals Load forecast and weather type (deviation type) classification for the next 18 hours: Period 1 (0-6h): Sunny days in spring -> Type II deviation. Forecast load: 75-88 tons / hour.

[0057] Period 2 (6-12h): Spring and Autumn Transition Day -> Type I Deviation. Forecast Load: 85-105 tons / hour.

[0058] Period 3 (12-18h): Typical winter day -> Type II deviation. Forecast load: 92-98 tons / hour.

[0059] Known "matching heating ranges" (from historical analysis): The matching range for sunny days in spring (Category II) is B (70-85 tons / hour); the matching range for transitional days in spring and autumn (Category I) is non-existent; the matching range for typical days in winter (Category II) is C (85-95 tons / hour). Step 3: Identify "Reliable Matching Periods" Logic: For a given time period, if its predicted load range falls entirely within the matching heating range for the corresponding weather type, then that time period is marked as a "reliable matching time period".

[0060] Analysis: Period 1: The predicted load (75-88) largely overlaps with the matching interval B (70-85), but 88 > 85, indicating a partial overlap. Therefore, it is not a reliable matching period. Period 2: There is no matching interval for this weather type (Class I). Therefore, it is definitely not a reliable matching period. Period 3: The predicted load (92-98) falls entirely within its matching interval C (85-95). Therefore, it is a reliable matching period.

[0061] Reliable matching time period: Only time period 3 (12-18h, 6 hours), other time periods: time period 1 (0-6h, 6 hours) and time period 2 (6-12h, 6 hours), total duration 12 hours. Reliable matching time period under type II deviation: time period 3, total 6 hours. Reliable matching time period under type I deviation: 0 hours.

[0062] First-level judgment: Check the total duration of "other time periods". Question: Is the total duration of other time periods (12 hours) greater than T_other (4 hours)? Judgment: Yes, 12>4. Decision: According to the rules, directly determine that real-time intervention of the steam heating network heater is required.

[0063] This multi-level decision-making model embodies a progressive approach from "absolute rejection" to "comprehensive risk assessment": Absolute rejection: If the uncontrollable period is too long, intervene directly; Quality check: Even if the uncontrollable period is short, if the system relies excessively on accurately predicted periods (indicating a weak ability to cope with inaccurate periods), intervene; Extreme cases: If all reliable periods come from inaccurate predictions (which may mean the system is very conservative or the demand is extremely stable), it is safe not to intervene; Comprehensive assessment: Finally, by calculating a comprehensive risk coefficient, the most refined trade-off is made in the "grey area".

[0064] This approach ensures that decisions are both safe and reliable, without being overly conservative, and finds the optimal balance between safety and economy in complex operating environments.

[0065] Furthermore, the method for determining the intervention control strategy of the steam heating network heater in the future preset time period is as follows: Based on the predicted heating demand load data after a preset future period and the matching of the heating supply range, the time period within a unit of time after the preset future period that falls within the matching heating supply range is determined and used as the future matching time period. Based on the predicted deviation type within a unit of time after a preset future period, a time period of a certain type of deviation is determined and used as a deviation time period. Based on the future matching period and a type of deviation period, the intervention control strategy for the steam heating network heater in the future preset period is determined.

[0066] It should be noted that the value of the unit duration is between 6 hours and 24 hours, and is used to determine the intervention strategy of the steam heating network heater after a preset period of time in the future.

[0067] It is understood that, based on the future matching period and a type of deviation period, the intervention control strategy for the steam heating network heater in the future preset period is determined, specifically including: When there is no future matching period or all of them belong to the same type of deviation period within the unit time, the steam heating network heater needs to be intervened in real time after the preset period in the future. Therefore, as long as the cumulative duration of the minimum endpoint of the flue gas temperature range with the largest proportion of monitored temperatures being less than the monitored time is greater than the preset duration threshold, the steam heating network heater is controlled to perform real-time intervention control processing. When there are future matching periods or periods that do not all belong to the same type of deviation within the unit time, the input demand factor of the steam heating network heater is determined by the average of the proportion of the future matching period in the unit time and the proportion of the period that does not belong to the same type of deviation. When the input demand factor is greater than a preset demand factor threshold, the steam heating network heater is controlled to perform real-time intervention control processing as long as the cumulative duration of the minimum endpoint corresponding to the flue gas temperature range with the largest proportion of monitored temperatures lower than the monitored time is greater than a second preset duration threshold. When the input demand factor is not greater than the preset demand factor threshold, there is no need to control the steam heating network heater to perform real-time intervention control processing until the cumulative duration of the heat supply of the waste heat recovery system being less than the heat supply demand load is greater than a preset cumulative duration threshold, at which point real-time intervention control processing of the steam heating network heater is required.

[0068] It is understandable that the real-time intervention control of the steam heating network heater is only required when the cumulative duration of the heating demand load is less than the preset cumulative duration threshold. The real-time intervention control will only be performed when the idle duration of the steam heating network heater exceeds the preset idle duration threshold.

[0069] In this embodiment, instead of simply determining "whether intervention is needed now", the intervention triggering conditions are dynamically adjusted based on the prediction of future trends, achieving a leap from "passive response" to "active adaptive control".

[0070] Example: Adaptive Intervention Control Strategy Based on Future Trends Step 1: Basic Scene and Parameter Settings Time Definition: Future Preset Time Period: Refers to the upcoming period requiring refined control, such as the next 12 hours. Unit Duration (Looking-Out Period): A time window used to predict future trends, valued at 12 hours (i.e., 12 hours after the "future preset time period"). Preset Duration Threshold (T1): 10 minutes (Strict Intervention Condition). Second Preset Duration Threshold (T2): 30 minutes (Relaxed Intervention Condition). Preset Cumulative Duration Threshold (T3): 45 minutes (Passive Response Condition). Preset Demand Factor Threshold (T_demand): 0.5. Preset Idle Duration Threshold (T_idle): 2 hours (Exit Condition). Step 2: Analyze future trends (forecast analysis) We conduct predictive analysis on the 12 hours following the "future preset time period" (i.e., the 13th to 24th hours).

[0071] Identify “future matching periods”: Analyze the predicted periods within the next 12 hours when the load falls into the “matching heating range” corresponding to the weather type.

[0072] Hypothetical outcome: Within this 12-hour outlook period, 4 hours of future matching time periods were found, identifying "Type I deviation time periods": Analyzing the time periods within this 12-hour period that belong to the Type I deviation type of prediction inaccuracy, hypothetical outcome: Within this 12-hour outlook period, 3 hours were identified as Type I deviation time periods.

[0073] Step 3: Determine the control strategy (based on the prospective results) Now, based on the analysis results of the prospective period, specific intervention and control strategies will be developed for the "next 12 hours" (the future preset period).

[0074] Scenario 1: Extremely pessimistic outlook -> Adopt aggressive intervention strategy Assuming the data for the outlook period: within the 12-hour outlook period, there are no future matching periods, or all 12 hours belong to a type of bias period.

[0075] Strategy Determination: This indicates a severe future situation where the waste heat system may not be able to support the system independently, necessitating the deployment of the steam heaters. Therefore, the system has formulated Strategy A for the "next 12 hours": Aggressive Intervention. Control Logic: Within the next 12 hours, if the cumulative duration of flue gas temperature below the target temperature range (120°C) exceeds T1 (10 minutes), the steam heating network heaters will be immediately activated for real-time intervention.

[0076] Scenario 2: Optimistic outlook -> Adopt a moderate intervention strategy Assuming the forward data (continuing from step one): there is a 4-hour future matching period, and not all of them are of the first type of deviation period. Strategy determined: Calculate the input demand factor. Calculate: the proportion of future matching periods = 4h / 12h ≈ 0.33, the proportion of non-first-class deviation periods = (12h - 3h) / 12h = 0.75, the input demand factor = (0.33 + 0.75) / 2 = 0.54, and determine: the input demand factor (0.54) > the preset demand factor threshold (0.5).

[0077] Strategy Determination: Strategy B-1 is adopted: Gentle intervention. Control logic: Over the next 12 hours, a relatively lenient triggering condition will be used. The steam heat network heater will only be activated if the cumulative duration of the flue gas temperature being below 120°C exceeds T2 (30 minutes). Strategy Interpretation: Because the waste heat system may also be adjusted in the future, the system allows the current waste heat system a longer "performance time," avoiding frequent activation of the backup heat source due to short-term fluctuations, thus being more economical.

[0078] Scenario 3: Very Optimistic Outlook -> Passive Response Strategy. Assuming the outlook data shows the input demand factor is no greater than 0.5 (e.g., many future matches, few Type I deviations, factor 0.4), the strategy is determined as follows: The waste heat recovery system is fully adjustable in the future. Strategy B-2 is adopted: passive response. Control logic: In the next 12 hours, flue gas temperature is not actively monitored as an intervention basis. Instead, the steam network heater is only activated when the cumulative duration of "waste heat supply < actual heating demand" exceeds T3 (45 minutes). This is the most economical mode. The system completely trusts the waste heat recovery system until a clear supply-demand gap appears and persists for a period of time before remedial action is taken. This maximizes the utilization of waste heat.

[0079] Step 4: Mechanism for deactivating backup heat sources The steam heating network heater will not operate continuously if the cumulative duration of "waste heat supply < actual heating demand" exceeds T3 (45 minutes). The system has an intelligent shutdown mechanism. Logic: When the steam heating network heater starts, the system monitors its idle time (i.e., the time its output is zero or very low). Action: Once the continuous idle time exceeds T_idle (2 hours), the system determines that the crisis has been resolved and automatically shuts off the real-time intervention of the steam heating network heater, restoring the system to the baseline mode of complete heat supply from the waste heat system. Purpose: To prevent unnecessary long-term operation of the backup heat source and save energy costs.

[0080] The essence of this control strategy lies in "adjusting the present based on the future." It uses a forward-looking window to predict future supply and demand conditions, thereby dynamically adjusting the sensitivity and initiative of the current control strategy. A pessimistic future outlook leads to a sensitive (aggressive) present outlook: proactively preventing problems using short-term thresholds. An optimistic future outlook leads to a tolerant (moderate / passive) present outlook: pursuing economic operation using long-term thresholds or passive conditions.

[0081] This strategy successfully transforms asymmetric information (future trends) in the time dimension into asymmetric advantages in the control dimension (adaptive triggering conditions), achieving a higher level of unity between security and economy.

[0082] Example 2 In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for heat network scheduling and processing considering a unit waste heat utilization system when running the computer program.

[0083] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0084] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0085] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for scheduling and processing a heat network considering a waste heat utilization system of a generating unit, characterized in that, Specifically, it includes: Based on the monitoring data of flue gas temperature, the variation data of the monitoring data of flue gas temperature is determined. Based on the variation data, when it is not necessary to intervene in the steam heating network heater in real time, the matching status of the flue gas temperature and the heating network scheduling demand is determined. Combined with the prediction deviation type of heating demand load under different weather types, the matching heating range under different prediction deviation types is determined. Based on the predicted heating demand load data for the future preset period and the matching heating range data under different prediction deviation types, when it is determined that real-time intervention of the steam heating network heater is not required, proceed to the next step. Based on the predicted heating demand load data after a preset future period and the matching status of the matching heating range, as well as the type of prediction deviation, the intervention control strategy of the steam heating network heater in the preset future period is determined.

2. The heat network dispatching and processing method considering the waste heat utilization system of the generating unit as described in claim 1, characterized in that, The flue gas temperature monitoring data is determined based on the monitoring results of the flue gas temperature monitoring device.

3. The heat network dispatching and processing method considering the waste heat utilization system of the generating unit as described in claim 1, characterized in that, The variation data of the flue gas temperature monitoring data is determined based on the distribution data of monitoring time in different flue gas temperature ranges.

4. The heat network dispatching and processing method considering the waste heat utilization system of the generating unit as described in claim 1, characterized in that, The flue gas temperature range is divided into a preset number of intervals based on historical monitoring data of the flue gas temperature, using an equal-interval approach.

5. The heat network dispatching and processing method considering the waste heat utilization system of the generating unit as described in claim 1, characterized in that, It was determined that no real-time intervention was required for the steam heating network heaters, specifically including: Based on the variation data of the flue gas temperature monitoring data, the distribution data of monitoring time in different flue gas temperature ranges are determined; Based on the distribution data of monitoring times in different flue gas temperature ranges, the proportion of monitoring times in different flue gas temperature ranges is determined. Based on the proportion of monitoring times within different flue gas temperature ranges, it is determined whether real-time intervention of the steam heating network heater is necessary.

6. The heat network dispatching and processing method considering the waste heat utilization system of the generating unit as described in claim 5, characterized in that, The proportion of monitoring times in different flue gas temperature ranges is determined based on the proportion of monitoring times in the flue gas temperature ranges among all monitoring times.

7. The heat network dispatching and processing method considering the waste heat utilization system of the generating unit as described in claim 5, characterized in that, Based on the proportion of monitoring times within different flue gas temperature ranges, it is determined whether real-time intervention of the steam heating network heater is necessary, specifically including: When there is no flue gas temperature range where the proportion of monitoring times is greater than the preset monitoring time proportion threshold, it indicates that the flue gas temperature is changing rapidly. Therefore, it is possible that the flue gas temperature is changing so that the waste heat recovery system cannot reliably provide heat energy. Therefore, it is determined that the steam heating network heater needs to be intervened in real time. When the heat energy of the waste heat recovery system cannot meet the demand, heat energy should be supplemented in a timely manner.

8. The heat network dispatching and processing method considering the waste heat utilization system of the generating unit as described in claim 1, characterized in that, The method for determining the intervention control strategy of the steam heating network heater in the future preset time period is as follows: Based on the predicted heating demand load data after a preset future period and the matching of the heating supply range, the time period within a unit of time after the preset future period that falls within the matching heating supply range is determined and used as the future matching time period. Based on the predicted deviation type within a unit of time after a preset future period, a time period of a certain type of deviation is determined and used as a deviation time period. Based on the future matching period and a type of deviation period, the intervention control strategy for the steam heating network heater in the future preset period is determined.

9. The heat network dispatching and processing method considering the waste heat utilization system of the generating unit as described in claim 8, characterized in that, Based on the future matching time period and a type of deviation time period, the intervention control strategy for the steam heating network heater in the future preset time period is determined, specifically including: When there is no future matching period or all periods belong to the same type of deviation period within the unit time, the steam heating network heater needs to be intervened in real time after the preset future period. Therefore, as long as the cumulative duration of the minimum endpoint of the flue gas temperature range with the largest proportion of monitored temperatures being lower than the monitored time is greater than the preset duration threshold, the steam heating network heater is controlled to perform real-time intervention control processing.

10. A computer system, comprising: A memory and processor connected by communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a heat network scheduling and processing method considering a unit waste heat utilization system as described in any one of claims 1-9.

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