Unit flexible powder making method and system based on intermediate bin

Abstract

The invention provides a unit flexible powder making method and system based on an intermediate bin, and belongs to the technical field of intelligent power plants, and the method specifically comprises the steps that a plurality of powder making alternative schemes are generated with the lowest safety allowance of the intermediate bin in different time periods and the reserve capacity of the intermediate bin as constraint conditions and the powder making amount in different time periods as the basis, according to the method, load fluctuation conditions among different time periods are determined on the basis of pulverization power consumption and unit basic loads in different time periods, and selectable schemes in pulverization alternative schemes are determined according to the load fluctuation conditions among different time periods, so that on-grid prices in different time periods are obtained; and on the basis of the pulverization power consumption and the on-grid price in different time periods, the optimal scheme in the selectable schemes is determined by further combining the deviation condition of the pulverized coal reserve of the intermediate bin in different time periods and the minimum safety margin, so that the operation flexibility of the unit is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart power plants, and in particular relates to a flexible powder making method and system for a unit based on an intermediate bin. Background Art

[0002] In the units that use raw coal as power generation fuel, most power plant boilers use pulverized coal to feed the boilers. For direct-blowing boilers, the existing technical solutions often use the needs of power plant boilers to prepare pulverized coal. This makes it a technical problem that needs to be solved urgently to combine the energy storage characteristics of the intermediate bin (i.e., use the intermediate bin to store pulverized coal) to improve the flexibility of load regulation of the unit.

[0003] In order to solve the above technical problems, in the invention patent application CN202410438352.9 "Method and device for coupling small intermediate warehouse and molten salt energy storage to improve the flexibility of thermal power plants", the small intermediate warehouse flexible storage and supply system and the molten salt energy storage system are coupled to the coal-fired thermal power unit, which can effectively improve the rapid load lifting and lowering capacity of the coal-fired thermal power unit and achieve efficient use of energy. However, there are the following technical problems:

[0004] In the electricity spot market, there are large differences in the electricity price benefits of the units at different times of the day, which leads to large differences in the economic benefits of using electricity to prepare coal powder at different times. Therefore, if the preparation of coal powder cannot be dynamically adjusted in combination with the differences in power generation prices at different times in the electricity spot market, the units will not be able to obtain the maximum economic benefits.

[0005] In response to the above technical problems, the present invention generates differentiated coal powder preparation adjustment strategies by considering the on-grid electricity prices in different time periods of the day, so as to achieve the beneficial effects of improving the flexibility of the unit and increasing the economic benefits. Specifically, the present application provides a flexible pulverizing method and system for the unit based on an intermediate bin. Summary of the invention

[0006] A flexible milling method based on an intermediate bin, characterized by comprising:

[0007] S1 predicts the unit load at different time periods during the day, and uses the unit load at different time periods to determine the pulverized coal consumption at different time periods, and determines the minimum safety margin of the intermediate bin of the unit at different time periods based on the pulverized coal consumption and the historical frequency regulation data of the unit;

[0008] S2 takes the minimum safety margin of the intermediate warehouse in different time periods and the reserve capacity of the intermediate warehouse as constraints, generates multiple milling alternative plans based on the milling amount in different time periods, determines the load fluctuation between different time periods based on the milling power consumption and the basic load of the unit, and determines the optional plans in the milling alternative plans based on the load fluctuation between different time periods;

[0009] S3 obtains the on-grid electricity price in different time periods, and determines the optimal solution among the optional solutions based on the pulverizing power consumption and on-grid electricity price in different time periods, and further combines the deviation between the pulverized coal reserves in the intermediate bin and the minimum safety margin in different time periods.

[0010] A further technical solution is to predict the load of the unit at different times of the day: statistics on processes and equipment involving interruptible loads, use advanced forecasting algorithms to predict the load demand of the power grid, including short-term and long-term power demand forecasts, and combine price fluctuations in the power spot market to provide basic data for milling scheduling;

[0011] A further technical solution is that the method for determining the best solution among the optional solutions is:

[0012] Obtaining the milling power consumption of the optional scheme at different time periods, and determining the economic feasibility of the optional scheme in combination with the on-grid electricity prices at different time periods;

[0013] Based on the pulverizing amount and pulverizing amount of the optional scheme at different time periods, the pulverized coal reserves of the intermediate bin at different time periods are determined, the frequency regulation safety factor of the intermediate bin at different time periods is determined by the deviation between the pulverized coal reserves of the intermediate bin at different time periods and the minimum safety margin, and the safety of the optional scheme is determined by the average value of the frequency regulation safety factor of the intermediate bin at different time periods;

[0014] A comprehensive evaluation amount of the optional solutions is determined based on the economy and safety of the optional solutions, and the optimal solution among the optional solutions is determined through the comprehensive evaluation amount.

[0015] A further technical solution is that the calculation formula of the comprehensive evaluation amount of the optional solution is:

[0016] T=K1T1+K2T2

[0017] Where T is the comprehensive evaluation quantity, K1 and K2 are the preset weight coefficients of economy and safety respectively, and T1 and T2 are economy and safety respectively.

[0018] On the other hand, an embodiment of the present application provides a flexible milling system based on an intermediate silo, which adopts the above-mentioned flexible milling method based on an intermediate silo, and is characterized in that it specifically includes:

[0019] Intermediate warehouse evaluation module, optional solution screening module, solution output module;

[0020] The intermediate storage evaluation module is responsible for predicting the unit load at different time periods during the day, and using the unit load at different time periods to determine the coal powder consumption at different time periods, and based on the coal powder consumption and the historical frequency modulation data of the unit, determining the minimum safety margin of the intermediate storage of the unit at different time periods;

[0021] The optional scheme screening module is responsible for generating multiple milling alternative schemes based on the milling amount in different time periods and the minimum safety margin of the intermediate warehouse in different time periods and the reserve capacity of the intermediate warehouse as constraints, and determining the load fluctuation between different time periods based on the milling power consumption and the basic load of the unit, and determining the optional schemes in the milling alternative schemes according to the load fluctuation between different time periods;

[0022] The scheme output module is responsible for obtaining the on-grid electricity price in different time periods, and determines the optimal scheme among the optional schemes based on the pulverizing power consumption and on-grid electricity price in different time periods, and further combining the deviation between the pulverized coal reserves in the intermediate bin and the minimum safety margin in different time periods.

[0023] The beneficial effects of the present invention are:

[0024] 1. In the present invention, the minimum safety margin of the intermediate bin of the unit in different time periods is determined based on the powder consumption and the historical frequency regulation data of the unit, and the powder consumption demand of the unit in the frequency regulation process is fully considered, thereby avoiding the technical problem of insufficient frequency regulation flexibility of the unit due to insufficient coal powder, and realizing dynamic adjustment of the minimum safety margin of the intermediate bin based on the frequency regulation demand of the unit.

[0025] 2. In the present invention, the best option among the optional options is determined based on the deviation between the pulverizing power consumption and the on-grid electricity price in different time periods, and the pulverized coal reserves in the intermediate bin and the minimum safety margin. This ensures the economy of pulverizing in the pulverizing scheme due to the difference in on-grid electricity prices, and realizes the adjustment of pulverizing in periods with lower electricity prices. It also ensures that the pulverized coal reserves in the intermediate bin and the minimum safety margin can be reasonably controlled within a certain range. While ensuring the safety of frequency regulation, it also improves the overall operating safety of the unit, and realizes the determination of the best option among the optional options from multiple angles. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0027] Figure 1It is a flow chart of a flexible powder making method for a unit based on an intermediate bin according to Example 1.

[0028] Figure 2 It is a flow chart of a method for determining the minimum safety margin of an intermediate warehouse;

[0029] Figure 3 It is a framework diagram of a flexible pulverizing system for a unit based on an intermediate silo;

[0030] Figure 4 It is the framework diagram of the intermediate storage silo type flour making system.

[0031] Figure numerals: 1-coal feeder; 2-coal mill; 3-fine powder separator, 4-coal powder bin; 5-coal powder exhaust fan; 6-burner; 7-boiler; 8-air preheater; 9-air blower; 10-coal feeder. DETAILED DESCRIPTION

[0032] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of ways and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the present invention will be comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar structures, and thus their detailed description will be omitted.

[0033] The terms "a", "an", "the", and "said" are used to indicate the presence of one or more elements / components / etc.; the terms "comprising" and "having" are used to express an open-ended inclusive meaning and mean that additional elements / components / etc. may be present in addition to the listed elements / components / etc.

[0034] Example 1

[0035] To solve the above problems, Figure 4 As shown, it is a framework diagram of the intermediate storage silo type flour making system involved in the present invention. According to one aspect of the present invention, as Figure 1 As shown, a flexible milling method based on an intermediate bin is provided, which is characterized by comprising:

[0036] S1 predicts the unit load at different time periods during the day, and uses the unit load at different time periods to determine the pulverized coal consumption at different time periods, and determines the minimum safety margin of the intermediate bin of the unit at different time periods based on the pulverized coal consumption and the historical frequency regulation data of the unit;

[0037] S2 takes the minimum safety margin of the intermediate warehouse in different time periods and the reserve capacity of the intermediate warehouse as constraints, generates multiple milling alternative plans based on the milling amount in different time periods, determines the load fluctuation between different time periods based on the milling power consumption and the basic load of the unit, and determines the optional plans in the milling alternative plans based on the load fluctuation between different time periods;

[0038] S3 obtains the on-grid electricity price in different time periods, and determines the optimal solution among the optional solutions based on the pulverizing power consumption and on-grid electricity price in different time periods, and further combines the deviation between the pulverized coal reserves in the intermediate bin and the minimum safety margin in different time periods.

[0039] Furthermore, the pulverized coal consumption is determined according to the amount of coal required for boiler combustion under the load of the unit.

[0040] Specifically, Figure 2 As shown, the method for determining the minimum safety margin of the intermediate bin is:

[0041] Determine the frequency modulation times of the unit in different time periods in historical dates based on the historical frequency modulation data of the unit, and determine the number of time periods in different frequency modulation time intervals through the frequency modulation times in different time periods;

[0042] Based on the number of time periods in different frequency modulation frequency intervals, weight coefficients of different frequency modulation frequency intervals are determined;

[0043] Determine the coal consumption compensation amount within different frequency regulation intervals, and determine the frequency regulation coal consumption demand of the unit in combination with the weight coefficients of different frequency regulation intervals; determine the minimum safety margin of the intermediate bin of the unit in different time periods based on the sum of the frequency regulation coal consumption demand and the powder consumption.

[0044] It should be noted that the weight coefficient of the frequency modulation number interval is determined according to the ratio of the number of time periods to the number of preset time periods.

[0045] In another embodiment, the method for determining the minimum safety margin of the intermediate warehouse is:

[0046] Determine the frequency modulation times of the unit in different time periods in historical dates based on the historical frequency modulation data of the unit, and when there is no time period in which the frequency modulation times are greater than the preset frequency modulation times, determine the minimum safety margin of the intermediate bin in different time periods using the powder consumption in the different time periods;

[0047] When there is a period of time when the frequency modulation number is greater than the preset frequency modulation number:

[0048] The time period in which the frequency modulation number is greater than the preset frequency modulation number is regarded as the attention period. When the proportion of the number of the attention periods is not greater than the preset proportion, the powder consumption in the different time periods is used to determine the minimum safety margin of the intermediate bin in the different time periods.

[0049] When the proportion of the attention time periods is greater than the preset proportion:

[0050] When the average frequency modulation times of different attention periods is greater than the preset frequency modulation times:

[0051] Determine the minimum safety margin of the intermediate bunker of the unit at different time periods by using the sum of the preset coal consumption and the powder consumption;

[0052] When the average frequency modulation times in different attention periods is not greater than the preset frequency modulation times:

[0053] The frequency modulation frequency coefficient is determined by using the frequency modulation times in different attention periods and the proportion of the number of attention periods. When the frequency modulation frequency coefficient does not meet the requirements, the minimum safety margin of the intermediate bunker of the unit in different periods is determined by using the sum of the preset coal consumption and the powder consumption;

[0054] When the frequency modulation frequency coefficient meets the requirements:

[0055] Determine the number of time periods in different frequency modulation intervals according to the frequency modulation times in different time periods, and determine the weight coefficients of different frequency modulation intervals based on the number of time periods in different frequency modulation intervals;

[0056] Determine the coal consumption compensation amount within different frequency regulation intervals, and determine the frequency regulation coal consumption demand of the unit in combination with the weight coefficients of different frequency regulation intervals; determine the minimum safety margin of the intermediate bin of the unit in different time periods based on the sum of the frequency regulation coal consumption demand and the powder consumption.

[0057] It should be noted that multiple milling alternatives are generated, including:

[0058] Taking the storage capacity of the intermediate warehouse as the constraint condition and the minimum safety margin of the intermediate warehouse that can meet the needs of different periods of time as the basis, the milling amount in different periods of time is determined;

[0059] Based on the milling amount in different time periods, multiple milling alternatives are generated by free combination.

[0060] Furthermore, the method for determining the optional scheme in the milling alternative scheme is:

[0061] Obtaining boiler loads at different time periods, and determining the amount of change in boiler loads between different adjacent time periods based on the boiler loads;

[0062] Based on the change in boiler load between different adjacent time periods, the load fluctuation coefficient between different adjacent time periods is determined;

[0063] The load stability of the alternative milling scheme is determined by the load fluctuation coefficient between different adjacent time periods, and the optional scheme in the alternative milling scheme is determined based on the load stability.

[0064] In a possible embodiment, the method for determining the load stability of the milling alternative is:

[0065] The load stability of the milling alternative is determined by using a CNN-BiGRU based prediction model, specifically including:

[0066] Step 1 Input layer: Preprocess the original data, i.e., the load fluctuation coefficient between different adjacent time periods, process and normalize the outliers and missing values ​​in the data, and use the processed data as the input of the prediction model. Suppose the input data with a sequence length equal to n is X: [x1, x2, x3, ..., x n ] T .

[0067] Step 2 CNN layer: CNN mainly extracts features from the load sequence and is mainly composed of a one-dimensional convolutional layer, a pooling layer, and a fully connected layer. First, the one-dimensional convolutional layer is used to extract the features of the historical load sequence; then the maximum pooling method is used to filter the information to prevent the risk of overfitting; finally, the output of the CNN layer is obtained through the Sigmoid activation function. It is expressed as: Let the output length of the CNN layer be i, then its output H: [H c1 , H c2 , H c3 , …, H cn ] T .

[0068] H c =Sigmoid(P×W2+b1)

[0069] The convolutional layer output c can be expressed as:

[0070]

[0071] Relu(x)=max(0,x)

[0072] In the formula: C represents the output of the convolution layer; Hc represents the output of the CNN layer; P represents the output of the pooling layer; b, b1 represent the bias; the convolution layer uses the ReLU activation function.

[0073] Step 3 BiGRU layer: The BiGRU layer trains the feature vectors extracted by CNN to explore the changing rules within the historical load data. The BiGRU is composed of the forward GRU layer and the reverse GRU layer. The BiGRU layer can transmit information in both directions, and its output is jointly determined by the forward GRU and the reverse GRU. The output of the BiGRU layer at time t can be expressed as:

[0074] h t =BiGRU(h t-1 , H c )

[0075] Where: BiGRU represents the expression after BiGRU model.

[0076] Step 4: Self-attention mechanism layer: The input of the self-attention mechanism layer is the output vector calculated by the BiGRU layer. The weights of different input feature vectors can be obtained through the self-attention mechanism.

[0077] Step 5: Fully connected layer: The fully connected layer calculates the predicted value through the Sigmoid activation function. Let the predicted value at time t be Y t , which can be expressed as:

[0078] y t =Sigmoid(w0s t +b0)

[0079] Where: Yt represents the predicted value at time t; w0 represents the weight matrix; b0 represents the bias vector.

[0080] Step 6 Output layer: The output layer is to denormalize the data obtained from the model training in this paper and finally output the preset prediction step size of m, which is the load stability of the alternative powder making scheme.

[0081] Specifically, determining the optional scheme in the milling alternative scheme based on load stability specifically includes:

[0082] When the load stability of the alternative milling scheme is within a preset stability range, the alternative milling scheme is determined to be an optional scheme.

[0083] It can be understood that the load fluctuation coefficient between adjacent time periods is determined according to the load variation interval corresponding to the change amount of the boiler load between adjacent time periods, and specifically the preset fluctuation coefficient corresponding to the load variation interval is used as the load fluctuation coefficient between adjacent time periods.

[0084] It should be noted that the method for determining the optional scheme in the milling alternative scheme is:

[0085] Obtaining boiler loads at different time periods, and determining the amount of change in boiler loads between different adjacent time periods based on the boiler loads;

[0086] Based on the change in boiler load between different adjacent time periods, the load fluctuation coefficient between different adjacent time periods is determined;

[0087] The load fluctuation period in the period is determined based on the load fluctuation coefficient, and whether the milling alternative is an optional scheme is determined according to the number of load fluctuation periods.

[0088] Furthermore, when the number of load fluctuation periods is greater than the preset number of fluctuation periods, it is determined that the milling alternative scheme is not an optional scheme.

[0089] It should also be noted that the method for determining the optional scheme in the milling alternative scheme is:

[0090] Obtaining boiler loads in different time periods, and determining the amount of change in boiler loads between different adjacent time periods based on the boiler loads, and determining that the pulverizing alternative scheme is an optional scheme when the amount of change in boiler loads between different adjacent time periods is within a preset range;

[0091] When there is a period when the boiler load change is not within the preset range:

[0092] The period when the change of boiler load is not within the preset range is regarded as the fluctuation period. When there is no fluctuation period when the change of boiler load is greater than the preset change threshold:

[0093] When the number of the fluctuation time periods is within a preset number range, the milling alternative is determined to be an optional option;

[0094] When the number of the fluctuation time periods is not within the preset number range, the number of fluctuation time periods and the change amount of the boiler load in different fluctuation time periods are used to determine the screening fluctuation coefficient. When the screening fluctuation coefficient meets the requirements, the milling alternative scheme is determined to be an optional scheme;

[0095] When there is a fluctuation period in which the boiler load change is greater than the preset change threshold or the screening fluctuation coefficient does not meet the requirements:

[0096] Based on the change in boiler load between different adjacent time periods, the load fluctuation coefficient between different adjacent time periods is determined, the load stability of the powder making alternative scheme is determined through the load fluctuation coefficient between different adjacent time periods, and the optional scheme in the powder making alternative scheme is determined based on the load stability.

[0097] Furthermore, the load of the units at different times of the day is predicted: the processes and equipment involved in interruptible loads are counted, and the load demand of the power grid is predicted using advanced prediction algorithms, including short-term and long-term power demand predictions, combined with price fluctuations in the power spot market, to provide basic data for milling scheduling;

[0098] Power load forecasting is based on the following types of data:

[0099] Historical load data: Real-time load data obtained from the historical operation records of the power plant, including daily, weekly, monthly and annual load changes; these data are usually recorded and stored by the power plant's power monitoring system (EMS) or energy management system (EMS);

[0100] Grid operation data: regional power demand data and power transmission status obtained from the grid dispatch center, including transmission line load, substation load, etc.; these data can be obtained through the API interface or data sharing platform provided by the grid company;

[0101] Meteorological data: Weather forecast data obtained from meteorological service providers, including temperature, humidity, rainfall, wind speed, etc., as an important influencing factor for load forecasting; these data are usually obtained through the meteorological data API interface or data service platform.

[0102] In power load forecasting, data processing mainly includes the following steps:

[0103] Data preprocessing: Clean and normalize the acquired historical load data and power grid operation data, remove abnormal data, fill in missing data, and ensure data integrity and consistency.

[0104] Feature extraction: Based on the processed data, extract key features that affect power load, such as seasonal characteristics, time characteristics (such as working days and holidays), meteorological characteristics, etc.;

[0105] Prediction model training: Use machine learning algorithms (such as neural networks, support vector machines, random forests, etc.) or time series models (such as ARIMA, LSTM, etc.) to train the extracted features and build a power load prediction model;

[0106] Real-time prediction and update: In actual operation, the model is updated and calibrated using real-time collected data to ensure the accuracy and timeliness of the prediction results.

[0107] It is understandable that before the minimum safety margin of the intermediate compartment is determined, the operating status of the boiler needs to be evaluated. When the operating status of the boiler meets the requirements, the minimum safety margin of the intermediate compartment is determined. If not, the preset minimum safety margin is used to determine the minimum safety margin of the intermediate compartment.

[0108] Boiler operation status monitoring: Real-time monitoring of boiler operating parameters, such as steam temperature, pressure, combustion conditions, etc., to ensure that the powder production can meet the immediate needs of the boiler and maintain stable operation of the boiler.

[0109] Boiler operation status monitoring involves the following types of data:

[0110] Boiler sensor data: data collected in real time by various sensors installed in the boiler system (such as temperature sensors, pressure sensors, flow meters, oxygen content analyzers, etc.). These data are usually collected and stored by the boiler control system (BCS) or distributed control system (DCS).

[0111] Fuel quality data: coal quality data obtained from the coal quality report provided by the fuel supplier, including coal calorific value, volatile matter, ash content, moisture, etc. These data are usually obtained through regular coal sample test reports or online coal quality analyzers.

[0112] In boiler operation status monitoring, data processing mainly includes the following steps:

[0113] Data collection and transmission: Through the boiler control system (BCS) or distributed control system (DCS), the data of each boiler sensor is collected in real time, and the data is transmitted to the central monitoring system through industrial communication protocols (such as Modbus, Profibus, etc.).

[0114] Data cleaning and calibration: Clean and calibrate the collected sensor data to remove noise and abnormal data to ensure the accuracy and reliability of the data.

[0115] Status analysis and evaluation: Using the boiler operation model and combustion optimization algorithm, the cleaned data is analyzed to evaluate the boiler's operating status (such as combustion efficiency, emission level, thermal efficiency, etc.) and identify potential operating problems or optimization opportunities.

[0116] Real-time monitoring and alarm: Based on the analysis results, the operating status of the boiler is monitored in real time. When abnormal conditions are detected or operating parameters exceed the safe range, alarms and protective measures are triggered.

[0117] Furthermore, when determining the reserve capacity of the intermediate warehouse, it is also necessary to further combine the coal intermediate storage status monitoring: real-time monitoring of the inventory, coal powder quality, temperature and humidity of the coal intermediate warehouse, and use inert gas for protection to ensure that the coal powder is stored within a reasonable range and avoid safety hazards caused by excessive storage.

[0118] The following types of data are required for monitoring the status of coal intermediate storage:

[0119] Storage equipment sensor data: real-time data collected by sensors (such as level meters, temperature sensors, humidity sensors, etc.) installed on coal intermediate storage equipment (such as coal intermediate silos, transportation pipelines, coal mills, etc.). These data are obtained through warehouse management systems (WMS) or automated control systems (ACS).

[0120] Pulverized coal quality data: Pulverized coal quality data obtained through regular sampling and testing, including coal fineness, moisture, ash content, etc. These data are usually provided by laboratory testing equipment or online pulverized coal analyzers.

[0121] In the monitoring of coal intermediate storage status, data processing mainly includes the following steps:

[0122] Data collection and integration: Collect sensor data from the warehouse management system (WMS) or automated control system (ACS) in real time, and integrate the data from each device to form complete warehouse status information.

[0123] Data cleaning and anomaly detection: Clean the collected sensor data to remove noise and abnormal data, and use anomaly detection algorithms to identify possible equipment failures or safety hazards.

[0124] Inventory status analysis: Based on the data after cleaning, analyze the inventory status of coal powder, including inventory quantity, storage cycle, quality change trend, etc., to ensure that the coal powder is stored within a reasonable range and avoid safety hazards or waste of resources caused by excessive storage.

[0125] Early warning and regulation: Based on the results of inventory status analysis, the reserve capacity is set. When the inventory level is higher than the reserve capacity, an early warning is triggered and corresponding regulatory measures are taken, such as adjusting the milling speed or temporarily stopping milling.

[0126] Specifically, in addition to using optional solutions to determine the milling solution, a multi-objective optimization scheduling algorithm can also be used to determine the optimal solution.

[0127] Use a multi-objective optimization scheduling algorithm to comprehensively consider factors such as power generation load, boiler demand, pulverized coal storage and use, economy and operational flexibility to generate the optimal pulverizing scheduling plan;

[0128] Step S21 Multi-objective optimization scheduling algorithm: Design a multi-objective optimization algorithm, taking factors such as power generation load demand, boiler operation requirements, coal powder storage and use requirements, economy and operational flexibility as input variables to generate the optimal pulverizing scheduling plan.

[0129] Step S211 objective function setting: define multiple objective functions, such as minimizing power generation cost, maximizing equipment operation stability, maximizing coal powder storage stability, etc., and use weighted methods or Pareto optimization methods to comprehensively balance various objectives.

[0130] 1) Objective function

[0131] Assuming the scheduling time interval is Δt and the number of daily scheduling is n, the objective function of minimum daily operation cost is constructed as

[0132] as follows:

[0133]

[0134] in:

[0135]

[0136] Where: C is the daily operating cost; The total electricity cost of the electrical equipment in each scheduling time period; is the total startup cost of the electrical equipment in each scheduling time period;

[0137] is the material storage cost in each scheduling period; W l ,W m ,W h They are the off-peak, mid-peak and peak time-of-use electricity prices stipulated by the local enterprise; and They are the electricity consumption of enterprises during off-peak, average and peak hours respectively;

[0138] 2) Decision variables

[0139] The decision variables are the operating time of each power-consuming equipment and the material storage volume in each scheduling time period;

[0140] T t ={T t (1),T t (2),…T t (i)…,T t (m)},t=1,2,…,n

[0141] S={S 1 ,S 2 ,…S t …,S n}

[0142] Where: T t (i) is the operating time of the i-th interruptible load electrical equipment during the t-th scheduling time, and 0 means that the equipment is not operating; S t is the material storage volume of the storage unit in the tth scheduling time; m is the number of interruptible load power equipment;

[0143] Step S212 Constraints: Set various constraints, including boiler operating time constraints, minimum pulverizing capacity, maximum pulverizing capacity, intermediate coal storage capacity restrictions, power constraints and energy constraints, etc., to ensure that the scheduling plan is physically feasible.

[0144] 3) Constraint equation

[0145] a. Time constraints

[0146]

[0147] T l+s m +T h =24

[0148] Where: T(i) is the average daily operating time of the power equipment calculated based on historical operating data, and are the operating time of the i-th interruptible load power equipment in the t-th dispatch time during the valley, flat and peak periods; T l 、T m and T h The time periods are the low, flat and peak periods respectively.

[0149] b. Storage constraints

[0150] M a ≤M t ≤M b

[0151] S a ≤S t ≤S b

[0152]

[0153] Where: M a and M b is the lower and upper limits of coal mill output in the tth scheduling time, M t is the coal mill output in the tth scheduling time; S a and S b is the lower and upper limits of the buffer storage capacity within the t-th scheduling time; S t is the storage capacity of the buffer storage in the tth scheduling time; is the demand for pulverized coal by the boiler in the clinker preparation process within the tth scheduling time;

[0154] c. Power Constraints

[0155] Assuming that the maximum power demand required by the enterprise is P, then:

[0156] P(i)=αP,i=1,2,…,m

[0157] Where: P(i) is the average power generation of each electrical equipment; α is the margin coefficient, and the value range of α is 0.85-0.95;

[0158] d. Energy Constraint

[0159]

[0160] In the formula, is the power consumption of the i-th power-consuming equipment in the t-th scheduling time period; and They are respectively the amount of electricity consumed by the i-th power-consuming equipment in the t-th scheduling time period during the valley, flat and peak periods.

[0161] Furthermore, the method for determining the best solution among the optional solutions is:

[0162] Obtaining the milling power consumption of the optional scheme at different time periods, and determining the economic feasibility of the optional scheme in combination with the on-grid electricity prices at different time periods;

[0163] Based on the pulverizing amount and pulverizing amount of the optional scheme at different time periods, the pulverized coal reserves of the intermediate bin at different time periods are determined, the frequency regulation safety factor of the intermediate bin at different time periods is determined by the deviation between the pulverized coal reserves of the intermediate bin at different time periods and the minimum safety margin, and the safety of the optional scheme is determined by the average value of the frequency regulation safety factor of the intermediate bin at different time periods;

[0164] A comprehensive evaluation amount of the optional solutions is determined based on the economy and safety of the optional solutions, and the optimal solution among the optional solutions is determined through the comprehensive evaluation amount.

[0165] Specifically, the calculation formula for the comprehensive evaluation amount of the optional scheme is:

[0166] T=K1T1+K2T2

[0167] Where T is the comprehensive evaluation quantity, K1 and K2 are the preset weight coefficients of economy and safety respectively, and T1 and T2 are economy and safety respectively.

[0168] It should also be noted that the method for determining the best solution among the optional solutions is:

[0169] Using the on-grid electricity prices of different time periods, the time periods are divided into a high electricity price interval and a low electricity price interval. When the proportion of the milling power consumption of the optional scheme in the high electricity price interval does not meet the requirements, it is determined that the optional scheme is not the optimal scheme;

[0170] When the proportion of the power consumption of milling in the high electricity price range of the optional scheme meets the requirements:

[0171] Based on the pulverizing amount and pulverizing amount of the optional scheme in different time periods, the pulverized coal reserves of the intermediate bin in different time periods are determined. When the deviations between the pulverized coal reserves of the intermediate bin in different time periods and the minimum safety margin are all less than the preset deviation threshold, it is determined that the optional scheme is not the optimal scheme.

[0172] When there is a period in which the deviation between the coal powder reserves in the intermediate bin and the minimum safety margin is not less than the preset deviation threshold:

[0173] Obtain the milling power consumption of the optional scheme in different time periods, and determine the economic efficiency of the optional scheme in combination with the on-grid electricity prices in different time periods. When the economic efficiency of the optional scheme is not within a preset economic efficiency range, it is determined that the optional scheme is not the optimal scheme;

[0174] When the economy of the optional scheme is within the preset economy range, the frequency regulation safety factor of the intermediate bin in different time periods is determined by the deviation between the coal powder reserves of the intermediate bin in different time periods and the minimum safety margin, and the safety of the optional scheme is determined by the average value of the frequency regulation safety factor of the intermediate bin in different time periods. When the safety of the optional scheme does not meet the requirements, it is determined that the optional scheme is not the optimal scheme;

[0175] When the security of the alternatives meets the requirements:

[0176] A comprehensive evaluation amount of the optional solutions is determined based on the economy and safety of the optional solutions, and the optimal solution among the optional solutions is determined through the comprehensive evaluation amount.

[0177] Example 2

[0178] On the other hand, the embodiment of the present application provides a flexible milling system based on an intermediate bin, such as Figure 3 As shown, the flexible milling method based on the intermediate bin is adopted, which is characterized by specifically comprising:

[0179] Intermediate warehouse evaluation module, optional solution screening module, solution output module;

[0180] The intermediate storage evaluation module is responsible for predicting the unit load at different time periods during the day, and using the unit load at different time periods to determine the coal powder consumption at different time periods, and based on the coal powder consumption and the historical frequency modulation data of the unit, determining the minimum safety margin of the intermediate storage of the unit at different time periods;

[0181] The optional scheme screening module is responsible for generating multiple milling alternative schemes based on the milling amount in different time periods and the minimum safety margin of the intermediate warehouse in different time periods and the reserve capacity of the intermediate warehouse as constraints, and determining the load fluctuation between different time periods based on the milling power consumption and the basic load of the unit, and determining the optional schemes in the milling alternative schemes according to the load fluctuation between different time periods;

[0182] The scheme output module is responsible for obtaining the on-grid electricity price in different time periods, and determines the optimal scheme among the optional schemes based on the pulverizing power consumption and on-grid electricity price in different time periods, and further combining the deviation between the pulverized coal reserves in the intermediate bin and the minimum safety margin in different time periods.

[0183] The beneficial effects of the present invention are:

[0184] 1. In the present invention, the minimum safety margin of the intermediate bin of the unit in different time periods is determined based on the powder consumption and the historical frequency regulation data of the unit, and the powder consumption demand of the unit in the frequency regulation process is fully considered, thereby avoiding the technical problem of insufficient frequency regulation flexibility of the unit due to insufficient coal powder, and realizing dynamic adjustment of the minimum safety margin of the intermediate bin based on the frequency regulation demand of the unit.

[0185] 2. In the present invention, the best option among the optional options is determined based on the deviation between the pulverizing power consumption and the on-grid electricity price in different time periods, and the pulverized coal reserves in the intermediate bin and the minimum safety margin. This ensures the economy of pulverizing in the pulverizing scheme due to the difference in on-grid electricity prices, and realizes the adjustment of pulverizing in periods with lower electricity prices. It also ensures that the pulverized coal reserves in the intermediate bin and the minimum safety margin can be reasonably controlled within a certain range. While ensuring the safety of frequency regulation, it also improves the overall operating safety of the unit, and realizes the determination of the best option among the optional options from multiple angles.

[0186] In the embodiments of the present invention, the term "plurality" refers to two or more than two, unless otherwise clearly defined. The terms "installation", "connection", "fixation" and the like should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection. For those skilled in the art, the specific meanings of the above terms in the embodiments of the present invention can be understood according to the specific circumstances.

[0187] In the description of the embodiments of the present invention, it needs to be understood that the directions or positional relationships indicated by the terms "upper" and "lower" are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or unit referred to must have a specific direction, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the embodiments of the present invention.

[0188] In the description of this specification, the description of the terms "one embodiment", "a preferred embodiment", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0189] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A flexible milling method based on an intermediate bin, characterized in that: Specifically include: Predicting the unit load at different time periods during the day, and using the unit load at different time periods to determine the pulverized coal consumption at different time periods, and determining the minimum safety margin of the intermediate bunker of the unit at different time periods based on the pulverized coal consumption and the historical frequency regulation data of the unit; Taking the minimum safety margin of the intermediate warehouse in different time periods and the reserve capacity of the intermediate warehouse as constraints, multiple milling alternative plans are generated based on the milling amount in different time periods, and the load fluctuation between different time periods is determined based on the milling power consumption and the basic load of the unit, and the optional plans in the milling alternative plan are determined based on the load fluctuation between different time periods; Obtain the on-grid electricity price in different time periods, and determine the best option among the optional options based on the pulverizing power consumption and on-grid electricity price in different time periods, and further combine the deviation between the pulverized coal reserves in the intermediate bin and the minimum safety margin in different time periods.

2. The flexible milling method based on the intermediate bin according to claim 1 is characterized in that: The pulverized coal consumption is determined according to the amount of coal required for boiler combustion under the load of the unit.

3. The flexible milling method based on the intermediate bin according to claim 1 is characterized in that: The method for determining the minimum safety margin of the intermediate bin is: Determine the frequency modulation times of the unit in different time periods in historical dates based on the historical frequency modulation data of the unit, and determine the number of time periods in different frequency modulation time intervals through the frequency modulation times in different time periods; Based on the number of time periods in different frequency modulation frequency intervals, weight coefficients of different frequency modulation frequency intervals are determined; Determine the coal consumption compensation amount within different frequency regulation intervals, and determine the frequency regulation coal consumption demand of the unit in combination with the weight coefficients of different frequency regulation intervals; determine the minimum safety margin of the intermediate bin of the unit in different time periods based on the sum of the frequency regulation coal consumption demand and the powder consumption.

4. The flexible milling method based on the intermediate bin according to claim 3 is characterized in that: The weight coefficient of the frequency modulation number interval is determined according to the ratio of the number of time periods to the number of preset time periods.

5. The flexible milling method based on the intermediate bin according to claim 1, characterized in that: Generate multiple milling alternatives, including: Taking the storage capacity of the intermediate warehouse as the constraint condition and the minimum safety margin of the intermediate warehouse that can meet the needs of different periods of time as the basis, the milling amount in different periods of time is determined; Based on the milling amount in different time periods, multiple milling alternatives are generated by free combination.

6. The flexible milling method based on the intermediate bin according to claim 1, characterized in that: The method for determining the optional scheme in the milling alternative scheme is: Obtaining boiler loads at different time periods, and determining the amount of change in boiler loads between different adjacent time periods based on the boiler loads; Based on the change in boiler load between different adjacent time periods, the load fluctuation coefficient between different adjacent time periods is determined; The load stability of the alternative milling scheme is determined by the load fluctuation coefficient between different adjacent time periods, and the optional scheme in the alternative milling scheme is determined based on the load stability.

7. The flexible milling method based on the intermediate bin according to claim 6 is characterized in that: Determining the optional scheme in the milling alternative scheme based on load stability specifically includes: When the load stability of the alternative milling scheme is within a preset stability range, the alternative milling scheme is determined to be an optional scheme.

8. The flexible milling method based on the intermediate bin according to claim 1, characterized in that: The method for determining the optional scheme in the milling alternative scheme is: Obtaining boiler loads in different time periods, and determining the amount of change in boiler loads between different adjacent time periods based on the boiler loads, and determining that the pulverizing alternative scheme is an optional scheme when the amount of change in boiler loads between different adjacent time periods is within a preset range; When there is a period when the boiler load change is not within the preset range: The period when the change of boiler load is not within the preset range is regarded as the fluctuation period. When there is no fluctuation period when the change of boiler load is greater than the preset change threshold: When the number of the fluctuation time periods is within a preset number range, the milling alternative is determined to be an optional option; When the number of the fluctuation time periods is not within the preset number range, the number of fluctuation time periods and the change amount of the boiler load in different fluctuation time periods are used to determine the screening fluctuation coefficient. When the screening fluctuation coefficient meets the requirements, the milling alternative scheme is determined to be an optional scheme; When there is a fluctuation period in which the boiler load change is greater than the preset change threshold or the screening fluctuation coefficient does not meet the requirements: Based on the change in boiler load between different adjacent time periods, the load fluctuation coefficient between different adjacent time periods is determined, the load stability of the powder making alternative scheme is determined through the load fluctuation coefficient between different adjacent time periods, and the optional scheme in the powder making alternative scheme is determined based on the load stability.

9. The flexible milling method based on the intermediate bin according to claim 1, characterized in that: The method for determining the best solution among the optional solutions is: Obtaining the milling power consumption of the optional scheme at different time periods, and determining the economic feasibility of the optional scheme in combination with the on-grid electricity prices at different time periods; Based on the pulverizing amount and pulverizing amount of the optional scheme at different time periods, the pulverized coal reserves of the intermediate bin at different time periods are determined, the frequency regulation safety factor of the intermediate bin at different time periods is determined by the deviation between the pulverized coal reserves of the intermediate bin at different time periods and the minimum safety margin, and the safety of the optional scheme is determined by the average value of the frequency regulation safety factor of the intermediate bin at different time periods; A comprehensive evaluation amount of the optional solutions is determined based on the economy and safety of the optional solutions, and the optimal solution among the optional solutions is determined through the comprehensive evaluation amount.

10. A unit flexible milling system based on an intermediate silo, using a unit flexible milling method based on an intermediate silo as claimed in any one of claims 1 to 9, characterized in that: Specifically include: Intermediate warehouse evaluation module, optional solution screening module, solution output module; The intermediate storage evaluation module is responsible for predicting the unit load at different time periods during the day, and using the unit load at different time periods to determine the coal powder consumption at different time periods, and based on the coal powder consumption and the historical frequency modulation data of the unit, determining the minimum safety margin of the intermediate storage of the unit at different time periods; The optional scheme screening module is responsible for generating multiple milling alternative schemes based on the milling amount in different time periods and the minimum safety margin of the intermediate warehouse in different time periods and the reserve capacity of the intermediate warehouse as constraints, and determining the load fluctuation between different time periods based on the milling power consumption and the basic load of the unit, and determining the optional schemes in the milling alternative schemes according to the load fluctuation between different time periods; The scheme output module is responsible for obtaining the on-grid electricity price in different time periods, and determines the optimal scheme among the optional schemes based on the pulverizing power consumption and on-grid electricity price in different time periods, and further combining the deviation between the pulverized coal reserves in the intermediate bin and the minimum safety margin in different time periods.

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