A peak load regulation method and device for thermal power generation under high frequency variable load scenario

Abstract

The present application discloses a peak-shaving scheduling method and device in a high-frequency variable load scenario of thermal power. In this method, a thermal power plant calculates the heat storage margin of an energy storage device in the thermal power plant; sends the associated data between the load, performance and cost of each thermal power unit in the thermal power plant and the heat storage margin to the power grid side, so that the power grid side determines the target unit for expected auxiliary peak-shaving and issues peak-shaving scheduling instructions based on the changes in thermal power load demand, the associated data provided by each thermal power plant and the heat storage margin; the thermal power plant receives the peak-shaving scheduling instructions issued by the power grid side; the peak-shaving scheduling instructions carry the unit identification of the target unit and the peak-shaving requirement information for the target unit; the peak-shaving requirement information includes the peak-shaving load curve; and the auxiliary peak-shaving service is executed based on the peak-shaving requirement information. Based on a variety of data fed back by thermal power plants, the present application takes into account the load capacity, performance, cost and changes in thermal power load demand of thermal power units to ensure the safety and stability of peak-shaving scheduling.

Description

Technical Field

[0001] The present application relates to the technical field of power dispatching, and in particular to a peak-shaving dispatching method and device in a high-frequency variable load scenario of thermal power. Background Art

[0002] Compared with thermal power generation (hereinafter referred to as thermal power), renewable energy power generation has outstanding instability. Specifically, wind power generation is easily affected by fluctuations in wind speed, and photovoltaic power generation is affected by changes in sunshine intensity. The above influences may cause instability in the power generation capacity of renewable energy. In the scenario where thermal power and renewable energy power generation are in parallel, the load of thermal power generation changes frequently due to the high instability of renewable energy power generation. For example, in the scenario where photovoltaic power generation and thermal power generation are in parallel, the photovoltaic power generation is affected by the intensity of sunshine and undergoes multiple ups and downs within a day. Correspondingly, the load of thermal power plants also fluctuates frequently due to the cooperation with photovoltaic power generation. For example, when the sunshine is strong and the photovoltaic power generation is relatively high, the load of thermal power is relatively low; when the sunshine is weak and the photovoltaic power generation is relatively low, the load of thermal power needs to be increased to make up for the problem of insufficient photovoltaic power generation.

[0003] Peak shaving refers to the measure of changing the output of generators to meet the changes in power load, so as to maintain the balance of power supply and demand during peak power consumption and keep the system frequency stable. Since electric energy cannot be stored in large quantities, the generation and use of electric energy must be synchronized. In the scenario of high-frequency load variation of thermal power, in order to maintain the balance of work power and keep the system frequency stable, it is necessary to change the output of generators to adapt to the changes in power load. This is the role of peak shaving.

[0004] The high frequency of load changes in thermal power generation will bring great challenges to both the power plant side and the power grid side. On the power plant side, the high frequency of load changes in thermal power generation is related to the power production process and affects the power production cost. On the power grid side, there is a lack of safe and stable peak-shaving scheduling solutions suitable for the power grid for the scenario of high frequency load changes in thermal power generation. How to implement peak-shaving scheduling technically safely and stably while considering the cost of power plants is an urgent problem to be solved. Summary of the invention

[0005] Based on the above problems, the present application provides a peak-shaving scheduling method and device in a high-frequency variable load scenario of thermal power. The purpose is to use digital technology to analyze the changing relationship between power plant cost and thermal power load in a high-frequency variable load scenario of thermal power, so as to implement peak-shaving scheduling safely and stably.

[0006] The embodiments of the present application disclose the following technical solutions:

[0007] The first aspect of the present application provides a peak load scheduling method in a thermal power high frequency variable load scenario, the method comprising:

[0008] Calculating the heat storage margin of the energy storage device in the thermal power plant; the heat storage margin includes a heat storage margin characterization value and a heat release capacity characterization value;

[0009] Sending the correlation data between the load, performance and cost of each thermal power unit in the thermal power plant and the heat storage and access margin to the power grid side, so that the power grid side can determine the target unit for expected auxiliary peak load regulation and issue peak load regulation instructions based on the change of thermal power load demand, the correlation data provided by each thermal power plant and the heat storage and access margin;

[0010] receiving a peak-shaving dispatch instruction issued by the power grid side; the peak-shaving dispatch instruction carries the unit identification of the target unit and peak-shaving requirement information for the target unit; the peak-shaving requirement information includes a peak-shaving load curve;

[0011] An auxiliary peak shaving service is performed based on the peak shaving requirement information.

[0012] In an optional implementation, before determining the target unit for expected auxiliary peak load regulation, the method further includes:

[0013] According to the peak load curve of each thermal power unit, calculating the quotation information of the auxiliary peak load shaving service provided by the thermal power unit that meets the requirements of the corresponding peak load curve;

[0014] Sending the quotation information to the power grid side;

[0015] The grid side determines the target unit for expected auxiliary peak load regulation based on the change of thermal power load demand, the associated data provided by each thermal power plant, and the heat storage and access margin, specifically:

[0016] The grid side determines the target unit for expected auxiliary peak regulation by weighing costs and indicators through data-driven means based on changes in thermal power load demand, the associated data provided by each thermal power plant, the heat storage and access margin, and the quotation information.

[0017] In an optional implementation, the grid side determines the target unit for expected auxiliary peak load regulation by weighing costs and indicators through data-driven based on the change of thermal power load demand, the associated data provided by each thermal power plant, the heat storage and access margin and the quotation information, specifically including:

[0018] The grid side generates a peak load curve for each thermal power unit based on the change in thermal power load demand, the associated data provided by each thermal power plant, and the heat storage and access margin, and calculates the climbing coefficient of each thermal power unit;

[0019] Preliminarily determine multiple thermal power units expected to assist in peak load regulation based on the ramp coefficients of each thermal power unit;

[0020] If there is a thermal power unit whose bid information is less than or equal to K times the threshold bid among the multiple thermal power units expected to assist in peak load regulation, the thermal power unit whose bid information is less than or equal to K times the threshold bid is determined as the target unit for expected auxiliary peak load regulation; the K is within the range of 1.1 to 1.3;

[0021] Among them, the threshold quotation is a quotation reference value obtained by deep learning historical quotation information on the power grid side.

[0022] In an optional implementation, after the preliminarily determining of a plurality of thermal power generating units expected to assist in peak load regulation, the method further includes:

[0023] The power grid side sends a heat pre-dispatching instruction to the multiple thermal power generating units expected to assist in peak load regulation;

[0024] The target unit among the multiple thermal power units expected to assist in peak load regulation performs heat pre-scheduling in cooperation with the heat storage device in the thermal power plant to which it belongs based on the heat pre-scheduling instruction.

[0025] In an optional implementation, the peak-shaving requirement information further includes climbing speed requirement information; and performing the auxiliary peak-shaving service based on the peak-shaving requirement information includes:

[0026] Determine whether there is equipment failure and communication failure in the target unit, and determine whether the target unit meets the climbing speed requirement information;

[0027] If there is no equipment failure and communication failure in the target unit, and the target unit meets the climbing speed requirement information, then according to the peak load curve and the climbing speed requirement information in the peak load requirement information, perform auxiliary peak load shaving service;

[0028] If there is a device failure or a communication failure in the target unit, a fault message is reported to the power grid side;

[0029] If the target unit does not meet the climbing speed requirement information, a prompt message indicating that the operating condition is not met is reported to the power grid side.

[0030] In an optional implementation, the peak load scheduling method in the thermal power high frequency variable load scenario also includes:

[0031] The power grid side adjusts the peak-shaving requirement information in the peak-shaving scheduling instruction according to the prompt information that the operating condition is not met.

[0032] In an optional implementation, the peak load scheduling method in the thermal power high frequency variable load scenario also includes:

[0033] The grid side relearns to increase the threshold quotation based on the prompt information of the operating condition not being met fed back by each thermal power plant, so that more units are used as target units for expected auxiliary peak regulation.

[0034] In an optional implementation, the climbing coefficient of the thermal power unit is calculated as follows:

[0035] The ramp speed, steam pressure and rated power generation capacity are extracted from the correlation data among the load, performance and cost of the thermal power unit;

[0036] The climbing coefficient of the thermal power unit is calculated according to the climbing speed, steam pressure, rated power generation capacity, heat storage and access margin of the thermal power unit and the climbing time required by the power plant side for the thermal power unit.

[0037] In an optional implementation, the heat storage margin is expressed as a percentage; the climbing coefficient of the thermal power unit is calculated according to the climbing coefficient, steam pressure, rated power generation capacity, heat storage margin and the climbing time required by the power plant side for the thermal power unit, including:

[0038] Calculate the product of the thermal power unit's ramp-up speed, ramp-up time, steam pressure and heat storage margin;

[0039] The ratio of the product to the rated power generation capacity of the thermal power unit is calculated, and the ratio is used as the climbing coefficient of the thermal power unit.

[0040] In an optional implementation, the peak load scheduling method in the thermal power high frequency variable load scenario also includes:

[0041] According to the climbing coefficient of each thermal power unit, the corresponding relationship between the thermal power unit and the peak load dispatching level is established;

[0042] The method of preliminarily determining a plurality of thermal power units expected to assist in peak load regulation according to the ramp coefficient of each thermal power unit specifically includes:

[0043] According to the peak-shaving dispatch level corresponding to each thermal power unit, a number of thermal power units expected to assist in peak-shaving are preliminarily determined.

[0044] In an optional implementation, the calculating, based on the peak load curve of each thermal power unit, the quotation information of the auxiliary peak load shaving service provided by the thermal power unit that meets the requirements of the corresponding peak load curve specifically includes:

[0045] According to the peak load curve and power supply quality of each thermal power unit, generating quotation information for providing the auxiliary peak load shaving service by the thermal power unit that meets the requirements of the corresponding peak load curve;

[0046] Among them, the peak load curve is selected from a typical load curve set, and the typical load curve set is established after analysis and research on the power grid side based on the load increase and decrease change data of the thermal power units under various working conditions, as well as the correlation data between the load, performance and cost of each thermal power unit.

[0047] In an optional implementation, the heat storage and access margin and the associated data are calculated based on the coal type parameters of the selected target coal type; the target coal type is selected by:

[0048] According to the high calorific value and the low calorific value corresponding to each of the multiple types of coal to be selected, the difference percentage between the high and low calorific value of each type of coal to be selected is obtained;

[0049] The coal type with the smallest percentage difference between high and low calorific values ​​among the multiple coal types to be selected is determined as the target coal type.

[0050] The second aspect of the present application provides a peak load scheduling device in a thermal power high frequency variable load scenario, the device comprising:

[0051] A calculation module, used to calculate the heat storage margin of the energy storage device in the thermal power plant; the heat storage margin includes a heat storage margin characterization value and a heat release capacity characterization value;

[0052] A sending module, used for sending the correlation data between the load, performance and cost of each thermal power unit in the thermal power plant and the heat storage and access margin to the power grid side, so that the power grid side can determine the target unit for expected auxiliary peak load regulation and issue peak load regulation scheduling instructions based on the change of thermal power load demand, the correlation data provided by each thermal power plant and the heat storage and access margin;

[0053] A receiving module, configured to receive a peak load scheduling instruction issued by the power grid side; the peak load scheduling instruction carries the unit identification of the target unit and the peak load requirement information for the target unit; the peak load requirement information includes a peak load curve;

[0054] The peak-shaving module is used to perform auxiliary peak-shaving services based on the peak-shaving requirement information.

[0055] Compared with the prior art, this application has the following beneficial effects:

[0056] In the technical solution of the present application, the thermal power plant calculates the heat storage and access margin of the energy storage device in the thermal power plant, including the heat storage margin characterization value and the heat release capacity characterization value; then, the associated data between the load, performance and cost of each thermal power unit in the thermal power plant and the heat storage and access margin are sent to the power grid side, so that the power grid side can determine the target unit for expected auxiliary peak regulation and issue the peak regulation scheduling instruction based on the change of thermal power load demand, the associated data provided by each thermal power plant and the heat storage and access margin. If the thermal power plant contains the target unit determined by the power grid side, it can receive the peak regulation scheduling instruction issued by the power grid side. The peak regulation scheduling instruction carries the unit identification of the target unit and the peak regulation requirement information for the target unit; the peak regulation requirement information includes the peak regulation load curve. Finally, the target unit in the thermal power plant performs the auxiliary peak regulation service based on the peak regulation requirement information. Based on the various data fed back by the thermal power plant, the present application can take into account the load capacity, performance, cost and thermal power load demand changes of the thermal power unit, and analyze the changing relationship between the power plant cost and the thermal power load by using digital technology, so as to ensure the safety and stability of peak regulation scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0058] Figure 1 A schematic diagram of a power supply scenario provided in an embodiment of the present application;

[0059] Figure 2 A flow chart of a peak load scheduling method in a thermal power high frequency variable load scenario provided in an embodiment of the present application;

[0060] Figure 3 A signaling interaction diagram of a peak load scheduling method in a thermal power high frequency variable load scenario provided in an embodiment of the present application;

[0061] Figure 4 A flowchart for determining a target unit for expected auxiliary peak load regulation provided in an embodiment of the present application;

[0062] Figure 5 A flowchart of a fault judgment and working condition judgment provided in an embodiment of the present application;

[0063] Figure 6 A schematic diagram of the structure of a peak load dispatching device for a thermal power plant with high frequency load variation provided in an embodiment of the present application;

[0064] Figure 7A flowchart of another peak load scheduling method for thermal power in a high-frequency variable load scenario provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] In the scenario where thermal power generation and renewable energy generation are carried out in parallel, the load of thermal power generation changes frequently due to the instability of renewable energy generation. Under the condition of high-frequency load changes in thermal power scenarios, there is currently a lack of supporting peak-shaving scheduling solutions with digital calculation costs, making it difficult to implement peak-shaving scheduling safely and stably, which can easily affect the life of the power system. How to ensure the life of the power system is a key issue. In view of this, the inventor proposes a peak-shaving scheduling method and device in the scenario of high-frequency load changes in thermal power in this application. Based on a variety of data fed back by thermal power plants, it can take into account the load capacity, performance, cost and thermal power load demand changes of thermal power units. By using digital technology, the relationship between the cost of the power plant and the change of thermal power load is analyzed, thereby ensuring the safety and stability of peak-shaving scheduling.

[0066] Figure 1 A power supply scenario is shown. Figure 1 As shown in the figure, this power supply scenario involves multiple power generation methods, including thermal power generation and photovoltaic power generation. Among them, photovoltaic power generation, as a new energy power generation method, has gradually increased its share in the energy consumption structure due to its zero emission, zero pollution, and sustainability. However, compared with thermal power generation, photovoltaic power generation has more prominent instability. For this reason, Figure 1 In the power supply scenario shown, the thermal power plant that generates electricity through thermal power needs to change the load frequently. In the specific implementation, the thermal power plant needs to cooperate with the peak load regulation on the grid side. Figure 1 For example, Figure 1 The left side shows three thermal power plants, each of which can store and release electricity. The thermal power plants transmit the released electricity through their own electrical connection with the power grid, and this electricity ultimately serves thousands of households that purchase electricity from the power grid.

[0067] Each thermal power plant generally has multiple thermal power units, and the equipment performance, supporting completeness, maintenance costs, etc. of each thermal power plant are often different. Furthermore, the equipment durability, safety attributes, climbing ability to cooperate with peak-shaving scheduling, power generation costs, etc. of each thermal power unit are often different. For thermal power plants, when cooperating with peak-shaving scheduling, it is necessary to pay attention to whether the thermal power units will cause operating losses due to cooperating with peak-shaving scheduling; in addition, it is necessary to determine whether they can cooperate with peak-shaving conditions. For the power grid side, it is necessary not only to combine the quotations of each unit in the thermal power plant and the unit's own capabilities and performance, but also to consider many aspects in scheduling, so as to ensure the safe and stable implementation of peak-shaving scheduling.

[0068] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0069] Figure 2 A flowchart of a peak load scheduling method for a thermal power plant with a high frequency variable load is provided in an embodiment of the present application. Figure 3 This is a signaling interaction diagram of a peak load scheduling method in a thermal power plant with high frequency load variation scenario. Figure 2 In this paper, the implementation of the scheme is mainly introduced from the perspective of thermal power plants. Figure 3 In the demonstration, the solution is implemented through interactive introduction on both sides (power plant side and grid side).

[0070] like Figure 2 As shown, the embodiment of the present application provides a peak load scheduling method in a thermal power high frequency variable load scenario, including:

[0071] S201. Calculate the heat storage margin of the energy storage device in the thermal power plant.

[0072] Each thermal power plant is equipped with energy storage devices. The energy storage devices store the heat generated by the coal combustion of the thermal power units and release it when needed. The heat is converted into electrical energy and transmitted to the power grid. In a possible implementation, the energy storage devices in the thermal power plant include but are not limited to: boilers, steam turbine systems, heaters, deaerators, etc.

[0073] The heat storage and access margin of each energy storage device is divided into two different types, one is the heat storage margin characterization value, and the other is the heat release capacity characterization value. The heat storage margin characterization value is used to indicate the ability of the energy storage device to further store heat. The heat release capacity characterization value is used to indicate the ability of the energy storage device to further release heat. The heat storage margin characterization value can be expressed by a specific heat value or by a percentage compared to the rated storage heat. Similarly, the heat release capacity characterization value can be expressed by a specific heat value or by a percentage compared to the pre-rated storage heat. Both the heat storage margin and the heat release capacity are related to the size and performance of the unit.

[0074] The heat storage and access margin of the energy storage devices in the thermal power plant reflects the current output capacity of the thermal power plant from the two aspects of "storage" and "access". In the specific implementation, according to the actual needs of the power grid, the heat storage margin can be counted according to two different dimensions. For example, statistics can be made according to the dimensions of "day before" and "day". The thermal power plant provides the heat storage and access margin of the energy storage device the day before, so that the power grid can prepare in advance according to the heat storage and access margin the day before and carry out orderly peak-shaving scheduling. The thermal power plant provides the heat storage and access margin of the energy storage device within the day, so that the power grid can realize accurate, flexible and reliable peak-shaving scheduling based on high real-time data according to the heat storage and access margin within the day.

[0075] In a possible implementation, the heat storage margin sent by the thermal power plant to the power grid side can be specifically refined to the heat storage margin corresponding to each thermal power unit of the thermal power plant. It should be noted that the heat storage margin can be calculated by the thermal power plant based on the coal type parameters of the selected target coal type and the thermal system design. The choice of coal type affects heat consumption and coal consumption. Table 1 is a data table of a digital selection model for coal types. In Table 1, test items for various types of coal such as GEB 5200, GEB 4800, GEB4700, etc. are shown. Here, the test items can also be regarded as the coal type parameters mentioned above. These coal type parameters include but are not limited to total moisture, air dried basis moisture, received basis ash, dry ash-free basis powdering, received basis carbon, received basis hydrogen, total sulfur, low calorific value, etc. In Table 1, LHV represents the low calorific value of the coal type, and HHV represents the high calorific value of the coal type.

[0076] Table 1

[0077]

[0078] The coal type digital selection model can realize the selection of coal types by extracting features from the data shown in Table 1. Because the H (hydrogen) part of carbon combines with oxygen to produce water in the physical combustion of the boiler, this part of water absorbs a large amount of heat called "latent heat of vaporization", so it is a relatively important factor in coal selection. This hydrogen content is measured by the percentage difference between HHV and LHV associated with it, as shown in the last row of data in Table 1. The smaller the value, the smaller the heat loss of latent heat of vaporization, the more heat generated by unit coal for power generation, and the better the economic benefits. In practical applications, by using the coal type digital selection model, according to the high calorific value and low calorific value corresponding to each of the multiple coal types to be selected, the percentage difference between the high and low calorific value of each coal type to be selected is obtained (this value can be understood as a low-order abstract feature); the coal type with the smallest percentage difference between the high and low calorific value among the multiple coal types to be selected is determined as the target coal type. Taking Table 1 as an example, the coal type GEB 5200 has the smallest percentage difference between high and low calorific values, which is 5.90%. Therefore, the coal type GEB 5200 can be selected as the target coal type. By selecting the coal type with the smallest percentage difference between high and low calorific values ​​as the target coal type, the heat generated by unit coal can be maximized.

[0079] The calculation formulas for the higher calorific value HHV and lower calorific value LHV of coal are as follows:

[0080] HHV=LHV+25*M+25*9*H

[0081] M is the total water content of coal, and H is the basic hydrogen content of coal. In the above formula, the units of HHV and LHV are both kcal / kg. In one example, M=23, H=4.21. It should be noted that in the above formula, when it is necessary to convert between the heat unit kcal and kJ, the values ​​of M and H need to be converted according to the conversion coefficients of different heat units.

[0082] S202, sending the correlation data between the load, performance and cost of each thermal power unit in the thermal power plant and the heat storage and access margin to the power grid side, so that the power grid side can determine the target units for expected auxiliary peak load regulation and issue peak load regulation scheduling instructions based on the changes in thermal power load demand, the correlation data provided by each thermal power plant and the heat storage and access margin.

[0083] In order to facilitate the data acquisition, data analysis and peak-shaving scheduling of each thermal power plant and each thermal power unit on the power grid side, in this application, each thermal power plant needs to provide its own data to the power grid side. In specific implementation, the thermal power plant sends the associated data between the load, performance and cost of each thermal power unit to the power grid side, and sends the heat storage and access margin calculated in step S201. It should be noted that in the embodiment of the present application, the associated data sent by the thermal power plant to the power grid side is also calculated based on the coal type parameters of the selected target coal type.

[0084] Table 2 is a table of parameters related to heat consumption, coal consumption and electricity price under different loads. As shown in the first column of Table 2, a variety of different power plant loads are displayed (such as 100% load, 95% load, 65% load, 45% load, etc.). As shown in columns 2 to 24 of Table 2, various heat consumption, coal consumption and system parameters under different loads are displayed. Combined with Table 2, under the high-frequency variable load dispatch, the actual power consumption rate of the thermal power plant (column 7 of Table 2) and other factors are constantly changing. For example, at 100% power plant load, the actual value of the power consumption rate is 5.71%, and at 45% power plant load, the actual value of the power consumption rate is 7.37%.

[0085] By reporting the data shown in Table 2 to the power grid, the power grid has the load conditions and thermal energy conditions of each thermal power plant, and can better classify and aggregate the data, so that different weights and priority calls can be given to the units according to the unit conditions and heat storage and access margins. Since the calculation in this process is affected by multiple variables, the features extracted from the data shown in Table 2 can be called mid-order data abstract features (abbreviated as mid-order features). The mid-order data abstract features are transmitted in the chain rule to reflect the energy consumption level and thermal energy reserve level of the power plant, and assist in the peak-shaving scheduling under the high-frequency variable load scenario of thermal power.

[0086] Table 2

[0087]

[0088] Table 3 shows the electricity price and cost data of a selected target coal type under different working conditions (power plant load). The grid compensation coefficient for 1 kWh of electricity payment is the grid electricity price (excluding limestone costs), which is the fuel part of the capacity price, which generally accounts for more than 50% of the entire electricity price.

[0089] The example calculation formula for the grid compensation coefficient for 1 kWh of electricity payment is as follows:

[0090] CERm=U*N / E (1-AUX)

[0091] Among them, U represents the coal price, which is also the fuel cost subsidy, in US dollars per ton (expressed as USD / t). As an example, U=69USD / t. E represents the unit power generation heat consumption rate, that is, the heat consumption of the unit per kilowatt-hour of electricity, in kcal / kWh. N represents the specific heat index, specifically the heat required per kilowatt-hour of electricity, in kcal / kWh, and AUX represents the plant power consumption rate, in %.

[0092] In Table 3, SHRW represents the weighted specific heat index, in kJ / (kW·h). Ea represents the power supply (or power generation), in kW·h. Ep represents the power consumption and electricity fee, in USD.

[0093] SHRcc is the specific heat index, in kJ / (kW·h). U is the coal price, in USD / t. ECRm is the electricity cost per kilowatt-hour, in USD / (kW·h). ECRm represents the electricity charge ratio, that is, the coal cost per kilowatt-hour of electricity produced, in USD / (kW·h). It should be noted that if the load is evenly distributed every day in a month, the weighted specific heat index is equal to the weighted index, that is, SHRW=SHRcc.

[0094] The calculation formula of ECRm is:

[0095] ECRm=SHRcc×(1 / HHV)×U / 1000

[0096] The calculation formula of Ep is:

[0097] Ep=NEOm*CERx

[0098] In the above formula, NEOm represents net electricity generation, in kW·h. CERx represents electricity price, in USD / (kW·h).

[0099] As shown in Table 3, through the correlation between different values ​​of electricity prices and loads, the cost reflected by the fuel of each thermal power plant in the power grid can be given, so that the power grid can extract the unit performance, environmental protection and fuel cost of the power plant under different load conditions based on these data. The extracted features affected by multiple variables (including coal cost price) are called high-order abstract features. The linear high-order features generated by integrating coal price cost on the basis of mid-order features include the correlation between fuel, electricity and electricity price.

[0100] That is to say, in the technical solution of the present application, the grid side determines the target unit for expected auxiliary peak load regulation and issues peak load regulation instructions based on the change of thermal power load demand, the associated data provided by each thermal power plant, and the heat storage and access margin, including:

[0101] The grid side extracts intermediate features based on the associated data and the heat storage and access margin provided by each thermal power plant, and the intermediate features include features reflecting the energy consumption level and thermal energy reserve level of the power plant; the grid side fuses the coal price cost in the associated data with the intermediate features to obtain high-order features, and the high-order features include the relationship between fuel, electricity and electricity price;

[0102] The grid side determines the target units for expected auxiliary peak load regulation and issues peak load regulation dispatching instructions based on the changes in thermal power load demand, the mid-order characteristics and the high-order characteristics.

[0103] It can be seen from Table 3 that the higher the load, the smaller the number of the grid compensation coefficient for 1 kWh of electricity payment, which means the lower the fuel cost. The lower the fuel cost under the same operating conditions, the better the unit performance and the lower the coal consumption. To this end, the grid side can know the performance of different thermal power units by comparing the fuel costs of different thermal power units under the same operating conditions (such as the grid compensation coefficient for 1 kWh of electricity payment shown in Table 3). For example, when thermal power unit A is at 100% load, the grid compensation coefficient for 1 kWh of electricity payment is 0.0311 USD / (Kw·h); when thermal power unit B is at 100% load, the grid compensation coefficient for 1 kWh of electricity payment is 0.0319 USD / (Kw·h), and the coal types selected by the two units are the same. By comparing this coefficient, it can be known that the performance of thermal power unit A is better than that of thermal power unit B.

[0104] Table 3

[0105]

[0106] Based on the data reported by each thermal power plant (including the correlation data between the load, performance and cost of each thermal power unit in the thermal power plant, as well as the heat storage and access margin, etc., where the correlation data can be combined with the examples in Table 2 and Table 3), the power grid side can have a full understanding of the thermal power units of each thermal power plant. Based on this, the power grid side can exercise the peak-shaving scheduling function. In specific implementation, the power grid side can determine the target unit for expected auxiliary peak-shaving based on the changes in thermal power load demand, the correlation data provided by each thermal power plant, and the heat storage and access margin, and then issue peak-shaving scheduling instructions to the target unit.

[0107] Before the grid side determines the target unit for the expected auxiliary peak-shaving dispatch, the grid side can send the peak-shaving load curve for each thermal power unit to each thermal power plant. In an example implementation, the peak-shaving load curve is in a two-dimensional form, with the horizontal axis representing time and the vertical axis representing power. The peak-shaving load curves for different thermal power units may be different. The peak-shaving load curve essentially expresses the load requirements for the thermal power units.

[0108] The thermal power plant can determine the thermal power units that meet the requirements of the corresponding peak load curve according to the peak load curve of each thermal power unit, and calculate the quotation information of the auxiliary peak load service provided by such thermal power units. Since the peak load dispatch is an additional auxiliary work provided by the thermal power unit, a certain fee needs to be charged to the power grid. The amount of the charge is presented in the form of a quotation. The thermal power plant calculates the quotation information of providing auxiliary peak load service in combination with the performance of the thermal power unit, the requirements of the peak load curve, the type of coal burned, the coal consumption, heat consumption and other fuel information, fuel cost, equipment cost, etc. The quotation information is sent to the power grid side, so that the power grid side can compare and determine the expected auxiliary peak load units based on the quotation information, and call such units target units. The number of target units in a thermal power plant is uncertain. For example, thermal power plant A may have 3 thermal power units determined as target units; thermal power plant B may have 1 thermal power unit determined as target unit; thermal power plant C may have no target units determined. Combining Table 2 and Table 3, the grid side can use data-driven and deep learning functions to weigh costs and indicators based on changes in thermal power load demand, related data provided by each thermal power plant, heat storage and access margin, and quotation information reported by each thermal power plant, and determine the target units for the expected auxiliary peak-shaving service through data-driven and deep learning functions.

[0109] Here are some examples:

[0110] Each thermal power plant provides its own Table 2 data to the power grid, and the power grid makes a comparison and selection based on Table 2 uploaded by each thermal power plant. For example, the power grid knows that there is a plant A with a 95% load and a plant B with a 60% load, and the corresponding heat release capacity characterization values ​​of plant A and plant B are 70% and 80% respectively. In this case, it is necessary to issue a dispatch instruction based on the power grid's deep learning under this condition, focusing on the heat release capacity characterization value priority or the load margin priority, and select the unit to go online for power generation. This selection strategy is based on data-driven and deep learning historical data-driven, in order to avoid system safety hazards.

[0111] In a possible implementation, the power grid side can establish a typical load curve set after analysis and research based on the load rise and fall variation data of the thermal power units under various different working conditions, as well as the correlation data between the load, performance and cost of each thermal power unit. The peak load curve of the thermal power unit on the power grid side is selected from a pre-established typical load curve set. The thermal power plant calculates the quotation information of the auxiliary peak-shaving service provided by the thermal power units that meet the requirements of the corresponding peak load curve based on the peak load curve of each thermal power unit. More specifically, it can be based on the peak load curve of each thermal power unit and the power supply quality to generate the quotation information of the auxiliary peak-shaving service provided by the thermal power units that meet the requirements of the corresponding peak load curve. Considering the power supply quality while referring to the peak load curve can match the quotation with the power supply quality and avoid the problem of inflated quotation and poor power supply quality.

[0112] Figure 4 A flowchart for determining a target unit for expected auxiliary peak load regulation is provided in an embodiment of the present application. Figure 4 In order to determine the target unit for expected auxiliary peak load regulation, the technical solution of this application can combine the climbing coefficient and quotation information of each thermal power unit, and through data-driven weighing of costs and indicators, select the thermal power unit with suitable quotation as the target unit from multiple thermal power units expected to assist in peak load regulation. Figure 4 As shown, the process includes:

[0113] S2021. Based on the changes in thermal power load demand, the related data provided by each thermal power plant and the heat storage and access margin, the grid side generates a peak load curve for each thermal power unit and calculates the climbing coefficient of each thermal power unit.

[0114] It is understandable that during the period of thermal power generation, since thermal power generation and renewable energy generation are carried out in parallel, the demand for thermal power load may fluctuate frequently. For example, the demand for thermal power load is low at noon and high at night; the demand for thermal power load is high during rainy periods and low during sunny days. Combined with the correlation data on load, performance and cost provided by each thermal power plant, it is possible to generate a peak load curve for each thermal power unit through feature extraction and other methods using artificial intelligence algorithm analysis. The peak load curve relies on the extraction and analysis of multi-level and multi-step data to realize a data-driven peak load scheduling scheme, the specific presentation of which includes the generated peak load curve.

[0115] Furthermore, in the technical solution of the present application, in order to select the target unit, the grid side can first calculate the climbing coefficient of each thermal power unit.

[0116] In the technical solution of this application, the climbing coefficient is a parameter used to measure the performance of a thermal power unit. The higher the climbing coefficient, the better the performance (climbing ability) of the thermal power unit, and the better it can perform auxiliary peak load regulation. The following is an exemplary method for calculating the climbing coefficient of a thermal power unit.

[0117] For a certain thermal power unit, in order to calculate its climbing coefficient, the grid side can extract the climbing speed, steam pressure and rated power generation capacity from the correlation data between the load, performance and cost of the thermal power unit. Then, according to the climbing speed, steam pressure, rated power generation capacity, heat storage and access margin of the thermal power unit and the climbing time required by the power plant side for the thermal power unit, the climbing coefficient of the thermal power unit is calculated. The following is an exemplary formula for calculating the climbing coefficient of a thermal power unit.

[0118] P=S*T*Q*C / R

[0119] In the formula, P is the climbing coefficient, S is the climbing speed, T is the climbing time required by the power grid, Q is the steam pressure, C is the heat storage margin, and R is the rated power generation capacity. Table 4 shows the operating conditions, climbing speeds, climbing time required by the power grid, steam pressure, heat storage margin, and calculated climbing coefficients of three different thermal power units (W1, W2, and W3). Among them, the steam pressure Q is expressed as a percentage of the rated pressure. Combined with the above formula, the calculation method of the climbing coefficient of the thermal power unit shown in the formula is: calculate the product of the climbing speed, climbing time, steam pressure, and heat storage margin of the thermal power unit; calculate the ratio of the product to the rated power generation capacity of the thermal power unit, and use the ratio as the climbing coefficient of the thermal power unit. The calculation of the climbing coefficient enables the capacity level of each thermal power unit to be quantitatively measured and compared horizontally with each other. The data-driven quantitative comparison of thermal power units enriches the theoretical basis of peak-shaving scheduling from a data level, and helps to achieve more reliable and orderly high-frequency peak-shaving scheduling.

[0120] Taking Table 4 as an example, in Table 4, the heat storage margin is represented by C. As one of the parameters for calculating the climbing coefficient of the thermal power unit, the C value can be specifically used to represent the heat release capacity characterization value in the heat storage margin. Taking Table 4 as an example, the C values ​​of units W1, W2 and W3 are 90%, 50% and 20% respectively, which means: the heat release capacity characterization value of unit W1 is 90%, and the heat storage margin characterization value is 10%; the heat release capacity characterization value of unit W2 is 50%, and the heat storage margin characterization value is 50%; the heat release capacity characterization value of unit W3 is 20%, and the heat storage margin characterization value is 80%. In other words, for the same unit, the sum of its heat release capacity characterization value and heat storage margin characterization value is 100%.

[0121] Table 4

[0122]

[0123] S2022. Preliminarily determine a number of thermal power units that are expected to assist in peak load regulation based on the ramp coefficient of each thermal power unit.

[0124] In the embodiment of the present application, the power grid side can preliminarily determine multiple units based on the relative size of the climbing coefficient of the thermal power unit, and expect these units to assist in peak load regulation. As shown in the last column of Table 4, thermal power units with different climbing coefficients can be rated (peak load dispatching level) according to the size of the climbing coefficient. For example, thermal power units with a climbing coefficient greater than or equal to 0.8 are classified as Class A; thermal power units with a climbing coefficient less than 0.8 but greater than or equal to 0.5 are classified as Class B; thermal power units with a climbing coefficient less than 0.5 are classified as Class C. In this way, a correspondence between thermal power units with different climbing coefficients and peak load dispatching levels is established.

[0125] In specific implementation, multiple thermal power units expected to assist in peak load regulation are preliminarily determined according to the peak load regulation dispatching level corresponding to each thermal power unit. For example, the power grid side can give priority to the thermal power units of grade A. If the thermal power units of grade A are still not enough for dispatching, the thermal power units of grade B can be further selected as the thermal power units expected to assist in peak load regulation.

[0126] S2023. Determine whether there is a thermal power unit among the multiple thermal power units expected to assist in peak load regulation whose quotation information is less than or equal to K times the threshold quotation. If yes, proceed to S2024; if not, proceed to S2025.

[0127] S2024. The thermal power unit is identified as the target unit for expected auxiliary peak regulation.

[0128] S2025. This thermal power unit is not selected as the target unit for expected auxiliary peak regulation for the time being.

[0129] It is understandable that if each thermal power unit needs to cooperate with the auxiliary peak-shaving service, it may generate heat consumption and energy consumption beyond the expected power generation task, which will bring certain cost expenditures. To this end, the thermal power unit can calculate the quotation and report it to the power grid. The power grid compares the quotation information reported by each thermal power unit with K times the threshold quotation. If the quotation is less than or equal to K times the threshold quotation, the power grid side believes that the quotation is relatively reasonable and can be tolerated. As shown in step S2024, such units can be designated as target units to assist in peak-shaving. On the contrary, if the quotation is higher than K times the threshold quotation, the power grid side believes that the quotation is too high and no longer prefers such units as target units.

[0130] Among them, the threshold quotation is the quotation reference value obtained by deep learning historical quotation information on the power grid side. In practical applications, the power grid side can comprehensively learn the historical quotation information of thermal power in high-frequency variable load scenarios, combine the historical quotation information and the performance and cost of the thermal power unit itself, and use data-driven to weigh the cost and indicators to analyze a reasonable quotation reference value. K is a coefficient greater than 1. In one example, K takes a value in the range of 1.1 to 1.3. For example, K takes a value of 1.3, that is, if the quotation information of the thermal power unit overflows by more than 30% compared to the threshold quotation, it is considered that it cannot be selected as the target unit. On the contrary, if the quotation information of the thermal power unit overflows by no more than 30% compared to the threshold quotation, it is considered that it can be selected as the target unit.

[0131] For the target unit, the grid side can send peak-shaving dispatch instructions to the thermal power unit to which the unit belongs.

[0132] S203: Receive peak load dispatching instructions issued by the power grid side.

[0133] The peak-shaving dispatch instruction carries the unit identification of the target unit and the peak-shaving requirement information for the target unit. For example, the peak-shaving dispatch instruction carries the unit identification W1. After the thermal power plant receives the peak-shaving dispatch instruction, it can be known that the instruction is issued to the thermal power unit with the unit identification W1. If a thermal power plant contains multiple target units, the power grid side can send peak-shaving dispatch instructions to each thermal power unit in parallel. This group dispatch improves the peak-shaving efficiency. The peak-shaving dispatch instruction carries the unit identification, so the thermal power plant can accurately forward the peak-shaving requirement information.

[0134] S204: Execute auxiliary peak-shaving service based on the peak-shaving requirement information.

[0135] As mentioned above, the peak-shaving dispatch instruction carries the peak-shaving requirement information for the target unit. Specifically, the peak-shaving requirement information includes the peak-shaving load curve. In one possible implementation, the peak-shaving requirement information also includes climbing speed requirement information. The peak-shaving load curve makes requirements for the target unit from the perspective of load, while the climbing speed requirement information makes requirements for the target unit from the perspective of climbing speed. When the target unit receives the peak-shaving requirement information, it needs to make a judgment based on its own actual situation to confirm whether it can meet the above requirements.

[0136] Figure 5 A flowchart of a fault judgment and working condition judgment provided in the embodiment of the present application. Figure 5 , an implementation method of this step is introduced exemplarily.

[0137] like Figure 5 As shown, after identifying the peak load requirement information in the peak load scheduling instruction, first, the target unit of the thermal power plant determines whether it has equipment failure and communication failure, and determines whether the target unit meets the ramp speed requirement information. Specifically, it can be as follows Figure 5 As shown, first determine whether there is an equipment failure and a communication failure. After the determination is completed, based on the premise that there is no equipment failure and no communication failure, further determine whether the climbing speed requirement information is met. If there is no equipment failure and communication failure in the target unit (that is, there is neither equipment failure nor communication failure), and the target unit meets the climbing speed requirement information, then the auxiliary peak-shaving service is performed according to the peak-shaving load curve and the climbing speed requirement information in the peak-shaving requirement information. If there is an equipment failure or a communication failure in the target unit, it means that the target unit cannot cooperate to complete the auxiliary peak-shaving service in terms of hardware conditions or communication conditions, and the thermal power plant needs to report the fault message of the thermal power unit to the power grid side, so that the power grid side can be informed of the fault status of the target unit in a timely manner. If the target unit does not meet the climbing speed requirement information, a prompt message that the working condition is not met is reported to the power grid side.

[0138] It should be noted that in the embodiment of the present application, the power grid side can adjust the peak-shaving dispatching instruction in response to the prompt information of the operating condition not being met fed back by the thermal power plant. For example, according to the prompt information of the operating condition not being met, the peak-shaving load curve in the peak-shaving requirement information is adjusted, and / or the value in the climbing speed requirement information is adjusted. By adjusting the peak-shaving load curve (for example, lowering the vertical coordinate value of the load curve) or lowering the value in the climbing speed requirement information, the threshold of the requirements for the target unit can be lowered, so that more units can become the target units serving the peak-shaving dispatching.

[0139] In addition, in the embodiment of the present application, the grid side can also relearn to improve the threshold quotation according to the prompt information of the operating conditions not being met fed back by each thermal power plant, so that more units can be used as target units for expected auxiliary peak regulation. More units are included in the queue of target units for expected auxiliary peak regulation, which expands the range of selected units and avoids the problem of insufficient ability to provide auxiliary peak regulation services in units with suitable quotations. Including more units in the queue of target units can give more units the opportunity to participate in the auxiliary peak regulation work, and also avoid the problem of limited output of thermal power units with better performance in the auxiliary peak regulation work due to constraints on transaction prices. Continuous in-depth learning of threshold quotations can always update the threshold quotations in a timely manner according to the status and quotations of thermal power units, making peak regulation scheduling dynamic and flexible.

[0140] As mentioned above, in this application, the thermal power units, especially the selected target units, should cooperate with the dispatching of the power grid to assist in peak load regulation. The peak load regulation dispatching method for thermal power in the high frequency variable load scenario provided in this application, after the power grid preliminarily determines multiple thermal power units expected to assist in peak load regulation, the method also includes:

[0141] The power grid side sends a heat pre-dispatch instruction to the multiple thermal power units expected to assist in peak load regulation. The purpose of issuing the heat pre-dispatch instruction is to enable each thermal power unit expected to assist in peak load regulation to make thermal preparations for the upcoming load adjustment work. Specifically, heat pre-dispatch may require the energy storage device to further store more heat, or it may require the energy storage device to further release some heat. The heat pre-dispatch instruction may be issued to the thermal power unit by the power grid side after knowing the heat storage and access margin of the thermal power unit. On the premise of knowing the heat storage and access margin, when the power grid side performs peak load regulation and issues the heat pre-dispatch instruction, it can make the instruction content and heat adjustment range of the issued heat pre-dispatch instruction more match the performance and capacity of the thermal power unit.

[0142] As mentioned earlier, the grid side will determine the target unit for the expected auxiliary peak load regulation based on the quotation information. For the target unit, it is considered that the thermal power plant and the grid side have reached an agreement on the quotation. The target unit selected by the grid side will cooperate with the heat storage device in the thermal power plant to which it belongs to perform heat pre-dispatch after receiving the heat pre-dispatch instruction. For example, the heat pre-dispatch instruction requires the target unit to further store 12% of heat, and the target unit further stores 12% of heat according to the heat pre-dispatch instruction. Therefore, when auxiliary peak load regulation service is required, the corresponding load adjustment can be completed through the pre-stored heat.

[0143] Figure 3 The peak load dispatching method process under the high-frequency load variation scenario of thermal power is presented, showing the interaction content and interaction sequence from both the thermal power plant and the power grid side. Figure 3 As shown, the thermal power plant selects the type of coal and calculates the heat storage and access margin. The calculated heat storage and access margin and the associated data (specifically, the associated data between the load, performance and cost of each thermal power unit in the thermal power plant) are sent to the power grid side. The specific types of associated data can be referred to Table 2, Table 3, etc. The power grid side digitally processes the data received from each thermal power plant, performs multi-level abstract extraction and analysis on the data, and uses artificial intelligence technology to capture the connection between the data. Then, based on the above extraction and analysis, the power grid side generates a peak load curve for each thermal power unit and sends the curve to the corresponding thermal power plant. In addition, heat pre-dispatch instructions can also be issued. Based on the actual situation of each thermal power unit, the thermal power plant calculates the quotation information that meets the requirements of the peak load curve to assist in peak regulation, and reports the quotation information to the power grid side. At this point, the power grid side can obtain the corresponding quotation information of each thermal power unit in each thermal power plant, and compares it horizontally in combination with the quotation information and the calculated climbing coefficient of the thermal power unit, and selects the target unit by weighing the cost and indicators through data drive. The power grid issues peak-shaving dispatch instructions to the thermal power plants, and the target units cooperate to execute heat pre-dispatching and perform auxiliary peak-shaving services based on the peak-shaving requirement information in the peak-shaving dispatch instructions.

[0144] The technical solution of this application has the following outstanding advantages:

[0145] As the proportion of renewable energy generation in the power system increases, thermal power plants, from design to operation, must pay attention to the storage of heat to meet the needs of auxiliary peak-shaving services for the power grid. The quotation of auxiliary peak-shaving services needs to be market-oriented and coordinated. To this end, the technical solution of this application can meet the needs of market-oriented coordination through the above-mentioned method.

[0146] In addition, in the technical solution of this application, data analysis is introduced in the market-oriented dispatch of high-frequency variable loads of thermal power. Through the quantified Tables 1 to 3, the grid side (combined with Tables 2 to 3) can be digitally dispatched, and the power plant side (combined with Table 1) can be digitally coal selected. The key lies in feature extraction based on data. The data-driven dispatching process makes the execution of peak-shaving dispatch more standardized and digitalized, with better synergy and clearer costs. The power grid can sort and set priorities according to the performance reflected by the cost and indicators, so as to better select the units suitable for peak-shaving based on the data as a theoretical basis, making the peak-shaving effect more stable, safe and ideal.

[0147] In addition, the technical solution of this application also takes into account the safety of the power grid, such as the ramp coefficient and the peak load curve. Such key data can guide the pricing of power quality and is economical. The pricing of high-frequency peak-shaving dispatching and auxiliary peak-shaving services takes into account the cost of power plants in the market.

[0148] Based on the peak-shaving scheduling method in the high-frequency variable load scenario of thermal power introduced in the previous embodiment, accordingly, the present application also provides a peak-shaving scheduling device in the high-frequency variable load scenario of thermal power. Figure 6 This is a schematic diagram of the structure of a peak load dispatching device in a high frequency variable load scenario of a thermal power plant provided in an embodiment of the present application. Figure 6 As shown, the device comprises:

[0149] The calculation module 601 is used to calculate the heat storage margin of the energy storage device in the thermal power plant; the heat storage margin includes a heat storage margin characterization value and a heat release capacity characterization value;

[0150] The sending module 602 is used to send the correlation data between the load, performance and cost of each thermal power unit in the thermal power plant and the heat storage and access margin to the power grid side, so that the power grid side determines the target unit for expected auxiliary peak load regulation and issues peak load regulation scheduling instructions based on the change of thermal power load demand, the correlation data provided by each thermal power plant and the heat storage and access margin;

[0151] The receiving module 603 is used to receive the peak load scheduling instruction issued by the power grid side; the peak load scheduling instruction carries the unit identification of the target unit and the peak load requirement information for the target unit; the peak load requirement information includes a peak load curve;

[0152] The peak shaving module 604 is configured to perform auxiliary peak shaving services based on the peak shaving requirement information.

[0153] In an optional implementation, in the peak-shaving dispatching device in the high-frequency variable load scenario of thermal power, the calculation module 601 is further used to calculate the quotation information of the auxiliary peak-shaving service provided by the thermal power unit that meets the requirements of the corresponding peak-shaving load curve according to the peak-shaving load curve of each thermal power unit;

[0154] The sending module 602 is also used to send the quotation information to the power grid side;

[0155] The grid side determines the target unit for expected auxiliary peak regulation by weighing costs and indicators through data-driven means based on changes in thermal power load demand, the associated data provided by each thermal power plant, the heat storage and access margin and the quotation information.

[0156] In an optional implementation, the grid side generates a peak load curve for each thermal power unit based on the change in thermal power load demand, the associated data provided by each thermal power plant, and the heat storage and access margin, and calculates the climbing coefficient of each thermal power unit;

[0157] Preliminarily determine multiple thermal power units expected to assist in peak load regulation based on the ramp coefficients of each thermal power unit;

[0158] If there is a thermal power unit whose bid information is less than or equal to K times the threshold bid among the multiple thermal power units expected to assist in peak load regulation, the thermal power unit whose bid information is less than or equal to K times the threshold bid is determined as the target unit for expected auxiliary peak load regulation; the K is within the range of 1.1 to 1.3;

[0159] Among them, the threshold quotation is a quotation reference value obtained by deep learning historical quotation information on the power grid side.

[0160] In an optional implementation,

[0161] The power grid side sends a heat pre-dispatching instruction to the multiple thermal power generating units expected to assist in peak load regulation;

[0162] The peak-shaving module 604 of the peak-shaving scheduling device in the high-frequency variable load scenario of thermal power is also used to perform heat pre-scheduling based on the heat pre-scheduling instruction through the cooperation of the heat storage device in the thermal power plant to which it belongs.

[0163] In an optional implementation, the peak shaving requirement information further includes climbing speed requirement information; the peak shaving module 604 is specifically configured to:

[0164] Determine whether there is equipment failure and communication failure in the target unit, and determine whether the target unit meets the climbing speed requirement information;

[0165] If there is no equipment failure and communication failure in the target unit, and the target unit meets the climbing speed requirement information, then according to the peak load curve and the climbing speed requirement information in the peak load requirement information, perform auxiliary peak load shaving service;

[0166] If there is a device failure or a communication failure in the target unit, a fault message is reported to the power grid side;

[0167] If the target unit does not meet the climbing speed requirement information, a prompt message indicating that the operating condition is not met is reported to the power grid side.

[0168] In an optional implementation,

[0169] The power grid side adjusts the peak-shaving requirement information in the peak-shaving scheduling instruction according to the prompt information that the operating condition is not met.

[0170] In an optional implementation,

[0171] The grid side relearns to increase the threshold quotation based on the prompt information of the operating condition not being met fed back by each thermal power plant, so that more units are used as target units for expected auxiliary peak regulation.

[0172] In an optional implementation, the climbing coefficient of the thermal power unit is calculated as follows:

[0173] The ramp speed, steam pressure and rated power generation capacity are extracted from the correlation data among the load, performance and cost of the thermal power unit;

[0174] The climbing coefficient of the thermal power unit is calculated according to the climbing speed, steam pressure, rated power generation capacity, heat storage and access margin of the thermal power unit and the climbing time required by the power plant side for the thermal power unit.

[0175] In an optional implementation, the heat storage and access margin is expressed as a percentage; the grid side calculates the climbing coefficient of the thermal power unit according to the climbing coefficient, steam pressure, rated power generation capacity, heat storage and access margin of the thermal power unit and the climbing time required by the power plant side for the thermal power unit, including:

[0176] The product of the climbing speed, climbing time, steam pressure and heat storage margin of the thermal power unit is calculated; the ratio of the product to the rated power generation capacity of the thermal power unit is calculated, and the ratio is used as the climbing coefficient of the thermal power unit.

[0177] In an optional implementation, the grid side is also used to establish a corresponding relationship between the thermal power units and the peak load dispatching level according to the climbing coefficient of each thermal power unit;

[0178] The grid side preliminarily determines multiple thermal power units expected to assist in peak load regulation based on the ramp coefficients of each thermal power unit. The specific method is as follows:

[0179] According to the peak-shaving dispatch level corresponding to each thermal power unit, a number of thermal power units expected to assist in peak-shaving are preliminarily determined.

[0180] In an optional implementation, the calculation module 601 is specifically used for:

[0181] Based on the peak-shaving load curve and power supply quality of each thermal power unit, quotation information for providing the auxiliary peak-shaving service for the thermal power units that meet the requirements of the corresponding peak-shaving load curve is generated; wherein the peak-shaving load curve is selected from a typical load curve set, and the typical load curve set is established after analysis and research on the power grid side based on the load increase and decrease change data of the thermal power units under various different operating conditions, as well as the correlation data between the load, performance and cost of each thermal power unit.

[0182] In an optional implementation, the heat storage and access margin reported by the peak-shaving dispatching device in the thermal power high-frequency variable load scenario and the associated data are calculated based on the coal type parameters of the selected target coal type; the peak-shaving dispatching device in the thermal power high-frequency variable load scenario also includes a coal type selection module, which is specifically used to:

[0183] According to the high calorific value and low calorific value corresponding to each of the multiple types of coal to be selected, the difference percentage between the high and low calorific value of each type of coal to be selected is obtained; and the coal type with the smallest difference percentage between the high and low calorific value among the multiple types of coal to be selected is determined as the target coal type.

[0184] Figure 7 This is a flow chart of another peak load scheduling method in a thermal power high frequency variable load scenario provided by the embodiment of the present application. Figure 7 Provide a brief description.

[0185] like Figure 7 In the process shown, the thermal power plant first selects the coal type based on the hydrogen content of each type of coal, then calculates the thermal storage margin of the thermal power plant, and digitizes the correlation data between the load, performance and cost of each thermal power unit in the thermal power plant. The thermal power plant sends this data to the power grid, which uses data to weigh costs and indicators and determine the target unit for expected auxiliary peak regulation.

[0186] In specific implementation, the grid side generates the peak load curve for each thermal power unit based on the changes in thermal power load demand and the corresponding data provided by each thermal power plant, and sends it to the thermal power unit. In addition, the climbing coefficient of each thermal power unit is calculated, and multiple thermal power units expected to assist in peak load regulation are preliminarily determined, and heat pre-dispatch instructions are sent to them. The thermal power plant calculates the quotation for the auxiliary peak load regulation service based on the peak load curve and sends it to the grid side. The grid side continues to combine cost factors and the performance data of the thermal power plant, and weighs the quotation information with the K-fold threshold quotation to select the target unit. If the quotations all exceed the K-fold threshold quotation, it means that the threshold quotation is too low, which means that the threshold quotation needs to be deeply learned again to improve the pass rate of the thermal power units with the load quotation threshold.

[0187] The target unit cooperates with the heat pre-scheduling, and after the pre-scheduling is completed, the heat storage is reported to the power grid again. The power grid determines whether there is equipment failure and communication failure in the target unit, and determines whether the climbing speed requirement information is met. If there is equipment failure or communication failure, it needs to be reported to the power grid; if there is a situation where the target unit does not meet the climbing speed requirement information, it also needs to be reported to the power grid. Figure 7 As shown by the middle dotted line, if the operating conditions are not met, measures such as re-learning the threshold quotation can be taken, or the peak-shaving requirement information in the peak-shaving dispatch instruction issued to the target unit can be adjusted.

[0188] It should be noted that each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is merely schematic, in which the unit described as a separate component may or may not be physically separated, and the component prompted as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0189] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A peak load dispatching method for thermal power in a high frequency load variation scenario, characterized in that: include: Calculating the heat storage margin of the energy storage device in the thermal power plant; the heat storage margin includes a heat storage margin characterization value and a heat release capacity characterization value; Sending the correlation data between the load, performance and cost of each thermal power unit in the thermal power plant and the heat storage and access margin to the power grid side, so that the power grid side can determine the target unit for expected auxiliary peak load regulation and issue peak load regulation instructions based on the change of thermal power load demand, the correlation data provided by each thermal power plant and the heat storage and access margin; receiving a peak-shaving dispatch instruction issued by the power grid side; the peak-shaving dispatch instruction carries the unit identification of the target unit and peak-shaving requirement information for the target unit; the peak-shaving requirement information includes a peak-shaving load curve; Execute auxiliary peak-shaving service based on the peak-shaving requirement information; Before determining the target unit for expected auxiliary peak load regulation, the method further includes: According to the peak load curve of each thermal power unit, calculating the quotation information of the auxiliary peak load shaving service provided by the thermal power unit that meets the requirements of the corresponding peak load curve; Sending the quotation information to the power grid side; The grid side determines the target unit for expected auxiliary peak load regulation based on the change of thermal power load demand, the associated data provided by each thermal power plant, and the heat storage and access margin, specifically: The grid side determines the target unit for expected auxiliary peak load regulation by weighing costs and indicators through data-driven means based on the change in thermal power load demand, the associated data provided by each thermal power plant, the heat storage and access margin and the quotation information; The grid side determines the target unit for expected auxiliary peak load regulation based on the change of thermal power load demand, the associated data provided by each thermal power plant, the heat storage and access margin and the quotation information, by weighing the cost and indicators through data-driven, specifically including: The grid side generates a peak load curve for each thermal power unit based on the change in thermal power load demand, the associated data provided by each thermal power plant and the heat storage and access margin, and calculates the climbing coefficient of each thermal power unit; Preliminarily determine multiple thermal power units expected to assist in peak load regulation based on the climbing coefficient of each thermal power unit; If there is a thermal power unit whose bid information is less than or equal to K times the threshold bid among the multiple thermal power units expected to assist in peak load regulation, the thermal power unit whose bid information is less than or equal to K times the threshold bid is determined as the target unit for expected auxiliary peak load regulation; the K is within the range of 1.1 to 1.3; Wherein, the threshold quotation is a quotation reference value obtained by deep learning historical quotation information on the grid side; The calculation method of the climbing coefficient of thermal power units is: The ramp speed, steam pressure and rated power generation capacity are extracted from the correlation data among the load, performance and cost of the thermal power unit; The climbing coefficient of the thermal power unit is calculated according to the climbing speed, steam pressure, rated power generation capacity, heat storage and access margin of the thermal power unit and the climbing time required by the power plant side for the thermal power unit.

2. The method according to claim 1, characterized in that After the multiple thermal power generating units expected to assist in peak load regulation are preliminarily determined, the method further includes: The power grid side sends a heat pre-dispatching instruction to the multiple thermal power generating units expected to assist in peak load regulation; The target unit among the multiple thermal power units expected to assist in peak load regulation performs heat pre-scheduling in cooperation with the heat storage device in the thermal power plant to which it belongs based on the heat pre-scheduling instruction.

3. The method according to claim 1, characterized in that: The peak shaving requirement information also includes climbing speed requirement information; and performing auxiliary peak shaving service based on the peak shaving requirement information includes: Determine whether there is equipment failure and communication failure in the target unit, and determine whether the target unit meets the climbing speed requirement information; If there is no equipment failure and communication failure in the target unit, and the target unit meets the climbing speed requirement information, then according to the peak load curve and the climbing speed requirement information in the peak load requirement information, perform auxiliary peak load shaving service; If there is a device failure or a communication failure in the target unit, a fault message is reported to the power grid side; If the target unit does not meet the climbing speed requirement information, a prompt message indicating that the operating condition is not met is reported to the power grid side.

4. The method according to claim 3, characterized in that The method further comprises: The power grid side adjusts the peak-shaving requirement information in the peak-shaving scheduling instruction according to the prompt information that the operating condition is not met.

5. The method according to claim 3, characterized in that: The method further comprises: The grid side relearns to increase the threshold quotation based on the prompt information of the operating condition not being met fed back by each thermal power plant, so that more units are used as target units for expected auxiliary peak regulation.

6. The method according to claim 1, characterized in that The heat storage margin is expressed as a percentage; the climbing coefficient of the thermal power unit is calculated according to the climbing speed, steam pressure, rated power generation capacity, heat storage margin and the climbing time required by the power plant side for the thermal power unit, including: Calculate the product of the ramp speed, ramp time, steam pressure and heat storage margin of the thermal power unit; The ratio of the product to the rated power generation capacity of the thermal power unit is calculated, and the ratio is used as the climbing coefficient of the thermal power unit.

7. The method according to claim 1, characterized in that The method further comprises: According to the climbing coefficient of each thermal power unit, the corresponding relationship between the thermal power unit and the peak load dispatching level is established; The method of preliminarily determining a plurality of thermal power units expected to assist in peak load regulation according to the climbing coefficient of each thermal power unit specifically includes: According to the peak-shaving dispatch level corresponding to each thermal power unit, a number of thermal power units expected to assist in peak-shaving are preliminarily determined.

8. The method according to claim 1, characterized in that The calculation of the quotation information of the auxiliary peak-shaving service provided by the thermal power generation units that meet the requirements of the corresponding peak-shaving load curve according to the peak-shaving load curve of each thermal power generation unit specifically includes: According to the peak load curve and power supply quality of each thermal power unit, generating quotation information for providing the auxiliary peak load shaving service by the thermal power unit that meets the requirements of the corresponding peak load curve; Among them, the peak load curve is selected from a typical load curve set, and the typical load curve set is established after analysis and research on the power grid side based on the load increase and decrease change data of the thermal power units under various working conditions, as well as the correlation data between the load, performance and cost of each thermal power unit.

9. The method according to any one of claims 1 to 8, characterized in that The heat storage and access margin and the associated data are calculated based on the coal type parameters of the selected target coal type; the target coal type is selected in the following manner: According to the high calorific value and the low calorific value corresponding to each of the multiple types of coal to be selected, the difference percentage between the high and low calorific value of each type of coal to be selected is obtained; The coal type with the smallest percentage difference between high and low calorific values ​​among the multiple coal types to be selected is determined as the target coal type.

10. The method according to claim 9, characterized in that The percentage of the difference between high and low calorific values ​​is a low-order feature obtained by extracting features based on the coal type parameters of the corresponding coal type in the thermal power plant; The grid side determines the target unit for expected auxiliary peak load regulation and issues a peak load regulation instruction based on the change of thermal power load demand, the associated data provided by each thermal power plant, and the heat storage and access margin, including: The grid side extracts intermediate features based on the associated data and the heat storage and access margin provided by each thermal power plant, wherein the intermediate features include features reflecting the energy consumption level and thermal energy reserve level of the power plant; The power grid side fuses the coal price cost in the associated data with the mid-order features to obtain high-order features, wherein the high-order features include the relationship between fuel, electricity quantity and electricity price; The grid side determines the target units for expected auxiliary peak load regulation and issues peak load regulation dispatching instructions based on the changes in thermal power load demand, the mid-order characteristics and the high-order characteristics.

11. A peak load dispatching device for thermal power in high frequency load variation scenario, characterized in that: include: A calculation module, used to calculate the heat storage margin of the energy storage device in the thermal power plant; the heat storage margin includes a heat storage margin characterization value and a heat release capacity characterization value; A sending module is used to send the correlation data between the load, performance and cost of each thermal power unit in the thermal power plant and the heat storage and access margin to the power grid side, so that the power grid side can determine the target unit for expected auxiliary peak load regulation and issue peak load regulation scheduling instructions based on the change of thermal power load demand, the correlation data provided by each thermal power plant and the heat storage and access margin, through data-driven weighing of costs and indicators; A receiving module, configured to receive a peak load scheduling instruction issued by the power grid side; the peak load scheduling instruction carries the unit identification of the target unit and the peak load requirement information for the target unit; the peak load requirement information includes a peak load curve; A peak-shaving module, used for performing auxiliary peak-shaving services based on the peak-shaving requirement information; The calculation module is further used to calculate the quotation information of the auxiliary peak-shaving service provided by the thermal power units that meet the requirements of the corresponding peak-shaving load curve according to the peak-shaving load curve of each thermal power unit; The sending module is further used to send the quotation information to the power grid side; The grid side determines the target unit for expected auxiliary peak regulation based on the change of thermal power load demand, the associated data provided by each thermal power plant, the heat storage and access margin and the quotation information, by weighing the cost and index through data-driven; wherein the specific method for determining the target unit for expected auxiliary peak regulation is: The grid side generates a peak load curve for each thermal power unit based on the change in thermal power load demand, the associated data provided by each thermal power plant and the heat storage and access margin, and calculates the climbing coefficient of each thermal power unit; Preliminarily determine multiple thermal power units expected to assist in peak load regulation based on the climbing coefficient of each thermal power unit; If there is a thermal power unit whose bid information is less than or equal to K times the threshold bid among the multiple thermal power units expected to assist in peak load regulation, the thermal power unit whose bid information is less than or equal to K times the threshold bid is determined as the target unit for expected auxiliary peak load regulation; the K is within the range of 1.1 to 1.3; Wherein, the threshold quotation is a quotation reference value obtained by deep learning historical quotation information on the grid side; The calculation method of the climbing coefficient of thermal power units is: The ramp speed, steam pressure and rated power generation capacity are extracted from the correlation data among the load, performance and cost of the thermal power unit; The climbing coefficient of the thermal power unit is calculated according to the climbing speed, steam pressure, rated power generation capacity, heat storage and access margin of the thermal power unit and the climbing time required by the power plant side for the thermal power unit.

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