Method, device and electronic equipment for operating a solar thermal power plant

Abstract

The present specification provides a method and device for operating control of a solar thermal power station and an electronic device. First, meteorological data of a current time period is obtained; and the meteorological data of the current time period is processed by using a preset frost prediction model to obtain a first prediction result; the meteorological data of the current time period is processed by using a preset solar normal direct radiation prediction model to obtain a second prediction result; then, a target mirror field heat collection power model is constructed according to the first prediction result, the second prediction result, and an initial mirror field heat collection power model of a target solar thermal power station; the mirror field heat collection power of the next time period of the target solar thermal power station is determined by using the target mirror field heat collection power model; whether to start the target solar thermal power station in the next time period is determined according to the mirror field heat collection power of the next time period of the target solar thermal power station; and in the case of determining to start the target solar thermal power station in the next time period, a target operation control strategy is determined according to the first prediction result.

Description

Technical Field

[0001] This manual belongs to the field of new energy technology, and in particular relates to the operation control methods, devices and electronic equipment of solar thermal power plants. Background Technology

[0002] With the development of new energy technologies, solar thermal power generation technology, with its energy storage and peak-shaving characteristics, has become one of the important supporting technologies to make up for the intermittent defects of wind power / solar power and ensure the stable operation of the power grid.

[0003] However, the solar thermal power plant's collector reflectors are usually exposed to complex outdoor environments, and frost can easily form on the mirrors in winter. This means that a frost layer forms on the surface of the collector reflectors, which significantly reduces the reflectivity of the mirrors, hinders the effective collection of solar radiation, and consequently leads to a decline in heat collection efficiency, a sharp reduction in power generation, and affects the normal operation of the solar thermal power plant.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This specification provides an operation control method, device, and electronic equipment for a concentrated solar power (CSP) plant. It is well-suited for complex environmental scenarios with mirror frost formation, predicts and determines the operation control strategy matching the next time period in advance, and then accurately and stably controls the start-up and operation of the CSP plant in the next time period based on the operation control strategy. This effectively reduces the power generation loss caused by mirror frost formation and ensures the efficient and stable operation of the CSP plant.

[0006] This manual provides a method for the operation and control of a concentrated solar power (CSP) plant, including: Obtain meteorological data for the current time period; The meteorological data for the current time period is processed using a preset frost prediction model to obtain a first prediction result; the meteorological data for the current time period is processed using a preset solar radiation prediction model to obtain a second prediction result; wherein, the first prediction result includes at least: frost indication parameters, frost degree characteristics, and defrosting time indicating whether there is mirror frost on the solar thermal power plant in the next time period; the second prediction result includes at least: solar normal direct radiation parameters for the next time period. Based on the first prediction result, the second prediction result, and the initial mirror field heat collection power model of the target solar thermal power plant, a matching target mirror field heat collection power model is constructed. Using the target mirror field heat collection power model, the mirror field heat collection power of the target solar thermal power plant in the next time period is determined; Based on the mirror field heat collection power of the target solar thermal power plant in the next time period, determine whether to start the target solar thermal power plant in the next time period; Given that the target solar thermal power plant will be started in the next time period, a matching target operation control strategy is determined based on the first prediction result; wherein, the target operation control strategy is used to control the start-up and operation of the target solar thermal power plant in the next time period.

[0007] In one embodiment, the meteorological data includes at least one of the following: weather, satellite cloud image, solar radiation parameters, ambient humidity, ambient temperature, ambient wind speed, and ambient wind direction.

[0008] In one embodiment, the preset frost prediction model includes a multi-timescale time-series prediction model based on a TCN-GRU structure.

[0009] In one embodiment, the initial mirror field heat collection power model for the target solar thermal power plant is constructed as follows: Determine the type of the target solar thermal power plant; wherein, the type of power plant includes: tower solar thermal power plants and trough solar thermal power plants; Based on the type of the target solar thermal power plant, construct a mirror field concentration efficiency model that matches the time and meteorological data; Based on the aforementioned mirror field concentration efficiency model, an absorbed heat power model is constructed considering time, meteorological data, and solar normal direct radiation parameters. Based on the absorbed heat power model and the heat dissipation loss power, an initial mirror field heat collection power model for the target solar thermal power plant is constructed.

[0010] In one embodiment, the method includes: constructing an initial mirror field heat collection power model for the target solar thermal power plant according to the following formula:

[0011] Among them, when the power plant type is a tower solar thermal power plant:

[0012] When the power plant type is a parabolic trough solar thermal power plant:

[0013] in, For the heat collection power of the mirror field, The heat power theoretically absorbed by the receiver. To dissipate heat and reduce power loss, These are the parameters for direct solar radiation in the normal direction. A For the mirror field area, To improve the light-gathering efficiency of the mirror field. This refers to the operating temperature of the heat absorber. For ambient temperature, For ambient wind speed, To reduce efficiency, For atmospheric attenuation efficiency, For cosine efficiency, To reduce efficiency due to shadows, To avoid the loss of efficiency due to shading, The first specular reflectivity, As a cleaning agent, For the optical efficiency of the solar collector, For the geometric efficiency of the solar collector, The second specular reflectivity, For the heat collection tube transmittance, The absorption rate of the heat collection tube coating, This is the effective area coefficient of the heat collection tube. To track the error coefficient, As an interception factor, For cosine loss, This is the shadow effect coefficient. This is the end loss coefficient. IAM This is the correction factor for the angle of incidence of sunlight.

[0014] In one embodiment, when the frost indication parameter indicates that the solar thermal power plant will experience mirror frost in the next time period, the step of constructing a matching target mirror field heat collection power model based on the first prediction result, the second prediction result, and the initial mirror field heat collection power model of the target solar thermal power plant includes: Based on the second prediction result, the initial mirror field heat collection power model is processed to obtain the intermediate mirror field heat collection power model; Based on the defrosting time, the next time period is divided into the first sub-time period and the second sub-time period; Based on the characteristics of frost formation and defrosting time, a time-varying mirror frost influence function is constructed through model fitting. Multiply the intermediate mirror field heat collection power model with the mirror surface frost influence function to obtain the corresponding modified mirror field heat collection power model; In the first sub-time period, the modified mirror field heat collection power model is used as the target mirror field heat collection power model; in the second sub-time period, the intermediate mirror field heat collection power model is used as the target mirror field heat collection power model.

[0015] In one embodiment, the first prediction result further includes: the amount of frost remaining in the next time period; Accordingly, determining the matching target operation control strategy based on the first prediction result includes: Based on the remaining amount of frost in the next time period, determine the characteristics of the change in the remaining amount of frost. The first trigger time point is determined based on the changing characteristics of the remaining frosting amount; Based on the defrosting time, a second trigger time point is determined; and a time point with a preset interval before the second trigger time point is determined as the third trigger time point. The first trigger time point is determined as the first start-up time for starting the mirror field of the target solar thermal power plant; the third trigger time point is determined as the second start-up time for preheating and starting the steam turbine using the waste heat stored in the heat storage tank; the second trigger time point is determined as the switching time for switching and using the currently generated mirror field heat collection power of the target solar thermal power plant to drive the steam turbine; Based on the first startup time, the second startup time, and the switching time, a matching target operation control strategy is established.

[0016] In one embodiment, the method further includes: Obtain data on fluctuations in grid electricity prices; Based on the variation characteristics of the remaining frost, a first regulation rule for the load regulation of the steam turbine in the third sub-time period is generated; wherein, the third sub-time period is the time period between the second start-up time and the switching time; Based on the fluctuation data of the grid electricity price and the solar normal direct radiation parameters for the next time period, a second regulation rule for the load regulation of the steam turbine in the fourth sub-time period is generated; wherein, the fourth sub-time period is the time period between the switching time and the end time of the next time period; Based on the first start time, the second start time, the switching time, the first adjustment rule, and the second adjustment rule, a matching target operation control strategy is established.

[0017] In one embodiment, after acquiring meteorological data for the current time period, the method further includes: Obtain the solar normal direct radiation parameters for the current time period, and measure the measured solar collector power for the current time period; Based on the initial mirror field heat collection power model, the meteorological data for the current time period, and the solar normal direct radiation parameters for the current time period, the mirror field heat collection power under the condition of no frost in the current time period is determined. Time alignment is performed between the mirror field heat collection power under the condition of no frost in the current time period and the measured heat collection power in the current time period; Based on the mirror field heat collection power under the influence of no frost in the current time period after time alignment and the measured heat collection power in the current time period, calculate the power difference between the mirror field heat collection power under the influence of no frost and the measured heat collection power at the same time point in the current time period. Based on the power difference between the mirror field heat collection power under the condition of no frost at the same time point in the current time period and the measured heat collection power, the heat loss of the mirror field due to frost in the current time period is determined by time integration. Based on the heat loss of the mirror field due to frost and the average thermoelectric efficiency of the steam turbine during the current time period, the power generation loss caused by mirror frost during the current time period is determined.

[0018] This specification also provides an operation control device for a solar thermal power plant, including: The acquisition module is used to acquire meteorological data for the current time period; The prediction module is used to process meteorological data for the current time period using a preset frost prediction model to obtain a first prediction result; and to process meteorological data for the current time period using a preset solar radiation prediction model to obtain a second prediction result; wherein, the first prediction result includes at least: frost indication parameters indicating whether there is mirror frost on the solar thermal power plant in the next time period, frost degree characteristics, and defrosting time; the second prediction result includes at least: solar normal direct radiation parameters for the next time period; The module is used to construct a matching target mirror field heat collection power model based on the first prediction result, the second prediction result, and the initial mirror field heat collection power model of the target solar thermal power plant. The first determining module is used to determine the solar thermal power of the target solar thermal power plant in the next time period by using the target mirror field heat collection power model. The second determining module is used to determine whether to start the target solar thermal power plant in the next time period based on the mirror field heat collection power of the target solar thermal power plant in the next time period. The third determining module is used to determine a matching target operation control strategy based on the first prediction result when the target solar thermal power plant is determined to start in the next time period; wherein the target operation control strategy is used to control the start-up and operation of the target solar thermal power plant in the next time period.

[0019] This specification also provides an electronic device, including a processor and a memory for storing processor-executable instructions, wherein the processor executes the instructions to implement the steps of the operation control method for the solar thermal power plant.

[0020] This specification also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the steps of the operation control method for the solar thermal power plant.

[0021] This specification also provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of the operation control method for the solar thermal power plant.

[0022] Based on the operation control methods, devices, and electronic equipment of the solar thermal power plant provided in this manual, a preset frost prediction model, a preset solar radiation prediction model, and an initial mirror field heat collection power model of the target solar thermal power plant can be constructed before implementation. In specific implementation, meteorological data for the current time period is first acquired; then, a preset frost prediction model is used to process the meteorological data for the current time period to obtain a first prediction result; a preset solar radiation prediction model is used to process the meteorological data for the current time period to obtain a second prediction result; wherein, the first prediction result includes at least: frost indication parameters, frost severity characteristics, and defrosting time indicating whether the solar thermal power plant will experience mirror frost in the next time period; the second prediction result includes at least: solar normal direct radiation parameters for the next time period; then, based on the first prediction result, the second prediction result, and the initial mirror field heat collection power model of the target solar thermal power plant, a matching target mirror field heat collection power model is constructed; using the target mirror field heat collection power model, the mirror field heat collection power of the target solar thermal power plant in the next time period is determined; based on the mirror field heat collection power of the target solar thermal power plant in the next time period, it is determined whether the target solar thermal power plant will be started in the next time period; if it is determined that the target solar thermal power plant will be started in the next time period, a matching target operation control strategy is determined based on the first prediction result; wherein, the target operation control strategy is used to control the start-up and operation of the target solar thermal power plant in the next time period. By fully considering and utilizing the impact of mirror frost on the operation and power generation of concentrated solar power (CSP) plants, this approach can be well applied to complex environmental scenarios where mirror frost exists. It allows for the prediction and determination of an operation control strategy that matches the next time period. Based on this strategy, the startup and operation of the CSP plant in the next time period can be precisely and stably controlled, thereby effectively reducing the power generation loss caused by mirror frost and ensuring the efficient and stable operation of the CSP plant, ultimately leading to better power generation revenue. Attached Figure Description

[0023] To more clearly illustrate the embodiments of this specification, the accompanying drawings used in the embodiments will be briefly introduced below. The drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating an embodiment of the operation control method for a solar thermal power plant provided in this specification. Figure 2 This is a schematic diagram of one embodiment of the operation control method for a solar thermal power plant provided in this specification, applied in a scenario example. Figure 3This is a schematic diagram of one embodiment of the operation control method for a solar thermal power plant provided in this specification, applied in a scenario example. Figure 4 This is a schematic diagram of one embodiment of the operation control method for a solar thermal power plant provided in this specification, applied in a scenario example. Figure 5 This is a schematic diagram of one embodiment of the operation control method for a solar thermal power plant provided in this specification, applied in a scenario example. Figure 6 This is a schematic diagram of one embodiment of the operation control method for a solar thermal power plant provided in this specification, applied in a scenario example. Figure 7 This is a schematic diagram of the structural composition of an electronic device provided in one embodiment of this specification; Figure 8 This is a schematic diagram of the structural composition of an operation control device for a solar thermal power plant provided in one embodiment of this specification; Figure 9 This is a schematic diagram of one embodiment of the operation control method for a solar thermal power plant provided in this specification, applied in a scenario example. Figure 10 This is a schematic diagram of one embodiment of the operation control method for a solar thermal power plant provided in this specification, applied in a scenario example. Detailed Implementation

[0025] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0026] It should be noted that the information and data related to users involved in the embodiments of this specification are all information and data authorized by the user or fully authorized by the relevant parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with relevant laws, regulations, and standards, and necessary confidentiality measures have been taken. They do not violate public order and good morals, and corresponding operation entry points are provided for users or relevant parties to choose to authorize or refuse.

[0027] It should also be noted that in the embodiments of this specification, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0028] See Figure 1 As shown in the embodiments of this specification, an operation control method for a concentrated solar power (CSP) plant is provided. Specifically, this method is applied to one side of the target CSP plant. In specific implementation, the method may include the following: S101: Obtain meteorological data for the current time period; S102: Process the meteorological data of the current time period using a preset frost prediction model to obtain a first prediction result; process the meteorological data of the current time period using a preset solar radiation prediction model to obtain a second prediction result; wherein, the first prediction result includes at least: frost indication parameters indicating whether there is mirror frost on the solar thermal power plant in the next time period, frost degree characteristics, and defrosting time; the second prediction result includes at least: solar normal direct radiation parameters for the next time period; S103: Based on the first prediction result, the second prediction result, and the initial mirror field heat collection power model of the target solar thermal power plant, a matching target mirror field heat collection power model is constructed. S104: Using the target mirror field heat collection power model, determine the mirror field heat collection power of the target solar thermal power plant in the next time period; S105: Based on the mirror field heat collection power of the target solar thermal power plant in the next time period, determine whether to start the target solar thermal power plant in the next time period. S106: If it is determined that the target solar thermal power plant will be started in the next time period, a matching target operation control strategy is determined based on the first prediction result; wherein, the target operation control strategy is used to control the start-up and operation of the target solar thermal power plant in the next time period.

[0029] The aforementioned target solar thermal power plant may include at least the following structures: a solar collector field, a thermal storage tank, and a steam turbine unit. The solar collector field is specifically used for concentrating sunlight and absorbing heat, converting direct solar radiation into corresponding thermal energy. The steam turbine unit is specifically used to generate electricity by utilizing the thermal energy, converting the thermal energy into corresponding electrical energy. The thermal storage tank is specifically used to store unused thermal energy.

[0030] The aforementioned solar thermal field can include at least a tower-type solar thermal field or a trough-type solar thermal field. Specifically, the tower-type solar thermal field can include at least a heliostat, an absorber tower, and an absorber. The trough-type solar thermal field can include at least a trough-shaped reflector array and collector tubes. Correspondingly, a solar thermal power plant with a tower-type solar thermal field can be referred to as a tower-type solar thermal power plant, and a solar thermal power plant with a trough-type solar thermal field can be referred to as a trough-type solar thermal power plant.

[0031] The aforementioned current time period can specifically be today, and the corresponding next time period can be tomorrow. Of course, in practice, the aforementioned current time period can also be this week, or this month, and the corresponding next time period can be next week, or next month, etc. This instruction manual does not limit this.

[0032] The meteorological data for the current time period mentioned above can specifically include meteorological data of the environmental area where the solar thermal power plant is located, which are monitored and collected at various time points (e.g., sampling time points) within the current time period.

[0033] Specifically, the meteorological data mentioned above may include at least one of the following: weather, satellite cloud imagery, solar radiation parameters, ambient humidity, ambient temperature, ambient wind speed, and ambient wind direction. The weather data mentioned above may specifically include at least one of the following: sunny, rainy, snowing, or cloudy.

[0034] The aforementioned pre-defined frost prediction model can be understood as an algorithm model that can predict the mirror frost situation (e.g., whether mirror frost forms, the degree of frost, and the duration of defrosting) of the solar thermal power plant's collector field in future time periods based on meteorological data input for a certain time period and through multi-time-scale data analysis.

[0035] The aforementioned frosting characteristics can be specifically understood as features used to describe the degree of frosting on a mirror surface, and may include at least one of the following: frost area, frost thickness, frost shape, etc.

[0036] The aforementioned defrosting time can be specifically understood as the time elapsed from sunrise until the frost layer on the mirror surface disappears.

[0037] Furthermore, the aforementioned first prediction result may also include the variation characteristics of the remaining amount of frost within the first time period.

[0038] The aforementioned pre-defined solar radiation prediction model can be understood as a neural network model that can analyze meteorological data for a certain time period and predict the solar normal direct radiation parameters for future time periods based on the changing trend characteristics of the meteorological data.

[0039] Specifically, the aforementioned preset frost prediction model can be a multi-timescale time-series prediction model based on the TCN-GRU structure.

[0040] The aforementioned preset frost prediction model can specifically be a hybrid model based on the TCN-GRU structure.

[0041] The aforementioned TCN (Temporal Convolutional Network) can be understood as a type of convolutional network, suitable for processing long-term series data and supporting parallel computing capabilities. Accordingly, by introducing a TCN network into the model, on the one hand, the model can support parallel computing, improving its processing efficiency; on the other hand, it can leverage the advantages of TCN networks in processing long-term series data, facilitating subsequent long-range dependency modeling.

[0042] The aforementioned GRU (Gate Recurrent Unit) can be understood as a variant of a recurrent neural network (RNN). Based on this structure, the gate unit can solve problems such as the inability of RNNs to retain information for long periods and gradients in backpropagation. At the same time, it can also preserve information in long-term sequences without it being cleared over time or removed because it is irrelevant to the prediction.

[0043] The aforementioned pre-defined frost prediction model can be a model obtained by further modifying the TCN-GRU structure, resulting in a model that includes at least multiple time-scale branch processing structures. Each time-scale branch processing structure corresponds to one time scale and processes meteorological data time series at at least one time scale.

[0044] Specifically, the aforementioned preset frost prediction model includes at least: a convolutional network layer based on the TCN structure, a splicing layer, and a prediction layer based on the GRU structure.

[0045] The convolutional network layer includes multiple time-scale branch processing structures; each time-scale branch processing structure includes at least multiple connected dilated convolutional layers and max pooling layers; the number of dilated convolutional layers and the dilation rate of the dilated convolutional layers in each time-scale branch processing structure are determined according to the time scale corresponding to that time-scale branch processing structure; the splicing layer is also connected to an attention fusion module.

[0046] Furthermore, the aforementioned multiple time-scale branch processing structures may specifically include: a first time-scale branch processing structure corresponding to 10 minutes, a second time-scale branch processing structure corresponding to 30 minutes, and a third time-scale branch processing structure corresponding to 1 hour.

[0047] In practice, the above-mentioned method uses a preset frost prediction model to process the meteorological data for the current time period to obtain the first prediction result. In practice, this may include the following:

[0048] First, according to the preset time window division rules, the meteorological data of the current time period is divided into multiple initial time scale meteorological data time series sequences; wherein, the time scale includes at least: 10 minutes, 30 minutes, and 1 hour; the time scale with the longest duration is determined from the multiple time scales as the maximum time scale; and the corresponding reference time point is determined according to the end time point of the meteorological data time series sequence of the maximum time scale; according to the reference time point, the multiple initial time scale meteorological data time series sequences are time-aligned to obtain multiple time scale meteorological data time series sequences.

[0049] Then, multiple dilated convolutional layers in the multi-timescale branch processing structure are used to process the meteorological data time series at the corresponding time scales to obtain multiple initial feature sequences. The max pooling layer in the multi-timescale branch processing structure is used to downsample and align the corresponding initial feature sequences to obtain multiple intermediate feature sequences. A concatenation layer is used to concatenate the multiple intermediate feature sequences based on the feature channel dimension to obtain an initial comprehensive feature vector. An attention fusion module is used to perform global average pooling on the initial comprehensive feature vector and compress the sequence information of each feature channel along the time dimension to obtain a globally compressed feature vector. The attention fusion module, through a gating network, analyzes the nonlinear dependencies between feature channels based on the globally compressed feature vector and adaptively adjusts the weight coefficients of the feature channels. Based on the adjusted weight coefficients, the features in the initial comprehensive feature vector are weighted and adjusted to obtain the target comprehensive feature vector. Finally, a prediction layer, using a GRU structure, performs long-term dependency modeling analysis on the mirror frost situation in the next time period based on the target comprehensive feature vector to determine the corresponding first prediction result.

[0050] The aforementioned pre-defined solar radiation prediction model can specifically be a hybrid model based on gradient boosting trees and long short-term memory networks.

[0051] In practice, a pre-set solar radiation prediction model can be used to process meteorological data for the current time period, analyze and predict meteorological data for the next time period based on the changing trend characteristics of the meteorological data for the current time period; then, based on the meteorological data for the next time period, the solar normal direct radiation parameters for the next time period can be further predicted as a second prediction result.

[0052] Specifically, a pre-defined solar radiation prediction model can be used to output the solar normal direct radiation for the next time period based on the changing trend characteristics of meteorological data for the current time period, as well as other meteorological data besides the solar normal direct radiation parameters for the next time period. That is, the aforementioned second prediction result can also include meteorological data for the next time period.

[0053] In practice, the frost indication parameters in the first prediction result can be used to determine whether the target solar thermal power plant will have mirror frost in the next time period.

[0054] If, based on the frost indication parameters in the first prediction result, it is determined that there will be no mirror frost in the next time period, the operation control strategy corresponding to the normal state can be directly determined as the target operation control strategy; and the start-up and operation of the target solar thermal power plant in the next time period can be controlled according to the target operation control strategy.

[0055] If, based on the frost indication parameters in the first prediction result, it is determined that mirror frost will occur in the next time period, the initial mirror field heat collection power model of the target solar thermal power plant can be modified based on the first and second prediction results to construct a matching target mirror field heat collection power model that considers the role and impact of mirror frost for the next time period. Then, using the target mirror field heat collection power model, the mirror field heat collection power at multiple time points within the next time period of the target solar thermal power plant can be determined.

[0056] Specifically, the aforementioned initial mirror field heat collection power model can be understood as an algorithm model that is pre-constructed through big data analysis, without considering the effect and influence of mirror frost, and used to calculate the theoretical mirror field heat collection power of the target solar thermal power plant.

[0057] Furthermore, based on preset processing rules, the mirror field heat collection power of the target solar thermal power plant in the next time period can be integrated over time to obtain a predicted value of the electrical energy generated by starting and operating the target solar thermal power plant in the next time period. This predicted value is then checked to see if it exceeds a preset reference power generation threshold. If the predicted value exceeds the threshold, it can be determined that although mirror frost will have an effect, the electrical energy generated by starting and operating the target solar thermal power plant will still meet the requirements. In this case, it can be determined that the target solar thermal power plant should be started and operated in the next time period. Based on the first prediction result, a matching target operation control strategy is determined by analyzing and considering the changing characteristics of the remaining frost. Then, in the next time period, the target operation control strategy can be used to precisely optimize the start-up and operation of the target solar thermal power plant, aiming to start and operate it safely and stably with relatively low energy consumption, thereby achieving relatively high power generation benefits.

[0058] Conversely, if the predicted power generation value of the target solar thermal power plant in the next time period is less than or equal to the preset reference power generation threshold, it can be determined that the effect and impact of mirror frost will be relatively large in the next time period. In this case, the actual utility generated by starting and operating the target solar thermal power plant is small. Therefore, it can be determined that the target solar thermal power plant will not be started in the next time period to reduce energy waste and consumption.

[0059] Based on the above embodiments, by fully considering and utilizing the impact of mirror frost on the operation and power generation of solar thermal power plants, it can be well applied to complex environmental scenarios where mirror frost exists. It can predict and determine the operation control strategy that matches the next time period in advance. Then, according to the operation control strategy, the start-up and operation of the solar thermal power plant in the next time period can be accurately and stably controlled, effectively reducing the power generation loss caused by mirror frost, ensuring that the solar thermal power plant can operate efficiently and stably, and striving for a relatively large power generation benefit as much as possible.

[0060] In some embodiments, the meteorological data may specifically include at least one of the following: weather, satellite cloud image, solar radiation parameters, ambient humidity, ambient temperature, ambient wind speed, ambient wind direction, etc.

[0061] It should be noted that the meteorological data listed above is for illustrative purposes only. In actual implementation, depending on the specific circumstances and processing requirements, the meteorological data mentioned above may also include other types of parameter data. This instruction manual does not impose any limitations on this.

[0062] In some embodiments, the preset frost prediction model may specifically include a multi-timescale time-series prediction model based on a TCN-GRU structure.

[0063] Before implementation, an initial frost prediction model based on the TCN-GRU structure can be constructed first; historical sample data can be obtained; the historical sample data should include at least meteorological data of the sample solar thermal power plant in the earlier time period and mirror frost data in the later time period; a sample training set can be constructed based on the historical sample data; the initial frost prediction model can be trained using the sample training set to obtain a preset frost prediction model that meets the requirements.

[0064] In some embodiments, see Figure 2 As shown, the initial mirror field heat collection power model for the target solar thermal power plant can be constructed in the following manner: S2-1: Determine the power plant type of the target solar thermal power plant; wherein, the power plant type includes: tower solar thermal power plant and parabolic trough solar thermal power plant; S2-2: Based on the type of the target solar thermal power plant, construct a matching mirror field concentration efficiency model with time and meteorological data; S2-3: Based on the aforementioned mirror field focusing efficiency model, construct an absorbed heat power model considering time, meteorological data, and solar normal direct radiation parameters; S2-4: Based on the absorbed heat power model and the heat dissipation loss power, an initial mirror field heat collection power model for the target solar thermal power plant is constructed.

[0065] In specific implementation, firstly, based on the type of the target solar thermal power plant, a matching preset rule template can be determined from a pre-defined set of rule templates as the basic rule template. This basic rule template includes solar thermal power plant attribute parameters to be input as set values, and meteorological data parameters showing their time-varying relationship. The attribute parameters of the target solar thermal power plant are then obtained. These attribute parameters include at least one of the following: mirror field area, mirror field layout, mirror reflectivity, collector tube transmittance, collector tube coating absorptivity, collector tube effective area coefficient, tracking error coefficient, and interception factor. Simultaneously, based on the degree of dirtiness of the mirror surface, a numerical mapping is performed to determine the corresponding cleaning factor. Then, based on the target solar thermal power plant's attribute parameters, cleaning factor, and basic rule template, a matching mirror field concentrating efficiency model based on time and meteorological data is constructed. The independent variables of the aforementioned mirror field concentrating efficiency model include time and meteorological data; and the meteorological data is an independent variable that varies over time.

[0066] The aforementioned set of preset rule templates may include multiple preset rule templates, each of which corresponds to a power plant type.

[0067] Before implementation, historical solar thermal power records of multiple sample power plants can be obtained. Based on these historical solar thermal power records, and based on different power plant types, multiple preset rule templates corresponding to each power plant type are determined through cluster learning. These preset rule templates are then combined to construct a preset rule template set.

[0068] Next, the attribute parameters of the target solar thermal power plant can be acquired and utilized to further process the above-mentioned mirror field concentrating efficiency model, resulting in an absorbed heat power model that considers time, meteorological data, and solar normal direct radiation parameters. The independent variables of the above-mentioned absorbed heat power model include time, meteorological data, and solar normal direct radiation parameters; furthermore, the meteorological data and solar normal direct radiation parameters are independent variables that vary with time.

[0069] Then, by querying and statistically analyzing the operation logs of the target solar thermal power plant, the operating data of the target solar thermal power plant is obtained. Based on this operating data, the heat dissipation loss power of the target solar thermal power plant is determined. This operating data includes at least the receiver operating temperature. The heat dissipation loss power also includes meteorological data parameters showing its time-varying relationship.

[0070] Finally, based on the absorbed heat power model and the heat dissipation loss power, an initial mirror field heat collection power model for the target solar thermal power plant is constructed. This initial mirror field heat collection power model is a time-varying model and includes at least meteorological data parameters whose time-varying relationship is to be input, and solar normal direct radiation parameters whose time-varying relationship is to be input.

[0071] Based on the above embodiments, various factors affecting the operation of the target solar thermal power plant can be comprehensively considered to construct an initial mirror field heat collection power model with high reference value that does not consider the effect of mirror frost.

[0072] In some embodiments, the above method may include: constructing an initial mirror field heat collection power model for the target solar thermal power plant according to the following formula:

[0073] Among them, when the power plant type is a tower solar thermal power plant:

[0074] When the power plant type is a parabolic trough solar thermal power plant:

[0075] in, For the heat collection power of the mirror field, The heat power theoretically absorbed by the receiver. To dissipate heat and reduce power loss, These are the parameters for direct solar radiation in the normal direction. A For the mirror field area, To improve the light-gathering efficiency of the mirror field. This refers to the operating temperature of the heat absorber. For ambient temperature, For ambient wind speed, To reduce efficiency, For atmospheric attenuation efficiency, For cosine efficiency, To reduce efficiency due to shadows, To avoid the loss of efficiency due to shading, The first specular reflectivity, As a cleaning agent, For the optical efficiency of the solar collector, For the geometric efficiency of the solar collector, The second specular reflectivity, For the heat collection tube transmittance, The absorption rate of the heat collection tube coating, This is the effective area coefficient of the heat collection tube. To track the error coefficient, As an interception factor, For cosine loss, This is the shadow effect coefficient. This is the end loss coefficient. IAM This is the correction factor for the angle of incidence of sunlight.

[0076] It should be noted that the above-mentioned ambient temperature, ambient wind speed, atmospheric attenuation efficiency, and solar normal direct radiation parameters are time-varying data.

[0077] In some embodiments, when the frosting indicator parameter indicates that the solar thermal power plant will experience mirror frosting in the next time period, refer to Figure 3 As shown, based on the first prediction result, the second prediction result, and the initial mirror field heat collection power model of the target solar thermal power plant, a matching target mirror field heat collection power model is constructed. In specific implementation, it may include the following: S3-1: Based on the second prediction result, process the initial mirror field heat collection power model to obtain the intermediate mirror field heat collection power model; S3-2: Based on the defrosting time, divide the next time period into the first sub-time period and the second sub-time period; S3-3: Based on the characteristics of frosting degree and defrosting time, a mirror frosting influence function that varies with time is constructed through model fitting; S3-4: Multiply the intermediate mirror field heat collection power model with the mirror surface frost influence function to obtain the corresponding modified mirror field heat collection power model; S3-5: In the first sub-time period, the modified mirror field heat collection power model is used as the target mirror field heat collection power model; in the second sub-time period, the intermediate mirror field heat collection power model is used as the target mirror field heat collection power model.

[0078] In practice, based on the second prediction result, the meteorological data for the next time period can be fitted to obtain the relationship between the meteorological data and time for the next time period, which is denoted as the first relationship. At the same time, the solar normal direct radiation parameter for the next time period can be fitted to obtain the relationship between the solar normal direct radiation parameter and time for the next time period, which is denoted as the second relationship. The first and second relationships are then substituted into the initial mirror field heat collection model to obtain the corresponding intermediate mirror field heat collection power model. The only independent variable in the intermediate mirror field heat collection power model is time.

[0079] In practice, the time point at which natural defrosting ends can be determined based on the defrosting duration in the first prediction result, and this time point can be used as the defrosting end time point. Then, the time period before the defrosting end time point in the next time period is divided into the first sub-time period, and the time period after the defrosting end time point in the next time period is divided into the second sub-time period. The first sub-time period needs to consider the effect of mirror frost formation, while the second sub-time period does not need to consider this effect.

[0080] In practical implementation, based on the frosting degree characteristics and defrosting time in the first prediction result, a mirror frosting influence function varying with time can be constructed through model fitting. The intermediate mirror field heat collection power model is multiplied by the mirror frosting influence function to obtain a corresponding modified mirror field heat collection power model. In the first sub-time period, the modified mirror field heat collection power model is used as the target mirror field heat collection power model; in the second sub-time period, the intermediate mirror field heat collection power model is used as the target mirror field heat collection power model, resulting in a piecewise target mirror field heat collection power model for the next time period. The only independent variable in the above target mirror field heat collection power model is time.

[0081] Accordingly, in specific implementation, the target mirror field heat collection power model can be used to predict the mirror field heat collection power at multiple time points in the next time period of the target solar thermal power plant, which can then be used as the mirror field heat collection power for the next time period of the target solar thermal power plant.

[0082] Based on the above embodiments, the first and second prediction results can be fully utilized to adapt and adjust the initial mirror field heat collection power model of the target solar thermal power plant, resulting in a target mirror field heat collection power model with high reference value that fully considers the effect of mirror frost in the next time period.

[0083] In some embodiments, the first prediction result may further include: the amount of frost remaining in the next time period, for example, the amount of frost remaining at multiple time points in the next time period; Accordingly, see Figure 4As shown, the target operation control strategy determined based on the first prediction result can include the following in its specific implementation: S4-1: Determine the characteristics of the change in the remaining amount of frost based on the remaining amount of frost in the next time period; S4-2: Determine the first trigger time point based on the change characteristics of the remaining frosting amount; S4-3: Based on the defrosting time, determine the second trigger time point; and determine the time point with a preset time interval before the second trigger time point as the third trigger time point; S4-4: The first trigger time point is determined as the first start-up time for starting the mirror field of the target solar thermal power plant; the third trigger time point is determined as the second start-up time for preheating and starting the steam turbine using the waste heat stored in the heat storage tank; the second trigger time point is determined as the switching time for switching and using the currently generated mirror field heat collection power of the target solar thermal power plant to drive the steam turbine; S4-5: Based on the first startup time, the second startup time, and the switching time, establish a matching target operation control strategy.

[0084] In practice, based on the remaining amount of frost in the next time period, a change curve of the remaining amount of frost in the next time period can be determined through data fitting; then, based on the change curve, the corresponding change characteristics of the remaining amount of frost can be obtained.

[0085] Among them, the change characteristics of the remaining amount of frost mentioned above include at least the rate of change of the remaining amount of frost.

[0086] In practice, the time point at which the rate of change of the remaining frost exceeds a preset threshold can be determined based on the characteristics of the remaining frost amount, and this time point can be used as the first trigger time point. Typically, after the first trigger time point, the frost layer on the mirror surface will defrost at a relatively fast rate. At this point, the mirror field of the target solar thermal power plant can be started to collect heat, preparing for subsequent operation.

[0087] In practice, the defrosting end time can be determined based on the defrosting duration; the adjacent time range before the defrosting end time (e.g., half an hour before the defrosting end time) can be determined as a candidate time range; the target mirror field heat collection power model can then be used to determine the mirror field heat collection power at multiple time points within the candidate time range; based on the mirror field heat collection power at multiple time points within the candidate time range, the time point in the candidate time range where the difference between the heat collection power required for the turbine to operate at full load and the value of the heat collection power is greater than a preset reference power difference can be determined as the second trigger time point, used to switch to using the instantaneous mirror field heat collection power currently generated by the target solar thermal power plant to drive the turbine for power generation; based on the turbine's operating characteristics and combined with the ambient temperature of the next time period, the preheating time from turbine startup to normal operation can be determined as a preset time; and the time point before the second trigger time point at a preset time interval can be determined as the third trigger time point.

[0088] Furthermore, the first trigger time point can be determined as the first start-up time for starting the mirror field of the target solar thermal power plant; the third trigger time point can be determined as the second start-up time for preheating and starting the steam turbine using the waste heat stored in the heat storage tank; the second trigger time point can be determined as the switching time for switching and using the currently generated mirror field heat collection power of the target solar thermal power plant to drive the steam turbine; and a matching target operation control strategy can be established based on the first start-up time, the second start-up time, and the switching time.

[0089] Based on the above embodiments, multiple influencing factors can be comprehensively considered to accurately construct a target operation control strategy with better results for the next time period.

[0090] In some embodiments, after determining a matching target operation control strategy based on the first prediction result, the startup and operation of the target solar thermal power plant in the next time period can be specifically controlled according to the target operation control strategy.

[0091] In practice, based on the target operation control strategy, the time can be monitored. When the time reaches the first start-up time, the mirror field of the target solar thermal power plant can be started to collect heat, preparing for the subsequent operation of the entire target solar thermal power plant. When the time reaches the second start-up time, the waste heat stored in the heat storage tank can be used to preheat and start the steam turbine, allowing the steam turbine to gradually start running. The heat storage tank stores some of the heat collected and absorbed in the previous time period but not used for power generation. When the time reaches the switching time, the steam turbine is driven to switch and utilize the heat collection power of the mirror field currently generated by the target solar thermal power plant, so that the steam turbine can convert the currently generated heat collection power of the mirror field into power generation power in a timely manner, achieving high-performance power generation.

[0092] In some embodiments, see Figure 5As shown, the above method may also include the following in its specific implementation: S5-1: Obtain data on fluctuations in grid electricity prices; S5-2: Based on the variation characteristics of the remaining frosting amount, generate a first regulation rule for the load regulation of the steam turbine in the third sub-time period; wherein, the third sub-time period is the time period between the second start-up time and the switching time; S5-3: Based on the fluctuation data of the grid electricity price and the solar normal direct radiation parameters for the next time period, generate a second regulation rule for the load regulation of the steam turbine for the fourth sub-time period; wherein, the fourth sub-time period is the time period between the switching time and the end time of the next time period; S5-4: Based on the first start time, the second start time, the switching time, the first adjustment rule, and the second adjustment rule, establish a matching target operation control strategy.

[0093] In practice, the first regulation rule can be generated by optimizing the solution based on the changing characteristics of the remaining amount of frost and the residual heat in the heat storage tank.

[0094] Based on the aforementioned first adjustment rule, the relatively small amount of residual heat in the heat storage tank can be effectively utilized, taking into account the changes caused by the effect of mirror frost, and the turbine load can be finely adjusted. This allows the turbine to be started and preheated safely and stably, while simultaneously generating corresponding electrical energy from the residual heat, thereby improving overall profitability.

[0095] In practical implementation, the solar thermal power of the target solar field can be determined first using the target solar field collection power model. Then, based on the solar thermal power of the target solar field and the fluctuation data of the grid electricity price in the fourth sub-time period, an objective function for the total power generation revenue of the fourth sub-time period can be constructed, where the independent variable of the objective function is the power generation of the fourth sub-time period. Finally, based on energy balance constraints, grid dispatch constraints, and solar thermal unit operating characteristic constraints, target constraints for the power generation of the fourth sub-time period can be constructed.

[0096] Based on the above objective constraints, the objective function is optimized and solved to determine the power generation of the fourth sub-time period. Based on the power generation of the fourth sub-time period, the operating load of the steam turbine at multiple time points within the fourth sub-time period is mapped. Then, based on the operating load of the steam turbine at multiple time points within the fourth sub-time period, a second regulation rule for the load regulation of the steam turbine in the fourth sub-time period is constructed.

[0097] Based on the above embodiments, the variation characteristics of the remaining frost and the fluctuation data of the grid electricity price can be fully considered and utilized to construct a more refined and effective target operation control strategy, so that the operation of the target solar thermal power plant can be controlled based on the target operation control strategy to obtain relatively higher returns.

[0098] In some embodiments, if mirror frost also exists during the current time period, the power generation loss caused by mirror frost can also be assessed. Accordingly, after obtaining the meteorological data for the current time period, refer to... Figure 6 As shown, the above method may also include the following in its specific implementation: S6-1: Obtain the solar normal direct radiation parameters for the current time period and measure the measured solar collector power for the current time period; S6-2: Based on the initial mirror field heat collection power model, the meteorological data for the current time period, and the solar normal direct radiation parameters for the current time period, determine the theoretical mirror field heat collection power under the condition of no frost in the current time period. S6-3: Time alignment is performed between the theoretical heat collection power of the mirror field under the condition of no frost in the current time period and the measured heat collection power in the current time period; S6-4: Based on the theoretical heat collection power of the mirror field under the influence of no frost in the current time period after time alignment and the measured heat collection power in the current time period, calculate the power difference between the theoretical heat collection power of the mirror field and the measured heat collection power under the influence of no frost at the same time point in the current time period. S6-5: Based on the power difference between the theoretical and measured heat collection power of the mirror field under the condition of no frost at the same time point in the current time period, the heat collection loss of the mirror field due to frost in the current time period is determined by time integration. S6-6: Based on the heat loss of the mirror field due to frost and the average thermoelectric efficiency of the turbine during the current time period, determine the power generation loss caused by frost on the mirror surface during the current time period.

[0099] The aforementioned average thermoelectric efficiency can be calculated based on the turbine's operating monitoring data.

[0100] In practice, the power difference between the theoretical and measured heat collection power of the mirror field at the same time point without frost can be calculated based on multiple different time scales. Then, based on the power difference between the theoretical and measured heat collection power of the mirror field at the same time point without frost, a weighted calculation is performed to obtain a more accurate power difference between the theoretical and measured heat collection power at the same time point without frost.

[0101] In practice, the power generation loss due to mirror frost in the current time period can be determined using the following formula: Power generation loss = Mirror field heat collection loss Average thermoelectric efficiency.

[0102] In practice, after determining the power generation loss caused by mirror frost in the current time period, the power generation loss in the current time period can be recorded in the loss log of the target solar thermal power plant.

[0103] Furthermore, it can query loss logs to obtain multiple power generation losses prior to the current time period; based on these losses and the current time period's power generation loss, it can assess the impact of mirror frost on the overall power generation of the target solar thermal power plant; when the impact exceeds a preset threshold, it can generate suggestions for deploying auxiliary defrosting equipment to the target solar thermal power plant to accelerate defrosting and reduce the impact of mirror frost; and based on the impact level, combined with a pre-trained large language model, it can determine a matching deployment scheme for auxiliary defrosting equipment; and then push the suggestions and deployment scheme to technical personnel to guide the deployment and modification of the target solar thermal power plant in complex environmental scenarios.

[0104] Based on the above embodiments, the power generation loss caused by mirror frost can be accurately assessed, providing valuable reference and guidance for the operation and maintenance management of target solar thermal power plants in complex environmental scenarios.

[0105] As can be seen from the above, based on the operation control method of the solar thermal power plant provided in the embodiments of this specification, before specific implementation, a preset frost prediction model, a preset solar radiation prediction model, and an initial mirror field heat collection power model of the target solar thermal power plant can be constructed to meet the requirements. In specific implementation, meteorological data for the current time period is first acquired; then, a preset frost prediction model is used to process the meteorological data for the current time period to obtain a first prediction result; a preset solar radiation prediction model is used to process the meteorological data for the current time period to obtain a second prediction result; wherein, the first prediction result includes at least: frost indication parameters, frost severity characteristics, and defrosting time indicating whether the solar thermal power plant will experience mirror frost in the next time period; the second prediction result includes at least: solar normal direct radiation parameters for the next time period; then, based on the first prediction result, the second prediction result, and the initial mirror field heat collection power model of the target solar thermal power plant, a matching target mirror field heat collection power model is constructed; using the target mirror field heat collection power model, the mirror field heat collection power of the target solar thermal power plant in the next time period is determined; based on the mirror field heat collection power of the target solar thermal power plant in the next time period, it is determined whether the target solar thermal power plant will be started in the next time period; if it is determined that the target solar thermal power plant will be started in the next time period, a matching target operation control strategy is determined based on the first prediction result; wherein, the target operation control strategy is used to control the start-up and operation of the target solar thermal power plant in the next time period. By fully considering and utilizing the impact of mirror frost on the operation and power generation of solar thermal power plants, this approach can be well applied to complex environmental scenarios where mirror frost exists. It can predict and determine the operation control strategy that matches the next time period in advance. Based on this operation control strategy, the startup and operation of the solar thermal power plant in the next time period can be accurately and stably controlled, effectively reducing the power generation loss caused by mirror frost and ensuring that the solar thermal power plant can operate efficiently and stably.

[0106] This specification provides an electronic device through its embodiments. (See attached document.) Figure 7 As shown. The electronic device includes a network communication port 701, a processor 702, and a memory 703. These structures are connected by internal cables so that they can perform specific data interaction.

[0107] Specifically, the network communication port 701 can be used to acquire meteorological data for the current time period.

[0108] The processor 702 can specifically be used to process meteorological data for the current time period using a preset frost prediction model to obtain a first prediction result; and to process meteorological data for the current time period using a preset solar radiation prediction model to obtain a second prediction result. The first prediction result includes at least: frost indication parameters, frost severity characteristics, and defrosting time indicating whether mirror frost will occur at the solar thermal power plant in the next time period. The second prediction result includes at least: solar normal direct radiation parameters for the next time period. Based on the first prediction result, the second prediction result, and the initial mirror field heat collection power model for the target solar thermal power plant, a matching target mirror field heat collection power model is constructed. Using the target mirror field heat collection power model, the mirror field heat collection power for the target solar thermal power plant in the next time period is determined. Based on the mirror field heat collection power for the target solar thermal power plant in the next time period, it is determined whether the target solar thermal power plant should be started in the next time period. If it is determined that the target solar thermal power plant will be started in the next time period, a matching target operation control strategy is determined based on the first prediction result. The target operation control strategy is used to control the start-up and operation of the target solar thermal power plant in the next time period.

[0109] The memory 703 can be used to store the corresponding instruction program and related intermediate data.

[0110] Based on the above method, the relevant structural performance of electronic equipment can be effectively utilized to improve the data processing speed of electronic equipment and efficiently realize the data processing for operation control of solar thermal power plants.

[0111] In this embodiment, the network communication port 701 can be a virtual port bound to different communication protocols, thereby enabling the sending or receiving of different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM or CDMA; it can also be a Wi-Fi chip; or it can be a Bluetooth chip.

[0112] In this embodiment, the processor 702 can be implemented in any suitable manner. For example, the processor can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers, etc. This specification is not limiting.

[0113] In this embodiment, the memory 703 may include multiple layers. In a digital system, anything that can store binary data can be a memory. In an integrated circuit, a circuit with storage function but no physical form is also called a memory, such as RAM, FIFO, etc. In a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0114] This specification also provides a computer-readable storage medium based on the above-described operation control method for a solar thermal power plant. The computer-readable storage medium stores computer program instructions that, when executed, perform the following: acquire meteorological data for the current time period; process the meteorological data for the current time period using a preset frost prediction model to obtain a first prediction result; process the meteorological data for the current time period using a preset solar radiation prediction model to obtain a second prediction result; wherein the first prediction result includes at least: frost indication parameters indicating whether mirror frost will occur at the solar thermal power plant in the next time period, frost severity characteristics, and defrosting time; the second prediction result includes at least: the next... The solar normal direct radiation parameters for the time period are determined; based on the first prediction result, the second prediction result, and the initial mirror field heat collection power model of the target solar thermal power plant, a matching target mirror field heat collection power model is constructed; using the target mirror field heat collection power model, the mirror field heat collection power of the target solar thermal power plant in the next time period is determined; based on the mirror field heat collection power of the target solar thermal power plant in the next time period, it is determined whether the target solar thermal power plant should be started in the next time period; if it is determined that the target solar thermal power plant should be started in the next time period, a matching target operation control strategy is determined based on the first prediction result; wherein, the target operation control strategy is used to control the start-up and operation of the target solar thermal power plant in the next time period.

[0115] In this embodiment, the storage medium includes, but is not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), Cache, Hard Disk Drive (HDD), or Memory Card. The memory can be used to store computer program instructions. The network communication unit can be an interface configured according to standards specified in the communication protocol for network connection communication.

[0116] In this embodiment, the specific functions and effects implemented by the program instructions stored in the computer-readable storage medium can be explained in comparison with other embodiments, and will not be repeated here.

[0117] This specification also provides a computer program product, comprising at least a computer program, which, when executed by a processor, performs the following method steps: acquiring meteorological data for the current time period; processing the meteorological data for the current time period using a preset frost prediction model to obtain a first prediction result; processing the meteorological data for the current time period using a preset solar radiation prediction model to obtain a second prediction result; wherein the first prediction result includes at least: frost indication parameters indicating whether mirror frost will occur at the solar thermal power plant in the next time period, frost severity characteristics, and defrosting time; the second prediction result includes at least: the solar normal direct radiation in the next time period. The parameters are as follows: Based on the first prediction result, the second prediction result, and the initial mirror field heat collection power model of the target solar thermal power plant, a matching target mirror field heat collection power model is constructed; using the target mirror field heat collection power model, the mirror field heat collection power of the target solar thermal power plant in the next time period is determined; based on the mirror field heat collection power of the target solar thermal power plant in the next time period, it is determined whether the target solar thermal power plant should be started in the next time period; if it is determined that the target solar thermal power plant should be started in the next time period, a matching target operation control strategy is determined based on the first prediction result; wherein, the target operation control strategy is used to control the start-up and operation of the target solar thermal power plant in the next time period.

[0118] See Figure 8 As shown, at the software level, this specification also provides an operation control device for a concentrated solar power plant, which may specifically include the following structural modules: The acquisition module 801 can be used to acquire meteorological data for the current time period. The prediction module 802 can be specifically used to process the meteorological data of the current time period using a preset frost prediction model to obtain a first prediction result; and to process the meteorological data of the current time period using a preset solar radiation prediction model to obtain a second prediction result; wherein, the first prediction result includes at least: frost indication parameters indicating whether there is mirror frost on the solar thermal power plant in the next time period, frost degree characteristics, and defrosting time; the second prediction result includes at least: solar normal direct radiation parameters for the next time period; The construction module 803 can be used to construct a matching target mirror field heat collection power model based on the first prediction result, the second prediction result, and the initial mirror field heat collection power model of the target solar thermal power plant. The first determining module 804 can be used to determine the solar thermal power of the target solar thermal power plant in the next time period by using the target mirror field heat collection power model. The second determining module 805 can be used to determine whether to start the target solar thermal power plant in the next time period based on the mirror field heat collection power of the target solar thermal power plant in the next time period. The third determining module 806 can be used to determine a matching target operation control strategy based on the first prediction result when the target solar thermal power plant is determined to start in the next time period; wherein, the target operation control strategy is used to control the start-up and operation of the target solar thermal power plant in the next time period.

[0119] In some embodiments, the meteorological data may specifically include at least one of the following: weather, satellite cloud image, solar radiation parameters, ambient humidity, ambient temperature, ambient wind speed, ambient wind direction, etc.

[0120] In some embodiments, the preset frost prediction model may specifically include a multi-timescale time-series prediction model based on a TCN-GRU structure.

[0121] In some embodiments, the initial mirror field heat collection power model for the target solar thermal power plant can be constructed as follows: determining the power plant type of the target solar thermal power plant; wherein, the power plant type includes: tower solar thermal power plants and trough solar thermal power plants; constructing a matching mirror field concentrating efficiency model based on time and meteorological data according to the power plant type of the target solar thermal power plant; constructing an absorbed heat power model based on time, meteorological data, and solar normal direct radiation parameters according to the mirror field concentrating efficiency model; and constructing the initial mirror field heat collection power model for the target solar thermal power plant based on the absorbed heat power model and the heat dissipation loss power.

[0122] In some embodiments, when the above-described device is implemented, an initial mirror field heat collection power model for the target solar thermal power plant can be constructed according to the following formula:

[0123] Among them, when the power plant type is a tower solar thermal power plant:

[0124] When the power plant type is a parabolic trough solar thermal power plant:

[0125] in, For the heat collection power of the mirror field, The heat power theoretically absorbed by the receiver. To dissipate heat and reduce power loss, These are the parameters for direct solar radiation in the normal direction. A For the mirror field area, To improve the light-gathering efficiency of the mirror field. This refers to the operating temperature of the heat absorber. For ambient temperature, For ambient wind speed, To reduce efficiency, For atmospheric attenuation efficiency, For cosine efficiency, To reduce efficiency due to shadows, To avoid the loss of efficiency due to shading, The first specular reflectivity, As a cleaning agent, For the optical efficiency of the solar collector, For the geometric efficiency of the solar collector, The second specular reflectivity, For the heat collection tube transmittance, The absorption rate of the heat collection tube coating, This is the effective area coefficient of the heat collection tube. To track the error coefficient, As an interception factor, For cosine loss, This is the shadow effect coefficient. This is the end loss coefficient. IAM This is the correction factor for the angle of incidence of sunlight.

[0126] In some embodiments, when the frost indication parameter indicates that there is mirror frost at the solar thermal power plant in the next time period, the aforementioned construction module 803 can, in its specific implementation, construct a matching target mirror field heat collection power model based on the first prediction result, the second prediction result, and the initial mirror field heat collection power model of the target solar thermal power plant in the following manner: Based on the second prediction result, the initial mirror field heat collection power model is processed to obtain an intermediate mirror field heat collection power model; based on the defrosting time, the next time period is divided into a first sub-time period and a second sub-time period; based on the frost degree characteristics and defrosting time, a mirror frost influence function that varies with time is constructed through model fitting; the intermediate mirror field heat collection power model is multiplied by the mirror frost influence function to obtain a corresponding modified mirror field heat collection power model; in the first sub-time period, the modified mirror field heat collection power model is used as the target mirror field heat collection power model; in the second sub-time period, the intermediate mirror field heat collection power model is used as the target mirror field heat collection power model.

[0127] In some embodiments, the first prediction result may further include: the amount of frost remaining in the next time period; Accordingly, when the third determining module 806 is specifically implemented, it can determine the matching target operation control strategy based on the first prediction result in the following manner: determine the change characteristics of the remaining frost amount based on the remaining frost amount in the next time period; determine the first trigger time point based on the change characteristics of the remaining frost amount; determine the second trigger time point based on the defrosting time; and determine a time point with a preset interval before the second trigger time point as the third trigger time point; determine the first trigger time point as the first start-up time for starting the mirror field of the target solar thermal power plant; determine the third trigger time point as the second start-up time for preheating and starting the turbine using the waste heat stored in the heat storage tank; determine the second trigger time point as the switching time for switching and using the currently generated mirror field heat collection power of the target solar thermal power plant to drive the turbine; and establish a matching target operation control strategy based on the first start-up time, the second start-up time, and the switching time.

[0128] In some embodiments, when the third determining module 806 is specifically implemented, it can also: acquire grid electricity price fluctuation data; generate a first regulation rule for the load regulation of the turbine in the third sub-time period based on the variation characteristics of the remaining frost; wherein the third sub-time period is the time period between the second start-up time and the switching time; generate a second regulation rule for the load regulation of the turbine in the fourth sub-time period based on the grid electricity price fluctuation data and the solar normal direct radiation parameters of the next time period; wherein the fourth sub-time period is the time period between the switching time and the end time of the next time period; and establish a matching target operation control strategy based on the first start-up time, the second start-up time, the switching time, the first regulation rule, and the second regulation rule.

[0129] In some embodiments, after acquiring meteorological data for the current time period, the above-mentioned device can also be used to: acquire the solar normal direct radiation parameters for the current time period and measure the measured heat collection power for the current time period; determine the theoretical heat collection power of the mirror field under the condition of no frost in the current time period based on the initial mirror field heat collection power model, the meteorological data for the current time period, and the solar normal direct radiation parameters for the current time period; perform time alignment between the theoretical heat collection power of the mirror field under the condition of no frost in the current time period and the measured heat collection power for the current time period; and perform time alignment based on the current time period. Calculate the power difference between the theoretical and measured heat collection power of the mirror field under frost-free conditions at the same time point within the current time period. Based on this power difference, determine the heat collection loss caused by frost during the current time period by performing time integration. Finally, determine the power generation loss caused by mirror frost during the current time period based on the heat collection loss caused by frost and the average thermoelectric efficiency of the turbine.

[0130] It should be noted that the units, devices, or modules described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. For ease of description, the above devices are described by dividing them into various modules according to their functions. Of course, in implementing this specification, the functions of each module can be implemented in one or more software and / or hardware, or the module that implements the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection between the devices or units shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0131] As can be seen from the above, the operation control device for the solar thermal power plant provided in the embodiments of this specification, by fully considering and utilizing the characteristics of the impact of mirror frost on the operation and power generation of the solar thermal power plant, can be well applied to complex environmental scenarios where mirror frost exists. It can predict and determine the operation control strategy that matches the next time period in advance, and then, according to the operation control strategy, accurately and stably control the start-up and operation of the solar thermal power plant in the next time period, effectively reduce the power generation loss caused by mirror frost, and ensure that the solar thermal power plant can operate efficiently and stably.

[0132] In a specific scenario example, the operation control methods for concentrated solar power (CSP) plants provided in this manual can be applied to assess and address the power generation loss caused by mirror frost formation. For detailed implementation procedures, please refer to the following content.

[0133] In this scenario, the reflectors of a concentrated solar power (CSP) power plant's collector field, due to long-term exposure to the complex outdoor environment, face a common technical challenge of frost formation in winter, hindering the plant's efficient operation. Specifically, frost coverage significantly reduces the reflectivity of the mirrors, impeding the effective concentration of solar radiation, leading to a decline in collector efficiency and a sharp reduction in power generation. Some project practices have shown that winter mirror frost can cause direct economic losses of tens of millions of yuan, highlighting the serious impact of this problem on the economic benefits of the power plant. Current industry research focuses primarily on the development of anti-frost materials and the optimization of defrosting processes, lacking methods for assessing the power generation loss caused by frost. Existing assessments often rely on qualitative descriptions or analogical analyses of the impact of snow and ice on the photovoltaic field, making it difficult to accurately quantify the lost power generation and providing scientific support for optimizing anti-frost strategies, making operation and maintenance decisions, and evaluating power generation benefits. Furthermore, frost formation on CSP power plant mirror fields is typically large-scale, and daily weather conditions vary, making it difficult to accurately assess using control groups.

[0134] To address the aforementioned technical issues and their root causes, this scenario example considers a method for assessing the power generation loss in a concentrated solar power (CSP) plant due to mirror frost. By constructing an accurate mirror field performance evaluation model, the theoretical heat collection power of the mirror field is obtained. This theoretical power collection power is then compared with the actual heat collection power to determine the heat collection loss. Further analysis of the power generation efficiency yields the total power generation loss. For detailed procedures, please refer to [link / reference needed]. Figure 9 As shown, it includes the following content.

[0135] First, establish the following mirror field performance model.

[0136] For tower-type solar thermal power plants:

[0137] in, The light-gathering efficiency of the mirror field; The truncation efficiency is related to the mirror field coordinates and the current time. The atmospheric attenuation efficiency is related to the mirror field coordinates and the receiver height. The efficiency is cosine-dependent and depends on the mirror field coordinates and the current time. The efficiency of shadow loss is related to the mirror field layout and the time of day. The efficiency of masking is related to the layout of the mirror field and the time of day. Specular reflectivity is a property parameter of the heliostat itself; It is a cleaning factor and is related to the cleanliness of the heliostat surface.

[0138] For parabolic trough solar thermal power plants:

[0139] in, For the optical efficiency of the solar collector, This refers to the geometric efficiency of the solar collector. , , , , , These are, respectively, specular reflectance, collector tube transmittance, collector tube coating absorptivity, collector tube effective area coefficient, tracking error coefficient, and interception factor; , , , IAM These are the cosine loss, shadow effect coefficient, end loss coefficient, and incident angle correction factor, all of which are related to the solar incident angle, i.e., to the mirror field coordinates and the time.

[0140] As can be seen from the above, the light-gathering efficiency of the mirror field... t represents the current time, meaning that for a mirror field with fixed coordinates and layout, its light-gathering efficiency is dynamically adjusted as the current time changes.

[0141] Therefore, the theoretical heat power absorbed by the heat absorber is:

[0142] in, , DNI A and B represent the heat power absorbed by the absorber, the direct solar radiation in the normal direction, and the mirror field area, respectively.

[0143] Because the absorber experiences heat loss, the amount of which is related to the absorber's operating temperature, ambient temperature, and ambient wind speed, namely:

[0144] in, , , , These are heat dissipation loss power, absorber operating temperature, ambient temperature, and ambient wind speed, respectively.

[0145] The final theoretical heat collection power of the mirror field (e.g., the initial mirror field heat collection power model of the target solar thermal power plant) is as follows: .

[0146] After establishing the mirror field performance model, the reliability of the model is first verified. Under non-frost weather conditions, meteorological data such as DNI, ambient temperature, and ambient wind speed, as well as equipment operating data such as receiver operating temperature and mirror field heat collection power are collected. By substituting DNI, ambient temperature, ambient wind speed, and receiver operating temperature into the above model, the theoretical heat collection power of the mirror field is obtained. This theoretical heat collection power is then compared with the actual heat collection power of the mirror field to verify the reliability of the model and ensure the accuracy of the assessment basis.

[0147] Next, a quantitative assessment of power generation loss on frosty days was conducted. Specifically, under frosty conditions, based on the validated mirror field performance model, the required meteorological and equipment operating data were input to calculate the theoretical heat collection power of the mirror field. The heat collection loss for that day was obtained by integrating the difference between the theoretical and actual heat collection power. Combined with the project's average thermoelectric efficiency, the power generation loss was calculated as follows: power generation loss = heat collection loss. Thermoelectric efficiency is the value of power generation loss caused by frost formation on the mirror surface.

[0148] In particular, the aforementioned mirror field performance model possesses a dual construction path: it can construct a precise mechanistic model based on the aforementioned principles, or it can generate a data-driven model by training traditional machine learning or deep learning algorithms using massive historical operating data, thus balancing accuracy requirements and construction costs in different scenarios. Simultaneously, this evaluation method is compatible with multi-frequency data sources, supporting data access from minute-level to hour-level, and can be universally adapted to concentrated solar power (CSP) projects with different data acquisition conditions, demonstrating strong versatility. This invention's solution can not only be used to assess daily power generation losses but also provide real-time evaluation data at various times, and can use this real-time evaluation data to determine the daily defrosting duration.

[0149] In practice, we can take the actual data of a certain solar thermal project on a certain frosting day as an example, and after constructing the mirror field performance model, calculate the theoretical heat collection power on that day and compare it with the actual heat collection power.

[0150] For details, please refer to [link / reference]. Figure 10 As shown in the figure, the red line represents the DNI data, the green line represents the actual heat collection power of the mirror field on that day, and the dashed line represents the theoretical heat collection power output by the constructed mirror field performance model. Due to frost formation that day, even with excellent DNI conditions, the mirror field's heat collection capacity only significantly recovered in the afternoon, resulting in a relatively low heat collection volume for the day. According to the curve in the figure, after approximately 16:00, the theoretical and actual heat collection power of the mirror field almost completely overlapped, meaning that defrosting was complete only after approximately 16:00. Based on the data calculation, the theoretical heat collection volume for that day was 2068.26 MWh, while the actual heat collection volume was only 1062.75 MWh, a difference of 1005.51 MWh. Considering the power plant's average thermoelectric efficiency of approximately 38%, the lost power generation is estimated to be 382 MWh.

[0151] The above scenario examples validate the operation and control method of the concentrated solar power (CSP) plant provided in this manual. Based on the constructed mirror field performance model, the difference between theoretical and actual heat collection is calculated to accurately assess the power generation loss caused by frost. The approach of "model verification on non-frost days + theoretical / actual comparison on frost days" is adopted. On non-frost days, meteorological and operational data are used to verify the reliability of the mirror field performance model. On frost days, the theoretical heat collection power is calculated using the model, and the difference between the theoretical and actual heat collection power is integrated to obtain the heat collection loss. This is then combined with power generation efficiency to calculate the power generation loss, avoiding the difficulty of setting up a frost control group for CSP mirror fields and achieving quantification of losses without physical comparison. The constructed mirror field performance model can be built using a dual-path approach, integrating the advantages of mechanistic modeling and data-driven modeling. This ensures the assessment accuracy in core scenarios while providing a low-cost, efficient modeling option for scenarios with sufficient data, allowing selection based on data conditions and accuracy requirements. The proposed assessment method supports multi-dimensional data access from minute to hourly levels, adapting to CSP projects with different data acquisition capabilities without requiring modification of existing data acquisition systems, thus improving the method's engineering applicability. It can also obtain the real-time difference between the theoretical heat collection and the actual heat collection of the mirror field, and determine the duration of the impact of frost on the power generation of the day.

[0152] While this specification provides the steps of operation for the methods described in the embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible order of execution among many steps and does not represent the only possible order. In actual device or client product execution, the methods shown in the embodiments or drawings may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, the presence of other identical or equivalent elements in a process, method, product, or apparatus that includes said elements is not excluded. The terms "first," "second," etc., are used to denote names and do not indicate any particular order.

[0153] Those skilled in the art will also know that, besides implementing the controller using purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller function as logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices within it used to implement various functions can also be considered structures within that hardware component. Alternatively, the devices used to implement various functions can be considered as both software modules implementing the method and structures within a hardware component.

[0154] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer-readable storage media, including storage devices.

[0155] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this specification can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions of this specification can essentially be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments of this specification.

[0156] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. This specification can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0157] Although this specification has been described by way of examples, those skilled in the art will recognize that many variations and modifications are possible without departing from the spirit of this specification, and it is intended that the appended text include such variations and modifications without departing from the spirit of this specification.

Claims

1. A method for operating and controlling a solar thermal power plant, characterized in that, include: Obtain meteorological data for the current time period; The meteorological data for the current time period is processed using a pre-set frost prediction model to obtain the first prediction result; The meteorological data for the current time period is processed using a preset solar radiation prediction model to obtain a second prediction result; wherein, the first prediction result includes at least: frost indication parameters, frost degree characteristics, and defrosting time of the solar thermal power plant indicating whether there is mirror frost in the next time period; the second prediction result includes at least: solar normal direct radiation parameters for the next time period; Based on the first prediction result, the second prediction result, and the initial mirror field heat collection power model of the target solar thermal power plant, a matching target mirror field heat collection power model is constructed. Using the target mirror field heat collection power model, the mirror field heat collection power of the target solar thermal power plant in the next time period is determined; Based on the mirror field heat collection power of the target solar thermal power plant in the next time period, determine whether to start the target solar thermal power plant in the next time period; Given that the target solar thermal power plant will be started in the next time period, a matching target operation control strategy is determined based on the first prediction result; wherein, the target operation control strategy is used to control the start-up and operation of the target solar thermal power plant in the next time period.

2. The method according to claim 1, characterized in that, The meteorological data includes at least one of the following: weather, satellite cloud image, solar radiation parameters, ambient humidity, ambient temperature, ambient wind speed, and ambient wind direction.

3. The method according to claim 1, characterized in that, The preset frost prediction model includes a multi-timescale time-series prediction model based on the TCN-GRU structure.

4. The method according to claim 1, characterized in that, The initial mirror field heat collection power model for the target solar thermal power plant is constructed in the following manner: Determine the type of the target solar thermal power plant; wherein, the type of power plant includes: tower solar thermal power plants and trough solar thermal power plants; Based on the type of the target solar thermal power plant, construct a mirror field concentration efficiency model that matches the time and meteorological data; Based on the aforementioned mirror field concentration efficiency model, an absorbed heat power model is constructed considering time, meteorological data, and solar normal direct radiation parameters. Based on the absorbed heat power model and the heat dissipation loss power, an initial mirror field heat collection power model for the target solar thermal power plant is constructed.

5. The method according to claim 4, characterized in that, The method includes: constructing an initial mirror field heat collection power model for the target solar thermal power plant according to the following formula: Among them, when the power plant type is a tower solar thermal power plant: When the power plant type is a parabolic trough solar thermal power plant: in, For the heat collection power of the mirror field, The heat power theoretically absorbed by the receiver. To dissipate heat and reduce power loss, These are the parameters for direct solar radiation in the normal direction. A For the mirror field area, To improve the light-gathering efficiency of the mirror field. This refers to the operating temperature of the heat absorber. For ambient temperature, For ambient wind speed, To reduce efficiency, For atmospheric attenuation efficiency, For cosine efficiency, To reduce efficiency due to shadows, To avoid the loss of efficiency due to shading, The first specular reflectivity, As a cleaning agent, For the optical efficiency of the solar collector, For the geometric efficiency of the solar collector, The second specular reflectivity, For the heat collection tube transmittance, The absorption rate of the heat collection tube coating, This is the effective area coefficient of the heat collection tube. To track the error coefficient, As an interception factor, For cosine loss, This is the shadow effect coefficient. This is the end loss coefficient. IAM This is the correction factor for the angle of incidence of sunlight.

6. The method according to claim 1, characterized in that, When the frost indication parameter indicates that the solar thermal power plant will experience mirror frost in the next time period, the process of constructing a matching target mirror field heat collection power model based on the first prediction result, the second prediction result, and the initial mirror field heat collection power model of the target solar thermal power plant includes: Based on the second prediction result, the initial mirror field heat collection power model is processed to obtain the intermediate mirror field heat collection power model; Based on the defrosting time, the next time period is divided into the first sub-time period and the second sub-time period; Based on the characteristics of frost formation and defrosting time, a time-varying mirror frost influence function is constructed through model fitting. Multiply the intermediate mirror field heat collection power model with the mirror surface frost influence function to obtain the corresponding modified mirror field heat collection power model; In the first sub-time period, the modified mirror field heat collection power model is used as the target mirror field heat collection power model; in the second sub-time period, the intermediate mirror field heat collection power model is used as the target mirror field heat collection power model.

7. The method according to claim 1, characterized in that, The first prediction result also includes: the amount of frost remaining in the next time period; Accordingly, determining the matching target operation control strategy based on the first prediction result includes: Based on the remaining amount of frost in the next time period, determine the characteristics of the change in the remaining amount of frost. The first trigger time point is determined based on the changing characteristics of the remaining frosting amount; Based on the defrosting time, a second trigger time point is determined; and a time point with a preset interval before the second trigger time point is determined as the third trigger time point. The first trigger time point is determined as the first start-up time for starting the mirror field of the target solar thermal power plant; the third trigger time point is determined as the second start-up time for preheating and starting the steam turbine using the waste heat stored in the heat storage tank; the second trigger time point is determined as the switching time for switching and using the currently generated mirror field heat collection power of the target solar thermal power plant to drive the steam turbine; Based on the first startup time, the second startup time, and the switching time, a matching target operation control strategy is established.

8. The method according to claim 7, characterized in that, The method further includes: Obtain data on fluctuations in grid electricity prices; Based on the variation characteristics of the remaining frost, a first regulation rule for the load regulation of the steam turbine in the third sub-time period is generated; wherein, the third sub-time period is the time period between the second start-up time and the switching time; Based on the fluctuation data of the grid electricity price and the solar normal direct radiation parameters for the next time period, a second regulation rule for the load regulation of the steam turbine in the fourth sub-time period is generated; wherein, the fourth sub-time period is the time period between the switching time and the end time of the next time period; Based on the first start time, the second start time, the switching time, the first adjustment rule, and the second adjustment rule, a matching target operation control strategy is established.

9. The method according to claim 1, characterized in that, After acquiring meteorological data for the current time period, the method further includes: Obtain the solar normal direct radiation parameters for the current time period, and measure the measured solar collector power for the current time period; Based on the initial mirror field heat collection power model, meteorological data for the current time period, and solar normal direct radiation parameters for the current time period, the theoretical mirror field heat collection power under the condition of no frost in the current time period is determined. Time alignment is performed between the theoretical heat collection power of the mirror field under the condition of no frost in the current time period and the measured heat collection power in the current time period; Based on the theoretical heat collection power of the mirror field under the influence of no frost in the current time period after time alignment and the measured heat collection power in the current time period, calculate the power difference between the theoretical heat collection power of the mirror field and the measured heat collection power under the influence of no frost at the same time point in the current time period. Based on the power difference between the theoretical and measured heat collection power of the mirror field under the condition of no frost at the same time point in the current time period, the heat collection loss of the mirror field due to frost in the current time period is determined by time integration. Based on the heat loss of the mirror field due to frost and the average thermoelectric efficiency of the steam turbine during the current time period, the power generation loss caused by mirror frost during the current time period is determined.

10. An operation control device for a solar thermal power plant, characterized in that, include: The acquisition module is used to acquire meteorological data for the current time period; The prediction module is used to process the meteorological data for the current time period using a preset frost prediction model to obtain the first prediction result; The meteorological data for the current time period is processed using a preset solar radiation prediction model to obtain a second prediction result; wherein, the first prediction result includes at least: frost indication parameters, frost degree characteristics, and defrosting time of the solar thermal power plant indicating whether there is mirror frost in the next time period; the second prediction result includes at least: solar normal direct radiation parameters for the next time period; The module is used to construct a matching target mirror field heat collection power model based on the first prediction result, the second prediction result, and the initial mirror field heat collection power model of the target solar thermal power plant. The first determining module is used to determine the solar thermal power of the target solar thermal power plant in the next time period by using the target mirror field heat collection power model. The second determining module is used to determine whether to start the target solar thermal power plant in the next time period based on the mirror field heat collection power of the target solar thermal power plant in the next time period. The third determining module is used to determine a matching target operation control strategy based on the first prediction result when the target solar thermal power plant is determined to start in the next time period; wherein the target operation control strategy is used to control the start-up and operation of the target solar thermal power plant in the next time period.

11. An electronic device, characterized in that, It includes a processor and a memory for storing processor-executable instructions, wherein the processor, when executing the instructions, implements the steps of the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, It stores computer instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 9.

13. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 9.

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