Auxiliary power generation method and device based on ultrahigh-temperature fluidized energy storage and release equipment

Abstract

The invention provides an auxiliary power generation method and device based on ultra-high-temperature fluidized energy storage and release equipment, and the method comprises the steps: estimating the load power of a power grid in a future time period after a current moment, obtaining the predicted power generation power of a power plant in the future time period, obtaining the current temperature of the ultra-high-temperature fluidized energy storage and release equipment, and obtaining the current temperature of the ultra-high-temperature fluidized energy storage and release equipment; acquiring a starting moment of a future time period, and estimating a target heat release moment and a target heat release rate of the ultrahigh-temperature fluidized energy storage and release equipment according to the predicted power generation power, the load power, the current temperature, the current moment and the starting moment under the condition that the predicted power generation power is smaller than the load power; the ultrahigh-temperature fluidized energy storage and release equipment is controlled to start to continuously release heat energy at the target heat release rate at the target heat release moment to assist the power plant in power generation, so that the actual power generation power of the power plant is larger than or equal to the load power when the starting moment is reached; the electric quantity generated by the power plant can meet the requirement of the user side for the electric quantity when the peak period of the power grid load is just entered.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to an auxiliary power generation method, device, electronic equipment and storage medium based on ultra-high temperature fluidized energy storage and release equipment. Background Art

[0002] With the development of industry and technology, and with the continuous improvement of people's living standards, the role of electricity in people's lives and work is becoming increasingly important.

[0003] Currently, power plants usually generate electricity and transmit the electricity to the power grid, which then transmits the electricity to users for use.

[0004] However, statistics show that power grid loads have peak and valley periods. For example, during valley periods, user electricity consumption decreases, meaning the grid load decreases. During peak periods, user electricity consumption increases, meaning the grid load increases. Since power generation typically maintains a stable level of energy, energy may be insufficient during peak periods, while energy may be wasted during valley periods.

[0005] To this end, the demand for peak shaving and valley filling has been proposed. The main purpose of peak shaving and valley filling here is to increase the load of the power grid by making full use of electricity during the low period of the power grid load, or to assist power plants in generating more electricity during the peak period of the power grid load to ensure that the amount of electricity can meet the user's demand. Summary of the Invention

[0006] The present application illustrates an auxiliary power generation method, device, electronic equipment, and storage medium based on ultra-high temperature fluidized energy storage and release equipment.

[0007] In a first aspect, the present application provides an auxiliary power generation method based on an ultra-high temperature fluidized bed energy storage and release device, wherein the heat output port of the ultra-high temperature fluidized bed energy storage and release device and the heat output port of a power plant boiler converge to an integrated heat output port, and the heat output from the integrated heat output port is used to heat the working fluid of the power plant; the method comprises:

[0008] Estimate the load power of the power grid in a future time period after the current moment; obtain the expected power generation power of the power plant in the future time period; obtain the current temperature of the ultra-high temperature fluidized bed energy storage and release equipment; obtain the starting time of the future time period; the current time is earlier than the starting time;

[0009] When the expected generated power is less than the load power, a target heat release time and a target heat release rate of the ultra-high temperature fluidized bed energy storage and discharge equipment are estimated based on the expected generated power, the load power, the current temperature, the current time, and the starting time; the target heat release time is earlier than the starting time; wherein, when the ultra-high temperature fluidized bed energy storage and discharge equipment releases heat at the target heat release rate at the target heat release time, the actual generated power of the power plant is greater than or equal to the load power when the starting time is reached;

[0010] The ultra-high temperature fluidized bed energy storage and discharge equipment is controlled to start continuous heat release at the target heat release rate at the target heat release time to assist the power plant in generating electricity.

[0011] In an optional implementation, estimating the target heat release time and target heat release rate of the ultra-high temperature fluidized bed energy storage and discharge device based on the expected generated power, the load power, the current temperature, the current time, and the start time includes:

[0012] Obtaining a power difference between the load power and the expected generated power;

[0013] Calculate the duration between the starting time and the current time;

[0014] The expected generated power, the load power, the current temperature, the current time, the starting time, the power difference and the duration are all input into the trained estimation model, so that the estimation model processes the expected generated power, the load power, the current temperature, the current time, the starting time, the power difference and the duration to obtain the target heat release time and the target heat release rate of the ultra-high temperature fluidized energy storage and release equipment.

[0015] In an optional implementation, the method further includes:

[0016] Obtain multiple training data sets;

[0017] A training data set includes: sample data and labeled data;

[0018] The sample data includes: the real load power of the power grid during the sample time period, the real power generation power of the power plant during the sample time period, the sample duration between the start time of the sample time period and the sample time, the sample temperature of the ultra-high temperature fluidized bed energy storage and release equipment at the sample time, and the sample difference between the real load power and the real power generation power; the real load power is greater than the real power generation power; the sample time is earlier than the start time of the sample time period;

[0019] The annotated data includes: an annotated heat release time of the ultra-high temperature fluidized energy storage and release equipment and an annotated heat release rate of the ultra-high temperature fluidized energy storage and release equipment; the annotated heat release time is later than the sample time and earlier than the start time of the sample time period;

[0020] Get the model to be trained;

[0021] The model to be trained is trained based on the multiple training data sets until the parameters in the model to be trained converge, thereby obtaining an estimated model.

[0022] In an optional implementation, the training of the to-be-trained model based on the multiple training data sets includes:

[0023] Processing the sample data based on the to-be-trained model to obtain an estimated heat release rate and an estimated heat release time of the ultra-high temperature fluidized energy storage and release device;

[0024] Obtaining a heat release rate loss value based on the estimated heat release rate and the marked heat release rate, and obtaining a heat release time loss value based on the estimated heat release time and the marked heat release time;

[0025] Obtaining a comprehensive loss value based on the heat release rate loss value and the heat release time loss value;

[0026] According to the comprehensive loss value, the parameters in the to-be-trained model are adjusted.

[0027] In a second aspect, the present application illustrates an auxiliary power generation device based on an ultra-high temperature fluidized bed energy storage and release device, wherein the heat output port of the ultra-high temperature fluidized bed energy storage and release device and the heat output port of the boiler of a power plant converge to an integrated heat output port, and the heat output from the integrated heat output port is used to heat the working fluid of the power plant; the device comprises:

[0028] An acquisition module is used to estimate the load power of the power grid in a future time period after the current moment; obtain the expected power generation power of the power plant in the future time period; obtain the current temperature of the ultra-high temperature fluidized bed energy storage and discharge equipment; obtain the starting time of the future time period; the current time is earlier than the starting time;

[0029] an estimating module, configured to estimate, when the estimated generated power is less than the load power, a target heat release time and a target heat release rate for the ultra-high temperature fluidized bed energy storage and discharge equipment based on the estimated generated power, the load power, the current temperature, the current time, and the starting time; the target heat release time is earlier than the starting time; wherein, when the ultra-high temperature fluidized bed energy storage and discharge equipment releases heat at the target heat release rate at the target heat release time, the actual generated power of the power plant is greater than or equal to the load power when the starting time is reached;

[0030] The control module is used to control the ultra-high temperature fluidized energy storage and release equipment to start continuous heat release at the target heat release rate at the target heat release time to assist the power plant in generating electricity.

[0031] In an optional implementation, the estimation module includes:

[0032] A first acquiring unit, configured to acquire a power difference between the load power and the expected generated power;

[0033] a calculation unit, configured to calculate the duration between the start time and the current time;

[0034] A processing unit is used to input the expected generated power, the load power, the current temperature, the current time, the starting time, the power difference and the duration into a trained estimation model, so that the estimation model processes the expected generated power, the load power, the current temperature, the current time, the starting time, the power difference and the duration to obtain the target heat release time and the target heat release rate of the ultra-high temperature fluidized energy storage and release equipment.

[0035] In an optional implementation, the estimation module further includes:

[0036] A second acquisition unit is used to acquire multiple training data sets;

[0037] A training data set includes: sample data and labeled data;

[0038] The sample data includes: the real load power of the power grid during the sample time period, the real power generation power of the power plant during the sample time period, the sample duration between the start time of the sample time period and the sample time, the sample temperature of the ultra-high temperature fluidized bed energy storage and release equipment at the sample time, and the sample difference between the real load power and the real power generation power; the real load power is greater than the real power generation power; the sample time is earlier than the start time of the sample time period;

[0039] The annotated data includes: an annotated heat release time of the ultra-high temperature fluidized energy storage and release equipment and an annotated heat release rate of the ultra-high temperature fluidized energy storage and release equipment; the annotated heat release time is later than the sample time and earlier than the start time of the sample time period;

[0040] A third acquisition unit is used to acquire a model to be trained;

[0041] A training unit is used to train the model to be trained based on the multiple training data sets until the parameters in the model to be trained converge, thereby obtaining an estimated model.

[0042] In an optional implementation, the training unit includes:

[0043] a processing subunit, configured to process the sample data based on the to-be-trained model to obtain an estimated heat release rate and an estimated heat release time of the ultra-high temperature fluidized energy storage and release device;

[0044] a first acquiring subunit, configured to acquire a heat release rate loss value according to the estimated heat release rate and the marked heat release rate, and to acquire a heat release time loss value according to the estimated heat release time and the marked heat release time;

[0045] A second acquisition subunit is configured to acquire a comprehensive loss value based on the heat release rate loss value and the heat release moment loss value;

[0046] The adjustment subunit is used to adjust the parameters in the model to be trained according to the comprehensive loss value.

[0047] In a third aspect, the present application shows an electronic device, which includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the method described in any of the above aspects.

[0048] In a fourth aspect, the present application shows a non-temporary computer-readable storage medium, which, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute the method described in any of the above aspects.

[0049] In a fifth aspect, the present application illustrates a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to execute the method as described in any one of the above aspects.

[0050] The technical solution provided by this application may have the following beneficial effects:

[0051] In the present application, the load power of the power grid in the future time period after the current moment is estimated. The expected power generation power of the power plant in the future time period is obtained. The current temperature of the ultra-high temperature fluidized energy storage and release equipment is obtained. The starting time of the future time period is obtained. In the case that the expected power generation power is less than the load power, the target heat release time and target heat release rate of the ultra-high temperature fluidized energy storage and release equipment are estimated based on the expected power generation power, the load power, the current temperature, the current moment and the starting moment. The target heat release time is earlier than the starting moment. In the case that the ultra-high temperature fluidized energy storage and release equipment releases heat at the target heat release rate at the target heat release time, when the starting moment is reached, the actual power generation power of the power plant is greater than or equal to the load power. The ultra-high temperature fluidized energy storage and release equipment is controlled to start continuous heat release at the target heat release rate at the target heat release time to assist the power plant in generating electricity.

[0052] Through this application, the actual power generation power of the power plant can be greater than or equal to the load power when the starting moment is reached, so that the power generated by the power plant can meet the power demand of the user end when "just entering the peak period of grid load". BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flowchart of the steps of an auxiliary power generation method based on ultra-high temperature fluidized energy storage and release equipment in this application.

[0054] Figure 2 This is a structural block diagram of an auxiliary power generation device based on ultra-high temperature fluidized energy storage and release equipment in this application.

[0055] Figure 3 This is a block diagram of an electronic device of the present application.

[0056] Figure 4 This is a block diagram of an electronic device of the present application. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0058] In order to achieve the purpose of peak shaving and valley filling, during the low load period of the power grid, the ultra-high temperature fluidized bed energy storage and discharge equipment can store heat in a thermal energy storage tank through the unused electric energy in the power grid (for example, convert the electric energy into thermal energy and store it), so as to achieve the purpose of increasing the load during the low load period of the power grid and the purpose of storing energy (for example, converting the electric energy into thermal energy and storing it).

[0059] Afterwards, during the peak period of grid load, the ultra-high temperature fluidized bed energy storage and release equipment releases the heat stored in the thermal energy storage tank. The released heat is used to fuse with the heat generated by the boiler of the power plant. The fused heat heats the working fluid of the power plant (for example, water or water vapor, or a mixture of water vapor, or other substances, etc.). The heated mixture enters the steam turbine to assist in driving the steam turbine to rotate, thereby assisting in increasing the kinetic energy of the steam turbine, and then assisting in driving the generator to generate more electricity, increasing the power generation of the power plant, and then increasing the amount of electricity transmitted to the power grid, so as to make up for the problem that the electricity originally generated by the power plant cannot meet the electricity demand of the user end.

[0060] After entering the peak period of grid load, the ultra-high temperature fluidized bed energy storage and release equipment will release the heat stored in the thermal energy storage tank to assist the power plant in generating electricity through the released heat, increase the amount of electricity generated by the power plant, and meet the user's demand for electricity as much as possible.

[0061] However, the inventors discovered that the process of using the released heat to assist the power plant in generating more electricity takes some time, which will result in a window period between the moment "just when the grid load enters the peak period" and the moment "when the amount of electricity generated by the power plant after the released heat assists the power plant to meet the user's demand for electricity". If the ultra-high temperature fluidized bed energy storage and release equipment releases the heat stored in the heat storage tank to assist the power plant in generating electricity through the released heat when "just when the grid load enters the peak period", there will be a window period after "just when the grid load enters the peak period". During this window period, the amount of electricity generated by the power plant cannot meet the user's demand for electricity.

[0062] Therefore, how to ensure that the amount of electricity generated by power plants can meet the power demand of users when the power grid just enters the peak period of load is a technical problem that needs to be solved urgently.

[0063] Therefore, in order to solve the above problems, the technical solution of the present application is proposed.

[0064] Specifically, refer to Figure 1 , showing a step flow chart of an auxiliary power generation method based on an ultra-high temperature fluidized energy storage and release device of the present application. The heat output port of the ultra-high temperature fluidized energy storage and release device and the heat output port of the boiler of the power plant converge to an integrated heat output port. The heat output from the integrated heat output port is used to heat the working fluid of the power plant. The heated mixture enters the steam turbine to assist in driving the steam turbine to rotate, thereby assisting in increasing the kinetic energy of the steam turbine, and then assisting in driving the generator to generate more electricity.

[0065] The method is applied to electronic equipment, which may include a control terminal of an ultra-high temperature fluidized bed energy storage and release device.

[0066] The method includes:

[0067] In step S101, the load power of the power grid in a future time period after the current moment is estimated. The expected power generation power of the power plant in the future time period is obtained. The current temperature of the ultra-high temperature fluidized bed energy storage device is obtained. The start time of the future time period is obtained.

[0068] The current time is earlier than the start time.

[0069] Among them, for the load power of the power grid in the future time period after the current moment, it can be estimated using the currently existing estimation method, and this application does not limit the specific estimation method.

[0070] In one embodiment, the historical load of the power grid in the historical time period before the current moment can be obtained, and the current load of the power grid in the current time period at the current moment can be obtained; the historical additional electricity-related events in the historical time period within the power consumption area supplied by the power grid can be obtained, the current additional electricity-related events in the current time period within the power consumption area can be obtained, and the future additional electricity-related events in the future time period within the power consumption area can be obtained; the historical weather data of the power consumption area in the historical time period can be obtained, the current weather data of the power consumption area in the current time period can be obtained, and the future weather data of the power consumption area in the future time period can be obtained; the historical calendar data of the historical time period can be obtained, the current calendar data of the current time period can be obtained, and the future calendar data of the future time period can be obtained; the historical electricity price in the historical time period within the power consumption area supplied by the power grid can be obtained, and the current time within the power consumption area can be obtained. The current electricity price within the segment is obtained, and the future electricity price within the future time period within the electricity consumption area is obtained; the historical load, current load, historical additional electricity-related events, current additional electricity-related events, future additional electricity-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices and future electricity prices are input into the power grid load power prediction model, so that the power grid load prediction model processes the historical load, current load, historical additional electricity-related events, current additional electricity-related events, future additional electricity-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices and future electricity prices (for example, collaborative processing, etc.) to obtain the load power of the power grid in the future time period after the current moment.

[0071] The power plant may include a thermal power plant, etc.

[0072] The power generated by the power plant in the future is expected to be provided directly by the power plant.

[0073] The expected power generation of the power plant in the future time period can be understood as: the expected power generation of the power plant in the future time period without the assistance of ultra-high temperature fluidized energy storage and release equipment. The expected power generation of the power plant in the future time period has nothing to do with the factors of ultra-high temperature fluidized energy storage and release equipment.

[0074] The predicted power generation capacity of the power plant in the future period can be directly obtained through the control system of the power plant.

[0075] The current temperature of the ultra-high temperature fluidized energy storage and release equipment can be directly measured. The current temperature of the ultra-high temperature fluidized energy storage and release equipment can be understood as: the temperature of the energy storage medium in the thermal energy storage tank of the ultra-high temperature fluidized energy storage and release equipment at the current moment.

[0076] The load power of a power grid refers to the total power consumed by all power users (including industrial users, commercial users, and residential users) in the grid. The size of the power load directly reflects the power demand of the grid.

[0077] This application divides time into time periods, and the length of each time period can be the same. For example, the length of the time period includes 1 hour, 2 hours, 3 hours, 6 hours, 12 hours, 24 hours, 36 hours, 48 ​​hours, 72 hours, 7 days, 14 days or one month, etc. In two adjacent time periods, the end time of the previous time period is adjacent to the start time of the next time period.

[0078] The time period of the current moment is the current time period, and the time period adjacent to and after the current time period is the future time period.

[0079] In step S102, when the expected power generation power is less than the load power, the target heat release time and target heat release rate of the ultra-high temperature fluidized bed energy storage and release equipment are estimated based on the expected power generation power, the load power, the current temperature, the current time and the start time.

[0080] The target heat release time is earlier than the start time. When the ultra-high temperature fluidized bed energy storage and discharge equipment releases heat at the target heat release rate at the target heat release time, the actual power generation power of the power plant is greater than or equal to the load power when the start time is reached.

[0081] In one embodiment, the ultra-high temperature fluidized energy storage and discharge equipment releases heat at the target heat release rate at the target heat release moment, which can be understood as: during the heat release process, the heat release rate continues to be the target heat release rate, that is, uniform release.

[0082] In one embodiment of the present application, when the expected power generation power is greater than or equal to the load power, it means that the expected power generation power of the power plant in the future time period can meet the load power of the power grid in the future time period and can meet the user's demand for electricity in the future time period. In this way, there is no need for ultra-high temperature fluidized bed energy storage and release equipment to assist the power plant in power generation, and the process can be terminated.

[0083] Alternatively, in another embodiment of the present application, when the expected power generation power is less than the load power, it means that the expected power generation power of the power plant in the future time period cannot meet the load power of the power grid in the future time period, and cannot meet the user's demand for electricity in the future time period. In this case, ultra-high temperature fluidized bed energy storage and release equipment is needed to assist the power plant in generating electricity, and steps S102 and S103 can be executed.

[0084] In one embodiment, in the present application, an estimation model is trained in advance, and the specific training process includes:

[0085] Get multiple training datasets.

[0086] A training data set includes: sample data and labeled data.

[0087] The sample data includes: the actual load power of the power grid during the sample time period, the actual power generation power of the power plant during the sample time period, the sample duration between the start time of the sample time period and the sample time, the sample temperature of the ultra-high temperature fluidized bed energy storage device at the sample time, the sample time prior to the start time of the sample time period, and the sample difference between the actual load power and the actual power generation power. The actual load power is greater than the actual power generation power. The sample time is prior to the start time of the sample time period.

[0088] Among them, the sample data in different training data sets are different, and the different sample data can be specifically understood as: for any two sample data, at least one of the real load power, real power generation power, sample duration, sample temperature and sample difference is different.

[0089] The actual power generated by the power plant during the sample period is directly provided by the power plant.

[0090] The actual power generation of the power plant during the sample period can be understood as: the actual power generation of the power plant during the sample period without the assistance of ultra-high temperature fluidized energy storage and release equipment. The actual power generation of the power plant during the sample period has nothing to do with the factors of ultra-high temperature fluidized energy storage and release equipment.

[0091] The labeled data includes: the labeled heat release time of the ultra-high temperature fluidized energy storage and release equipment and the labeled heat release rate of the ultra-high temperature fluidized energy storage and release equipment. The labeled heat release time is later than the sample time and earlier than the start time of the sample time period.

[0092] The marked heat release time of the ultra-high temperature fluidized energy storage and discharge equipment is the actual and real heat release time of the ultra-high temperature fluidized energy storage and discharge equipment.

[0093] The marked heat release rate of the ultra-high temperature fluidized energy storage and discharge equipment is the actual, real heat release rate of the ultra-high temperature fluidized energy storage and discharge equipment.

[0094] Get the model to be trained.

[0095] The model to be trained is trained based on the multiple training data sets until the parameters in the model to be trained converge, thereby obtaining an estimated model.

[0096] When the to-be-trained model is trained based on the multiple training data sets, the sample data can be processed based on the to-be-trained model to obtain the estimated heat release rate and the estimated heat release time of the ultra-high temperature fluidized energy storage and release equipment.

[0097] Then, a heat release rate loss value is obtained based on the estimated heat release rate and the marked heat release rate, and a heat release time loss value is obtained based on the estimated heat release time and the marked heat release time.

[0098] The comprehensive loss value is obtained based on the heat release rate loss value and the heat release time loss value.

[0099] According to the comprehensive loss value, the parameters in the training model are adjusted.

[0100] For example, the sum of the heat release moment loss value and the heat release rate loss value may be directly calculated, and the sum may be determined as the comprehensive loss value.

[0101] Alternatively, when calculating the comprehensive loss value, the heat release moment loss value and the heat release rate loss value may be weightedly summed to obtain the comprehensive loss value.

[0102] For example, technicians can set the weight corresponding to the loss value at the heat release moment and the weight corresponding to the heat release rate loss value in advance based on experience. The present application does not limit the specific values ​​of the weight corresponding to the loss value at the heat release moment and the weight corresponding to the heat release rate loss value, respectively. It is sufficient that the sum of the weight corresponding to the loss value at the heat release moment and the weight corresponding to the heat release rate loss value can be a specific value, which can include a value of 1 or a value of 2, etc.

[0103] For example, the weight corresponding to the loss value at the heat release moment is 0.45, and the weight corresponding to the loss value at the heat release rate is 0.55.

[0104] For another example, the weight corresponding to the loss value at the heat release moment is 0.4, and the weight corresponding to the loss value at the heat release rate is 0.6.

[0105] Estimation models may include: random forest model, XGBoost (eXtreme Gradient Boosting), LSTM (Long Short Term Memory), or Transformer (a deep learning architecture based on the self-attention mechanism, originally used for natural language processing tasks such as machine translation).

[0106] In this way, in step S102, the power difference between the load power and the expected power generation power can be obtained, the duration between the starting moment and the current moment is calculated, and the expected power generation power, the load power, the current temperature, the current moment, the starting moment, the power difference and the duration are all input into the trained estimation model, so that the estimation model processes the expected power generation power, the load power, the current temperature, the current moment, the starting moment, the power difference and the duration to obtain the target heat release time and the target heat release rate of the ultra-high temperature fluidized energy storage and release equipment.

[0107] Specifically, the expected generated power, the load power, the current temperature, the current time, the starting time, the power difference and the duration can all be input into the trained estimation model, and the feature extraction network in the estimation model can respectively extract the feature vector corresponding to the expected generated power, the feature vector corresponding to the load power, the feature vector corresponding to the current temperature, the feature vector corresponding to the current time, the feature vector corresponding to the starting time, the feature vector corresponding to the power difference and the feature vector corresponding to the duration.

[0108] Among them, the feature extraction network can include an encoding layer and a multi-head self-attention layer. For the expected power generation, the encoding layer can be used to encode the expected power generation (for example, one-hot encoding, etc.) to obtain a sparse vector corresponding to the expected power generation, and then the multi-head self-attention layer is used to perform multi-head self-attention weighting on the sparse vector corresponding to the expected power generation to obtain a feature vector corresponding to the expected power generation.

[0109] The same is true for the load power, the current temperature, the current time, the starting time, the power difference and the duration, which will not be described in detail here.

[0110] The coding layer can include one-hot etc.

[0111] The multi-head self-attention layer can include Multi-head Self-attention Layer, etc.

[0112] Then, the characteristic vector corresponding to the expected generated power, the characteristic vector corresponding to the load power, the characteristic vector corresponding to the current temperature, the characteristic vector corresponding to the current moment, the characteristic vector corresponding to the starting moment, the characteristic vector corresponding to the power difference, and the characteristic vector corresponding to the duration are input into the aggregation network in the estimation model, so that the aggregation network aggregates the characteristic vector corresponding to the expected generated power, the characteristic vector corresponding to the load power, the characteristic vector corresponding to the current temperature, the characteristic vector corresponding to the current moment, the characteristic vector corresponding to the starting moment, the characteristic vector corresponding to the power difference, and the characteristic vector corresponding to the duration into an aggregation vector.

[0113] For example, the characteristic vector corresponding to the expected generated power, the characteristic vector corresponding to the load power, the characteristic vector corresponding to the current temperature, the characteristic vector corresponding to the current moment, the characteristic vector corresponding to the starting moment, the characteristic vector corresponding to the power difference, and the characteristic vector corresponding to the duration can be concatenated end to end in sequence to obtain an aggregated vector.

[0114] Alternatively, the dimensions of the feature vectors mentioned above are the same, and the feature vector corresponding to the expected generated power, the feature vector corresponding to the load power, the feature vector corresponding to the current temperature, the feature vector corresponding to the current moment, the feature vector corresponding to the starting moment, the feature vector corresponding to the power difference, and the feature vector corresponding to the duration can be average pooled or maximum pooled to obtain an aggregated vector.

[0115] Then, the prediction network in the prediction model is used to process the aggregate vector to obtain the target heat release time and the target heat release rate of the ultra-high temperature fluidized energy storage and release equipment.

[0116] The estimation network includes a logistic regression function, a normalized exponential function, a fully connected layer or an activation function, for example, Sigmoid, Softmax or ReLU.

[0117] The estimation network can first use MLP (Multilayer Perceptron) to process the aggregated vector to obtain an intermediate vector, then use the activation function tanh to process the intermediate vector to obtain an activation vector, and then use Softmax, Sigmoid or ReLU to process the activation vector to obtain the target heat release time and the target heat release rate of the ultra-high temperature fluidized energy storage and release equipment.

[0118] In step S103, the ultra-high temperature fluidized bed energy storage and discharge equipment is controlled to start continuously releasing heat at the target heat release rate at the target heat release time to assist the power plant in generating electricity.

[0119] Alternatively, after the future time period ends, the ultra-high temperature fluidized bed energy storage and discharge device may be controlled to stop releasing heat.

[0120] For example, the heat output port of the thermal energy storage tank of the ultra-high temperature fluidized bed energy storage and release equipment has a switch valve, and the opening size of the switch valve can be adjusted. The larger the switch valve is opened, the faster the heat output rate, that is, the faster the heat release rate; or, the smaller the switch valve is opened, the slower the heat output rate, that is, the slower the heat release rate.

[0121] Different heat release rates correspond to different opening sizes of the switching valve.

[0122] To this end, the opening size of the switch valve corresponding to the target heat release rate can be determined, and then the switch valve can be opened to the opening size of the switch valve corresponding to the target heat release rate at the target heat release moment, so as to achieve continuous heat release at the target heat release rate at the target heat release moment.

[0123] The opening sizes of the switch valves corresponding to different target heat release rates are different and are set in advance, for example, by technicians based on actual conditions.

[0124] In the present application, the load power of the power grid in the future time period after the current moment is estimated. The expected power generation power of the power plant in the future time period is obtained. The current temperature of the ultra-high temperature fluidized energy storage and release equipment is obtained. The starting time of the future time period is obtained. In the case that the expected power generation power is less than the load power, the target heat release time and target heat release rate of the ultra-high temperature fluidized energy storage and release equipment are estimated based on the expected power generation power, the load power, the current temperature, the current moment and the starting moment. The target heat release time is earlier than the starting moment. In the case that the ultra-high temperature fluidized energy storage and release equipment releases heat at the target heat release rate at the target heat release time, when the starting moment is reached, the actual power generation power of the power plant is greater than or equal to the load power. The ultra-high temperature fluidized energy storage and release equipment is controlled to start continuous heat release at the target heat release rate at the target heat release time to assist the power plant in generating electricity.

[0125] Through this application, the actual power generation power of the power plant can be greater than or equal to the load power when the starting moment is reached, so that the power generated by the power plant can meet the power demand of the user end when "just entering the peak period of grid load".

[0126] In addition, through this application, intelligent technology for peak-valley power regulation can be realized. During the low period of grid load, the estimation model is used to predict the peak-shaving time of the grid. The energy storage system predicts and purchases cheap valley electricity from the grid, which is converted into thermal energy through ultra-high temperature fluidized energy storage heating for storage to achieve real-time coupling. During the peak period of grid load, thermal energy is released to participate in electricity production, so as to increase the load during the low period of the grid to store thermal energy and release ultra-high temperature stored thermal energy during the peak period of the grid load.

[0127] Achieve economical operating indicators for unit units and reduce operational safety risks caused by downtime or load reduction. When power grid units are operated with loads lower than the design value, there are potential risks such as safety issues and reduced lifespan. To compensate for the practical problems caused by the changes in peak and valley unit loads, the present application proposes a solution for converting and storing peak and valley electricity.

[0128] With the goal of making full use of valley electricity and maximizing corporate benefits, peak-valley regulation of electricity can improve the power generation efficiency of unit units and optimize the economic indicators of plant electricity consumption, achieving economy and stability.

[0129] The high-temperature fluidized bed heat storage tank is a site for both electricity conversion and steam production, providing technical support for the smooth and timely operation of the high-temperature fluidized bed heat storage tank by coupling with grid fluctuations. It also provides technical support for the real-time switching of electric heating devices, generating high-temperature thermal energy through the heating of heat storage materials through staged heating, and providing data support for forecasting and decision-making on actual needs.

[0130] The solution of this application has good economic benefits, improves equipment operation planning, increases system safety, and extends the service life of the production system.

[0131] It should be noted that for the method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions involved are not necessarily required by this application.

[0132] Reference Figure 2 , shows an auxiliary power generation device based on an ultra-high temperature fluidized bed energy storage and release device of the present application, wherein the heat output port of the ultra-high temperature fluidized bed energy storage and release device and the heat output port of the boiler of the power plant converge to an integrated heat output port, and the heat output from the integrated heat output port is used to heat the working medium of the power plant; the device includes:

[0133] The acquisition module 11 is used to estimate the load power of the power grid in a future time period after the current time; obtain the expected power generation power of the power plant in the future time period; obtain the current temperature of the ultra-high temperature fluidized bed energy storage device; obtain the starting time of the future time period; the current time is earlier than the starting time;

[0134] an estimating module 12 for estimating, when the estimated generated power is less than the load power, a target heat release time and a target heat release rate for the ultra-high temperature fluidized energy storage and discharge equipment based on the estimated generated power, the load power, the current temperature, the current time, and the starting time; the target heat release time is earlier than the starting time; wherein, when the ultra-high temperature fluidized energy storage and discharge equipment releases heat at the target heat release rate at the target heat release time, the actual generated power of the power plant is greater than or equal to the load power when the starting time is reached;

[0135] The control module 13 is used to control the ultra-high temperature fluidized bed energy storage and release equipment to start continuous heat release at the target heat release rate at the target heat release time to assist the power plant in generating electricity.

[0136] In an optional implementation, the estimation module includes:

[0137] A first acquiring unit, configured to acquire a power difference between the load power and the expected generated power;

[0138] a calculation unit, configured to calculate the duration between the start time and the current time;

[0139] A processing unit is used to input the expected generated power, the load power, the current temperature, the current time, the starting time, the power difference and the duration into a trained estimation model, so that the estimation model processes the expected generated power, the load power, the current temperature, the current time, the starting time, the power difference and the duration to obtain the target heat release time and the target heat release rate of the ultra-high temperature fluidized energy storage and release equipment.

[0140] In an optional implementation, the estimation module further includes:

[0141] A second acquisition unit is used to acquire multiple training data sets;

[0142] A training data set includes: sample data and labeled data;

[0143] The sample data includes: the real load power of the power grid during the sample time period, the real power generation power of the power plant during the sample time period, the sample duration between the start time of the sample time period and the sample time, the sample temperature of the ultra-high temperature fluidized bed energy storage and release equipment at the sample time, and the sample difference between the real load power and the real power generation power; the real load power is greater than the real power generation power; the sample time is earlier than the start time of the sample time period;

[0144] The annotated data includes: an annotated heat release time of the ultra-high temperature fluidized energy storage and release equipment and an annotated heat release rate of the ultra-high temperature fluidized energy storage and release equipment; the annotated heat release time is later than the sample time and earlier than the start time of the sample time period;

[0145] A third acquisition unit is used to acquire a model to be trained;

[0146] A training unit is used to train the model to be trained based on the multiple training data sets until the parameters in the model to be trained converge, thereby obtaining an estimated model.

[0147] In an optional implementation, the training unit includes:

[0148] a processing subunit, configured to process the sample data based on the to-be-trained model to obtain an estimated heat release rate and an estimated heat release time of the ultra-high temperature fluidized energy storage and release device;

[0149] a first acquiring subunit, configured to acquire a heat release rate loss value according to the estimated heat release rate and the marked heat release rate, and to acquire a heat release time loss value according to the estimated heat release time and the marked heat release time;

[0150] A second acquisition subunit is configured to acquire a comprehensive loss value based on the heat release rate loss value and the heat release moment loss value;

[0151] The adjustment subunit is used to adjust the parameters in the model to be trained according to the comprehensive loss value.

[0152] In the present application, the load power of the power grid in the future time period after the current moment is estimated. The expected power generation power of the power plant in the future time period is obtained. The current temperature of the ultra-high temperature fluidized energy storage and release equipment is obtained. The starting time of the future time period is obtained. In the case that the expected power generation power is less than the load power, the target heat release time and target heat release rate of the ultra-high temperature fluidized energy storage and release equipment are estimated based on the expected power generation power, the load power, the current temperature, the current moment and the starting moment. The target heat release time is earlier than the starting moment. In the case that the ultra-high temperature fluidized energy storage and release equipment releases heat at the target heat release rate at the target heat release time, when the starting moment is reached, the actual power generation power of the power plant is greater than or equal to the load power. The ultra-high temperature fluidized energy storage and release equipment is controlled to start continuous heat release at the target heat release rate at the target heat release time to assist the power plant in generating electricity.

[0153] Through this application, the actual power generation power of the power plant can be greater than or equal to the load power when the starting moment is reached, so that the power generated by the power plant can meet the power demand of the user end when "just entering the peak period of grid load".

[0154] Optionally, an embodiment of the present application also provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and runnable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0155] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the various processes of the above-described method embodiments are implemented and the same technical effects are achieved. To avoid repetition, the details are not described here. The computer-readable storage medium may be, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0156] Figure 3 8 is a block diagram of an electronic device 800 shown in the present application. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0157] Reference Figure 3, the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0158] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 802 may include one or more modules to facilitate interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

[0159] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, images, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0160] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0161] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also monitor the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0162] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), which is configured to receive external audio signals when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0163] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0164] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can monitor the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the electronic device 800. The sensor assembly 814 can also monitor the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to monitor the presence of nearby objects without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0165] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, an operator network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast operation information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0166] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0167] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by the processor 820 of the electronic device 800 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0168] Figure 4 1 is a block diagram of an electronic device 1900 shown in the present application. For example, the electronic device 1900 can be provided as a server.

[0169] Reference Figure 4 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute the instructions to perform the above-described method.

[0170] The electronic device 1900 may further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or the like.

[0171] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0172] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0173] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

[0174] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0175] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0176] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0177] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0178] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0179] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0180] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. An auxiliary power generation method based on ultra-high temperature fluidized bed energy storage and release equipment, characterized in that: The heat output port of the ultra-high temperature fluidized bed energy storage and discharge device and the heat output port of the boiler of the power plant are converged into an integrated heat output port, and the heat output from the integrated heat output port is used to heat the working medium of the power plant. The method includes: Estimate the load power of the power grid in a future time period after the current moment; obtain the expected power generation power of the power plant in the future time period; obtain the current temperature of the ultra-high temperature fluidized bed energy storage and release equipment; obtain the starting time of the future time period; the current time is earlier than the starting time; When the expected generated power is less than the load power, a target heat release time and a target heat release rate of the ultra-high temperature fluidized bed energy storage and discharge equipment are estimated based on the expected generated power, the load power, the current temperature, the current time, and the starting time; the target heat release time is earlier than the starting time; wherein, when the ultra-high temperature fluidized bed energy storage and discharge equipment releases heat at the target heat release rate at the target heat release time, the actual generated power of the power plant is greater than or equal to the load power when the starting time is reached; The ultra-high temperature fluidized bed energy storage and discharge equipment is controlled to start continuous heat release at the target heat release rate at the target heat release time to assist the power plant in generating electricity.

2. The method according to claim 1, characterized in that The estimating the target heat release time and target heat release rate of the ultra-high temperature fluidized bed energy storage and release device based on the expected generated power, the load power, the current temperature, the current time, and the start time includes: Obtaining a power difference between the load power and the expected generated power; Calculate the duration between the starting time and the current time; The expected generated power, the load power, the current temperature, the current time, the starting time, the power difference and the duration are all input into the trained estimation model, so that the estimation model processes the expected generated power, the load power, the current temperature, the current time, the starting time, the power difference and the duration to obtain the target heat release time and the target heat release rate of the ultra-high temperature fluidized energy storage and release equipment.

3. The method according to claim 2, characterized in that The method further comprises: Obtain multiple training data sets; A training data set includes: sample data and labeled data; The sample data includes: the real load power of the power grid during the sample time period, the real power generation power of the power plant during the sample time period, the sample duration between the start time of the sample time period and the sample time, the sample temperature of the ultra-high temperature fluidized bed energy storage and release equipment at the sample time, and the sample difference between the real load power and the real power generation power; the real load power is greater than the real power generation power; the sample time is earlier than the start time of the sample time period; The annotated data includes: an annotated heat release time of the ultra-high temperature fluidized energy storage and release equipment and an annotated heat release rate of the ultra-high temperature fluidized energy storage and release equipment; the annotated heat release time is later than the sample time and earlier than the start time of the sample time period; Get the model to be trained; The model to be trained is trained based on the multiple training data sets until the parameters in the model to be trained converge, thereby obtaining an estimated model.

4. The method according to claim 3, characterized in that The training of the to-be-trained model based on the multiple training data sets includes: Processing the sample data based on the to-be-trained model to obtain an estimated heat release rate and an estimated heat release time of the ultra-high temperature fluidized energy storage and release device; Obtaining a heat release rate loss value based on the estimated heat release rate and the marked heat release rate, and obtaining a heat release time loss value based on the estimated heat release time and the marked heat release time; Obtaining a comprehensive loss value based on the heat release rate loss value and the heat release time loss value; According to the comprehensive loss value, the parameters in the to-be-trained model are adjusted.

5. An auxiliary power generation device based on ultra-high temperature fluidized bed energy storage and release equipment, characterized in that: The heat output port of the ultra-high temperature fluidized bed energy storage and discharge device and the heat output port of the boiler of the power plant are converged into an integrated heat output port, and the heat output from the integrated heat output port is used to heat the working medium of the power plant; the device includes: An acquisition module is used to estimate the load power of the power grid in a future time period after the current moment; obtain the expected power generation power of the power plant in the future time period; obtain the current temperature of the ultra-high temperature fluidized bed energy storage and discharge equipment; obtain the starting time of the future time period; the current time is earlier than the starting time; an estimating module, configured to estimate, when the estimated generated power is less than the load power, a target heat release time and a target heat release rate for the ultra-high temperature fluidized bed energy storage and discharge equipment based on the estimated generated power, the load power, the current temperature, the current time, and the starting time; the target heat release time is earlier than the starting time; wherein, when the ultra-high temperature fluidized bed energy storage and discharge equipment releases heat at the target heat release rate at the target heat release time, the actual generated power of the power plant is greater than or equal to the load power when the starting time is reached; The control module is used to control the ultra-high temperature fluidized energy storage and release equipment to start continuous heat release at the target heat release rate at the target heat release time to assist the power plant in generating electricity.

6. The device according to claim 5, characterized in that The estimation module includes: A first acquiring unit, configured to acquire a power difference between the load power and the expected generated power; a calculation unit, configured to calculate the duration between the start time and the current time; A processing unit is used to input the expected generated power, the load power, the current temperature, the current time, the starting time, the power difference and the duration into a trained estimation model, so that the estimation model processes the expected generated power, the load power, the current temperature, the current time, the starting time, the power difference and the duration to obtain the target heat release time and the target heat release rate of the ultra-high temperature fluidized energy storage and release equipment.

7. The device according to claim 6, characterized in that The estimation module further includes: A second acquisition unit is used to acquire multiple training data sets; A training data set includes: sample data and labeled data; The sample data includes: the real load power of the power grid during the sample time period, the real power generation power of the power plant during the sample time period, the sample duration between the start time of the sample time period and the sample time, the sample temperature of the ultra-high temperature fluidized bed energy storage and release equipment at the sample time, and the sample difference between the real load power and the real power generation power; the real load power is greater than the real power generation power; the sample time is earlier than the start time of the sample time period; The annotated data includes: an annotated heat release time of the ultra-high temperature fluidized energy storage and release equipment and an annotated heat release rate of the ultra-high temperature fluidized energy storage and release equipment; the annotated heat release time is later than the sample time and earlier than the start time of the sample time period; A third acquisition unit is used to acquire a model to be trained; A training unit is used to train the model to be trained based on the multiple training data sets until the parameters in the model to be trained converge, thereby obtaining an estimated model.

8. The device according to claim 7, characterized in that The training unit comprises: a processing subunit, configured to process the sample data based on the to-be-trained model to obtain an estimated heat release rate and an estimated heat release time of the ultra-high temperature fluidized energy storage and release device; a first acquiring subunit, configured to acquire a heat release rate loss value according to the estimated heat release rate and the marked heat release rate, and to acquire a heat release time loss value according to the estimated heat release time and the marked heat release time; A second acquisition subunit is configured to acquire a comprehensive loss value based on the heat release rate loss value and the heat release moment loss value; The adjustment subunit is used to adjust the parameters in the model to be trained according to the comprehensive loss value.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the method according to any one of claims 1 to 4 when executed by the processor.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which implements the method according to any one of claims 1 to 4 when executed by a processor.

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