Renewable energy generation scheduling method and system based on multiple factors and different time scales

Abstract

The present disclosure relates to the field of electric power technology, and discloses a method and system for scheduling new energy power generation at different time scales based on multiple factors. The specific scheme is as follows: based on the first training sample set of seasonal frozen soil areas, a geothermal energy extraction efficiency prediction model for cross-variable seasonal frozen soil areas is constructed; based on the second training sample set of cross-variable seasonal frozen soil areas, a power generation prediction model for cross-variable seasonal frozen soil areas is constructed; on the basis of the cross-variable geothermal energy extraction efficiency prediction model and the cross-variable power generation prediction model, the geothermal pipe layout mode and the power generation mode of the generator set are used as independent variables, and the geothermal energy extraction efficiency and the power generation are used as dependent variables to construct an objective function, wherein the cross-variable geothermal pipe layout mode includes the geothermal pipe layout position; the cross-variable objective function is solved for the maximum value, and the target geothermal pipe layout mode and the target power generation mode of the generator set in the cross-variable seasonal frozen soil area are obtained.

Description

Technical Field

[0001] The present disclosure relates to the field of electric power technology, and in particular to a method, system, electronic device and storage medium for scheduling renewable energy power generation at different time scales based on multiple factors. Background Art

[0002] When extracting shallow geothermal energy in seasonally frozen areas, the impact of frozen soil thawing on geothermal energy extraction efficiency is a complex technical issue.

[0003] During the thawing process of frozen soil, the latent heat of phase change of ice in the soil will significantly affect the thermophysical parameters of the soil, causing changes in parameters such as soil thermal conductivity and specific heat capacity, which in turn affects the conduction process of geothermal energy. At the same time, the thawing of frozen soil will also change the permeability of the soil, causing the flow pattern of groundwater to change, forming a complex soil water-heat-mechanical coupling process. The combined effect of these factors makes the extraction of shallow geothermal energy in frozen soil areas face completely different technical challenges from those in non-frozen soil areas.

[0004] Therefore, how to extract geothermal energy in permafrost areas and convert it into electrical energy to improve the efficiency of heat extraction and power generation is a technical problem that urgently needs to be solved in this field.

[0005] In this context, predicting the power generation from shallow geothermal energy extraction becomes particularly difficult.

[0006] However, due to the nonlinearity and time-varying nature of the frozen soil thawing process, traditional geothermal energy extraction models and power generation prediction methods are difficult to apply directly. Therefore, how to accurately describe the changes in soil thermophysical parameters and water flow conditions during the dynamic process of frozen soil thawing, and based on this, establish a shallow geothermal energy extraction model that can reflect the characteristics of frozen soil, is a key technical problem that needs to be solved urgently. Only on this basis can we further study the new energy power generation scheduling scheme suitable for frozen soil areas and improve the efficiency of geothermal energy extraction and geothermal power generation. Summary of the invention

[0007] The present disclosure provides a method, system, electronic device and storage medium for scheduling renewable energy power generation at different time scales based on multiple factors, which can solve at least one of the above problems.

[0008] The present disclosure provides a new energy generation scheduling method based on multiple factors and different time scales, including:

[0009] Based on a first training sample set in a seasonal frozen soil area, a geothermal energy extraction efficiency prediction model is constructed in the seasonal frozen soil area, wherein each training sample in the first training sample set includes meteorological data at a specified time scale, soil temperature and water content corresponding to the geothermal pipe arrangement position under the meteorological data, and the corresponding geothermal energy extraction efficiency;

[0010] Based on the second training sample set of the seasonal frozen soil area, constructing a power generation prediction model for the seasonal frozen soil area, wherein each training sample in the second training sample set includes geothermal energy extraction efficiency, power generation mode of a generator set, and corresponding power generation efficiency;

[0011] Based on the geothermal energy extraction efficiency prediction model and the power generation prediction model, an objective function is constructed with the geothermal pipe arrangement mode and the power generation mode of the generator set as independent variables, and the geothermal energy extraction efficiency and the power generation power as dependent variables, wherein the geothermal pipe arrangement mode includes the geothermal pipe arrangement position;

[0012] The maximum value of the objective function is solved to obtain the target geothermal pipe layout and the target generator set power generation mode in the seasonal frozen soil area.

[0013] According to another aspect of the present disclosure, a new energy power generation scheduling system based on multiple factors and different time scales is provided, comprising:

[0014] A first model building module is used to build a geothermal energy extraction efficiency prediction model for the seasonal frozen soil area based on a first training sample set in the seasonal frozen soil area, wherein each training sample in the first training sample set includes meteorological data at a specified time scale, soil temperature and water content corresponding to the geothermal pipe arrangement position under the meteorological data, and the corresponding geothermal energy extraction efficiency;

[0015] A second model building module is used to build a power generation prediction model for the seasonal frozen soil area based on a second training sample set in the seasonal frozen soil area, wherein each training sample in the second training sample set includes a geothermal energy extraction efficiency, a power generation mode of a generator set, and a corresponding power generation efficiency;

[0016] An objective function construction module is used to construct an objective function based on the geothermal energy extraction efficiency prediction model and the power generation prediction model, taking the geothermal pipe arrangement mode and the power generation mode of the generator set as independent variables, and taking the geothermal energy extraction efficiency and the power generation power as dependent variables, wherein the geothermal pipe arrangement mode includes the geothermal pipe arrangement position;

[0017] The objective function solving module is used to solve the maximum value of the objective function to obtain the target geothermal pipe layout and the target generator set power generation mode in the seasonal frozen soil area.

[0018] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0019] at least one processor; and

[0020] a memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any new energy power generation scheduling method based on different time scales of multiple factors in the embodiments of the present disclosure.

[0022] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute any of the new energy power generation scheduling methods based on different time scales of multiple factors in the embodiments of the present disclosure.

[0023] The technical solution disclosed in the present invention is adopted, and the meteorological data of the seasonal frozen soil area at a specified time scale, the soil temperature and water content corresponding to the geothermal pipe layout position under the meteorological data, and the corresponding geothermal energy extraction efficiency are used to form a training sample, and a geothermal energy extraction efficiency prediction model for the seasonal frozen soil area is generated by training. At the same time, the geothermal energy extraction efficiency, the power generation mode of the generator set, and the corresponding power generation efficiency are used to form a training sample, and a power generation power prediction model is generated by training. On the basis of these two models, the geothermal pipe layout mode and the power generation mode of the generator set are used as independent variables, and the geothermal energy extraction efficiency and the power generation power are used as dependent variables to construct an objective function. In this way, the objective function can take into account the thermophysical parameters of the seasonal frozen soil area and the power generation conditions of the generator set to predict the geothermal energy extraction efficiency and the power generation power, so that by solving the maximum value, a suitable power generation scheduling plan can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flow chart of a new energy power generation scheduling method based on multiple factors and different time scales according to an embodiment of the present disclosure;

[0025] Figure 2 It is a structural block diagram of a renewable energy power generation dispatching system based on multiple factors and different time scales according to an embodiment of the present disclosure;

[0026] Figure 3 is a block diagram of an electronic device for implementing an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0028] Figure 1 It is a flow chart of a new energy power generation scheduling method based on different time scales of multiple factors according to an embodiment of the present disclosure.

[0029] like Figure 1 As shown, the new energy generation scheduling method based on multiple factors and different time scales may include:

[0030] S110, constructing a geothermal energy extraction efficiency prediction model for the seasonal frozen soil area based on a first training sample set in the seasonal frozen soil area, wherein each training sample in the first training sample set includes meteorological data at a specified time scale, soil temperature and water content corresponding to the geothermal pipe arrangement position under the meteorological data, and the corresponding geothermal energy extraction efficiency;

[0031] S120, constructing a power generation prediction model for the seasonal frozen soil area based on a second training sample set in the seasonal frozen soil area, wherein each training sample in the second training sample set includes a geothermal energy extraction efficiency, a power generation mode of a generator set, and a corresponding power generation efficiency;

[0032] S130, based on the geothermal energy extraction efficiency prediction model and the power generation prediction model, taking the geothermal pipe layout and the power generation mode of the generator set as independent variables, and taking the geothermal energy extraction efficiency and the power generation power as dependent variables, constructing an objective function, wherein the geothermal pipe layout includes the geothermal pipe layout position;

[0033] S140, solving the maximum value of the objective function to obtain the target geothermal pipe layout and the target power generation mode of the generator set in the seasonal frozen soil area.

[0034] For example, the time scale may be season, month, week, etc. Meteorological data at a certain time scale may include weather temperature, precipitation, etc.

[0035] For example, a typical location is selected in a seasonally frozen soil area, and a geothermal pipe, soil temperature and moisture content sensor array is laid out, with a depth range from the surface to 50 meters, an interval of 0.5 meters, and a sampling frequency set to once an hour. Then, the median filter algorithm is used to process the collected raw data, and the outliers are detected and marked by the box plot method. The interpolation algorithm is used to repair the data missing caused by sensor failure. In this way, a data set is obtained.

[0036] Exemplarily, data is extracted from the data set, and a first training sample set is generated. The first training sample set includes a plurality of training samples. Each training sample includes an input sample and an output sample. The input sample is meteorological data at a specified time scale, soil temperature and water content corresponding to the geothermal pipe arrangement position under the meteorological data, and the output sample is geothermal energy extraction efficiency.

[0037] For example, one or more generator sets may be arranged in a seasonally frozen soil area, and the power generation mode of these generator sets may include power generation time, the number of generator sets performing power generation operations, etc.

[0038] Exemplarily, the second training sample set may include multiple training samples, each training sample includes an input sample and an output sample, wherein the input sample is geothermal energy extraction efficiency and power generation mode of a generator set, and the output sample is power generation efficiency.

[0039] Exemplarily, a hybrid model can be constructed using a long short-term memory network LSTM and a temporal convolutional network TCN, and then the hybrid model is trained using the first training sample set mentioned above to generate a geothermal energy extraction efficiency prediction model.

[0040] Exemplarily, the LSTM contains two network layers, each of which includes 256 neurons to capture long-term dependencies. The TCN part uses three dilated convolutional layers, where the convolution kernel size of the dilated convolutional layer is 3 and the dilation rates are 1, 2, and 4, respectively, to extract local features. The model input layer contains the extracted features and time encoding, and the output layer is a single neuron representing the predicted geothermal energy extraction efficiency.

[0041] Exemplarily, the Adam optimizer can be used to train the above model.

[0042] Exemplarily, the second training sample set may be used to train a hybrid model combining a long short-term memory network LSTM and a gradient boosting decision tree to obtain a power generation prediction model.

[0043] Exemplarily, LSTM is used to capture time series characteristics. For example, LSTM includes two network layers, each of which includes 128 neurons. Gradient boosting decision tree is used for nonlinear feature mapping. For example, the number of trees is set to 300, the maximum depth is 10, and the learning rate is 0.01.

[0044] For example, after the power generation prediction model is trained, it can be adjusted. For example, in order to achieve fast prediction, the trained model is pruned and quantized, and the model is compressed to 20% of its original size, which increases the prediction speed by 5 times while ensuring that the accuracy loss does not exceed 2%.

[0045] Exemplarily, the geothermal pipe arrangement may also include pipe spacing, pipe size, and fluid parameters in the pipes, such as fluid flow rate and fluid flow rate.

[0046] Exemplarily, the objective function may be a single objective function or a multi-objective function, for example, maximizing the sum of geothermal energy extraction efficiency and power generation, or maximizing geothermal energy extraction efficiency and power generation respectively.

[0047] Exemplarily, a particle swarm optimization algorithm or a non-dominated sorting genetic algorithm may be used to solve the maximum value of the objective function to obtain a target geothermal pipe layout and a target generator set power generation mode in seasonally frozen soil areas.

[0048] Exemplarily, after obtaining the target geothermal pipe arrangement and the target generator set power generation mode, power generation scheduling is performed according to the target geothermal pipe arrangement and the target generator set power generation mode, wherein the power generation scheduling includes arranging geothermal pipes and setting the power generation mode of the generator set.

[0049] According to the above implementation, the meteorological data of the seasonal frozen soil area at a specified time scale, the soil temperature and water content corresponding to the geothermal pipe layout position under the meteorological data, and the corresponding geothermal energy extraction efficiency are used to form a training sample, and a geothermal energy extraction efficiency prediction model for the seasonal frozen soil area is generated by training. At the same time, the geothermal energy extraction efficiency, the power generation mode of the generator set, and the corresponding power generation efficiency are used to form a training sample, and a power generation prediction model is generated by training. On the basis of these two models, the geothermal pipe layout mode and the power generation mode of the generator set are used as independent variables, and the geothermal energy extraction efficiency and the power generation are used as dependent variables to construct an objective function. In this way, the objective function can take into account the thermophysical parameters of the seasonal frozen soil area and the power generation conditions of the generator set to predict the geothermal energy extraction efficiency and the power generation, so that by solving the maximum value, a suitable power generation scheduling plan can be obtained.

[0050] In one embodiment, the independent variables of the objective function also include meteorological data at various time scales within a year, and soil temperature and water content corresponding to the geothermal pipe arrangement location under the meteorological data.

[0051] In one embodiment, based on a geothermal energy extraction efficiency prediction model and a power generation prediction model, with geothermal pipe layout and generator set power generation mode as independent variables, and geothermal energy extraction efficiency and power generation power as dependent variables, an objective function is constructed, including: taking the geothermal pipe layout mode as the first independent variable, determining multiple second independent variables, wherein the second independent variable includes other variables except the geothermal pipe layout position in the first independent variable, as well as meteorological data at a time scale within a year, and soil temperature and moisture content corresponding to the geothermal pipe layout position in the first independent variable under the meteorological data; using the geothermal energy extraction efficiency prediction model to process each second independent variable, obtain a first processing result corresponding to each second independent variable, and sum and average each first processing result to obtain a first function; taking the generator set power generation mode as the third independent variable, using the power generation prediction model, respectively processing each first processing result and the third independent variable, obtain a second processing result corresponding to each first processing result, and sum and average each second processing result to obtain a second function; based on the first function and the second function, determining the objective function.

[0052] For example, other variables besides the geothermal pipe arrangement position in the first independent variable may include pipe spacing, pipe size, and fluid parameters in the pipe, such as fluid flow rate and fluid flow velocity.

[0053] Exemplarily, multiple second independent variables may include meteorological data at various time scales within a year, and soil temperature and water content corresponding to the geothermal pipe layout position in the first independent variable under the meteorological data. For example, a second independent variable includes meteorological data at a time scale, and soil temperature and water content corresponding to the geothermal pipe layout position in the first independent variable under the meteorological data. Different second independent variables correspond to different time scales. A first independent variable may correspond to the above-mentioned multiple second independent variables, that is, the geothermal pipe layout positions corresponding to these multiple independent variables are the same.

[0054] Exemplarily, the geothermal energy extraction efficiency prediction model can be converted into a corresponding formula, each second independent variable is input into the formula, and then the output results of the formula are summed and averaged to obtain the first function.

[0055] For example, the power generation prediction model can be converted into a corresponding formula, and for each first processing result, the first processing result and the third independent variable are respectively input into the formula to obtain multiple second processing results. Then, the multiple second processing results are summed and averaged to obtain the second function.

[0056] Exemplarily, the first function and the second function are combined into a piecewise function as the objective function. Alternatively, the first function and the second function are summed to obtain the objective function.

[0057] According to the above implementation, based on the geothermal energy extraction efficiency prediction model and the power generation prediction model, the objective function can be constructed with the geothermal pipe layout and the power generation method of the generator set as independent variables, and the geothermal energy extraction efficiency and power generation as dependent variables.

[0058] In one embodiment, the maximum value of the objective function is solved to obtain the target geothermal pipe layout and the target power generation mode of the generator set in the seasonal frozen soil area, including: solving the maximum value of the first function in the objective function to obtain the target geothermal pipe layout in the seasonal frozen soil area; based on the value of the maximum value of the first function, solving the maximum value of the second function in the objective function to obtain the target power generation mode of the generator set in the seasonal frozen soil area.

[0059] Exemplarily, a particle swarm algorithm or a genetic algorithm may be used to solve the maximum value of the first function in the objective function to obtain a target geothermal pipe arrangement in a seasonally frozen soil area.

[0060] Exemplarily, after solving for the target geothermal pipe arrangement in the seasonal frozen soil zone, the maximum value of the second function in the objective function can be combined with the maximum value of the first function to obtain the target power generation mode of the generator set in the seasonal frozen soil zone.

[0061] Exemplarily, the value of the maximum value of the first function is input into the objective function, and then a particle swarm algorithm or a genetic algorithm can be used to solve the maximum value of the input objective function to obtain the power generation mode of the target generator set in the seasonal frozen soil area.

[0062] In one embodiment, the maximum value of the first function in the objective function is solved to obtain the target geothermal pipe layout in the seasonal frozen soil area, including: determining the initial first population based on the first independent variable and each second independent variable, wherein each individual in the initial first population corresponds to a first independent variable and includes each second independent variable corresponding to the first independent variable; starting from the initial first population, the following population iteration operation is performed, including: based on the first function, calculating each individual in the first population of the current iteration respectively to obtain the first fitness of each individual in the first population of the current iteration; when the maximum fitness among the first fitness of each individual in the first population of the current iteration does not meet the first fitness condition, cross-mutating the first independent variable corresponding to each individual in the first population of the current iteration to obtain the first population of the next iteration. When the maximum fitness among the first fitness of each individual in the first population of the current iteration meets the first fitness condition, the above iteration operation is stopped, and the target geothermal pipe layout in the seasonal frozen soil area is determined based on the first independent variable corresponding to the individual corresponding to the maximum fitness.

[0063] It can be understood that each time the first population is subjected to crossover mutation, the first independent variable is mutated, and at the same time, multiple second independent variables corresponding to the mutated first independent variable may also be mutated, thereby obtaining the mutated first population.

[0064] It can be understood that when the first population of the next iteration is obtained, the above population iteration operation is returned to continue to be performed until the maximum fitness among the first fitness of each individual in the first population of the current iteration meets the first fitness condition, and then the above iteration operation is stopped.

[0065] Exemplarily, the first fitness condition may be that the maximum fitness among the fitness of each individual in the first population is greater than a preset fitness threshold.

[0066] Exemplarily, if the number of iterations exceeds a preset threshold, the above-mentioned iterative operation may be stopped, and the target geothermal pipe layout in the seasonal frozen ground area may be determined based on the first independent variable corresponding to the individual corresponding to the maximum fitness.

[0067] For example, the population size is set to 200 and the number of generations is 1000. The initial value of the crossover probability is 0.8 and the initial value of the mutation probability is 0.1. Dynamic adjustment is made according to the population diversity index δ. For example, when δ<0.6, the crossover probability increases by 0.05 and the mutation probability increases by 0.02; when δ>0.8, the crossover probability decreases by 0.05 and the mutation probability decreases by 0.02.

[0068] According to the above implementation, the maximum value of the first function in the objective function can be solved to obtain the target geothermal pipe arrangement mode in the seasonal frozen soil area.

[0069] In one embodiment, based on the value of the maximum value of the first function, the maximum value of the second function in the objective function is solved to obtain the target power generation mode of the generator set in the seasonal frozen soil area, including: based on the value of the maximum value of the first function and each third independent variable, the initial second population is determined, wherein each individual in the initial second population corresponds to a third independent variable and the value of the maximum value of the first function; starting from the initial second population, the following population iteration operation is performed, including: based on the second function, each individual in the second population of the current iteration is calculated to obtain the second fitness of each individual in the second population of the current iteration; when the maximum fitness of the second fitness of each individual in the second population of the current iteration does not meet the second fitness condition, the third independent variable corresponding to each individual in the second population of the current iteration is cross-mutated to obtain the second population of the next iteration. When the maximum fitness of the second fitness of each individual in the second population of the current iteration meets the second fitness condition, the above iteration operation is stopped, and the target power generation mode of the generator set in the seasonal frozen soil area is determined based on the third independent variable corresponding to the individual corresponding to the maximum fitness.

[0070] It can be understood that each time the second population is subjected to cross-mutation, the third independent variable corresponding to each individual is mutated, and at the same time, the value of the maximum value of the first function corresponding to each individual is kept unchanged.

[0071] It can be understood that when the second population of the next iteration is obtained, the above population iteration operation is returned to continue to be performed until the maximum fitness among the second fitness of each individual in the second population of the current iteration meets the second fitness condition, and then the above iteration operation is stopped.

[0072] Exemplarily, the second fitness condition may be that the maximum fitness among the second fitness of each individual in the second population is greater than a preset fitness threshold.

[0073] Exemplarily, if the number of iterations exceeds a preset threshold, the above-mentioned iterative operation may be stopped, and the power generation mode of the target generator set in the seasonal frozen soil area may be determined based on the third independent variable corresponding to the individual corresponding to the maximum fitness.

[0074] For example, the population size is set to 100 and the number of generations is 500. The initial value of the crossover probability is 0.8 and the initial value of the mutation probability is 0.1. Dynamic adjustment is made according to the population diversity index δ. For example, when δ<0.6, the crossover probability increases by 0.04 and the mutation probability increases by 0.01; when δ>0.8, the crossover probability decreases by 0.03 and the mutation probability decreases by 0.02.

[0075] According to the above implementation, the maximum value of the second function in the objective function can be solved based on the value of the maximum value of the first function to obtain the target power generation mode of the generator set in the seasonal frozen soil area.

[0076] Figure 2 It is a structural block diagram of a renewable energy power generation dispatching system based on multiple factors and different time scales according to an embodiment of the present disclosure.

[0077] like Figure 2 As shown, the system comprises:

[0078] A first model building module 210 is used to build a geothermal energy extraction efficiency prediction model for a seasonal frozen soil region based on a first training sample set in the seasonal frozen soil region, wherein each training sample in the first training sample set includes meteorological data at a specified time scale, soil temperature and water content corresponding to a geothermal pipe arrangement position under the meteorological data, and a corresponding geothermal energy extraction efficiency;

[0079] A second model building module 220 is used to build a power generation prediction model for the seasonal frozen soil area based on a second training sample set for the seasonal frozen soil area, wherein each training sample in the second training sample set includes a geothermal energy extraction efficiency, a power generation mode of a generator set, and a corresponding power generation efficiency;

[0080] The objective function construction module 230 is used to construct an objective function based on the geothermal energy extraction efficiency prediction model and the power generation prediction model, taking the geothermal pipe arrangement mode and the power generation mode of the generator set as independent variables, and taking the geothermal energy extraction efficiency and the power generation power as dependent variables, wherein the geothermal pipe arrangement mode includes the geothermal pipe arrangement position;

[0081] The objective function solving module 240 is used to solve the maximum value of the objective function to obtain the target geothermal pipe layout and the target generator set power generation mode in the seasonal frozen soil area.

[0082] In one embodiment, the independent variables of the objective function also include meteorological data at various time scales within a year, and soil temperature and moisture content corresponding to the geothermal pipe arrangement location under the meteorological data.

[0083] In one implementation, the objective function construction module 230 includes:

[0084] An independent variable determination unit is used to determine a plurality of second independent variables with the geothermal pipe arrangement as a first independent variable, wherein the second independent variables include other variables except the geothermal pipe arrangement position in the first independent variable, as well as meteorological data at a time scale within a year, and soil temperature and water content corresponding to the geothermal pipe arrangement position in the first independent variable under the meteorological data;

[0085] a first function determination unit, configured to process each of the second independent variables using the geothermal energy extraction efficiency prediction model to obtain a first processing result corresponding to each of the second independent variables, and to sum and average each of the first processing results to obtain a first function;

[0086] a second function determination unit, configured to use the power generation mode of the generator set as a third independent variable, adopt the power generation prediction model, process each of the first processing results and the third independent variable respectively, obtain a second processing result corresponding to each of the first processing results, and sum and average each of the second processing results to obtain a second function;

[0087] The third function determination unit is configured to determine the target function based on the first function and the second function.

[0088] In one implementation, the objective function solving module 240 includes:

[0089] A first solving unit is used to solve the maximum value of the first function in the objective function to obtain the target geothermal pipe arrangement mode in the seasonal frozen soil area;

[0090] The second solving unit is used to solve the maximum value of the second function in the objective function based on the value of the maximum value of the first function, so as to obtain the target power generation mode of the generator set in the seasonal frozen soil area.

[0091] In one implementation, the first solving unit is specifically configured to:

[0092] Based on the first independent variable and each of the second independent variables, determining an initial first population, wherein each individual in the initial first population corresponds to one first independent variable and includes each of the second independent variables corresponding to the first independent variable;

[0093] The following population iteration operations are performed starting from the initial first population, including:

[0094] Based on the first function, calculating each individual in the first population of the current iteration respectively to obtain a first fitness of each individual in the first population of the current iteration;

[0095] When the maximum fitness among the first fitness of each individual in the first population of the current iteration does not satisfy the first fitness condition, crossover mutation is performed on the first independent variable corresponding to each individual in the first population of the current iteration to obtain the first population of the next iteration;

[0096] When the maximum fitness among the first fitness of each individual in the first population of the current iteration satisfies the first fitness condition, the target geothermal pipe arrangement mode in the seasonal frozen ground area is determined based on the first independent variable corresponding to the individual corresponding to the maximum fitness.

[0097] In one implementation, the second solving unit is specifically configured to:

[0098] Based on the value of the maximum value of the first function and each of the third independent variables, an initial second population is determined, wherein each individual in the initial second population corresponds to a value of the maximum value of the third independent variable and the first function;

[0099] The following population iteration operations are performed starting from the initial second population, including:

[0100] Based on the second function, calculating each individual in the second population of the current iteration respectively to obtain a second fitness of each individual in the second population of the current iteration;

[0101] When the maximum fitness among the second fitness of each individual in the second population of the current iteration does not satisfy the second fitness condition, crossover mutation is performed on the third independent variable corresponding to each individual in the second population of the current iteration to obtain the second population of the next iteration;

[0102] When the maximum fitness among the second fitness of each individual in the second population of the current iteration satisfies the second fitness condition, the power generation mode of the target generator set in the seasonal frozen soil area is determined based on the third independent variable corresponding to the individual corresponding to the maximum fitness.

[0103] For the description of specific functions and examples of each module and sub-module of the system in the embodiment of the present disclosure, please refer to the relevant description of the corresponding steps in the above method embodiment, which will not be repeated here.

[0104] According to an embodiment of the present disclosure, the above method of the present disclosure may be applied to an electronic device and a readable storage medium.

[0105] Figure 3A schematic block diagram of an example electronic device 600 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0106] like Figure 3 As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0107] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0108] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above, such as a new energy generation scheduling method based on different time scales of multiple factors. For example, in some embodiments, a new energy generation scheduling method based on different time scales of multiple factors may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of a new energy generation scheduling method based on different time scales of multiple factors described above may be executed. Alternatively, in other embodiments, the computing unit 601 may be configured in any other appropriate manner (eg, by means of firmware) to execute a new energy power generation scheduling method based on multiple factors and at different time scales.

[0109] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0110] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0111] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0112] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0113] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0114] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0115] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0116] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A new energy generation scheduling method based on multiple factors and different time scales, characterized in that: include: Based on a first training sample set in a seasonal frozen soil area, a geothermal energy extraction efficiency prediction model is constructed in the seasonal frozen soil area, wherein each training sample in the first training sample set includes meteorological data at a specified time scale, soil temperature and water content corresponding to the geothermal pipe arrangement position under the meteorological data, and the corresponding geothermal energy extraction efficiency; Based on the second training sample set of the seasonal frozen soil area, constructing a power generation prediction model for the seasonal frozen soil area, wherein each training sample in the second training sample set includes geothermal energy extraction efficiency, power generation mode of a generator set, and corresponding power generation efficiency; Taking the geothermal pipe arrangement as the first independent variable, a plurality of second independent variables are determined, wherein the second independent variables include the pipe spacing, pipe size and fluid parameters in the pipe of the geothermal pipe, as well as meteorological data at a time scale within a year, and soil temperature and water content corresponding to the geothermal pipe arrangement position in the first independent variable under the meteorological data, and the geothermal pipe arrangement includes the geothermal pipe arrangement position; Using the geothermal energy extraction efficiency prediction model, processing each of the second independent variables to obtain a first processing result corresponding to each of the second independent variables, and summing and averaging each of the first processing results to obtain a first function; Taking the power generation mode of the generator set as the third independent variable, using the power generation prediction model, processing each of the first processing results and the third independent variable respectively, obtaining the second processing results corresponding to each of the first processing results, and summing and averaging each of the second processing results to obtain a second function; Determine an objective function based on the first function and the second function; Solving the objective function for a maximum value to obtain a target geothermal pipe arrangement mode and a target generator set power generation mode in the seasonally frozen soil zone; The time scale is season, month or week, the meteorological data includes weather temperature and precipitation, and the power generation mode of the generator set includes power generation time and the number of generator sets performing power generation operations.

2. The method according to claim 1, characterized in that The step of solving the objective function for the maximum value to obtain the target geothermal pipe arrangement mode and the target generator set power generation mode in the seasonally frozen soil area includes: Solving the maximum value of the first function in the objective function to obtain the target geothermal pipe arrangement mode in the seasonally frozen soil area; Based on the value of the maximum value of the first function, the maximum value of the second function in the objective function is solved to obtain the target power generation mode of the generator set in the seasonal frozen soil area.

3. The method according to claim 2, characterized in that Solving the maximum value of the first function in the objective function to obtain the target geothermal pipe arrangement mode in the seasonally frozen soil area includes: Based on the first independent variable and each of the second independent variables, determining an initial first population, wherein each individual in the initial first population corresponds to one first independent variable and includes each of the second independent variables corresponding to the first independent variable; The following population iteration operations are performed starting from the initial first population, including: Based on the first function, calculating each individual in the first population of the current iteration respectively to obtain a first fitness of each individual in the first population of the current iteration; When the maximum fitness among the first fitness of each individual in the first population of the current iteration does not satisfy the first fitness condition, crossover mutation is performed on the first independent variable corresponding to each individual in the first population of the current iteration to obtain the first population of the next iteration; When the maximum fitness among the first fitness of each individual in the first population of the current iteration satisfies the first fitness condition, the target geothermal pipe arrangement mode in the seasonal frozen ground area is determined based on the first independent variable corresponding to the individual corresponding to the maximum fitness.

4. The method according to claim 2, characterized in that: The maximum value of the second function in the objective function is solved based on the value of the maximum value of the first function to obtain the target power generation mode of the generator set in the seasonal frozen soil area, including: Based on the value of the maximum value of the first function and each of the third independent variables, an initial second population is determined, wherein each individual in the initial second population corresponds to a value of the maximum value of the third independent variable and the first function; The following population iteration operations are performed starting from the initial second population, including: Based on the second function, calculating each individual in the second population of the current iteration respectively to obtain a second fitness of each individual in the second population of the current iteration; When the maximum fitness among the second fitness of each individual in the second population of the current iteration does not satisfy the second fitness condition, crossover mutation is performed on the third independent variable corresponding to each individual in the second population of the current iteration to obtain the second population of the next iteration; When the maximum fitness among the second fitness of each individual in the second population of the current iteration satisfies the second fitness condition, the power generation mode of the target generator set in the seasonal frozen soil area is determined based on the third independent variable corresponding to the individual corresponding to the maximum fitness.

5. A new energy generation dispatching system based on multiple factors and different time scales, characterized in that: include: A first model building module is used to build a geothermal energy extraction efficiency prediction model for the seasonal frozen soil area based on a first training sample set in the seasonal frozen soil area, wherein each training sample in the first training sample set includes meteorological data at a specified time scale, soil temperature and water content corresponding to the geothermal pipe arrangement position under the meteorological data, and the corresponding geothermal energy extraction efficiency; A second model building module is used to build a power generation prediction model for the seasonal frozen soil area based on a second training sample set in the seasonal frozen soil area, wherein each training sample in the second training sample set includes a geothermal energy extraction efficiency, a power generation mode of a generator set, and a corresponding power generation efficiency; An objective function construction module is used to construct an objective function based on the geothermal energy extraction efficiency prediction model and the power generation prediction model, taking the geothermal pipe arrangement mode and the power generation mode of the generator set as independent variables, and taking the geothermal energy extraction efficiency and the power generation power as dependent variables, wherein the geothermal pipe arrangement mode includes the geothermal pipe arrangement position; An objective function solving module, used for solving the maximum value of the objective function to obtain a target geothermal pipe arrangement mode and a target generator set power generation mode in the seasonal frozen soil area; Wherein, the objective function construction module includes: An independent variable determination unit is used to determine a plurality of second independent variables with the geothermal pipe arrangement as a first independent variable, wherein the second independent variables include the pipe spacing, pipe size and fluid parameters in the pipe of the geothermal pipe, as well as meteorological data at a time scale within a year, and soil temperature and water content corresponding to the geothermal pipe arrangement position in the first independent variable under the meteorological data; a first function determination unit, configured to process each of the second independent variables using the geothermal energy extraction efficiency prediction model to obtain a first processing result corresponding to each of the second independent variables, and to sum and average each of the first processing results to obtain a first function; a second function determination unit, configured to use the power generation mode of the generator set as a third independent variable, adopt the power generation prediction model, process each of the first processing results and the third independent variable respectively, obtain a second processing result corresponding to each of the first processing results, and sum and average each of the second processing results to obtain a second function; A third function determination unit, configured to determine the target function based on the first function and the second function; The time scale is season, month or week, the meteorological data includes weather temperature and precipitation, and the power generation mode of the generator set includes power generation time and the number of generator sets performing power generation operations.

6. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method of any one of claims 1-4.

7. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are for causing a computer to execute a method according to any one of claims 1-4.

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