Resource scheduling method, device and equipment and sodium electricity energy storage system
By using ant colony optimization algorithm and prediction model in sodium-electric energy storage systems, the capacity output of energy modules is optimized, and the problem of how to rationally utilize renewable resources is solved, and efficient and low-carbon energy utilization is achieved.
Patent Information
- Application Number
- CN202411659128.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-19
- Publication Date
- 2025-03-07
AI Technical Summary
How to rationally utilize renewable resources, improve energy utilization, and reduce carbon emissions.
By applying ant colony optimization algorithm and pre-trained prediction model in the sodium-electric energy storage system, combining real-time energy data and weather forecast data, the production capacity values of each energy module are predicted, and the optimal capacity values are performed to determine the capacity value to schedule the production capacity output of the energy module.
It has achieved stability and low carbon emissions in energy module production capacity, maximized the use of renewable energy, reduced power generation costs, and improved energy utilization.
Smart Images
Figure CN119578805A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of energy technology, and in particular to a resource scheduling method, device, equipment and a sodium electricity energy storage system. Background Art
[0002] Electricity is essential to modern life, so power supply equipment needs to generate electricity to ensure the stable operation of society. Traditional power supply equipment usually uses fossil fuels to generate electricity, but in order to reduce greenhouse gas emissions and avoid environmental pollution, there are currently many ways to use green energy to generate electricity, such as hydropower, solar power, wind power, etc. These power generation methods can be based on renewable resources such as solar energy, wind energy, and light energy, but how to reasonably use these renewable resources is a problem that needs to be solved urgently. Summary of the invention
[0003] The present application provides a resource scheduling method, device, equipment and sodium electricity energy storage system for improving resource utilization.
[0004] In a first aspect, an embodiment of the present application provides a resource scheduling method, which is applied to a sodium-electric energy storage system, wherein the sodium-electric energy storage system includes a first energy module, and the first energy module includes a light energy module, a wind energy module and a tidal energy module; the method includes: predicting the production capacity value of each energy module in the first energy module in the first time period according to the real-time light energy data of the light energy module, the real-time wind energy data of the wind energy module and the real-time tidal energy data of the tidal energy module, as well as the weather forecast data of the area where the sodium-electric energy storage system is located in the first time period, combined with a pre-trained prediction model; the weather forecast data includes the illumination data, wind volume data and tidal data in the first time period; using a preset ant colony optimization algorithm and the stability evaluation value and carbon emissions of each energy module in the first energy module, constraining and optimizing the predicted production capacity value of each energy module in the first energy module in the first time period, and determining the optimal production capacity value of the first energy module; and scheduling the production capacity value of each energy module in the sodium-electric energy storage system according to the optimal production capacity value.
[0005] In an embodiment of the present application, the capacity value of the first energy module in the first time period is predicted by combining the weather forecast data and the real-time data of the first energy module through the prediction model, and further constrained optimization based on the ant colony optimization algorithm can determine the optimal capacity value of the first energy module with high capacity stability and low carbon emissions in the first time period. Based on the optimal capacity value, the capacity output of the first energy module in the first time period is adjusted, so that each energy module in the first energy module always maintains stable power generation and ensures that the carbon emissions generated by power generation are minimized, thereby achieving the technical effect of rational use of renewable resources. In addition, it is also possible to determine whether to enable backup power resources based on the optimal capacity value, that is, while ensuring that the power resources for new energy power generation in the first energy module are sufficient, reduce the use of hydrogen energy modules and city power modules for power generation, maximize the use of green and renewable energy for power generation, reduce power generation costs, and improve energy utilization.
[0006] In a possible implementation, a preset ant colony optimization algorithm and the stability evaluation value and carbon emissions of each energy module in the first energy module are used to perform constrained optimization on the predicted production capacity values of each energy module in the first energy module within the first time period to determine the optimal production capacity value of the first energy module, including: based on the predicted production capacity values, stability evaluation values, carbon emissions and preset constraints of each energy module in the first energy module within the first time period, while satisfying the constraints, using the ant colony optimization algorithm to optimize and solve a preset objective function to determine the maximum value of the objective function, wherein the maximum value satisfies the maximum weighted sum of the production capacity values, stability evaluation values and carbon emissions of each energy module in the first energy module within the first time period; and determining the maximum value as the optimal production capacity value.
[0007] In a possible implementation, the objective function is: max =ω1*Z cost +ω2*Z stability +ω3*Z carbon ; Among them, Z max represents the optimal capacity value, ω1 represents the weight coefficient of the capacity of the first energy module, ω2 represents the weight coefficient of the stability of the capacity of the first energy module, ω3 represents the weight coefficient of the carbon emission of the first energy module, and Z cost represents the capacity value of the first energy module, Z stability represents the stability evaluation value of the first energy module, Z carbon Represents the carbon emissions of the first energy module.
[0008] In one possible implementation, predicting the production capacity value of each energy module in the first energy module within the first time period includes: inputting the maximum power point of the light energy module within the first time period, the illumination data and the real-time light energy data into the prediction model, so that the prediction model predicts the production capacity value output by the light energy module under the maximum output power according to the maximum power point, and obtains the first predicted value of the light energy module within the first time period, wherein the maximum power point is the power point at which the photoelectric conversion efficiency of the light energy module within the first time period is the highest.
[0009] In a possible implementation, the sodium-electric energy storage system further includes a second energy module, which includes a hydrogen energy module and a mains module; according to the optimal capacity value, the capacity value of each energy module in the sodium-electric energy storage system is scheduled, including: if the first resource amount of the electric resources generated by the first energy module does not exceed the second resource amount, then based on a preset strategy, the second energy module is controlled to generate backup electric resources, the second resource amount is the electric resource amount required for the load connected to the sodium-electric energy storage system, and the preset strategy is used to indicate the use of the hydrogen energy module and / or the mains module to generate backup electric resources; if the first resource amount of the electric resources generated by the first energy module exceeds the second resource amount, then the third resource amount of the electric resources is stored in the sodium-electric energy storage module of the sodium-electric energy storage system, and the third resource amount is the resource amount in the first resource amount except the second resource amount.
[0010] In a second aspect, an embodiment of the present application provides a sodium-electric energy storage system, the sodium-electric energy storage system comprising a first energy module and a control module, the first energy module comprising a light energy module, a wind energy module and a tidal energy module; wherein the control module is used to predict the production capacity value of each energy module in the first energy module within the first time period based on the real-time light energy data of the light energy module, the real-time wind energy data of the wind energy module and the real-time tidal energy data of the tidal energy module, as well as the weather forecast data of the area where the sodium-electric energy storage system is located within the first time period, combined with a pre-trained prediction model; the weather forecast data comprises the illumination data, wind volume data and tidal data within the first time period; the control module is also used to use a preset ant colony optimization algorithm and the stability evaluation value and carbon emissions of each energy module in the first energy module to perform constrained optimization on the predicted production capacity value of each energy module in the first energy module within the first time period, and determine the optimal production capacity value of the first energy module; the control module is also used to dispatch the production capacity value of each energy module in the sodium-electric energy storage system according to the optimal production capacity value.
[0011] In one possible implementation, the control module is specifically used to: based on the production capacity value, stability evaluation value, carbon emissions and preset constraints of each energy module in the first energy module within the first time period, while satisfying the constraints, use the ant colony optimization algorithm to optimize and solve the preset objective function to determine the maximum value of the objective function, wherein the maximum value satisfies the maximum weighted sum of the production capacity value, stability evaluation value and carbon emissions of each energy module in the first energy module within the first time period; and determine the maximum value as the optimal production capacity value.
[0012] In a possible implementation, the objective function is: max =ω1*Z cost +ω2*Z stability +ω3*Z carbon ; Among them, Z max represents the optimal capacity value, ω1 represents the weight coefficient of the capacity of the first energy module, ω2 represents the weight coefficient of the stability of the capacity of the first energy module, ω3 represents the weight coefficient of the carbon emission of the first energy module, and Z cost represents the capacity value of the first energy module, Z stability represents the stability evaluation value of the first energy module, Z carbon Represents the carbon emissions of the first energy module.
[0013] In one possible implementation, the control module is specifically used to: input the maximum power point, illumination data and real-time light energy data of the light energy module within the first time period into the prediction model, so that the prediction model predicts the output capacity value of the light energy module under the maximum output power according to the maximum power point, and obtains the first prediction value of the light energy module within the first time period, and the maximum power point is the power point at which the photoelectric conversion efficiency of the light energy module within the first time period is the highest.
[0014] In a possible implementation, the sodium-electric energy storage system further includes a second energy module and a sodium-electric energy storage module, the second energy module including a hydrogen energy module and a mains power module; the control module is further used to: if the first resource amount of the electric resources generated by the first energy module does not exceed the second resource amount, then control the second energy module to generate backup electric resources based on a preset strategy, the second resource amount being the electric resource amount required for the load connected to the sodium-electric energy storage system, and the preset strategy being used to indicate the use of the hydrogen energy module and / or the mains power module to generate backup electric resources; if the first resource amount of the electric resources generated by the first energy module exceeds the second resource amount, then store a third resource amount of electric resources in the sodium-electric energy storage system's sodium-electric energy storage module, the third resource amount being the resource amount in the first resource amount excluding the second resource amount.
[0015] In a third aspect, an embodiment of the present application provides a resource scheduling device, which is applied to a sodium-electric energy storage system, wherein the sodium-electric energy storage system includes a first energy module and a control module, wherein the first energy module includes a light energy module, a wind energy module and a tidal energy module; the device includes: a prediction module, which predicts the production capacity value of each energy module in the first energy module in the first time period according to the real-time light energy data of the light energy module, the real-time wind energy data of the wind energy module and the real-time tidal energy data of the tidal energy module, as well as the weather forecast data of the area where the sodium-electric energy storage system is located in the first time period, combined with a pre-trained prediction model; the weather forecast data includes the illumination data, wind volume data and tidal data in the first time period; an optimization module, which is used to use a preset ant colony optimization algorithm and the stability evaluation value and carbon emissions of each energy module in the first energy module to perform constrained optimization on the predicted production capacity value of each energy module in the first energy module in the first time period, and determine the optimal production capacity value of the first energy module; a scheduling module, which is used to schedule the production capacity value of each energy module in the sodium-electric energy storage system according to the optimal production capacity value.
[0016] In one possible implementation, the optimization module is specifically used to: based on the predicted production capacity value, stability evaluation value, carbon emissions of each energy module in the first energy module within the first time period and preset constraints, and while satisfying the constraints, use the ant colony optimization algorithm to optimize and solve the preset objective function to determine the maximum value of the objective function, wherein the constraints are used to constrain the maximum value to satisfy the maximum weighted sum of the production capacity value, stability evaluation value and carbon emissions of each energy module in the first energy module within the first time period; and determine the maximum value as the optimal production capacity value.
[0017] In a possible implementation, the objective function is: max =ω1*Zcost +ω2*Z stability +ω3*Z carbon ; Among them, Z max represents the optimal capacity value, ω1 represents the weight coefficient of the capacity of the first energy module, ω2 represents the weight coefficient of the stability of the capacity of the first energy module, ω3 represents the weight coefficient of the carbon emission of the first energy module, and Z cost represents the capacity value of the first energy module, Z stability represents the stability evaluation value of the first energy module, Z carbon Represents the carbon emissions of the first energy module.
[0018] In one possible implementation, the prediction module is specifically used to: input the maximum power point, illumination data and real-time light energy data of the light energy module within the first time period into the prediction model, so that the prediction model predicts the output capacity value of the light energy module under the maximum output power according to the maximum power point, and obtains the first predicted value of the light energy module within the first time period, and the maximum power point is the power point at which the photoelectric conversion efficiency of the light energy module within the first time period is the highest.
[0019] In a possible implementation, the sodium-electric energy storage system also includes a second energy module, which includes a hydrogen energy module and a mains power module; the scheduling module is specifically used to: if the first resource amount of the electric resources generated by the first energy module does not exceed the second resource amount, then control the second energy module to generate backup electric resources based on a preset strategy, the second resource amount is the electric resource amount required for the load connected to the sodium-electric energy storage system, and the preset strategy is used to indicate the use of the hydrogen energy module and / or the mains power module to generate backup electric resources; if the first resource amount of the electric resources generated by the first energy module exceeds the second resource amount, then store a third resource amount of electric resources in the sodium-electric energy storage module of the sodium-electric energy storage system, and the third resource amount is the resource amount in the first resource amount excluding the second resource amount.
[0020] In a fourth aspect, an embodiment of the present application provides a resource scheduling device, comprising: at least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements the method described in the first aspect and any possible implementation manner by executing the instructions stored in the memory.
[0021] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions. When the computer instructions are executed on a computer, the computer executes the method described in the first aspect and any possible implementation manner.
[0022] In a sixth aspect, an embodiment of the present application provides a computer program product, comprising computer instructions, which, when executed on a computer, enables the method described in the first aspect and any possible implementation manner to be implemented.
[0023] The beneficial effects of the second to sixth aspects can be referred to the contents described in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] Figure 1 A schematic diagram of an application scenario of a resource scheduling method provided in an embodiment of the present application;
[0025] Figure 2 A schematic diagram of the structure of a sodium battery energy storage system provided in an embodiment of the present application;
[0026] Figure 3 A schematic diagram of a resource scheduling method provided in an embodiment of the present application;
[0027] Figure 4 A schematic diagram of the structure of a resource scheduling device provided in an embodiment of the present application;
[0028] Figure 5 A schematic diagram of the structure of a resource scheduling device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0029] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be arbitrarily combined with each other. In addition, although the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in an order different from that here.
[0030] The terms "first" and "second" in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the term "comprising" and any of their variations are intended to cover non-exclusive protection. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices. "Multiple" in the present application can mean at least two, for example, two, three or more, and the embodiments of the present application are not limited.
[0031] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description. It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, which should be considered as exemplary, and their purpose is only to illustrate the feasibility of the implementation of the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.
[0032] The terms involved in the embodiments of the present application are explained below.
[0033] 1. Ant Colony Optimization Algorithm: Ant Colony Optimization (ACO) is a group intelligence optimization algorithm that simulates the foraging behavior of ants in nature. In the process of looking for food, ants will release a pheromone to mark the path. The basic idea of applying the ant colony algorithm to solve the optimization problem is: use the walking path of the ants to represent the feasible solution to the problem to be optimized, and all the paths of the entire ant colony constitute the solution space of the problem to be optimized. Ants with shorter paths release more pheromones. As time goes by, the accumulated pheromone concentration on the shorter path gradually increases, and the number of ants choosing this path also increases. In the end, the entire ant will concentrate on the best path under the action of positive feedback, and the corresponding path is the optimal solution to the problem to be optimized.
[0034] 2. Peak reduction and valley filling: It is a measure to adjust the power load. According to the power consumption patterns of different users, the power consumption time of various users is reasonably and systematically arranged and organized. This can reduce the peak load and fill the valley load. It can reduce the peak-valley difference of the power grid load and make the power generation and power consumption tend to be balanced.
[0035] Please refer to Figure 1, is a schematic diagram of an application scenario of a resource scheduling method provided in an embodiment of the present application. Figure 1 As shown, the scenario diagram includes a sodium electric energy storage system 110, a resource scheduling device 120 and a load 130. Among them, wired or wireless communication can be performed between the resource scheduling device 120 and the sodium electric energy storage system 110. The sodium electric energy storage system 110 is used to provide power resources for the load 130. The resource scheduling device 120 can be a device or template with data processing capabilities and control functions, and can specifically be a functional module in the sodium electric energy storage system 110, or a terminal device or server that is communicatively connected to the sodium electric energy storage system 110. The terminal device includes but is not limited to: personal computers (PC), tablet computers, laptop computers, PDAs, mobile Internet devices (MID), wireless terminal devices in industrial control, wireless terminal devices in smart grids, wireless terminal devices in transportation safety, etc. It should be noted that Figure 1 The loads in the system may be user equipment, industrial equipment, communication equipment, etc., which are not illustrated one by one here. The number of loads connected to the sodium electric energy storage system may be one or more, which is not limited in the embodiments of the present application.
[0036] Combine the following Figure 2 The structural schematic diagram of a sodium battery energy storage system is shown in FIG. Figure 1 The sodium electricity energy storage system 110 shown is described in detail.
[0037] like Figure 2 As shown, the sodium electricity energy storage system 110 includes a first energy module 111 and a control module 112. Among them, the first energy module 111 includes a light energy module 111a, a wind energy module 111b and a tidal energy module 111c. The light energy module 111a is used to collect light energy in the area where the sodium electricity energy storage system 110 is located and convert it into electrical energy, the wind energy module 111b is used to collect wind energy in the area where the sodium electricity energy storage system 110 is located and convert it into electrical energy, and the tidal energy module 111c is used to collect tidal energy in the area where the sodium electricity energy storage system 110 is located and convert it into electrical energy. The control module 112 is used to predict the production capacity of each energy module in the first energy module 111 during the first time period based on the real-time light energy data of the light energy module 111a, the real-time wind energy data of the wind energy module 111b and the real-time tidal energy data of the tidal energy module 111c, as well as the weather forecast data of the area where the sodium-electric energy storage system 110 is located during the first time period, and schedule the production capacity of each energy module in the sodium-electric energy storage system 110 based on the predicted production capacity.
[0038] In one possible implementation, Figure 2 The control module 112 shown can implement Figure 1The functions of the resource scheduling device 120 are shown.
[0039] In a possible implementation, the control module 112 can also adjust the orientation of the photovoltaic panel in the light energy module 111a at preset intervals according to the preset sun movement trajectory, so that the photovoltaic panel always faces the sun to collect light energy. The preset duration can be set according to actual needs, for example, 0.5 hours, and this embodiment of the application is not limited to this.
[0040] In a possible implementation, the sodium electricity energy storage system 110 also includes a second energy module 113 and a sodium electricity energy storage module 114. The second energy module 113 includes a hydrogen energy module 113a and a mains module 113b, wherein the hydrogen energy module 113a is used to convert hydrogen energy into electrical energy, for example, methanol reforming to produce hydrogen for power generation. The mains module 113b is a power generation module for ensuring basic power supply needs, and its power generation method is, for example, thermal power generation, hydropower generation, etc. The sodium electricity energy storage module 114 refers to a module based on sodium ion battery energy storage. The sodium electricity energy storage module 114 has the characteristics of high energy density, and the sodium element reserves are rich, and the charge and discharge rate is large. The current sodium ion battery has a charge and discharge rate of 3C and above, which can achieve fast charging and discharging, and can meet the implementation of strategies such as peak shaving and valley filling, two charging and two discharging. The sodium electricity energy storage module 114 can store the power resources generated by each energy module in the first energy module 111 and the second energy module 113, and supply power to the load connected to the sodium electricity energy storage system 110.
[0041] In a possible implementation, the sodium electricity energy storage system 110 also includes a monitoring module 115, which is used to remotely monitor the power generation of each energy module in the sodium electricity energy storage module 110, and to issue control instructions to instruct the control module 120 to control the opening or closing of each energy module in the sodium electricity energy storage system when implementing strategies such as peak shaving and valley filling or other special circumstances, so as to ensure the normal operation of the sodium electricity energy storage system 110.
[0042] In a possible implementation, the sodium-electric energy storage system 110 further includes an inverter 116, which is used to convert direct current into alternating current, so as to realize the AC power supply of the sodium-electric energy storage module 114 to the external load, so that the sodium-electric energy storage system has the capability of AC power supply and DC power supply. Figure 2 The load 1 shown is a load with an AC power supply requirement, and the load 2 is a load with a DC power supply requirement.
[0043] Please refer to Figure 3 , is a flow chart of a resource scheduling method provided in an embodiment of the present application. Figure 3 The steps shown are for illustrative purposes only, but in actual applications, they can also be performed by other devices or apparatuses. Figure 3The resource scheduling device described below is, for example, Figure 1 The resource scheduling device 120 shown in the figure, or the resource scheduling device described below is, for example, Figure 2 The control module 112 shown. The sodium electricity energy storage system described below is, for example, Figure 1 or Figure 2 The sodium electricity energy storage system 110 shown in the figure, the first energy module described below is, for example, Figure 2 The first energy module 111 shown and the second energy module described below are, for example, Figure 2 The second energy module 113 shown and the sodium electricity storage module described below are, for example, Figure 2 The sodium electricity storage module 114 shown in the figure, the light energy module described below is, for example, Figure 2 The light energy module 111a shown in the figure, and the wind energy module described below are for example Figure 2 The wind energy module 111b shown in the figure and the tidal energy module described below are for example Figure 2 The tidal energy module 111c shown, the hydrogen energy module described below is, for example, Figure 2 The hydrogen energy module 113a shown in the figure, the mains power module described below is, for example, Figure 2 The mains module 113b is shown.
[0044] S301, predicting the capacity value of each energy module in the first energy module in the first time period according to the real-time light energy data of the light energy module, the real-time wind energy data of the wind energy module and the real-time tidal energy data of the tidal energy module, and the weather forecast data of the area where the sodium electric energy storage system is located in the first time period, combined with the pre-trained forecasting model; the weather forecast data includes the light data, wind volume data and tidal data in the first time period;
[0045] Among them, the real-time light energy data includes the solar irradiance, photovoltaic panel area and photovoltaic panel conversion efficiency collected by the light energy module at the current moment; the real-time wind energy data includes the air density, wind rotor swept area, wind speed and power coefficient collected by the wind energy module at the current moment; the real-time tidal energy data includes the real-time power generation of the tidal energy module.
[0046] The prediction model is used to predict the production capacity of the light energy module, the wind energy module and the tidal energy module in the future time period. The prediction model is, for example, a neural network model. In an embodiment of the present application, the pre-trained prediction model is obtained by training the historical data and historical weather data of the light energy module, the wind energy module and the tidal energy module. Among them, the historical data can correspond to the content of the real-time data described above, and the training method of the prediction model is similar to the training method of other models in the field, which will not be repeated here.
[0047] Specifically, the resource scheduling device can input the real-time light energy data, real-time wind energy data, real-time tidal energy data and weather forecast data into the pre-trained prediction model, and predict the capacity value of each energy module in the first energy module under the weather conditions in the first time period by combining the weather forecast data and the real-time data of each energy module in the current first energy module through the prediction model. It should be understood that the capacity value of each energy module in the first energy module output by the prediction model in the first time period may refer to the predicted maximum capacity value that each energy module can output in the first time period. The first time period described in the embodiment of the present application is a future time period.
[0048] In one possible implementation, when the resource scheduling device predicts the production capacity of the light energy module in the first time period, the maximum power point, illumination data and real-time light energy data of the light energy module in the first time period can be input into the prediction model, so that the prediction model predicts the production capacity output by the light energy module under maximum output power based on the maximum power point, and obtains the first predicted value of the light energy module in the first time period.
[0049] Among them, the maximum power point is the power point at which the photoelectric conversion efficiency of the light energy module is the highest in the first time period. The maximum power point can be predicted based on the real-time data of the light energy module, the illumination data in the first time period, and the pre-trained maximum power point prediction model. The pre-trained maximum power point prediction model is obtained by combining the maximum power point tracking (MPPT) algorithm, and the maximum power point prediction model is, for example, a neural network model. The pre-trained maximum power point prediction model can be trained based on the historical light energy data, historical maximum power point and historical weather data of the light energy module, and its training method can correspond to the training process of the prediction model described above, which will not be repeated here.
[0050] In a possible implementation, the weather forecast data is determined by the resource scheduling device based on weather forecast information or geographic information of the area where the sodium-electric energy storage system is located, wherein the weather forecast information includes information such as temperature, sunshine duration, wind force, wind direction, etc., and the geographic information includes location information and terrain information of the sodium-electric energy storage system. For example, the resource scheduling device can predict the sunshine data and wind volume data in the first time period based on the weather forecast model and weather forecast information. The resource scheduling device can predict the tidal time and tidal duration of the area where the sodium-electric energy storage system is located based on geographic information and historical tidal data.
[0051] Among them, the weather prediction model is, for example, a neural network model, and the training method of the weather prediction model can correspond to the training process of the prediction model described above, which will not be repeated here.
[0052] It should be noted that in actual applications, in order to simplify the processing flow, the weather prediction model and the tidal prediction function can be integrated into the prediction model, and then the weather forecast data and geographic information, and the real-time data of each energy module in the first energy module are input into the integrated prediction model to obtain the predicted production capacity value of each energy module in the first energy module in the first time period.
[0053] S302, using a preset ant colony optimization algorithm and the stability evaluation value and carbon emissions of each energy module in the first energy module, constrained optimization is performed on the predicted production capacity values of each energy module in the first energy module within the first time period to determine the optimal production capacity value of the first energy module.
[0054] Among them, the stability evaluation value is used to evaluate the stability of the output (capacity) of each energy module in the first energy module. The stability evaluation value is related to factors such as the number of power generation components included in each energy module and the power generation rate. The stability evaluation value can be predefined by the user in the resource scheduling device, or can be determined based on the corresponding relationship between the number of preset power generation components, the power generation rate and the stability evaluation value, which is not limited in the embodiments of the present application.
[0055] Carbon emissions are related to the capacity value and capacity mode of each energy module. For example, in the case of uniform solar power generation, the higher the capacity value of the solar module, the higher the carbon emissions, that is, there is a negative linear relationship between carbon emissions and capacity value. Under different capacity modes, the carbon emissions of solar energy are higher than those of tidal energy.
[0056] Specifically, the resource scheduling device can optimize and solve the preset objective function using the ant colony optimization algorithm based on the predicted capacity value, stability evaluation value, carbon emissions and preset constraints of each energy module in the first energy module in the first time period, while satisfying the constraints, to determine the maximum value of the objective function, and determine the maximum value as the optimal capacity value of the first energy module. The optimal capacity value is composed of the recommended capacity values of each energy module in the first energy module, or in other words, the sum of the recommended capacity values of each energy module in the first energy module is the optimal capacity value.
[0057] The maximum value satisfies the maximum weighted sum of the production capacity value, stability assessment value and carbon emissions of the first energy module in the first time period.
[0058] Exemplarily, the objective function and constraints provided in the embodiments of the present application are as follows:
[0059] Z max =ω1*Z cost +ω2*Z stability +ω3*Z carbon
[0060] Z cost =(∑(C1*i,i)+∑(C2*k,k)+∑(C3*j,j)) 2
[0061] Z stability =∑y(i, k, j)
[0062] Z carbon =-(∑(a1*i,i)+∑(a2*k,k)+∑(a3*j,j)) 2
[0063] i<first quantity;
[0064] k<second number;
[0065] j<third quantity;
[0066] ∑(C1*i, i)≤preset threshold;
[0067] Among them, Z max represents the optimal capacity value, ω1 represents the weight coefficient of the capacity of the first energy module, ω2 represents the weight coefficient of the stability of the capacity of the first energy module, and ω3 represents the weight coefficient of the carbon emissions of the first energy module. ω1, ω2 and ω3 are all predefined in the resource scheduling device. Z cost represents the capacity value of the first energy module, Z stability represents the stability evaluation value of the first energy module, Z carbon Represents the carbon emissions of the first energy module.
[0068] ∑(C1*i, i) represents the capacity value of the solar energy module, C1 represents the capacity value of a solar energy component in the solar energy module, ∑(C2*k, k) represents the capacity value of the wind energy module, C2 represents the capacity value of a wind energy component in the wind energy module, ∑(C3*j, j) represents the capacity value of the tidal energy module, and C3 represents the capacity value of a tidal energy component in the tidal energy module; y represents the stability assessment value; ∑(a1*i, i) represents the carbon emissions of the solar energy module, a1 represents the carbon emissions of a solar energy component in the solar energy module, ∑(a2*k, k) represents the carbon emissions of the wind energy module, a2 represents the carbon emissions of a wind energy component in the wind energy module, ∑(a3*j, j) represents the carbon emissions of the tidal energy module, and a3 represents the carbon emissions of a tidal energy component in the tidal energy module. i represents the number of light energy components included in the light energy module, k represents the number of wind energy components included in the wind energy module, and j represents the number of tidal energy components included in the tidal energy module. The first number, the second number, the third number and the preset threshold value can be set according to actual needs. The first number, the second number and the third number can be the same or different. For example, the first number, the second number and the third number are all 10, and the preset threshold value is 200KWH / day, which is not limited in the embodiment of the present application.
[0069] By way of example, the optimization idea based on the ant colony optimization algorithm is briefly described below.
[0070] (1) Initialize the number of ants m = 350, the maximum number of iterations tr_max = 100, the pheromone volatility factor Rho = 0.9, the transition probability constant P0 = 0.2, and the local search step step = 0.05.
[0071] (2) Randomly generate the initial position of the ant, calculate the fitness function value, set it as the initial pheromone, and calculate the state transition probability. The calculation formula is:
[0072] P(tr,i)=max(T au)-T au(i) / max(T au)(1)
[0073] Among them, max(Tau) represents the maximum value of pheromone, Tau(i) represents the pheromone of ant i, and P(tr,i) represents the transition probability value of ant i in the trth iteration.
[0074] (3) Position update: When the state transition probability is less than the transition probability constant, a local search is performed, and the search formula is new = old + r_1·step{2};
[0075] Where new is the position to be moved, old is the current position of the ant, r_1 is a random number between [-1,1], step is the local search step, and λ is the inverse of the current iteration number. When the state transition probability is greater than the transition probability constant, a global search is performed, and the search formula is new=old+r 2*range(3).
[0076] Among them, r 2 is a random number in [-0.5, 0.5], and range is the interval size of the independent variable.
[0077] Whether to update the current position of the ant is determined by judging the function value of the position to be moved and the function value of the current position, and the boundary condition is processed by using the boundary absorption method to limit the ant position within the value range.
[0078] (4) Calculate the fitness value of the new ant position, determine whether the ant has moved, and update the pheromone. The pheromone update formula is Tau = (1-Rho)*Tau+f(4)
[0079] Among them, Rho is the pheromone volatility factor, Tau is the pheromone, and f is the objective function value.
[0080] (5) Determine whether the termination condition is met: If so, the search process ends and the optimal capacity value is output; if not, the iterative optimization continues.
[0081] S303, adjusting the capacity value of each energy module in the sodium electricity energy storage system according to the optimal capacity value.
[0082] Since the optimal capacity value includes the recommended capacity value of each energy module in the first energy module, the resource scheduling device can adjust each energy module in the first energy module so that each energy module in the first energy module outputs power resources according to its corresponding recommended capacity value within the first time period.
[0083] In a possible implementation, the resource scheduling device may also determine whether the optimal capacity value of the first energy module in the first time period can meet the demand in the first time period, thereby determining whether to enable the backup power resources. Specifically, the resource scheduling device determines whether the first resource amount of the power resources generated by the first energy module in the first time period exceeds the second resource amount, the first resource amount is the power resource amount corresponding to the predicted optimal capacity value, and the second resource amount is the power resource amount required by the load connected to the sodium electric energy storage system. Among them, the second resource amount can be calculated based on the actual power supply in the same time period as the first time period in the historical date, or it can be obtained based on the real-time power supply prediction, and this embodiment of the present application is not limited to this.
[0084] If the resource scheduling device determines that the first resource amount does not exceed the second resource amount, the second energy module is controlled to generate backup power resources based on a preset strategy, and the preset strategy is used to indicate that the backup power resources are generated by using the hydrogen energy module and / or the mains module. If the first resource amount does not exceed the second resource amount, the power resources of the third resource amount are stored in the sodium energy storage module of the sodium energy storage system, and the third resource amount is the resource amount of the first resource amount other than the second resource amount.
[0085] When the first resource amount does not exceed the second resource amount, the resource scheduling device may determine to enable the hydrogen energy module and / or the mains power module according to the difference between the first resource amount and the second resource amount and the historical capacity value of the hydrogen energy module. Exemplarily, if the difference between the first resource amount and the second resource amount is large, and the capacity value of the hydrogen energy module determined according to the historical capacity value of the hydrogen energy module cannot meet the difference, the hydrogen energy module and the mains power module are enabled at the same time for power supply. Otherwise, the hydrogen energy module is enabled first for auxiliary power supply.
[0086] When the first resource amount exceeds the second resource amount, the resource scheduling device can adjust the charging rate of the sodium energy storage module according to the difference between the first resource amount and the second resource amount. Specifically, the resource scheduling device can determine and adjust the charging rate of the sodium energy storage module according to the corresponding relationship between the preset charging rate and the amount of resources to be stored.
[0087] In a possible implementation, when the sodium battery energy storage system supplies power to an external load, the load changes in real time, resulting in certain fluctuations in the output voltage. Therefore, the stability of the output voltage of the sodium battery energy storage module can be controlled by an automatic control algorithm, such as proportional-integral-derivative (PID).
[0088] In an embodiment of the present application, the capacity value of the first energy module in the first time period is predicted by combining the weather forecast data and the real-time data of the first energy module through the prediction model, and further constrained optimization based on the ant colony optimization algorithm can determine the optimal capacity value of the first energy module with high capacity stability and low carbon emissions in the first time period. Based on the optimal capacity value, the capacity output of the first energy module in the first time period is adjusted so that each energy module in the first energy module always maintains stable power generation and ensures that the carbon emissions generated by power generation are minimized. In addition, it is also possible to determine whether to enable backup power resources based on the optimal capacity value, that is, while ensuring that the power resources for new energy power generation in the first energy module are sufficient, reduce the use of hydrogen energy modules and city power modules for power generation, maximize the use of green and renewable energy for power generation, reduce power generation costs, and improve energy utilization.
[0089] Based on the same inventive concept, an embodiment of the present application provides a resource scheduling device, which is used to implement any of the above resource scheduling methods, for example, Figure 3 The resource scheduling method shown, and the device can also realize the functions of the resource scheduling device mentioned above.
[0090] Please refer to Figure 4 , is a schematic diagram of the structure of a resource scheduling device provided in an embodiment of the present application. Figure 4 As shown, the resource scheduling device 400 may include a prediction module 401, an optimization module 402 and a scheduling module 403. The resource scheduling device 400 may be applied to a sodium-electric energy storage system, the sodium-electric energy storage system includes a first energy module and a control module, the first energy module includes a light energy module, a wind energy module and a tidal energy module;
[0091] Exemplarily, the prediction module 401 predicts the production capacity of each energy module in the first energy module within the first time period based on the real-time light energy data of the light energy module, the real-time wind energy data of the wind energy module and the real-time tidal energy data of the tidal energy module, as well as the weather forecast data of the area where the sodium electric energy storage system is located within the first time period, combined with the pre-trained prediction model; the weather forecast data includes the light data, wind volume data and tidal data within the first time period; the optimization module 402 is used to use the preset ant colony optimization algorithm and the stability evaluation value and carbon emissions of each energy module in the first energy module to perform constrained optimization on the predicted production capacity of each energy module in the first time period to determine the optimal production capacity of the first energy module; the scheduling module 403 is used to schedule the production capacity of each energy module in the sodium electric energy storage system according to the optimal production capacity.
[0092] In one possible implementation, the optimization module 402 is specifically used to: based on the production capacity value, stability evaluation value, carbon emissions and preset constraints of each energy module in the first energy module within the first time period, while satisfying the constraints, use an ant colony optimization algorithm to optimize and solve the preset objective function, and determine the maximum value of the objective function, where the maximum value satisfies the maximum weighted sum of the production capacity value, stability evaluation value and carbon emissions of each energy module in the first energy module within the first time period; and determine the maximum value as the optimal production capacity value.
[0093] In a possible implementation, the objective function is: max =ω1*Z cost +ω2*Z stability +ω3*Z carbon Among them, Z max represents the optimal capacity value, ω1 represents the weight coefficient of the capacity of the first energy module, ω2 represents the weight coefficient of the stability of the capacity of the first energy module, ω3 represents the weight coefficient of the carbon emissions of the first energy module, and Zcost Represents the capacity of the first energy module, Z stability represents the stability evaluation value of the first energy module, Z carbon Represents the carbon emissions of the first energy module.
[0094] In one possible implementation, the prediction module 401 is specifically used to: input the maximum power point, illumination data, and real-time light energy data of the light energy module in the first time period into a prediction model, so that the prediction model predicts the output capacity value of the light energy module under the maximum output power according to the maximum power point, and obtains the first prediction value of the light energy module in the first time period, wherein the maximum power point is the power point at which the light energy module has the highest photoelectric conversion efficiency in the first time period.
[0095] In a possible implementation, the sodium-electric energy storage system also includes a second energy module, which includes a hydrogen energy module and a mains module; the scheduling module 403 is specifically used to: if the first resource amount of the electric resources generated by the first energy module does not exceed the second resource amount, then control the second energy module to generate backup electric resources based on a preset strategy, the second resource amount is the electric resource amount required for the load connected to the sodium-electric energy storage system, and the preset strategy is used to indicate the use of the hydrogen energy module and / or the mains module to generate backup electric resources; if the first resource amount of the electric resources generated by the first energy module exceeds the second resource amount, then store the third resource amount of electric resources in the sodium-electric energy storage system's sodium-electric energy storage module, the third resource amount being the resource amount in the first resource amount excluding the second resource amount.
[0096] Based on the same inventive concept, the embodiment of the present application provides a resource scheduling device for implementing any of the above resource scheduling methods, for example, Figure 3 The resource scheduling method shown, and the device can also realize the functions of the resource scheduling device mentioned above.
[0097] Please refer to Figure 5 , is a schematic diagram of the structure of a resource scheduling device provided in an embodiment of the present application. Figure 5 As shown, the resource scheduling device 500 includes at least one processor 501 and a memory 502 communicatively connected to the at least one processor 501 .
[0098] The processor 501 may be a general-purpose processor or a dedicated processor. The processor 501 may include, for example, a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data. The central processing unit may be used to control the resource scheduling device 500, execute software programs and / or process data. Different processors may be independent devices or may be arranged in one or more processing circuits, for example, integrated in one or more dedicated integrated circuits.
[0099] In one embodiment, the memory 502 stores instructions that can be executed by at least one processor 501. The at least one processor 501 implements the functions of the control module as described above by executing the instructions stored in the memory 502, and accordingly, can also implement the steps performed by the resource scheduling device as described above.
[0100] In this embodiment, the resource scheduling device 500 can also implement the functions of the resource scheduling apparatus 400 mentioned above, and at least one processor 501 in the resource scheduling device 500 can also implement the functions of the prediction module 401, the optimization module 402 and the scheduling module 403 mentioned above.
[0101] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on a computer, the computer executes any resource scheduling method as described above, for example, Figure 3 The resource scheduling method shown.
[0102] Based on the same inventive concept, an embodiment of the present application provides a computer program product, comprising computer instructions, which, when executed on a computer, enables any of the resource scheduling methods described above, for example, Figure 3 The resource scheduling method shown is implemented.
[0103] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.
[0104] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0105] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0107] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.
Claims
1. A resource scheduling method, characterized in that: Applied to a sodium-electric energy storage system, the sodium-electric energy storage system includes a first energy module, the first energy module includes a light energy module, a wind energy module and a tidal energy module; the method includes: According to the real-time light energy data of the light energy module, the real-time wind energy data of the wind energy module and the real-time tidal energy data of the tidal energy module, and the weather forecast data of the area where the sodium electricity energy storage system is located in the first time period, combined with the pre-trained prediction model, the production capacity value of each energy module in the first energy module in the first time period is predicted; the weather forecast data includes the light data, wind volume data and tidal data in the first time period; Using a preset ant colony optimization algorithm and the stability evaluation value and carbon emission of each energy module in the first energy module, constrained optimization is performed on the predicted capacity value of each energy module in the first energy module within the first time period to determine the optimal capacity value of the first energy module; According to the optimal capacity value, the capacity value of each energy module in the sodium electricity energy storage system is scheduled.
2. The method according to claim 1, characterized in that Using a preset ant colony optimization algorithm and the stability evaluation value and carbon emission of each energy module in the first energy module, constrained optimization is performed on the predicted capacity value of each energy module in the first energy module within the first time period to determine the optimal capacity value of the first energy module, including: According to the predicted capacity value, stability evaluation value, carbon emission of each energy module in the first energy module in the first time period and preset constraints, and under the condition that the constraints are met, the preset objective function is optimized and solved by using the ant colony optimization algorithm to determine the maximum value of the objective function, wherein the maximum value satisfies the maximum weighted sum of the capacity value, stability evaluation value and carbon emission of each energy module in the first energy module in the first time period; The maximum value is determined as the optimal capacity value.
3. The method according to claim 1, characterized in that The objective function is: WITH max =ω1*Z cost +ω2*Z stability +ω3*Z carbon Among them, Z max represents the optimal capacity value, ω1 represents the weight coefficient of the capacity of the first energy module, ω2 represents the weight coefficient of the stability of the capacity of the first energy module, ω3 represents the weight coefficient of the carbon emission of the first energy module, and Z cost represents the capacity value of the first energy module, Z stability represents the stability evaluation value of the first energy module, Z carbon Represents the carbon emissions of the first energy module.
4. The method according to claim 1, characterized in that: Predicting the capacity value of each energy module in the first energy module during the first time period includes: The maximum power point, illumination data and real-time light energy data of the light energy module within the first time period are input into the prediction model, so that the prediction model predicts the output capacity value of the light energy module under the maximum output power according to the maximum power point, and obtains the first prediction value of the light energy module within the first time period. The maximum power point is the power point at which the photoelectric conversion efficiency of the light energy module is the highest within the first time period.
5. The method according to any one of claims 1 to 4, characterized in that: The sodium-electric energy storage system further includes a second energy module, which includes a hydrogen energy module and a mains power module; according to the optimal capacity value, scheduling the capacity value of each energy module in the sodium-electric energy storage system includes: If the first resource amount of the electric power resources generated by the first energy module does not exceed the second resource amount, the second energy module is controlled to generate backup electric power resources based on a preset strategy, wherein the second resource amount is the amount of electric power resources required by the load connected to the sodium-electric energy storage system, and the preset strategy is used to indicate the use of the hydrogen energy module and / or the mains module to generate backup electric power resources; If the first resource amount of the electric power resources generated by the first energy module exceeds the second resource amount, the third resource amount of the electric power resources is stored in the sodium-electric energy storage module of the sodium-electric energy storage system, and the third resource amount is the resource amount in the first resource amount excluding the second resource amount.
6. A sodium electricity energy storage system, characterized in that: The sodium electricity energy storage system includes a first energy module and a control module, wherein the first energy module includes a light energy module, a wind energy module and a tidal energy module; wherein, The control module is used to predict the production capacity value of each energy module in the first energy module in the first time period according to the real-time light energy data of the light energy module, the real-time wind energy data of the wind energy module and the real-time tidal energy data of the tidal energy module, and the weather forecast data of the area where the sodium electricity energy storage system is located in the first time period, combined with the pre-trained prediction model; the weather forecast data includes the light data, wind volume data and tidal data in the first time period; The control module is further used to use a preset ant colony optimization algorithm and the stability evaluation value and carbon emission of each energy module in the first energy module to perform constrained optimization on the predicted capacity value of each energy module in the first time period to determine the optimal capacity value of the first energy module; The control module is also used to schedule the production capacity value of each energy module in the sodium electricity energy storage system according to the optimal production capacity value.
7. The system according to claim 6, characterized in that The sodium electricity energy storage system further includes a second energy module and a sodium electricity energy storage module, wherein the second energy module includes a hydrogen energy module and a mains power module; the control module is further used for: If the first resource amount of the electric power resources generated by the first energy module does not exceed the second resource amount, the second energy module is controlled to generate backup electric power resources based on a preset strategy, wherein the second resource amount is the amount of electric power resources required by the load connected to the sodium-electric energy storage system, and the preset strategy is used to indicate the use of the hydrogen energy module and / or the mains module to generate backup electric power resources; If the first resource amount of the electric power resources generated by the first energy module exceeds the second resource amount, the third resource amount of the electric power resources is stored in the sodium-electric energy storage module of the sodium-electric energy storage system, and the third resource amount is the resource amount in the first resource amount excluding the second resource amount.
8. A resource scheduling device, characterized in that: Applied to a sodium-electric energy storage system, the sodium-electric energy storage system comprises a first energy module and a control module, the first energy module comprises a light energy module, a wind energy module and a tidal energy module; the device comprises: A prediction module, based on the real-time light energy data of the light energy module, the real-time wind energy data of the wind energy module and the real-time tidal energy data of the tidal energy module, and the weather forecast data of the area where the sodium electricity energy storage system is located in the first time period, combined with a pre-trained prediction model, predicts the production capacity value of each energy module in the first energy module in the first time period; the weather forecast data includes light data, wind volume data and tidal data in the first time period; an optimization module, configured to perform constrained optimization on the predicted capacity values of each energy module in the first energy module within the first time period by using a preset ant colony optimization algorithm and the stability evaluation value and carbon emission of each energy module in the first energy module, so as to determine the optimal capacity value of the first energy module; A scheduling module is used to schedule the production capacity value of each energy module in the sodium electricity energy storage system according to the optimal production capacity value.
9. A resource scheduling device, characterized in that: include: at least one processor, and 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 at least one processor implements the method according to any one of claims 1 to 5 by executing the instructions stored in the memory.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 5.
11. A computer program product, characterized in that The invention comprises computer instructions, which, when executed on a computer, enable the method according to any one of claims 1 to 5 to be implemented.