CN118748425B - Control method and system of pumped storage power station - Google Patents
Control method and system of pumped storage power station Download PDFInfo
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- CN118748425B CN118748425B CN202410765691.8A CN202410765691A CN118748425B CN 118748425 B CN118748425 B CN 118748425B CN 202410765691 A CN202410765691 A CN 202410765691A CN 118748425 B CN118748425 B CN 118748425B
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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Abstract
本发明涉及电站调度技术领域，具体公开了一种抽水蓄能电站的控制方法及系统，所述方法包括获取抽水蓄能机组历史数据、抽水蓄能机组实时功率和电网负荷历史数据，建立抽水蓄能机组工作模型；在电网负荷历史数据中得到n条电网日负荷曲线，进而得到当天电网日负荷的预测负荷数据；依据当天电网日负荷的预测负荷数据和抽水蓄能机组工作模型得到抽水蓄能机组预测功率；调整抽水蓄能机组的工作状态。本发明依据历史数据进行建模和预测抽水蓄能机组的功率，然后计算预测功率与实际功率的差值，再将差值与预设阈值比对，依据比对结果决定是否调整抽水蓄能机组的工作状态，实现了自动和实时调节，无需依赖监控人员的经验和操作。
The present invention relates to the technical field of power station dispatching, and specifically discloses a control method and system for a pumped-storage power station, the method comprising obtaining historical data of a pumped-storage unit, real-time power of a pumped-storage unit and historical data of power grid load, establishing a working model of a pumped-storage unit; obtaining n daily load curves of a power grid from the historical data of power grid load, and then obtaining the predicted load data of the daily load of the power grid on that day; obtaining the predicted power of the pumped-storage unit based on the predicted load data of the daily load of the power grid on that day and the working model of the pumped-storage unit; and adjusting the working state of the pumped-storage unit. The present invention models and predicts the power of a pumped-storage unit based on historical data, then calculates the difference between the predicted power and the actual power, and then compares the difference with a preset threshold value, and decides whether to adjust the working state of the pumped-storage unit based on the comparison result, thereby realizing automatic and real-time adjustment without relying on the experience and operation of monitoring personnel.
Description
Technical Field
The invention relates to the technical field of power station scheduling, in particular to a control method and a control system of a pumped storage power station.
Background
With the rapid development of modern industry and power industry, the contradiction between peak and valley of load of a power system is more and more prominent, and the power system is urgently required to have stronger peak regulation, frequency modulation, phase modulation and standby capability so as to ensure the safe, stable and economic operation of the power system. Pumped storage power stations are one of the means for effectively and economically solving the above problems by virtue of their flexible, rapid, economical and reliable characteristics. The pumped storage power station pumps water to an upstream reservoir when the power grid is in a valley load, converts electric energy into potential energy and stores the potential energy, and discharges water to generate electricity when the power grid is in a peak load so as to provide electric energy for the power grid. Thus, pumped-storage power plants can convert low-value electrical energy during grid load off-peak periods into high-value electrical energy during load peak periods.
However, at present, the monitoring personnel manually adjusts the working state of the pumped storage unit according to the real-time condition, so that the working state of the pumped storage unit is not only required to depend on the experience of the monitoring personnel, but also can be adjusted by the operation of the monitoring personnel, and the requirement of automatic adjustment cannot be met.
Disclosure of Invention
The invention aims to provide a control method and a control system of a pumped storage power station, which are used for solving the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a method of controlling a pumped-storage power plant, the method comprising:
Acquiring historical data of a pumped storage unit, real-time power of the pumped storage unit and power grid load historical data, and establishing a working model of the pumped storage unit according to the power grid load historical data and the historical data of the pumped storage unit;
searching n pieces of daily load data of the power grid on the same date as the weather of the current day in the historical data of the power grid, obtaining n pieces of daily load curves of the power grid, and obtaining predicted load data of daily load of the current day according to the n pieces of daily load curves of the power grid;
Obtaining predicted power of the pumped storage unit according to predicted load data of daily loads of the power grid on the same day and a working model of the pumped storage unit;
And adjusting the working state of the pumped storage unit according to the predicted power of the pumped storage unit and the real-time power of the pumped storage unit.
The invention further provides a method for establishing a pump storage unit working model according to the power grid load historical data and the pump storage unit historical data, comprising the following steps of:
Randomly selecting part of power grid load historical data and part of pumped storage unit historical data of the same weather as a training set, and the rest data as a test set;
Substituting the training set into a long and short memory neural network algorithm, a SOM cluster neural network algorithm and a support vector machine algorithm respectively to obtain different initial pump storage unit working models;
Substituting the test set into different initial pump storage unit working models for verification, and obtaining different pump storage unit working models after verification.
The method for obtaining the predicted power of the pumped storage unit according to the predicted load data of the daily load of the power grid and the working model of the pumped storage unit comprises the following steps of:
training each pump storage unit working model according to the predicted load data of the daily load of the power grid on the same day to obtain the predicted power of each pump storage unit working model;
And carrying out weighted average on the predicted power of all the pump storage unit working models to obtain the predicted power of the pump storage unit.
As a further scheme of the invention, before the training set is respectively substituted into the long and short memory neural network algorithm, the SOM cluster neural network algorithm and the support vector machine algorithm, the training set is normalized, and the specific formula of the normalization is as followsWhere d (i) is the ith data in the training set,S is the variance of different d (i) and a (i) is the normalization of d (i) for the average of different d (i).
According to the further scheme of the invention, the step of adjusting the working state of the pumped storage unit according to the predicted power of the pumped storage unit and the real-time power of the pumped storage unit comprises the following steps:
Calculating a power change value of the pumped storage unit according to the predicted power of the pumped storage unit and the real-time power of the pumped storage unit;
and comparing the power change value with a preset power change threshold value, and if the power change value is larger than the preset power change threshold value, adjusting the working state of the pumped storage unit in real time.
The technical scheme of the invention also provides a control system of the pumped storage power station, which comprises:
the acquisition module is used for acquiring historical data of the pumped storage unit, real-time power of the pumped storage unit and power grid load historical data, and establishing a working model of the pumped storage unit according to the power grid load historical data and the pumped storage unit historical data;
The prediction module is used for searching n pieces of daily power grid load data with the same date as the weather of the current day in the historical power grid load data, obtaining n pieces of daily power grid load curves, and obtaining predicted load data of daily power grid load of the current day according to the n pieces of daily power grid load curves;
The generation module is used for obtaining the predicted power of the pumped storage unit according to the predicted load data of the daily load of the power grid on the same day and the working model of the pumped storage unit;
and the adjusting module is used for adjusting the working state of the pumped storage unit according to the predicted power of the pumped storage unit and the real-time power of the pumped storage unit.
The further scheme of the invention is that the acquisition module comprises:
the acquisition unit is used for acquiring historical data of the pumped storage unit, real-time power of the pumped storage unit and historical data of the power grid load;
The dividing unit is used for randomly selecting part of power grid load historical data and part of pumped storage unit historical data in the same weather as a training set, and the rest data as a test set;
The model generation unit is used for substituting the training set into a long and short memory neural network algorithm, a SOM cluster neural network algorithm and a support vector machine algorithm respectively to obtain different initial pumped storage unit working models;
And the model verification unit is used for substituting the test set into different initial pump storage unit working models to verify, and obtaining different pump storage unit working models after verification.
As a further scheme of the invention: the generation module comprises:
the generating unit is used for training each pump storage unit working model according to the predicted load data of the daily load of the power grid on the same day to obtain the predicted power of each pump storage unit working model;
and the weighting unit is used for carrying out weighted average on the predicted power of all the pump storage unit working models to obtain the predicted power of the pump storage unit.
As a further aspect of the present invention, the adjustment module includes:
The calculation unit is used for calculating the power change value of the pumped storage unit according to the predicted power of the pumped storage unit and the real-time power of the pumped storage unit;
The adjusting unit is used for comparing the power change value with a preset power change threshold value, and adjusting the working state of the pumped storage unit in real time if the power change value is larger than the preset power change threshold value.
Compared with the prior art, the method has the beneficial effects that modeling and predicting the power of the pumped storage unit are carried out according to historical data, then the difference value between the predicted power and the actual power is calculated, the difference value is compared with the preset threshold value, and whether the working state of the pumped storage unit is adjusted is determined according to the comparison result, so that automatic and real-time adjustment is realized, and the experience and operation of monitoring personnel are not required to be relied on.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
FIG. 1 is a flow chart diagram of a control method of a pumped-storage power station.
FIG. 2 is a partial first sub-flowchart of a control method of a pumped-storage power plant.
FIG. 3 is a third sub-flowchart of a control method of a pumped-storage power plant.
FIG. 4 is a fourth sub-flowchart of a method of controlling a pumped-storage power plant.
Fig. 5 is a block diagram of the control system of the pumped storage power station.
Fig. 6 is a block diagram of the acquisition module in the control system of the pumped storage power station.
Fig. 7 is a block diagram of the construction of the generation module in the control system of the pumped storage power station.
FIG. 8 is a block diagram of the components of the adjustment module in the control system of the pumped storage power station.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Embodiment 1 fig. 1 is a flow chart of a control method of a pumped-storage power station, in an embodiment of the invention, a control method of a pumped-storage power station includes:
Acquiring historical data of a pumped storage unit, real-time power of the pumped storage unit and power grid load historical data, and establishing a working model of the pumped storage unit according to the power grid load historical data and the historical data of the pumped storage unit;
The pumped storage unit generally has three working modes, namely a pumping mode, a power generation mode and an idle mode, wherein the load of a power grid at a certain moment is closely related to the power of the pumped storage unit at the moment, when the power grid is in an overload state (namely, the electric quantity generated by a power station is far lower than the load electric quantity of the power grid), the pumped storage unit is in the power generation mode and discharges water to a hydropower station for generating electricity in a lower reservoir, so that the overload state of the power grid is lightened, when the power grid is in a normal state (namely, the electric quantity generated by the power station is approximately the same as the load electric quantity of the power grid), the pumped storage unit is in the idle mode, and when the power grid is in a low-load state (namely, the electric quantity generated by the power station is far greater than the load electric quantity of the power grid), the pumped storage unit is in the pumping mode, and the water is pumped to the upper reservoir by redundant electric energy so as to be convenient for subsequent reutilization. The real-time power of the pumped storage unit is obtained for the subsequent judgment of whether adjustment is needed. Knowing the grid load data and then the pumped storage unit data, the correlation between the grid load data and the current pumped storage unit data can be found out, so that a pumped storage unit working model is constructed.
Searching n pieces of daily load data of the power grid on the same date as the weather of the current day in the historical data of the power grid, obtaining n pieces of daily load curves of the power grid, and obtaining predicted load data of daily load of the current day according to the n pieces of daily load curves of the power grid;
At the same time of day, the influence of wind power on the pumped storage unit, the influence of temperature on the pumped storage unit and the influence of humidity on the pumped storage unit are generally similar, the power grid loads are similar at the moment, the obtained predicted load curve of the daily load of the power grid on the day can be more accurate, and the predicted load data of the daily load of the power grid on the day can be obtained. The predicted load curve of the daily load of the current grid is r (k), the daily load curves of the n grids are h1 (k), h2 (k), and Bi is the fitting parameter.
Obtaining predicted power of the pumped storage unit according to predicted load data of daily loads of the power grid on the same day and a working model of the pumped storage unit;
and (3) inputting predicted load data of daily loads of the power grid on the same day into a working model of the pumped storage unit for iteration, wherein the output result is the predicted power of the pumped storage unit.
And adjusting the working state of the pumped storage unit according to the predicted power of the pumped storage unit and the real-time power of the pumped storage unit.
And comparing the predicted working state of the pumped storage unit with the actual working state of the pumped storage unit, and determining whether to adjust the working state of the pumped storage unit on line according to the comparison result.
FIG. 2 is a partial first sub-flowchart of a control method of a pumped-storage power plant, the steps of establishing a pumped-storage unit working model from grid load history data and pumped-storage unit history data including:
Randomly selecting part of power grid load historical data and part of pumped storage unit historical data of the same weather as a training set, and the rest data as a test set;
The power grid load historical data with the same weather is similar to the power grid load of the same day, the part of the power grid load historical data is taken as a total sample set, and a part of the total sample set is randomly selected as a training set, so that the established model has sufficient representativeness to the samples in the total sample set, the testing set is used for verifying whether the established model is accurate, the training set is normalized, the later data processing is convenient, the network learning speed is accelerated, and the specific formula of the normalization processing is as follows Where d (i) is the ith data in the training set,S is the variance of different d (i) and a (i) is the normalization of d (i) for the average of different d (i).
Substituting the training set into a long and short memory neural network algorithm, a SOM cluster neural network algorithm and a support vector machine algorithm respectively to obtain different initial pump storage unit working models;
To ensure that the model built is suitable for different operating situations, three algorithms are used for modeling. The core of the long and short memory neural network algorithm is the state of the neurons, the state of the neurons is just like a conveyor belt, and the linear effect of the neurons is very small and penetrates through the whole chain structure. Information is easily propagated on the conveyor belt without changing the state. Long and short memory neural network algorithms have the ability to delete or add information in the state of neurons, a mechanism carefully managed by a structure called a threshold. The threshold is a way to let information pass selectively, and is made up of a Sigmoid neural network layer and a point-by-point multiplier. The Sigmoid neural network layer outputs a number between 0 and 1, describing how much information a neuron should pass. The output of "0" means "all cannot pass", and the output of "1" means "all pass". The first step of the long and short memory neural network algorithm is to decide what information should be forgotten by the neurons. This is made up of a Sigmod neural network layer called the "forgetting gate layer". It inputs ht-1 and xt, and then outputs a number between 0-1 at each neuron state of ct-1. "1" means "keep this completely" and "0" means "forget this completely". Attempts to predict the language model of the next word from the previous word. In this problem, the neuron state may include gender information in the current subject, so the correct pronoun may be used. When we see a new subject, we will forget the previous gender information, ft=δ (W f[ht-1,xt]+bf). The next step is to decide what information we want to preserve in the neuronal cells, which consists of two parts. First, a Sigmod neural network layer called the "forget gate layer" determines the values we want to update. A tanh layer then generates a new candidate Cts, which is added to the neuron state. In the next step, we combine these two steps to generate an updated state value, adding the sex of the new subject to the neuron state, replacing the old subject ,it＝δ(Wi·[ht-1,xt]+bi),Cts＝tanh(Wc·[ht-1,xt]+bc). that we will forget to update the old neuron state Ct-1 to the new neuron state Ct. The previous step has decided what to do and we go to do next. We multiply the old state by an ft, forget the information we decided to forget before, and then we increment it Cts. This is a new candidate, measured by how much we want to update the value of each state, C t＝ft·Ct-1+it·Cts. Finally, we decide what to output. This output is based on our neuron state, but has a filter. First, we use Sigmod the neural network layer to decide which part of the neuron state needs to be output, then we let the neuron state pass through the tanh (let the output value become between-1 and 1) layer and multiply the output of Sigmod threshold, we output only O t＝δ(Wo[ht-1,xt]+bo),ht＝ot·tanh(Ct that we want to output.
The SOM clustering neural network is an unsupervised learning neural network, and input data is mapped to a two-dimensional topological structure in a competitive learning and proximity updating mode, so that data clustering is realized. SOM clustered neural networks consist of a two-dimensional structure, typically a grid-like structure, with each node called a neuron. The neurons are arranged in a two-dimensional topology, each neuron having a weight vector associated therewith, the vector having the same dimension as the input data. SOM cluster neural network adopts competition learning mode to train. For a given input sample, each neuron in the SOM clustered neural network calculates its distance between its weight vector and the input sample. The competition mechanism in SOM clustered neural networks is implemented based on competition between neurons. Given an input vector x, the activation value ai of neuron i can be calculated by: Wij is the weight between neuron i and input vector x, xj is the j-th component of the input vector. In order to ensure the relative importance of data in the SOM clustering neural network, the SOM clustering neural network performs clustering according to the similarity and characteristic self-organization of the data, and the similar fluctuation process is classified.
The support vector machine algorithm is a commonly used algorithm which is used for finding a class classification model of a hyperplane for dividing data into one class and other classes, and the separation interval is the largest and is different from a perceptron. The three algorithms obtain three different initial pump storage unit working models for subsequent preparation.
Substituting the test set into different initial pump storage unit working models for verification, and obtaining different pump storage unit working models after verification.
The test set is used for verifying whether the obtained initial pump storage unit working model is accurate or not, ensuring the accuracy of the predicted power of the next step, the verification passing standard is to see whether the number of times or performance of iteration reaches a preset threshold value or not, if the verification is not passed, the training set and the test set are randomly divided again, the initial pump storage unit working model is reconstructed, and if the verification is passed, the initial pump storage unit working model can be used as the pump storage unit working model.
FIG. 3 is a third sub-flowchart of a control method of a pumped-storage power station, wherein the step of obtaining the predicted power of the pumped-storage unit according to the predicted load data of the daily load of the power grid and the working model of the pumped-storage unit comprises the following steps:
training each pump storage unit working model according to the predicted load data of the daily load of the power grid on the same day to obtain the predicted power of each pump storage unit working model;
And substituting the predicted load data of the same day into a pumped storage unit working model obtained by a long and short memory neural network algorithm, a pumped storage unit working model obtained by an SOM cluster neural network algorithm and a pumped storage unit working model obtained by a support vector machine algorithm respectively to obtain three different predicted powers of the pumped storage unit.
And carrying out weighted average on the predicted power of all the pump storage unit working models to obtain the predicted power of the pump storage unit.
Because the conditions and advantages and disadvantages of each algorithm are different, the weight scores of the pumped storage unit working model obtained by the long and short memory neural network algorithm, the pumped storage unit working model obtained by the SOM clustering neural network algorithm and the pumped storage unit working model obtained by the support vector machine algorithm are preset, the sum of the weight score of each pumped storage unit working model divided by the weight score of all the pumped storage unit working models is the weight coefficient of the pumped storage unit working model, the predicted power obtained by each pumped storage unit working model is multiplied by the weight coefficient of the pumped storage unit working model, and the results are added together to obtain the final predicted power of the pumped storage unit.
FIG. 4 is a fourth sub-flowchart of a control method of a pumped-storage power plant, wherein the step of adjusting the working state of the pumped-storage unit according to the predicted power of the pumped-storage unit and the real-time power of the pumped-storage unit comprises:
Calculating a power change value of the pumped storage unit according to the predicted power of the pumped storage unit and the real-time power of the pumped storage unit;
the predicted power of the pumped storage unit is an ideal state, and the real-time power of the actual pumped storage unit needs to be seen, wherein the difference value of the real-time power of the pumped storage unit and the real-time power is the power change value of the pumped storage unit.
And comparing the power change value with a preset power change threshold value, and if the power change value is larger than the preset power change threshold value, adjusting the working state of the pumped storage unit in real time.
The power of the pumped storage unit needs to be adjusted at a certain cost, the cost for adjusting the power of the pumped storage unit is usually calculated by different pumped storage power stations, so that a critical value of power change can be obtained, the critical value is defined as a preset power change threshold value, when the power change value is smaller than or equal to the preset power change threshold value, if the working state of the pumped storage unit is adjusted, the cost and the obtained cost performance are lower, the difference between the two is regarded as a reasonable difference range, the working state of the pumped storage unit does not need to be adjusted, and only when the power change value is larger than the preset power change threshold value, the difference between the two is larger, and the working state of the pumped storage unit needs to be adjusted, otherwise, more economic cost is paid.
Embodiment 2 fig. 5 is a block diagram of a control system of a pumped-storage power station, in an embodiment of the present invention, a control system of a pumped-storage power station, the system includes:
the acquisition module is used for acquiring historical data of the pumped storage unit, real-time power of the pumped storage unit and power grid load historical data, and establishing a working model of the pumped storage unit according to the power grid load historical data and the pumped storage unit historical data;
The prediction module is used for searching n pieces of daily power grid load data with the same date as the weather of the current day in the historical power grid load data, obtaining n pieces of daily power grid load curves, and obtaining predicted load data of daily power grid load of the current day according to the n pieces of daily power grid load curves;
The generation module is used for obtaining the predicted power of the pumped storage unit according to the predicted load data of the daily load of the power grid on the same day and the working model of the pumped storage unit;
and the adjusting module is used for adjusting the working state of the pumped storage unit according to the predicted power of the pumped storage unit and the real-time power of the pumped storage unit.
The acquisition module comprises:
the acquisition unit is used for acquiring historical data of the pumped storage unit, real-time power of the pumped storage unit and historical data of the power grid load;
The dividing unit is used for randomly selecting part of power grid load historical data and part of pumped storage unit historical data in the same weather as a training set, and the rest data as a test set;
The model generation unit is used for substituting the training set into a long and short memory neural network algorithm, a SOM cluster neural network algorithm and a support vector machine algorithm respectively to obtain different initial pumped storage unit working models;
And the model verification unit is used for substituting the test set into different initial pump storage unit working models to verify, and obtaining different pump storage unit working models after verification.
The generation module comprises:
the generating unit is used for training each pump storage unit working model according to the predicted load data of the daily load of the power grid on the same day to obtain the predicted power of each pump storage unit working model;
and the weighting unit is used for carrying out weighted average on the predicted power of all the pump storage unit working models to obtain the predicted power of the pump storage unit.
The adjustment module includes:
The calculation unit is used for calculating the power change value of the pumped storage unit according to the predicted power of the pumped storage unit and the real-time power of the pumped storage unit;
The adjusting unit is used for comparing the power change value with a preset power change threshold value, and adjusting the working state of the pumped storage unit in real time if the power change value is larger than the preset power change threshold value.
The control method of the pumped storage power station can achieve all functions through computer equipment, the computer equipment comprises one or more processors and one or more memories, at least one program code is stored in the one or more memories, and the program code is loaded and executed by the one or more processors to achieve the functions of the big data-based user behavior prediction method.
The processor fetches instructions from the Memory, analyzes the instructions, then completes corresponding operation according to the instruction requirement, generates a series of control commands, makes each part of the computer automatically, continuously and coordinately act to become an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit, wherein the Memory comprises a Read-Only Memory (ROM) which is used for storing computer programs, and a protection device is arranged outside the Memory.
For example, a computer program may be split into one or more modules, one or more modules stored in memory and executed by a processor to perform the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in the terminal device.
It will be appreciated by those skilled in the art that the foregoing description of the service device is merely an example and is not meant to be limiting, and may include more or fewer components than the foregoing description, or may combine certain components, or different components, such as may include input-output devices, network access devices, buses, etc.
The Processor may be a central processing unit (Central Processing Unit, CPU), other general purpose Processor, digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal device described above, and which connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used for storing computer programs and/or modules, and the processor may implement various functions of the terminal device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area which may store an operating system, an application program required for at least one function (such as an information collection template display function, a product information distribution function, etc.), etc., and a storage data area which may store data created according to the use of the berth status display system (such as a product information collection template corresponding to different product types, product information required to be distributed by different product providers, etc.), etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card (SMART MEDIA CARD, SMC), secure Digital (SD) card, flash memory card (FLASH CARD), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
The modules/units integrated in the terminal device may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on this understanding, the present invention may implement all or part of the modules/units in the system of the above-described embodiments, or may be implemented by instructing the relevant hardware by a computer program, which may be stored in a computer-readable storage medium, and which, when executed by a processor, may implement the functions of the respective system embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include any entity or device capable of carrying computer program code, recording medium, USB flash disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random access Memory (RAM, random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media, among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
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| CN102184453A (en) * | 2011-05-16 | 2011-09-14 | 上海电气集团股份有限公司 | Wind power combination predicting method based on fuzzy neural network and support vector machine |
| CN102624017A (en) * | 2012-03-22 | 2012-08-01 | 清华大学 | Battery energy storage system peak clipping and valley filling real-time control method based on load prediction |
| CN117200280A (en) * | 2023-11-08 | 2023-12-08 | 国网信息通信产业集团有限公司 | Photovoltaic power station power control method and related equipment |
| CN117348439A (en) * | 2023-09-25 | 2024-01-05 | 南方电网调峰调频发电有限公司储能科研院 | Dynamic simulation test method and system for pumped storage unit operating conditions |
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| CN116565966B (en) * | 2023-06-29 | 2023-10-24 | 国能青海黄河玛尔挡水电开发有限公司 | Intelligent hydraulic power generation double microcomputer automatic synchronous control system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102184453A (en) * | 2011-05-16 | 2011-09-14 | 上海电气集团股份有限公司 | Wind power combination predicting method based on fuzzy neural network and support vector machine |
| CN102624017A (en) * | 2012-03-22 | 2012-08-01 | 清华大学 | Battery energy storage system peak clipping and valley filling real-time control method based on load prediction |
| CN117348439A (en) * | 2023-09-25 | 2024-01-05 | 南方电网调峰调频发电有限公司储能科研院 | Dynamic simulation test method and system for pumped storage unit operating conditions |
| CN117200280A (en) * | 2023-11-08 | 2023-12-08 | 国网信息通信产业集团有限公司 | Photovoltaic power station power control method and related equipment |
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