CN116632838A - Method and device for analyzing electric energy supply of power generation enterprise - Google Patents
Method and device for analyzing electric energy supply of power generation enterprise Download PDFInfo
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Abstract
The invention relates to the technical field of data analysis, and discloses a method for analyzing electric energy supply of a power generation enterprise, which comprises the following steps: and detecting data of the historical electric energy supply quantity and the historical electric energy demand quantity to obtain a standard electric energy supply quantity and a standard electric energy demand quantity, predicting the change trend of the standard electric energy supply quantity and the standard electric energy demand quantity, obtaining a reference electricity price, adjusting the reference electricity price based on the electric energy supply predicted value and the electric energy demand predicted value to obtain a pricing function, obtaining a profit optimal objective function of a power generation enterprise by using the pricing function, obtaining a target electricity price according to the profit optimal objective function and the pricing function, and completing electric energy supply analysis of the power generation enterprise. The invention also provides an electric energy supply analysis device, electronic equipment and a computer readable storage medium applied to the power generation enterprises. The method and the device can solve the problem that the result of power supply prediction is inaccurate due to the fact that the influence of abnormal data is not considered.
Description
Technical Field
The present invention relates to the field of data analysis technologies, and in particular, to a method and apparatus for analyzing electric energy supply of a power generation enterprise, an electronic device, and a computer readable storage medium.
Background
Due to the rapid development of big data and artificial intelligence, the concept of smart grid is proposed internationally. In the smart grid era, large-scale renewable energy sources are connected in grid, but because renewable energy sources have intermittent characteristics, the renewable energy sources are difficult to predict in advance. The power generation enterprises transmit the electric energy supply data to the data processor for data analysis through technical means such as sensors and communication, valuable information is extracted by utilizing artificial intelligence to predict future power supply and demand, and further, the electricity price is scientifically and intelligently formulated.
However, since the current method for predicting the trend of the power supply generally analyzes the collected data directly, the influence of the abnormal data is not considered, and the prediction result is often inaccurate.
Disclosure of Invention
The invention provides a method, a device and a computer readable storage medium for analyzing electric energy supply of a power generation enterprise, which mainly aim to solve the problem that the influence of abnormal data is not considered, so that the result of electric energy supply prediction is not accurate enough.
In order to achieve the above object, the present invention provides a method for analyzing power supply applied to a power generation enterprise, including:
Acquiring historical electric energy supply quantity and historical electric energy demand quantity of an electric power generation enterprise based on a pre-built intelligent sensor, wherein the historical electric energy supply quantity comprises historical new energy supply quantity and historical conventional electric energy supply quantity;
detecting data of the historical electric energy supply quantity and the historical electric energy demand quantity to obtain abnormal data, and eliminating the abnormal data to obtain a standard electric energy supply quantity and a standard electric energy demand quantity;
predicting the variation trend of the standard electric energy supply amount and the standard electric energy demand amount to obtain an electric energy supply predicted value and an electric energy demand predicted value in a preset time period:
；
；
wherein ,representation->Predicted value of power supply at time->Representation->Predicted value of the electrical energy demand at time-> and />Representing a horizontal smoothing constant, +.> and />Representing trend smooth constant, ++>Representation->Electric energy supply quantity acquired by the intelligent sensor at moment, < >>Representation->Electric energy demand acquired by the intelligent sensor at moment, < >>Representation->Level-smooth predictive value of the power supply quantity at the moment,/->Representation->The level of the electric power demand at the moment of time smoothes the predicted value,representation->Trend increment estimation value of electric energy supply quantity at moment, < >>Representation->Trend increment estimation value of electric energy demand at moment;
Acquiring a reference electricity price, and adjusting the reference electricity price based on the electric energy supply predicted value and the electric energy demand predicted value to obtain a pricing function;
obtaining an optimal profit objective function of the power generation enterprise by utilizing the pricing function;
and obtaining a target electricity price according to the optimal profit objective function and the pricing function, and completing the electric energy supply analysis of the power generation enterprises.
Optionally, the detecting the historical electric energy supply and the historical electric energy demand to obtain abnormal data, and rejecting the abnormal data to obtain the standard electric energy supply and the standard electric energy demand includes:
inputting the historical electric energy supply quantity and the historical electric energy demand quantity into a pre-trained electric energy data anomaly detection model to detect to obtain a historical error, wherein the electric energy data anomaly detection model is composed of a GRU deep neural network;
if the historical error is larger than a preset error threshold, the historical electric energy supply amount and the historical electric energy demand amount are abnormal data, and the abnormal data are removed to obtain a standard electric energy supply amount and a standard electric energy demand amount;
and if the historical error is not greater than a preset error threshold, outputting the historical error as the standard electric energy supply quantity and the standard electric energy demand quantity.
Optionally, the step of inputting the historical electric energy supply amount and the historical electric energy demand amount into a pre-trained electric energy data anomaly detection model to detect, so as to obtain a historical error includes:
taking the historical electric energy supply quantity and the historical electric energy demand quantity as input data, and encoding the input data based on a pre-constructed encoding formula to obtain a data hidden state;
decoding the data hidden state by using an electric energy data anomaly detection model to obtain a reconstruction sequence;
and subtracting the reconstruction sequence from the input data to obtain a history error.
Optionally, the coding formula is:
；
；
；
wherein ,representation->Implicit status of data of time of day->Representation->Reset gate state of time->Representation->Update door state of time->Time of presentation->Representation->Implicit status of data of time of day->Representing Sigmoid function->Representation->Time of day, historical power supply or historical power demand,/-or-> and />Representing a weight matrix, +.>、/> and />Representing the bias vector.
Optionally, before the obtaining the reference electricity price and adjusting the reference electricity price based on the electric energy supply predicted value and the electric energy demand predicted value to obtain the pricing function, the method further includes:
Comparing the power supply predicted value with the power demand predicted value in a preset observation period, and calculating a difference value between the power supply predicted value and the power demand predicted value;
outputting an electric energy shortage signal if the electric energy supply predicted value is smaller than the electric energy demand predicted value in the observation period;
and outputting an electric energy surplus signal if the electric energy supply predicted value is larger than the electric energy demand predicted value and the difference is larger than a preset threshold value in the observation period.
Optionally, the adjusting the reference electricity price based on the electricity supply predicted value and the electricity demand predicted value to obtain a pricing function includes:
establishing a power utilization utility function based on the power demand predicted value and the reference power price to obtain power utilization utility;
adjusting the predicted value of the electric energy demand according to the electricity utilization effect to obtain a target electric energy demand, wherein the target electric energy demand is:
；
wherein the saidIndicating the target electric energy demand,/->Indicating the utility of electricity, < >>Representing a predicted value of power demand,/-, for>Representing exogenous parameters;
establishing a pricing function based on the target electric energy demand and a new energy supply predicted value, wherein the pricing function is:
；
wherein ,representing the actual electricity price>Indicating a reference electricity price, & lt & gt>Representing new energy supply predictive value, +.> and />Representing pricing parameters.
Optionally, the electricity utility function is:
；
wherein ,indicating the utility of electricity, < >>Representing utility parameters.
Optionally, the obtaining the profit optimal objective function of the power generation enterprise by using the pricing function includes:
obtaining a cost function by utilizing the new energy supply predicted value and the target electric energy demand, wherein the cost function is as follows:
；
wherein ,representing the actual cost +.>Cost parameters representing new energy, +.>Cost parameters representing conventional electrical energy;
obtaining a profit function of the power generation enterprise based on the cost function and the pricing function;
and optimizing the profit function according to the profit maximization principle to obtain the profit optimal objective function.
Optionally, the profit optimization objective function is:
；
wherein ,indicating the profits of the power generation enterprises.
In order to solve the above problems, the present invention also provides an electric power supply analysis apparatus applied to an electric power generation enterprise, the apparatus comprising:
the power data acquisition module is used for acquiring historical power supply quantity and historical power demand quantity of a power generation enterprise based on a pre-built intelligent sensor, wherein the historical power supply quantity comprises historical new power supply quantity and historical conventional power supply quantity;
The data anomaly detection module is used for detecting the historical electric energy supply quantity and the historical electric energy demand quantity to obtain anomaly data, and eliminating the anomaly data to obtain a standard electric energy supply quantity and a standard electric energy demand quantity;
the electric energy data prediction module is used for predicting the variation trend of the standard electric energy supply quantity and the standard electric energy demand quantity to obtain an electric energy supply predicted value and an electric energy demand predicted value in a preset time period:
；
；
wherein ,representation->Predicted value of power supply at time->Representation->Predicted value of the electrical energy demand at time-> and />Representing a horizontal smoothing constant, +.> and />Representing trend smooth constant, ++>Representation->Electric energy supply quantity acquired by the intelligent sensor at moment, < >>Representation->Electric energy demand acquired by the intelligent sensor at moment, < >>Representation->Level-smooth predictive value of the power supply quantity at the moment,/->Representation->The level of the electric power demand at the moment of time smoothes the predicted value,representation->Trend increment estimation value of electric energy supply quantity at moment, < >>Representation->Trend increment estimation value of electric energy demand at moment;
the profit optimization module is used for obtaining a reference electricity price, and adjusting the reference electricity price based on the electric energy supply predicted value and the electric energy demand predicted value to obtain a pricing function;
Obtaining an optimal profit objective function of the power generation enterprise by utilizing the pricing function;
and the target electricity price acquisition module is used for acquiring the target electricity price according to the optimal profit target function and the pricing function and completing the electric energy supply analysis of the power generation enterprises.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor;
and a memory communicatively coupled to the at least one processor;
the memory stores instructions executable by the at least one processor to implement the method for power supply analysis for a power generation enterprise.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one instruction that is executed by a processor in an electronic device to implement the above-mentioned method for power supply analysis applied to a power generation enterprise.
In order to solve the problems described in the background art, firstly, acquiring historical electric energy supply quantity and historical electric energy demand quantity of a power generation enterprise based on a pre-built intelligent sensor, carrying out data detection on the historical electric energy supply quantity and the historical electric energy demand quantity to obtain abnormal data, and rejecting the abnormal data. Therefore, the method, the device, the electronic equipment and the computer readable storage medium for analyzing the electric energy supply of the power generation enterprise can solve the problem that the result of the electric energy supply prediction is inaccurate due to the fact that the influence of abnormal data is not considered.
Drawings
FIG. 1 is a flow chart of a method for power supply analysis for a power generation enterprise according to an embodiment of the present application;
FIG. 2 is a functional block diagram of an electrical energy supply analysis device for an electrical power generation enterprise according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device for implementing the method for analyzing power supply applied to a power generation enterprise according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
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 application.
The embodiment of the application provides a method for analyzing electric energy supply of a power generation enterprise. The execution subject of the method for power supply analysis applied to the power generation enterprise includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the method applied to the power supply analysis of the power generation enterprises may be performed by software or hardware installed at the terminal device or the server device. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Embodiment one: referring to fig. 1, a flow chart of a method for analyzing power supply applied to a power generation enterprise according to an embodiment of the invention is shown. In this embodiment, the method for power supply analysis applied to a power generation enterprise includes:
s1, acquiring historical electric energy supply quantity and historical electric energy demand quantity of a power generation enterprise based on a pre-built intelligent sensor, wherein the historical electric energy supply quantity comprises historical new energy supply quantity and historical conventional electric energy supply quantity.
It should be explained that the historical electric energy supply amount refers to the electric energy generation amount of the electric power generation enterprises, and can be divided into new energy power generation and conventional power generation, so that the historical electric energy supply amount includes a historical new energy supply amount and a historical conventional electric energy supply amount. The historical electric energy demand refers to the electric energy required by users of the area responsible for the power generation enterprise. Further, since the period and load of power generation of the new energy source are affected by weather conditions, the fluctuation is relatively large, and thus the period of power generation peaks and valleys may be exactly opposite to the period of power use by the user. According to the embodiment of the invention, the historical electric energy supply quantity and the historical electric energy demand quantity of the power generation enterprises are collected through the intelligent sensor such as the digital electric energy collector, and then are transmitted to the control computer for data analysis in a communication mode of the power line carrier.
It should be appreciated that the power supply for a power generation enterprise in embodiments of the present invention includes both new sources of energy and conventional power. Because the new energy power generation has intermittent characteristics, the new energy needs to be supplemented by conventional electric energy when the new energy cannot meet the electric energy requirement of a user. Therefore, the intelligent sensor is adopted to acquire the historical new energy supply quantity, the historical conventional electric energy supply quantity and the historical electric energy demand quantity to analyze the electric energy supply of the power generation enterprise.
For example, a manager of a small Zhang Shi power generation enterprise needs to collect and analyze power supply data of the power generation enterprise, so as to allocate power supply in a future time period to meet the increasing power demand of users, and to make electricity prices according to the power supply data. The intelligent sensor is adopted for the small sheets to collect the historical electric energy supply quantity and the historical electric energy demand quantity of the area in the past 365 days into a distributed computer, and then all data are uploaded into the core database server in a GSM/3G/4G/Ethernet mode for data analysis.
S2, detecting the historical electric energy supply amount and the historical electric energy demand amount to obtain abnormal data, and eliminating the abnormal data to obtain the standard electric energy supply amount and the standard electric energy demand amount.
In detail, the detecting the data of the historical electric energy supply amount and the historical electric energy demand amount to obtain abnormal data, and eliminating the abnormal data to obtain a standard electric energy supply amount and a standard electric energy demand amount includes:
inputting the historical electric energy supply quantity and the historical electric energy demand quantity into a pre-trained electric energy data anomaly detection model to detect to obtain a historical error, wherein the electric energy data anomaly detection model is composed of a GRU deep neural network;
if the historical error is larger than a preset error threshold, the historical electric energy supply amount and the historical electric energy demand amount are abnormal data, and the abnormal data are removed to obtain a standard electric energy supply amount and a standard electric energy demand amount;
and if the historical error is not greater than a preset error threshold, outputting the historical error as the standard electric energy supply quantity and the standard electric energy demand quantity.
It should be explained that the gate-controlled loop network GRU is a simplified version of a Long and Short Term Memory (LSTM) network, and can better capture the dependency relationship between the historical power supply and the historical power demand in different time periods. Compared with an LSTM network, the GRU network is simpler in structure and faster in training speed, and therefore historical electric energy supply and demand can be detected more rapidly, and abnormal data can be obtained.
It should be emphasized that, because of the long-term feature memory capability of the GRU network, the non-linear and unstable features of the historical power supply and demand can be fully extracted for implementing anomaly detection of the historical power supply and demand.
It should be understood that, in the embodiment of the invention, the abnormal data in the historical electric energy supply amount and the historical electric energy demand amount are detected by constructing the electric energy data abnormal detection model, so that the influence of the abnormal data is eliminated, and the standard electric energy supply amount and the standard electric energy demand amount are obtained.
In detail, the step of inputting the historical electric energy supply amount and the historical electric energy demand amount into the electric energy data anomaly detection model after the pre-training to detect, and obtaining the historical error includes:
taking the historical electric energy supply quantity and the historical electric energy demand quantity as input data, and encoding the input data based on a pre-constructed encoding formula to obtain a data hidden state;
decoding the data hidden state by using an electric energy data anomaly detection model to obtain a reconstruction sequence;
and subtracting the reconstruction sequence from the input data to obtain a history error.
In detail, the coding formula is:
；
；
；
wherein ,representation->Implicit status of data of time of day->Representation->Reset gate state of time->Representation->Update door state of time->Time of presentation->Representation->Implicit status of data of time of day->Representing Sigmoid function->Representation->Time of day, historical power supply or historical power demand,/-or-> and />Representing a weight matrix, +.>、/> and />Representing the bias vector.
It should be explained that, in the embodiment of the present invention, the historical electric energy supply amount and the historical electric energy demand amount are input into the GRU network to encode, and the historical electric energy supply amount and the historical electric energy demand amount are converted from a high-dimensional form into a low-dimensional implicit form, so as to obtain the data implicit state. And then decoding the data hidden state, and reconstructing the data hidden state back to a high-dimensional representation form to obtain a reconstructed sequence.
It should be emphasized that the historical error refers to an absolute value of a difference between the reconstruction sequence and the input historical power supply amount or the historical power demand amount, and if the historical power supply amount or the historical power demand amount is a normal data point, the reconstruction effect of the power data anomaly detection model is good, and the historical error is small; if the model is for abnormal points, the reconstruction effect of the model is poor, and the history error is large. In the data anomaly detection stage, the data points with the history errors larger than the error threshold are regarded as anomaly points based on the error threshold preset by the electric energy data anomaly detection model, so that anomaly detection of the history electric energy supply quantity and the history electric energy demand quantity is completed.
For example, the electric energy supply amounts [700000kWh, 710000kWh, 708000kWh ] of the small sheets selected from 1 to 3 days of 10 months are input into the electric energy data abnormality detection model to obtain the reconstruction sequences [702000kWh, 711000kWh, 713000kWh ], and then the history errors [2000kWh, 1000kWh, 5000kWh ] can be obtained according to the calculation formula of the history errors. If the preset error threshold value is 3000kWh, the electric energy supply amount acquired on the day of 10 months and 3 days is identified as an abnormal point due to the fact that the historical error is higher than 3000kWh, and the historical error of other time periods is lower than 3000kWh, and the data are normal.
S3, predicting the variation trend of the standard electric energy supply amount and the standard electric energy demand amount to obtain an electric energy supply predicted value and an electric energy demand predicted value in a preset time period.
In detail, the electric power supply predicted value and the electric power demand predicted value:
；
；
wherein ,representation->Predicted value of power supply at time->Representation->Electric energy demand pre-heating at timeMeasuring value of-> and />Representing a horizontal smoothing constant, +.> and />Representing trend smooth constant, ++>Representation->Electric energy supply quantity acquired by the intelligent sensor at moment, < >>Representation->Electric energy demand acquired by the intelligent sensor at moment, < > >Representation->Level-smooth predictive value of the power supply quantity at the moment,/->Representation->The level of the electric power demand at the moment of time smoothes the predicted value,representation->Trend delta estimates of the amount of electrical energy supplied at the moment,/>representation->Trend delta estimate of electrical energy demand at time.
It should be explained that, the prediction method adopted in the embodiment of the invention assumes that the variation trend in the historical power supply amount and the power demand data amount is maintained stable, predicts the data of the future time series by comparing the standard power supply amount and the variation of the standard power demand amount in different time intervals, and can grasp the trend information of the data and predict the future power supply and power demand values.
And S4, acquiring a reference electricity price, and adjusting the reference electricity price based on the electric energy supply predicted value and the electric energy demand predicted value to obtain a pricing function.
It should be explained that the reference electricity price is a basic electricity price determined according to the electricity coal source of the power generation enterprise and the internet electricity price of each landmark post. The power generation enterprises can comprehensively consider the power generation cost and various market factors and float the basic electricity price.
In detail, the method further includes, before the obtaining the reference electricity price, adjusting the reference electricity price based on the electric energy supply predicted value and the electric energy demand predicted value to obtain the pricing function:
Comparing the power supply predicted value with the power demand predicted value in a preset observation period, and calculating a difference value between the power supply predicted value and the power demand predicted value;
outputting an electric energy shortage signal if the electric energy supply predicted value is smaller than the electric energy demand predicted value in the observation period;
and outputting an electric energy surplus signal if the electric energy supply predicted value is larger than the electric energy demand predicted value and the difference is larger than a preset threshold value in the observation period.
In detail, the adjusting the reference electricity price based on the electricity supply predicted value and the electricity demand predicted value to obtain a pricing function includes:
establishing a power utilization utility function based on the power demand predicted value and the reference power price to obtain power utilization utility;
adjusting the predicted value of the electric energy demand according to the electricity utilization effect to obtain a target electric energy demand, wherein the target electric energy demand is:
；
wherein the saidIndicating the target electric energy demand,/->Indicating the utility of electricity, < >>Representing a predicted value of power demand,/-, for>Representing exogenous parameters;
establishing a pricing function based on the target electric energy demand and a new energy supply predicted value, wherein the pricing function is:
；
wherein ,representing the actual electricity price>Indicating a reference electricity price, & lt & gt>Representing new energy supply predictive value, +.> and />Representing pricing parameters.
It should be understood that the electricity utilization effect is an index for measuring the influence of electricity price on the electricity demand of the user, and the user can adjust the own electricity demand according to the electricity price given by the power generation enterprise so as to obtain the maximum satisfaction. Therefore, the embodiment of the invention constructs the electricity utilization utility function by considering the influence of the predicted value of the electric energy demand and the reference electricity price to obtain the electricity utilization utility, then adjusts the predicted value of the electric energy demand according to the electricity utilization utility to obtain the target electric energy demand, and establishes the pricing function.
It should be emphasized that when the new energy supply predicted value is greater than the target electric energy demand value, the power generation enterprises encourage the users to use electricity by adjusting the electricity price down, and when the new energy supply predicted value is less than the target electric energy demand value, the power generation enterprises control the users to use electricity by increasing the electricity price. Therefore, the embodiment of the invention establishes the pricing function according to the target electric energy demand and the predicted value of the new energy supply.
In detail, the electricity utility function is:
；
wherein ,indicating the utility of electricity, < >>Representing utility parameters.
And S5, obtaining the optimal profit objective function of the power generation enterprise by using the pricing function.
The obtaining the profit optimal objective function of the power generation enterprise by using the pricing function comprises the following steps:
obtaining a cost function by utilizing the new energy supply predicted value and the target electric energy demand, wherein the cost function is as follows:
；
wherein ,representing the actual cost +.>Cost parameters representing new energy, +.>Cost parameters representing conventional electrical energy;
obtaining a profit function of the power generation enterprise based on the cost function and the pricing function;
and optimizing the profit function according to the profit maximization principle to obtain the profit optimal objective function.
In detail, the profit optimization objective function is:
；
wherein ,representing profit.
It should be explained that the profit function of the power generation enterprise is:
；
it should be understood that, according to the profit maximization principle, the profit of the power generation enterprise is maximized, so that the max function is adopted to take the maximum value of the profit function, and the profit optimal objective function is obtained.
It should be emphasized that for the power supply costs of the power generation enterprises, when the new energy supply is capable of satisfying the electric energy demand, the required costs are the new energy supply costs, and when the new energy supply is incapable of satisfying the electric energy demand, the required costs consist of the new energy supply costs and the conventional electric energy supply costs.
And S6, obtaining a target electricity price according to the optimal profit objective function and the pricing function, and completing the electric energy supply analysis of the power generation enterprises.
It should be explained that the embodiment of the invention obtains the pricing parameters by solving the profit optimization function and />And substituting the pricing parameters into a pricing function to obtain target pricing of the power generation enterprises, thereby completing power supply analysis.
It should be understood that, the method for analyzing electric energy supply according to the embodiment of the invention eliminates abnormal data by detecting abnormality of the historical electric energy supply amount and the historical electric energy demand amount, so as to more accurately predict electric energy supply and demand in a future time period, and then scientifically formulates electricity price according to the relation between the electric energy supply amount and the electric energy demand amount, thereby highlighting the useful value of the electric energy supply data.
In order to solve the problems described in the background art, firstly, acquiring historical electric energy supply quantity and historical electric energy demand quantity of a power generation enterprise based on a pre-built intelligent sensor, carrying out data detection on the historical electric energy supply quantity and the historical electric energy demand quantity to obtain abnormal data, and rejecting the abnormal data. Therefore, the method, the device, the electronic equipment and the computer readable storage medium for analyzing the electric energy supply of the power generation enterprise can solve the problem that the result of the electric energy supply prediction is inaccurate due to the fact that the influence of abnormal data is not considered.
Embodiment two: fig. 2 is a functional block diagram of an electric power supply analysis device applied to an electric power generation enterprise according to an embodiment of the present invention.
The power supply analysis device 100 applied to a power generation enterprise according to the present invention may be installed in an electronic apparatus. Depending on the functions implemented, the power supply analysis device 100 applied to the power generation enterprises may include a power data acquisition module 101, a data anomaly detection module 102, a power data prediction module 103, a profit optimization module 104, and a target electricity price acquisition module 105. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
The electric energy data acquisition module 101 is configured to acquire a historical electric energy supply amount and a historical electric energy demand amount of a power generation enterprise based on a pre-built intelligent sensor, where the historical electric energy supply amount includes a historical new energy supply amount and a historical regular electric energy supply amount;
the data anomaly detection module 102 is configured to perform data detection on the historical power supply amount and the historical power demand amount to obtain anomaly data, and reject the anomaly data to obtain a standard power supply amount and a standard power demand amount;
The electric energy data prediction module 103 is configured to predict a trend of the standard electric energy supply amount and the standard electric energy demand amount, and obtain an electric energy supply predicted value and an electric energy demand predicted value within a preset time period:
；
；
wherein ,representation->Predicted value of power supply at time->Representation->Predicted value of the electrical energy demand at time-> and />Representing a horizontal smoothing constant, +.> and />Representing trend smooth constant, ++>Representation->Electric energy supply quantity acquired by the intelligent sensor at moment, < >>Representation->Electric energy demand acquired by the intelligent sensor at moment, < >>Representation->Level-smooth predictive value of the power supply quantity at the moment,/->Representation->The level of the electric power demand at the moment of time smoothes the predicted value,representation->Trend increment estimation value of electric energy supply quantity at moment, < >>Representation->Trend increment estimation value of electric energy demand at moment;
the profit optimization module 104 is configured to obtain a reference electricity price, adjust the reference electricity price based on the predicted value of power supply and the predicted value of power demand, obtain a pricing function, and obtain an optimal profit objective function of the power generation enterprise by using the pricing function;
the target electricity price obtaining module 105 is configured to obtain a target electricity price according to the profit optimization objective function and the pricing function, and complete the analysis of power supply of the power generation enterprise.
In detail, the modules in the power supply analysis device 100 for a power generation enterprise in the embodiment of the present invention use the same technical means as the method for power supply analysis for a power generation enterprise described in fig. 1, and can produce the same technical effects, which are not described herein.
Embodiment III: fig. 3 is a schematic structural diagram of an electronic device for implementing a method for analyzing power supply applied to a power generation enterprise according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a power supply analysis program applied to a power generation enterprise.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of a power supply analysis program applied to a power generation company, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective parts of the entire electronic device using various interfaces and lines, executes programs or modules stored in the memory 11 (for example, a power supply analysis program applied to a power generation company, etc.), and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process the data.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The power supply analysis program stored in the memory 11 of the electronic device 1 and applied to the power generation enterprises is a combination of a plurality of instructions, which when executed in the processor 10, can realize:
acquiring historical electric energy supply quantity and historical electric energy demand quantity of an electric power generation enterprise based on a pre-built intelligent sensor, wherein the historical electric energy supply quantity comprises historical new energy supply quantity and historical conventional electric energy supply quantity;
Detecting data of the historical electric energy supply quantity and the historical electric energy demand quantity to obtain abnormal data, and eliminating the abnormal data to obtain a standard electric energy supply quantity and a standard electric energy demand quantity;
predicting the variation trend of the standard electric energy supply amount and the standard electric energy demand amount to obtain an electric energy supply predicted value and an electric energy demand predicted value in a preset time period:
；
；
wherein ,representation->Predicted value of power supply at time->Representation->Predicted value of the electrical energy demand at time-> and />Representing a horizontal smoothing constant, +.> and />Representing trend smooth constant, ++>Representation->Electric energy supply quantity acquired by the intelligent sensor at moment, < >>Representation->Electric energy demand acquired by the intelligent sensor at moment, < >>Representation->Level-smooth predictive value of the power supply quantity at the moment,/->Representation->The level of the electric power demand at the moment of time smoothes the predicted value,representation->Trend increment estimation value of electric energy supply quantity at moment, < >>Representation->Trend increment estimation value of electric energy demand at moment;
acquiring a reference electricity price, and adjusting the reference electricity price based on the electric energy supply predicted value and the electric energy demand predicted value to obtain a pricing function;
obtaining an optimal profit objective function of the power generation enterprise by utilizing the pricing function;
And obtaining a target electricity price according to the optimal profit objective function and the pricing function, and completing the electric energy supply analysis of the power generation enterprises.
Specifically, the specific implementation method of the above instruction by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 2, which are not repeated herein.
Further, the modules/units integrated in the electronic device 1 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. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
acquiring historical electric energy supply quantity and historical electric energy demand quantity of an electric power generation enterprise based on a pre-built intelligent sensor, wherein the historical electric energy supply quantity comprises historical new energy supply quantity and historical conventional electric energy supply quantity;
Detecting data of the historical electric energy supply quantity and the historical electric energy demand quantity to obtain abnormal data, and eliminating the abnormal data to obtain a standard electric energy supply quantity and a standard electric energy demand quantity;
predicting the variation trend of the standard electric energy supply amount and the standard electric energy demand amount to obtain an electric energy supply predicted value and an electric energy demand predicted value in a preset time period:
；
；
wherein ,representation->Predicted value of power supply at time->Representation->Predicted value of the electrical energy demand at time-> and />Representing a horizontal smoothing constant, +.> and />Representing trend smooth constant, ++>Representation->Electric energy supply quantity acquired by the intelligent sensor at moment, < >>Representation->Electric energy demand acquired by the intelligent sensor at moment, < >>Representation->Level-smooth predictive value of the power supply quantity at the moment,/->Representation->The level of the electric power demand at the moment of time smoothes the predicted value,representation->Trend increment estimation value of electric energy supply quantity at moment, < >>Representation->Trend increment estimation value of electric energy demand at moment;
acquiring a reference electricity price, and adjusting the reference electricity price based on the electric energy supply predicted value and the electric energy demand predicted value to obtain a pricing function;
obtaining an optimal profit objective function of the power generation enterprise by utilizing the pricing function;
And obtaining a target electricity price according to the optimal profit objective function and the pricing function, and completing the electric energy supply analysis of the power generation enterprises.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method for power supply analysis for a power generation enterprise, the method comprising:
acquiring historical electric energy supply quantity and historical electric energy demand quantity of an electric power generation enterprise based on a pre-built intelligent sensor, wherein the historical electric energy supply quantity comprises historical new energy supply quantity and historical conventional electric energy supply quantity;
detecting data of the historical electric energy supply quantity and the historical electric energy demand quantity to obtain abnormal data, and eliminating the abnormal data to obtain a standard electric energy supply quantity and a standard electric energy demand quantity;
predicting the variation trend of the standard electric energy supply amount and the standard electric energy demand amount to obtain an electric energy supply predicted value and an electric energy demand predicted value in a preset time period:
；
；
wherein ,representation->Predicted value of power supply at time->Representation->Predicted value of the electrical energy demand at time-> and />Representing a horizontal smoothing constant, +.> and />Representing trend smooth constant, ++>Representation->Electric energy supply quantity collected by the intelligent sensor at moment，/>Representation->Electric energy demand acquired by the intelligent sensor at moment, < >>Representation->Level-smooth predictive value of the power supply quantity at the moment,/->Representation->Level smooth predictive value of the electrical energy demand at time,/->Representation->Trend increment estimation value of electric energy supply quantity at moment, < >>Representation->Trend increment estimation value of electric energy demand at moment;
acquiring a reference electricity price, and adjusting the reference electricity price based on the electric energy supply predicted value and the electric energy demand predicted value to obtain a pricing function;
obtaining an optimal profit objective function of the power generation enterprise by utilizing the pricing function;
and obtaining a target electricity price according to the optimal profit objective function and the pricing function, and completing the electric energy supply analysis of the power generation enterprises.
2. The method for power supply analysis applied to a power generation enterprise according to claim 1, wherein the detecting the historical power supply amount and the historical power demand amount to obtain abnormal data, and rejecting the abnormal data to obtain a standard power supply amount and a standard power demand amount comprises:
Inputting the historical electric energy supply quantity and the historical electric energy demand quantity into a pre-trained electric energy data anomaly detection model to detect to obtain a historical error, wherein the electric energy data anomaly detection model is composed of a GRU deep neural network;
if the historical error is larger than a preset error threshold, the historical electric energy supply amount and the historical electric energy demand amount are abnormal data, and the abnormal data are removed to obtain a standard electric energy supply amount and a standard electric energy demand amount;
and if the historical error is not greater than a preset error threshold, outputting the historical error as the standard electric energy supply quantity and the standard electric energy demand quantity.
3. The method for power supply analysis applied to a power generation enterprise according to claim 2, wherein the step of inputting the historical power supply amount and the historical power demand amount into a pre-trained power data anomaly detection model for detection to obtain a historical error comprises:
taking the historical electric energy supply quantity and the historical electric energy demand quantity as input data, and encoding the input data based on a pre-constructed encoding formula to obtain a data hidden state;
decoding the data hidden state by using an electric energy data anomaly detection model to obtain a reconstruction sequence;
And subtracting the reconstruction sequence from the input data to obtain a history error.
4. A method for power supply analysis for a power generation enterprise as claimed in claim 3, wherein said coding formula is:
；
；
；
wherein ,representation->Implicit status of data of time of day->Representation->Reset gate state of time->Representation->Update door state of time->Time of presentation->Representation->Implicit status of data of time of day->Representing Sigmoid function->Representation->Time of day, historical power supply or historical power demand,/-or-> and />Representing a weight matrix, +.>、/> and />Representing the bias vector.
5. The method for power supply analysis for a power generation enterprise of claim 1, wherein the obtaining a reference electricity price, adjusting the reference electricity price based on the predicted value of power supply and the predicted value of power demand, and before obtaining a pricing function, the method further comprises:
comparing the power supply predicted value with the power demand predicted value in a preset observation period, and calculating a difference value between the power supply predicted value and the power demand predicted value;
outputting an electric energy shortage signal if the electric energy supply predicted value is smaller than the electric energy demand predicted value in the observation period;
And outputting an electric energy surplus signal if the electric energy supply predicted value is larger than the electric energy demand predicted value and the difference is larger than a preset threshold value in the observation period.
6. The method for power supply analysis for a power generation enterprise of claim 5, wherein adjusting the baseline electricity price based on the power supply forecast and the power demand forecast results in a pricing function comprising:
establishing a power utilization utility function based on the power demand predicted value and the reference power price to obtain power utilization utility;
adjusting the predicted value of the electric energy demand according to the electricity utilization effect to obtain a target electric energy demand, wherein the target electric energy demand is:
；
wherein the saidIndicating the target electric energy demand,/->Indicating the utility of electricity, < >>Representing a predicted value of power demand,/-, for>Representing exogenous parameters;
establishing a pricing function based on the target electric energy demand and a new energy supply predicted value, wherein the pricing function is:
；
wherein ,representing the actual electricity price>Indicating a reference electricity price, & lt & gt>Representing new energy supply predictive value, +.> and />Representing pricing parameters.
7. The method for power supply analysis for a power generation enterprise of claim 6, wherein the power usage utility function is:
；
wherein ,indicating the utility of electricity, < >>Representing utility parameters.
8. The method for power supply analysis for a power generation facility in accordance with claim 6, wherein said utilizing said pricing function to obtain an optimal objective function of profit for the power generation facility comprises:
obtaining a cost function by utilizing the new energy supply predicted value and the target electric energy demand, wherein the cost function is as follows:
；
wherein ,representing the realityCost (S)/(S)>Cost parameters representing new energy, +.>Cost parameters representing conventional electrical energy;
obtaining a profit function of the power generation enterprise based on the cost function and the pricing function;
and optimizing the profit function according to the profit maximization principle to obtain the profit optimal objective function.
9. The method for power supply analysis applied to a power generation enterprise according to claim 8 or 6, wherein the profit optimization objective function is:
；
wherein ,indicating the profits of the power generation enterprises.
10. An electrical energy supply analysis device for use in an electrical power generation facility, the device comprising:
the power data acquisition module is used for acquiring historical power supply quantity and historical power demand quantity of a power generation enterprise based on a pre-built intelligent sensor, wherein the historical power supply quantity comprises historical new power supply quantity and historical conventional power supply quantity;
The data anomaly detection module is used for detecting the historical electric energy supply quantity and the historical electric energy demand quantity to obtain anomaly data, and eliminating the anomaly data to obtain a standard electric energy supply quantity and a standard electric energy demand quantity;
the electric energy data prediction module is used for predicting the variation trend of the standard electric energy supply quantity and the standard electric energy demand quantity to obtain an electric energy supply predicted value and an electric energy demand predicted value in a preset time period:
；
；
wherein ,representation->Predicted value of power supply at time->Representation->Predicted value of the electrical energy demand at time-> and />Representing a horizontal smoothing constant, +.> and />Representing trend smooth constant, ++>Representation->Electric energy supply quantity acquired by the intelligent sensor at moment, < >>Representation->Electric energy demand acquired by the intelligent sensor at moment, < >>Representation->Level-smooth predictive value of the power supply quantity at the moment,/->Representation->Level smooth predictive value of the electrical energy demand at time,/->Representation->Trend increment estimation value of electric energy supply quantity at moment, < >>Representation->Trend increment estimation value of electric energy demand at moment;
the profit optimization module is used for obtaining a reference electricity price, and adjusting the reference electricity price based on the electric energy supply predicted value and the electric energy demand predicted value to obtain a pricing function;
Obtaining an optimal profit objective function of the power generation enterprise by utilizing the pricing function;
and the target electricity price acquisition module is used for acquiring the target electricity price according to the optimal profit target function and the pricing function and completing the electric energy supply analysis of the power generation enterprises.
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