CN115641175A - Medium-and-long-term power transaction assistant decision-making determination method and device for new energy power station - Google Patents
Medium-and-long-term power transaction assistant decision-making determination method and device for new energy power station Download PDFInfo
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Abstract
The invention provides a method and a device for determining a middle-long term electric power transaction aid decision of a new energy power station, belonging to the field of electric power transaction and comprising the following steps: acquiring first historical meteorological data, and determining predicted meteorological data of a corresponding area of the new energy power station in a preset time period; inputting the predicted meteorological data to a meteorological transformation power generation model to obtain predicted power generation output by the meteorological transformation power generation model, wherein the meteorological transformation power generation model is determined based on second historical meteorological data and historical power generation data corresponding to the new energy power station; determining a predicted electricity price corresponding to the new energy power station based on a preset time series model; and determining an electric power transaction auxiliary decision according to the predicted meteorological data, the predicted power generation amount and the predicted electricity price. According to the method, the quotation strategy and the report strategy are optimized on the basis of forecasting meteorological data, forecasting generated energy and forecasting electricity price, the electric power transaction auxiliary decision is determined, and the problem of low automation efficiency of power generation enterprises such as manual transaction, manual statistics and manual replication is solved.
Description
Technical Field
The invention relates to the technical field of electric power transaction, in particular to a method and a device for determining a medium-long term electric power transaction auxiliary decision of a new energy power station.
Background
With the continuous deepening of the national power reform, the focus of the power market main body gradually returns to the medium-and-long-term market capable of locking the income from the spot market, and the contract of the medium-and-long-term market signs the decisive factor of the medium-and-long-term market, wherein the first factor is the accurate mastering of the self power generation capacity of the enterprise and the accurate prediction of the future power generation capacity of the enterprise, and the second factor is the prediction of the supply and demand condition of the whole market and the wind direction of the medium-and-long-term spot price. The existing auxiliary decision-making method for the medium and long term transaction of the new energy needs manual establishment of transaction strategies, manual statistics and manual duplication, and is low in efficiency.
Disclosure of Invention
The invention provides a method and a device for determining a medium-and-long-term electric power transaction aid decision of a new energy power station, which are used for solving the defects that in the prior art, transaction strategies need to be made manually, manual statistics and manual duplication are needed, and the efficiency is low.
The invention provides a middle-long term electric power transaction assistant decision-making determination method of a new energy power station, which comprises the following steps:
acquiring first historical meteorological data, and determining predicted meteorological data of a corresponding area of the new energy power station in a preset time period;
inputting the predicted meteorological data to a meteorological transformation power generation model to obtain predicted power generation output by the meteorological transformation power generation model, wherein the meteorological transformation power generation model is determined based on second historical meteorological data and historical power generation data corresponding to the new energy power station;
determining a predicted electricity price corresponding to the new energy power station based on a preset time series model;
and determining an electric power transaction auxiliary decision according to the predicted meteorological data, the predicted power generation amount and the predicted electricity price.
According to the method for determining the medium-term and long-term power transaction auxiliary decision of the new energy power station, the power transaction auxiliary decision is determined according to the predicted meteorological data, the predicted power generation amount and the predicted power price, and the method comprises the following steps:
obtaining predicted supply and demand data according to the predicted meteorological data and the predicted power generation amount;
determining declared electric quantity and declared electrovalence of the new energy power station in a preset time period according to the predicted supply and demand data and the predicted electrovalence;
and optimizing the declared electric quantity and the declared power price based on the electric power market data and the electric power spot market data in a preset time period to obtain an optimal signed electric quantity and an optimal signed power price so as to determine an electric power transaction auxiliary decision.
According to the auxiliary decision-making determination method for the medium-and-long-term electric power transaction of the new energy power station, the predicted supply and demand data are obtained according to the predicted meteorological data and the predicted power generation amount, and the method comprises the following steps:
extracting meteorological feature data from the predicted meteorological data based on a convolutional neural network;
determining the predicted load of the new energy power station according to the predicted power generation amount;
inputting the meteorological characteristic data, the historical available power of the new energy power station, the actual grid-connected power and the predicted load into a probability model to obtain predicted supply and demand data, wherein the predicted supply and demand data comprise predicted supply and demand conditions and predicted probabilities corresponding to the predicted supply and demand conditions.
According to the method for determining the middle-long term power transaction assistant decision of the new energy power station, the step of obtaining the first historical meteorological data and determining the predicted meteorological data of the corresponding area of the new energy power station in the preset time period comprises the following steps:
acquiring first historical meteorological data, and acquiring gridding meteorological data according to the first historical meteorological data;
decomposing the gridding meteorological data based on an empirical orthogonal function, and determining the predicted meteorological data of the corresponding area of the new energy power station in a preset time period.
According to the auxiliary decision-making determination method for the medium-long term power transaction of the new energy power station, which is provided by the invention, the meteorological transformation power generation model is determined based on the following modes:
acquiring second historical meteorological data and historical power generation data corresponding to the new energy power station;
determining a mapping relationship between the second historical meteorological data and the historical power generation data based on a plurality of initialization models respectively; the initialization model comprises a time sequence statistics model, a machine learning model, an optimization algorithm model and a deep learning model;
and model fusion is carried out on the initialization models of the target number based on preset evaluation parameters and the mapping relation, so as to obtain the meteorological transformation power generation model.
According to the method for determining the auxiliary decision-making of the medium-term and long-term power transaction of the new energy power station, the corresponding predicted electricity price of the new energy power station is determined based on the preset time series model, and the method comprises the following steps:
improving the bidirectional LSTM model based on an ELU activation function to obtain a first electricity price prediction model;
obtaining historical electricity price characteristic data and historical electricity price data to train the first electricity price prediction model to obtain a second electricity price prediction model;
and determining the corresponding predicted electricity price of the new energy power station according to the second electricity price prediction model.
The invention provides a method for determining a medium-long term electric power transaction assistant decision of a new energy power station, which further comprises the following steps:
and acquiring an electric power transaction contract, decomposing the electric power transaction contract, and determining an electric power transaction auxiliary decision according to the decomposed data.
The invention also provides a device for determining the medium-and-long-term power transaction aid decision of the new energy power station, which comprises:
the system comprises a forecast meteorological data determining module, a forecasting meteorological data determining module and a forecasting meteorological data analyzing module, wherein the forecast meteorological data determining module is used for acquiring first historical meteorological data and determining forecast meteorological data of a corresponding area of a new energy power station in a preset time period;
the predicted power generation amount determining module is used for inputting the predicted meteorological data to a meteorological transformation power generation model to obtain the predicted power generation amount output by the meteorological transformation power generation model, wherein the meteorological transformation power generation model is determined based on second historical meteorological data and historical power generation data corresponding to the new energy power station;
the predicted electricity price determining module is used for determining the predicted electricity price corresponding to the new energy power station based on a preset time series model;
and the auxiliary decision determining module is used for determining an auxiliary decision of the electric power transaction according to the predicted meteorological data, the predicted power generation amount and the predicted electricity price.
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the auxiliary decision-making method for the medium-long term power transaction of the new energy power station.
The invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of medium and long term power transaction aid decision determination for a new energy power station.
According to the method and the device for determining the medium-and-long-term electric power transaction auxiliary decision of the new energy power station, the quotation strategy and the report strategy are optimized on the basis of meteorological data prediction, generated energy prediction and electricity price prediction, the electric power transaction auxiliary decision is determined, and the problem of low pain point of the existing power generation enterprises in automation efficiency such as manual transaction, manual statistics and manual replication is solved.
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In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of a method for determining a medium-and-long-term electric power transaction aid decision of a new energy power station according to an embodiment of the present invention;
FIG. 2 is a schematic flowchart of step S110 in FIG. 1 according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a method for constructing a meteorological transformation power generation model according to an embodiment of the invention;
FIG. 4 is a flowchart illustrating step S130 in FIG. 1 according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating step S140 in FIG. 1 according to an embodiment of the present invention;
FIG. 6 is a functional block diagram of a joint gaming model provided by embodiments of the present invention;
fig. 7 is a schematic block diagram of a medium-and-long-term power transaction aid decision-making determination device of a new energy power station according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Fig. 1 is a schematic flow chart of a method for determining a medium-and-long-term power transaction aid decision of a new energy power station according to an embodiment of the present invention, and referring to fig. 1, the present invention provides a method for determining a medium-and-long-term power transaction aid decision of a new energy power station, including:
s110, acquiring first historical meteorological data, and determining predicted meteorological data of a corresponding area of the new energy power station in a preset time period;
s120, inputting the predicted meteorological data to a meteorological transformation power generation model to obtain predicted power generation output by the meteorological transformation power generation model, wherein the meteorological transformation power generation model is determined based on second historical meteorological data and historical power generation data corresponding to the new energy power station;
s130, determining a predicted electricity price corresponding to the new energy power station based on a preset time series model;
and S140, determining an electric power transaction auxiliary decision according to the predicted meteorological data, the predicted power generation amount and the predicted electricity price.
Optionally, in step S110, the first historical meteorological data may be historical meteorological data of a geographical location where the new energy power station is located, or may also be global meteorological data.
Alternatively, the predicted weather data may be obtained directly from a weather station.
Optionally, the new energy power station in the invention is a wind power station, and the predicted meteorological data includes a plurality of meteorological elements related to wind speed, such as temperature, pressure, wind speed, wind direction, irradiance, and the like.
Optionally, the preset time period is a medium-long period, specifically 45 days, and it can be known that the method is a medium-long period power transaction assistant decision-making determination method.
Optionally, in step S120, based on the predicted meteorological data, it is possible to predict the future multi-day and multi-month power generation amount, and the day-ahead prediction of the power station is considered in the medium-long term decomposition in the electric power spot transaction, and on the other hand, the monthly power generation plan, the residual power generation capacity, the day-ahead and real-time price relationship, and the like are considered. The prediction result related to the power generation amount prediction can be used as a main basis for medium and long term decomposition on one hand, and can be used as a certain reference for medium and long term annual and monthly transactions on the other hand. The method mainly comprises the steps of forecasting the power generation amount in multiple dimensions of days and months, manually maintaining and correcting actual data, analyzing the accuracy, inquiring, counting and recording the historical actual power generation amount in months.
Optionally, in step S130, the method uses a two-factor non-stationary sequence equation and a generalized autoregressive conditional variance equation to establish a random load model, and constructs a random planning model of optimal contract decomposition with probability that an actual standard deviation is smaller than an expected standard deviation as an opportunity constraint. The Monte Carlo random simulation and the augmented Lagrange genetic algorithm are combined to solve, and the model and the method are checked by adopting a domestic market actual data construction example.
Optionally, in step S140, various contract signing policies are given by simulating and predicting the spot market clearing price, and calculating various combinations of benefits and risks according to the medium-and-long term contract benefits and the restriction management of contract positions. The overall income condition of medium and long term contracts under different medium and long term positions and different simulation and prediction of the clear price of the spot market is researched; the research is based on the income analysis research under the condition of declaring the electric power under different confidence intervals; the method comprises the following steps of researching how to distribute signing of various types of contracts based on different transaction types of the power station, and ensuring the overall income maximization of the power station; the problem of profit balance is solved under the linkage of medium and long term contracts with the spot market and the real-time market.
The method and the device can optimize the quotation strategy and the report strategy on the basis of meteorological data prediction, power generation amount prediction and power price prediction to determine the electric power transaction auxiliary decision, and solve the problem of low pain point of the automatic efficiency of manual transaction, manual statistics, manual replication and the like of the power generation enterprises at present.
Fig. 2 is a schematic flowchart of the step S110 in fig. 1 according to an embodiment of the present invention, and with reference to fig. 2, on the basis of the above embodiment, as an optional embodiment, the acquiring first historical meteorological data and determining predicted meteorological data of an area corresponding to a new energy power station within a preset time period includes:
s210, acquiring first historical meteorological data, and obtaining gridding meteorological data according to the first historical meteorological data;
and S220, decomposing the gridding meteorological data based on an empirical orthogonal function, and determining the predicted meteorological data of the corresponding area of the new energy power station in a preset time period.
Optionally, in step S210, the first historical meteorological data includes European Centre (ECMWF) ERA-interior data and historical 30-year data of re-analysis data of the us NCAR/NCEP. And driving an atmospheric mode by using the first historical meteorological data as an initial field, and simulating to obtain gridding meteorological data.
Optionally, the grid meteorological data can be corrected or assimilated by combining with the years of observation data.
Optionally, in step S220, the empirical orthogonal function is suitable for analyzing the original sequence or the flat spatial distribution characteristics of each climate element, and can decompose irregularly distributed stations in a limited region, so that the expansion and convergence are fast, information of the variable field is easily concentrated on several modes, and the separated spatial structure has a certain physical significance.
And a method combining climate power and climate statistics is adopted to carry out medium-term and long-term wind power generation amount prediction. And (3) obtaining the atmospheric circulation forms of different months in the future for prediction on the basis of taking the climate mode result of the current mainstream meteorological mechanism as a climate dynamics prediction result, and predicting the meteorological elements of the predicted region by combining the atmospheric low-frequency oscillation and the remote correlation theory. Meanwhile, according to the characteristic that the long-term meteorological or climatic time series contains different time scale oscillations, a statistical method is utilized to generate a periodic basis function with various lengths for the historical multi-year time series of the key meteorological elements related to the wind speed in the forecast area, and the modeling prediction is carried out on the change of the key meteorological elements in the future period on the basis of the basis function.
The invention considers seasonal forecasting factors in the temperate zone, utilizes the remote correlation and the low-frequency change characteristics of atmospheric circulation, and particularly considers remote correlation information of ocean activities, such as Ernino-southern billow (ENSO), north Atlantic billow (NAO) and the like. Therefore, in medium-and long-term electric quantity forecasting, multi-time scale resolution forecasting of the future hour, day, month, year and the like can be carried out, and extreme values can be well forecasted.
It can be understood that the accuracy of weather data prediction is improved by performing back calculation on historical weather resources and partitioning the weather resources.
FIG. 3 is a schematic flow chart of a method for constructing a meteorological transformation power generation model according to an embodiment of the invention; referring to fig. 3, on the basis of the above embodiment, as an alternative embodiment, the meteorological transformation power generation model is determined based on the following manner:
s310, acquiring second historical meteorological data and historical power generation data corresponding to the new energy power station;
s320, respectively determining the mapping relation between the second historical meteorological data and the historical power generation data based on a plurality of initialization models; the initialization model comprises a time sequence statistics model, a machine learning model, an optimization algorithm model and a deep learning model;
and S330, performing model fusion on the target number of the initialization models based on preset evaluation parameters and the mapping relation to obtain the meteorological transformation power generation model.
Optionally, the meteorological transformation power generation model is a high-precision medium-and-long-term power generation prediction model, a meteorological transformation power model which is divided into regions or is accurate to a single machine is established based on historical meteorological resource data of the station and power data under actual working conditions, and the meteorological observation station data is used for correcting to deduce predicted power generation in the future 45 days.
Optionally, the method adopts various model selection and model fusion modes to predict the medium-term and long-term power generation, and mainly comprises a time sequence statistics model, a machine learning model, an optimization algorithm model and a deep learning model, then an evaluation target is set, an optimal model is selected and model fusion is carried out, and finally a medium-term and long-term power generation prediction result is output.
The time sequence statistic model comprises a seasonal autoregressive moving average model, a grey prediction model, an online sequence extreme learning machine and the like; the machine learning model comprises a random forest, a gradient lifting tree, a support vector machine, an elastic net and the like; the optimization algorithm model comprises a genetic algorithm, a particle swarm optimization, an artificial fish swarm algorithm, a squirrel optimization algorithm and the like; the deep learning type model comprises a convolutional neural network, a long-short term memory network, a fuzzy neural network, a deep forest, a bert, a transformer and the like.
Optionally, the model fusion refers to performing average or weighted average on the output result of the selected optimal initialization model to obtain high-precision predicted power generation amount.
Optionally, in the extreme weather prediction, if the difference between the prediction data and the recent real weather is found to be large, the long-term power generation amount prediction result can be manually determined, so that more accurate input information is provided for making a decision for power trading.
The method can realize the auxiliary decision support of medium-term and long-term trading and improve the accuracy of the auxiliary decision.
FIG. 4 is a flowchart illustrating step S130 in FIG. 1 according to an embodiment of the present invention; referring to fig. 4, on the basis of the foregoing embodiment, as an optional embodiment, the determining, based on a preset time series model, a predicted electricity price corresponding to the new energy power station includes:
s410, improving the bidirectional LSTM model based on an ELU activation function to obtain a first electricity price prediction model;
s420, obtaining historical electricity price characteristic data and historical electricity price data to train the first electricity price prediction model to obtain a second electricity price prediction model;
and S430, determining the corresponding predicted electricity price of the new energy power station according to the second electricity price prediction model.
Optionally, the historical electricity price characteristic data includes data of system load, new energy output, power coal price fluctuation, incoming water condition, unit quotation, line transmission limit, medium and long-term weather, extreme weather such as snowfall, cold tide, ice coating, sand storm, strong wind and the like.
Optionally, for the problem that the electricity price data is influenced by multiple factors, the data is preprocessed by using a fuzzy similarity principle, a neural network algorithm is introduced into the electricity price prediction model, and the sample data is learned and trained according to the correlation relationship among the time sequences, so that the experimental error is reduced.
Aiming at the problem of gradient disappearance in the back propagation calculation process, an E-BLSTM model for predicting the electricity price at the power generation side of the power market is designed according to the sensitivity of the power generation side of the power market to the electricity price and the characteristics of linearity and nonlinearity of the electricity price. The LSTM is used for keeping the long-term memory characteristic, the sigmoid function and the tanh function are improved, three types of valves are added to the LSTM model, the ELU activation function is introduced into the bidirectional LSTM electricity price prediction model, the step length is increased, and the problem of gradient disappearance is solved. And using an optimized ADAM gradient descent algorithm, iteratively updating the weight of the neural network according to the training data, selecting an optimal loss function, and improving the accuracy of the electricity price prediction.
It can be understood that the invention provides a bidirectional LSTM model based on an ELU activation function to predict the short-term and medium-term electricity price change of the power generation side of the power market by using the relevant theory of deep learning. Mainly aiming at the defects and shortcomings of the recurrent neural network, the method adopts the LSTM model by analyzing factors influencing electricity price, designs the E-BLSTM model after optimizing and improving the activation function, performs experimental analysis, and proves the accuracy of the model under the condition of reaching convergence by finite iterations.
Compared with the common LSTM model, the E-BLSTM model designed by the invention is compared with the common LSTM model, and compared with the SARIMA model, the experiment proves that the algorithm can converge to a lower loss rate, can accurately predict the electricity price with larger fluctuation at the power market supply side, and proves the effectiveness and the convergence of the model.
FIG. 5 is a schematic flowchart of step S140 in FIG. 1 according to an embodiment of the present invention; referring to fig. 5, on the basis of the above embodiment, as an alternative embodiment, the determining an electric power transaction aid decision according to the predicted meteorological data, the predicted power generation amount and the predicted electricity price includes:
s510, obtaining predicted supply and demand data according to the predicted meteorological data and the predicted power generation amount;
s520, determining declared electric quantity and declared electrovalence of the new energy power station in a preset time period according to the predicted supply and demand data and the predicted electrovalence;
and S530, optimizing the declared electric quantity and the declared price based on the electric power market data and the electric power spot market data in a preset time period to obtain an optimal signed electric quantity and an optimal signed price so as to determine an electric power transaction auxiliary decision.
Optionally, the obtaining of the predicted supply and demand data according to the predicted meteorological data and the predicted power generation amount includes:
extracting meteorological feature data from the predicted meteorological data based on a convolutional neural network;
determining the predicted load of the new energy power station according to the predicted power generation amount;
inputting the meteorological characteristic data, the historical available power of the new energy power station, the actual grid-connected power and the predicted load into a probability model to obtain predicted supply and demand data, wherein the predicted supply and demand data comprise predicted supply and demand conditions and predicted probabilities corresponding to the predicted supply and demand conditions.
FIG. 6 is a functional block diagram of a joint gaming model provided by embodiments of the present invention; referring to fig. 6, optionally, for a multi-sequence mode of a medium-and-long-term power market and a power spot market in a power market, a combined game model with a double-layer structure is constructed in the invention; the outer layer is a new energy power station reporting decision layer, and the strategy is designed according to the self operation characteristics and rules of the new energy power station: extracting multi-layer numerical weather resource forecast map data of the region based on a data source of a current mainstream mechanism according to historical available power and actual grid-connected power, generating characteristics of the region weather resource map through a convolutional neural network, and bringing the characteristics, the actual available power and the grid-connected power into a Bayesian probability model to predict future supply and demand conditions and probability.
Accurately predicting the next-day coming clear electricity price by using a wavelet decomposition time series model; determining declared electric quantity of electric power market transaction at each stage according to the predicted supply and demand conditions and the design strategy of the clear electricity price; the inner layer is a multi-time sequence trading decision layer, and the bid amount in the new energy power station is obtained according to the trading clearing principle and the power station declaration optimization clearing according to various time sequence market varieties contained in the medium-long term power market and the power spot market. The outer layer model optimizes the interval by a decision-making yield curve by adopting a heuristic optimization method aiming at maximizing the inner layer decision-making yield, and the inner layer model optimizes the optimization parameter of each interval of the decision-making yield curve by a random integer mixed optimization method on the basis of the decision result of the parameterized outer layer model; and iterating the internal and external models in an alternate solving mode, and finally outputting an optimal day-ahead declaration curve, sectional quotation information and medium-long term time-sharing recommended quantity and price.
On the basis of the above embodiment, as an optional embodiment, the method further includes:
and acquiring an electric power transaction contract, decomposing the electric power transaction contract, and determining an electric power transaction auxiliary decision according to the decomposed data.
Optionally, the method manages the medium-and-long-term contracts of the market main body, performs entry maintenance on various components (base numbers and market) quantities and prices of the medium-and-long-term contracts by taking monthly as a statistical period, performs monthly contract electric quantity decomposition, completion condition tracking and rolling balance, provides a reference basis for medium-and-long-term decomposition in daily transactions and electric power declared in the day and the future, and performs auxiliary decision support on the medium-and-long-term transactions by combining medium-and-long-term electric quantity prediction and medium-and-long-term market supply and demand conditions and market price prediction.
It can be understood that the invention gives various contract signing strategies by simulating and predicting the spot market clearing price and calculating various combinations of benefits and risks according to the medium-long term contract benefits and the limited management of the contract position. Researching the overall income condition of medium and long term contracts under the conditions of different medium and long term bin positions and different simulation prediction spot market clearing prices; the research is based on the income analysis research under the condition of declaring the electric power under different confidence intervals; the method is used for researching how to distribute signing of various types of contracts based on different transaction types of the power station, and the overall income maximization of the power station is ensured; and the problem of income balance is solved under the linkage of medium and long term contracts with the spot market and the real-time market.
The medium-and-long-term power transaction assistant decision-making determination device of the new energy power station provided by the invention is described below, and the medium-and-long-term power transaction assistant decision-making determination device of the new energy power station described below and the medium-and-long-term power transaction assistant decision-making determination method of the new energy power station described above can be correspondingly referred to each other.
Fig. 7 is a schematic block diagram of a medium-and-long-term power transaction aid decision-making determination device of a new energy power station according to an embodiment of the present invention; referring to fig. 7, the present invention further provides a medium-and-long-term power transaction aid decision-making determination apparatus for a new energy power station, including:
the forecast meteorological data determining module 710 is configured to obtain first historical meteorological data and determine forecast meteorological data of an area corresponding to the new energy power station within a preset time period;
the predicted power generation amount determining module 720 is configured to input the predicted meteorological data to a meteorological transformation power generation model to obtain the predicted power generation amount output by the meteorological transformation power generation model, where the meteorological transformation power generation model is determined based on second historical meteorological data and historical power generation data corresponding to the new energy power station;
the predicted electricity price determining module 730 is used for determining the predicted electricity price corresponding to the new energy power station based on a preset time series model;
and an auxiliary decision determining module 740, configured to determine an auxiliary decision for power transaction according to the predicted meteorological data, the predicted power generation amount, and the predicted electricity price.
As an embodiment, the assistant decision module 740 is further configured to:
obtaining predicted supply and demand data according to the predicted meteorological data and the predicted generated energy;
determining declared electric quantity and declared electrovalence of the new energy power station in a preset time period according to the predicted supply and demand data and the predicted electrovalence;
and optimizing the declared electric quantity and the declared power price based on the electric power market data and the electric power spot market data in a preset time period to obtain an optimal signed electric quantity and an optimal signed power price so as to determine an electric power transaction auxiliary decision.
As an embodiment, the assistant decision module 740 is further configured to:
extracting meteorological feature data from the predicted meteorological data based on a convolutional neural network;
determining the predicted load of the new energy power station according to the predicted power generation amount;
inputting the meteorological characteristic data, the historical available power of the new energy power station, the actual grid-connected power and the predicted load into a probability model to obtain predicted supply and demand data, wherein the predicted supply and demand data comprise predicted supply and demand conditions and predicted probabilities corresponding to the predicted supply and demand conditions.
As an embodiment, the predictive weather data determination module 710 is further configured to:
acquiring first historical meteorological data, and acquiring gridding meteorological data according to the first historical meteorological data;
decomposing the gridding meteorological data based on an empirical orthogonal function, and determining the predicted meteorological data of the corresponding area of the new energy power station in a preset time period.
As an embodiment, the predicted power generation amount determination module 720 is further configured to:
acquiring second historical meteorological data and historical power generation data corresponding to the new energy power station;
determining a mapping relation between the second historical meteorological data and the historical power generation data respectively based on a plurality of initialization models; the initialization model comprises a time sequence statistic model, a machine learning model, an optimization algorithm model and a deep learning model;
and model fusion is carried out on the target number of the initialization models based on preset evaluation parameters and the mapping relation, so that the meteorological transformation power generation model is obtained.
As an embodiment, the predictive electricity prices determination module 730 is further configured to:
improving the bidirectional LSTM model based on an ELU activation function to obtain a first electricity price prediction model;
obtaining historical electricity price characteristic data and historical electricity price data to train the first electricity price prediction model to obtain a second electricity price prediction model;
and determining the corresponding predicted electricity price of the new energy power station according to the second electricity price prediction model.
As an embodiment, the present invention further comprises:
and the contract management module is used for acquiring the electric power transaction contract, decomposing the electric power transaction contract and determining an electric power transaction auxiliary decision according to the decomposed data.
The invention also provides a medium-and-long-term electric power transaction assistant decision support system of the new energy power station, which comprises an electric power transaction assistant decision support system cloud platform, an electric power transaction assistant decision server, an electric power transaction cloud platform application server and a service platform server which are arranged at the side of the Internet cloud platform, a power station power prediction server, a meteorological server and an external power station workstation which are arranged at the side of the power station, wherein the power station power prediction server is connected with the meteorological server through an isolator and sends power, wind speed/irradiance files to the meteorological server at set time intervals (such as 15 minutes); the method comprises the steps that a meteorological server transmits data ftp back to an electric power transaction auxiliary decision server in real time, a power station external network workstation is connected with an electric power transaction cloud platform application server and used for accessing an electric power transaction auxiliary system, the electric power transaction cloud platform application server is connected with the electric power transaction auxiliary decision server, the electric power transaction auxiliary decision server obtains the ftp of a service platform server, and the ftp is downloaded, analyzed and stored in a warehouse and sent to an ftp write-back file.
Fig. 8 illustrates a physical structure diagram of an electronic device, and as shown in fig. 8, the electronic device may include: a processor (processor) 810, a communication Interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication Interface 820 and the memory 830 communicate with each other via the communication bus 840. The processor 810 may invoke logic instructions in the memory 830 to perform a method for medium and long term power transaction assistance decision determination for a new energy power plant, comprising:
acquiring first historical meteorological data, and determining predicted meteorological data of a corresponding area of the new energy power station in a preset time period;
inputting the predicted meteorological data to a meteorological transformation power generation model to obtain predicted power generation output by the meteorological transformation power generation model, wherein the meteorological transformation power generation model is determined based on second historical meteorological data and historical power generation data corresponding to the new energy power station;
determining a predicted electricity price corresponding to the new energy power station based on a preset time series model;
and determining an electric power transaction auxiliary decision according to the predicted meteorological data, the predicted power generation amount and the predicted electricity price.
In addition, the logic instructions in the memory 830 can be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of executing a method for medium and long term power transaction assistance decision determination for a new energy power station, comprising:
acquiring first historical meteorological data, and determining predicted meteorological data of a corresponding area of the new energy power station in a preset time period;
inputting the predicted meteorological data to a meteorological transformation power generation model to obtain predicted power generation output by the meteorological transformation power generation model, wherein the meteorological transformation power generation model is determined based on second historical meteorological data and historical power generation data corresponding to the new energy power station;
determining a predicted electricity price corresponding to the new energy power station based on a preset time series model;
and determining an electric power transaction auxiliary decision according to the predicted meteorological data, the predicted power generation amount and the predicted electricity price.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which when executed by a processor is implemented to execute a method for medium and long term power transaction aid decision determination for a new energy power station, comprising:
acquiring first historical meteorological data, and determining predicted meteorological data of a corresponding area of the new energy power station in a preset time period;
inputting the predicted meteorological data to a meteorological transformation power generation model to obtain predicted power generation output by the meteorological transformation power generation model, wherein the meteorological transformation power generation model is determined based on second historical meteorological data and historical power generation data corresponding to the new energy power station;
determining a predicted electricity price corresponding to the new energy power station based on a preset time series model;
and determining an electric power transaction auxiliary decision according to the predicted meteorological data, the predicted power generation amount and the predicted electricity price.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on the understanding, the above technical solutions substantially or otherwise contributing to the prior art may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A middle-long term electric power transaction assistant decision-making determination method of a new energy power station is characterized by comprising the following steps:
acquiring first historical meteorological data, and determining predicted meteorological data of a corresponding area of the new energy power station in a preset time period;
inputting the predicted meteorological data to a meteorological transformation power generation model to obtain predicted power generation output by the meteorological transformation power generation model, wherein the meteorological transformation power generation model is determined based on second historical meteorological data and historical power generation data corresponding to the new energy power station;
determining a predicted electricity price corresponding to the new energy power station based on a preset time series model;
and determining an electric power transaction auxiliary decision according to the predicted meteorological data, the predicted power generation amount and the predicted electricity price.
2. The method for determining the auxiliary decision of the medium-term and long-term power transaction of the new energy power station as claimed in claim 1, wherein the step of determining the auxiliary decision of the power transaction according to the predicted meteorological data, the predicted power generation amount and the predicted power price comprises the following steps:
obtaining predicted supply and demand data according to the predicted meteorological data and the predicted power generation amount;
determining declared electric quantity and declared electrovalence of the new energy power station in a preset time period according to the predicted supply and demand data and the predicted electrovalence;
and optimizing the declared electric quantity and the declared price to obtain an optimal signed electric quantity and an optimal signed price to determine an electric power transaction aid decision based on electric power market data and electric power spot market data in a preset time period.
3. The method for determining the auxiliary decision of the medium-and-long-term electric power transaction of the new energy power station as claimed in claim 2, wherein the step of obtaining the predicted supply and demand data according to the predicted meteorological data and the predicted power generation amount comprises the following steps:
extracting meteorological feature data from the predicted meteorological data based on a convolutional neural network;
determining the predicted load of the new energy power station according to the predicted power generation amount;
inputting the meteorological characteristic data, the historical available power of the new energy power station, the actual grid-connected power and the predicted load into a probability model to obtain predicted supply and demand data, wherein the predicted supply and demand data comprise predicted supply and demand conditions and predicted probabilities corresponding to the predicted supply and demand conditions.
4. The method for assisting decision-making in medium-and-long-term power transaction of the new energy power station as claimed in claim 1, wherein the step of obtaining the first historical meteorological data and determining the predicted meteorological data of the corresponding area of the new energy power station in the preset time period comprises the following steps:
acquiring first historical meteorological data, and acquiring gridding meteorological data according to the first historical meteorological data;
decomposing the gridding meteorological data based on an empirical orthogonal function, and determining the predicted meteorological data of the corresponding area of the new energy power station in a preset time period.
5. The method for assisting decision-making in medium-and-long-term power transaction of a new energy power station as claimed in claim 1, wherein the meteorological transformation power generation model is determined based on the following manner:
acquiring second historical meteorological data and historical power generation data corresponding to the new energy power station;
determining a mapping relation between the second historical meteorological data and the historical power generation data respectively based on a plurality of initialization models; the initialization model comprises a time sequence statistics model, a machine learning model, an optimization algorithm model and a deep learning model;
and model fusion is carried out on the target number of the initialization models based on preset evaluation parameters and the mapping relation, so that the meteorological transformation power generation model is obtained.
6. The method for assisting decision-making in medium-and-long-term power transaction of the new energy power station as claimed in claim 1, wherein the step of determining the corresponding predicted power rate of the new energy power station based on a preset time series model comprises the steps of:
improving the bidirectional LSTM model based on an ELU activation function to obtain a first electricity price prediction model;
obtaining historical electricity price characteristic data and historical electricity price data to train the first electricity price prediction model to obtain a second electricity price prediction model;
and determining the corresponding predicted electricity price of the new energy power station according to the second electricity price prediction model.
7. The method for assisting decision-making in medium-and-long-term power transaction of a new energy power station as claimed in claim 1, further comprising:
and acquiring an electric power transaction contract, decomposing the electric power transaction contract, and determining an electric power transaction auxiliary decision according to the decomposed data.
8. A middle-long term electric power transaction assistant decision-making determination device of a new energy power station is characterized by comprising:
the system comprises a forecast meteorological data determining module, a forecasting meteorological data determining module and a forecasting meteorological data analyzing module, wherein the forecast meteorological data determining module is used for acquiring first historical meteorological data and determining forecast meteorological data of a corresponding area of a new energy power station in a preset time period;
the predicted power generation amount determining module is used for inputting the predicted meteorological data to a meteorological transformation power generation model to obtain the predicted power generation amount output by the meteorological transformation power generation model, wherein the meteorological transformation power generation model is determined based on second historical meteorological data and historical power generation data corresponding to the new energy power station;
the predicted electricity price determining module is used for determining the predicted electricity price corresponding to the new energy power station based on a preset time series model;
and the auxiliary decision determining module is used for determining an auxiliary decision of the electric power transaction according to the predicted meteorological data, the predicted power generation amount and the predicted electricity price.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements a method for medium and long term power transaction aid decision determination of a new energy power station according to any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the method for determining a medium-and-long term power trading assistance decision for a new energy power station according to any one of claims 1 to 7.
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