CN110705771A

CN110705771A - Method and device for predicting and optimizing power generation power of new energy of regional power grid

Info

Publication number: CN110705771A
Application number: CN201910916345.4A
Authority: CN
Inventors: 杨健; 江长明; 柳玉; 牛四清; 李丹; 张勇; 陈之栩; 邓立; 郭骏; 史沛然
Original assignee: State Grid Corp of China SGCC; North China Electric Power Research Institute Co Ltd; North China Grid Co Ltd
Current assignee: State Grid Corp of China SGCC; North China Electric Power Research Institute Co Ltd; North China Grid Co Ltd
Priority date: 2019-09-26
Filing date: 2019-09-26
Publication date: 2020-01-17
Anticipated expiration: 2039-09-26
Also published as: CN110705771B

Abstract

The invention provides a method and a device for predicting and optimizing the generated power of a new energy of a regional power grid, wherein a simplified new energy-electricity conversion model of a new energy power generation station is established according to historical operation data; acquiring a total predicted error of the new energy power generation power according to the station predicted data and the station measured data; obtaining equivalent prediction data according to the simplified new energy-electricity conversion model, the station prediction meteorological data, the station prediction data, the station actual measurement data and the operation data; obtaining errors caused by each key link of the new energy power generation power prediction of the new energy power generation station according to the total new energy power generation power prediction error and the equivalent prediction data; and optimizing each key link of the new energy power generation power prediction according to the error caused by each key link, so that the power prediction precision can be effectively improved.

Description

Method and device for predicting and optimizing power generation power of new energy of regional power grid

Technical Field

The invention relates to the technical field of new energy power generation control, in particular to a method and a device for predicting and optimizing new energy power generation power of a regional power grid.

Background

The new energy generally refers to renewable energy developed and utilized on the basis of new technology, including solar energy, biomass energy, wind energy, geothermal energy, wave energy, ocean current energy, tidal energy, hydrogen energy and the like.

Most new energy sources have intermittency, randomness and volatility, and when the permeability of the new energy sources exceeds a certain proportion, the safe operation of a power system can be seriously influenced. The prediction of the new energy power generation power of the new energy power generation station is a core technology for guaranteeing safe and reliable operation of a high-proportion new energy power system, and a power grid dispatching department makes dispatching plans of various power sources according to the predicted new energy power generation power, namely, the new energy power generation is brought into a conventional power generation plan so as to be convenient for better management and utilization of the new energy power generation, so that the prediction accuracy of the new energy power generation power is directly related to the problems of power grid peak regulation, unit combination, unit economic operation and the like.

However, the current prediction level of the new energy power generation output cannot meet the actual operation requirement of the power system; in order to improve the prediction accuracy of the new energy power generation power, the factors influencing the prediction of the new energy power generation power can be analyzed by evaluating the prediction error of the new energy power generation power, and the prediction of the new energy power generation power is adjusted according to the influencing factors, so that the prediction accuracy of the new energy power generation power is improved.

The new energy power generation power prediction error evaluation technology adopted by the scheduling side of the existing regional power grid is mainly based on a macro overall result of a prediction result and an actual result, errors caused by each key link of new energy power generation power prediction of each new energy power generation station cannot be analyzed quantitatively, corresponding optimization measures cannot be carried out in a targeted mode, and power prediction accuracy is not improved favorably.

Disclosure of Invention

The invention provides a method, a device, electronic equipment and a computer-readable storage medium for predicting and optimizing the generated power of a new energy of a regional power grid, which can at least partially solve the problems in the prior art.

In order to achieve the purpose, the invention adopts the following technical scheme:

in a first aspect, a method for predicting and optimizing the generated power of a regional power grid new energy is provided, which includes:

acquiring historical operating data, station forecast meteorological data, station forecast data, station actual measurement data and operating data of a new energy power generation station in a regional power grid;

establishing a simplified new energy-electricity conversion model of the new energy power station according to the historical operation data;

acquiring a total predicted error of the new energy power generation power according to the station predicted data and the station measured data;

obtaining equivalent prediction data according to the simplified new energy-electricity conversion model, the station prediction meteorological data, the station prediction data, the station actual measurement data and the operation data;

obtaining errors caused by each key link of the new energy power generation power prediction of the new energy power generation station according to the total new energy power generation power prediction error and the equivalent prediction data;

and optimizing each key link of the new energy power generation power prediction according to the error caused by each key link.

Further, still include:

and according to errors caused by each key link of the new energy power generation power prediction of each new energy power generation station in the regional power grid, decoupling and evaluating the new energy power generation power prediction errors of the regional power grid.

Further, still include:

and preprocessing the historical operating data.

Further, the historical operating data includes: a plurality of power values and a plurality of station actual measurement meteorological factors corresponding to the power values;

the preprocessing of the historical operating data comprises:

dividing the power values into a plurality of power intervals according to a preset power interval;

calculating the probability density of actually measured meteorological factors of each station corresponding to the power value in a power interval by adopting a kernel density function;

and rejecting abnormal data according to actually measured meteorological factors and probability density of each station corresponding to the power value in the power interval.

Further, the expression of the kernel density function is:

wherein x represents the actually measured meteorological factors of the station,

the probability density of the field station actual measurement meteorological factors x is represented, h represents a preset power interval, n represents the number of the field station actual measurement meteorological factors corresponding to the power value in the power interval, x_iIndicating the actually measured meteorological factors of the ith station in the power interval; k () is a kernel function.

Further, the eliminating abnormal data according to the actually measured meteorological factors and the probability density thereof of each station corresponding to the power value in the power interval comprises:

sequencing the actually measured meteorological factors of the station corresponding to the power value in the power interval from small to large to obtain a meteorological factor sequence [ v [ [ v ])₁,v₂,…,v_n]And its corresponding probability density sequence [ P₁,P₂,…,P_n]；

Traverse the probability density sequence [ P₁,P₂,…,P_n]Obtaining the maximum value P of the probability density_k；

From the maximum value of the probability density P_kAt first, judge P_k+1-P_kIs less than P_k+2-P_k+1Whether the absolute value of (a) is true; if yes, continue to judge P_k+2-P_k+1Whether or not the absolute value of (A) is less than P_k+3-P_k+2Until the judgment condition is not satisfied, setting the meteorological factor corresponding to the probability density at the moment as the maximum meteorological factor v of the power interval_max；

From the maximum value of the probability density P_kAt first, judge P_k-P_k-1Is less than P_k-1-P_k-2Whether the absolute value of (a) is true; if yes, continue to judge P_k-1-P_k-2Whether or not the absolute value of (A) is less than P_k-2-P_k-3Until the judgment condition is not satisfied, setting the meteorological factor corresponding to the probability density at the moment as the minimum meteorological factor v of the power interval_min；

The meteorological factor sequence [ v ]₁,v₂,…,v_n]The middle-jiao qi pattern factors are in v_min～v_maxWeather factors other than those of the weather and their counterpartsThe power value is deleted.

Further, the establishing a simplified new energy-electricity conversion model of the new energy power generation station according to the historical operation data includes:

and fitting the historical operating data to obtain a simplified new energy-electricity conversion model of the new energy power generation station.

Further, the station prediction data includes: station predicted power; the station measured data comprises: the station actually measures meteorological data, and the operation data comprises: planned startup capacity, actual startup capacity, and rated installed capacity;

should obtain equivalent prediction data according to this simplified new forms of energy-electricity conversion model, this station prediction meteorological data, this station prediction data, this station measured data and this operational data, include:

calculating equivalent predicted power under the condition of accurate starting capacity according to the station predicted power, the planned starting capacity and the actual starting capacity;

obtaining equivalent prediction power under accurate meteorological resource conditions according to the actually measured meteorological data of the station, the simplified new energy-electricity conversion model, the planned starting capacity and the rated installed capacity;

and obtaining equivalent predicted power under the condition of predicted meteorological resources according to the station predicted meteorological data, the simplified new energy-electricity conversion model, the planned startup capacity and the rated installed capacity.

Further, the calculating the equivalent predicted power under the condition of accurate boot capacity according to the station predicted power, the planned boot capacity and the actual boot capacity includes:

and multiplying the actual starting capacity by the planned starting capacity by the station predicted power to obtain the equivalent predicted power under the condition of accurate starting capacity.

Further, the obtaining of the equivalent predicted power under the accurate meteorological resource condition according to the actually measured meteorological data of the station, the simplified new energy-electricity conversion model, the planned startup capacity, and the rated installed capacity includes:

inputting the actually measured meteorological data of the station into the simplified new energy-electricity conversion model to obtain the predicted generated energy under the accurate meteorological resource condition;

and multiplying the quotient of the planned starting capacity divided by the rated installed capacity by the predicted generating capacity under the accurate meteorological resource condition to obtain the equivalent predicted power under the accurate meteorological resource condition.

Further, the obtaining of the equivalent predicted power under the condition of the predicted meteorological resources according to the station predicted meteorological data, the simplified new energy-electricity conversion model, the planned startup capacity and the rated installed capacity includes:

inputting the station forecast meteorological data into the simplified new energy-electricity conversion model to obtain the forecast power generation amount under the condition of forecast meteorological resources;

and multiplying the quotient of the planned starting capacity divided by the rated installed capacity by the predicted power generation amount under the condition of the predicted meteorological resources to obtain the equivalent predicted power under the condition of the predicted meteorological resources.

Further, the key links include: a numerical weather forecasting link, a model link and a correction link;

the error caused by each key link of the new energy power generation power prediction of the new energy power generation station obtained according to the total new energy power generation power prediction error and the equivalent prediction data comprises the following steps:

obtaining an error caused by the numerical weather forecast link of the new energy power generation station according to the equivalent forecast power under the forecast weather resource condition and the equivalent forecast power under the accurate weather resource condition;

obtaining errors caused by the correction link of the new energy power generation station according to the station predicted power and the equivalent predicted power under the accurate starting capacity condition;

and subtracting the error caused by the numerical weather forecast link and the error caused by the correction link from the total predicted error of the new energy power generation power to obtain the error caused by the model link of the new energy power generation station.

Further, the station prediction data includes: station predicted power, the station measured data includes: actual power of the station;

should obtain new forms of energy generated power prediction total error according to this station prediction data and this station measured data, include:

and subtracting the actual power of the station from the predicted power of the station to obtain the predicted total error of the power generation power of the new energy.

In a second aspect, a prediction optimization device for new energy generated power of a regional power grid is provided, which includes:

the data acquisition module is used for acquiring historical operating data, station forecast meteorological data, station forecast data, station actual measurement data and operating data of a new energy power generation station in a regional power grid;

the modeling module is used for establishing a simplified new energy-electricity conversion model of the new energy power station according to the historical operation data;

the new energy power generation power prediction total error obtaining module is used for obtaining a new energy power generation power prediction total error according to the station prediction data and the station actual measurement data;

the equivalent prediction data acquisition module is used for obtaining equivalent prediction data according to the simplified new energy-electricity conversion model, the station prediction meteorological data, the station prediction data, the station actual measurement data and the operation data;

the error decoupling evaluation module is used for obtaining errors caused by each key link of the new energy power generation power prediction of the new energy power generation station according to the total new energy power generation power prediction error and the equivalent prediction data;

and the prediction optimization module optimizes each key link of the new energy power generation power prediction according to the error caused by each key link.

Further, still include:

and the regional error evaluation module is used for decoupling and evaluating the new energy power generation power prediction error of the regional power grid according to the error caused by each key link of the new energy power generation power prediction of each new energy power generation station in the regional power grid.

Further, still include:

and the data preprocessing module is used for preprocessing the historical operating data.

the data preprocessing module comprises:

the interval dividing unit divides the power values into a plurality of power intervals according to a preset power interval;

the probability density acquisition unit is used for calculating the probability density of actually measured meteorological factors of each station corresponding to the power value in a power interval by adopting a kernel density function;

and the abnormal data removing unit is used for removing the abnormal data according to the actually measured meteorological factors and the probability density of each station corresponding to the power value in the power interval.

Further, the modeling module includes:

and the data fitting unit is used for fitting the historical operating data to obtain a simplified new energy-electricity conversion model of the new energy power generation station.

the equivalent prediction data acquisition module comprises:

the first equivalent prediction power acquisition unit is used for calculating equivalent prediction power under the condition of accurate starting capacity according to the station prediction power, the planned starting capacity and the actual starting capacity;

the second equivalent prediction power acquisition unit is used for acquiring equivalent prediction power under the accurate meteorological resource condition according to the actually measured meteorological data of the station, the simplified new energy-electricity conversion model, the planned starting capacity and the rated installed capacity;

and the third equivalent prediction power acquisition unit is used for obtaining equivalent prediction power under the condition of predicting meteorological resources according to the station prediction meteorological data, the simplified new energy-electricity conversion model, the planned starting-up capacity and the rated installed capacity.

Further, the first equivalent prediction power obtaining unit includes:

and the first calculating subunit multiplies the station predicted power by the quotient of the actual boot capacity divided by the planned boot capacity to obtain the equivalent predicted power under the condition of accurate boot capacity.

Further, the second equivalent prediction power obtaining unit includes:

the first predicted power generation obtaining subunit inputs actually measured meteorological data of the station into the simplified new energy-electricity conversion model to obtain predicted power generation under the accurate meteorological resource condition;

and the second calculating subunit is used for obtaining the equivalent predicted power under the accurate meteorological resource condition by utilizing the quotient of dividing the planned starting capacity by the rated installed capacity and multiplying the predicted power generation under the accurate meteorological resource condition.

Further, the third equivalent prediction power obtaining unit includes:

the second predicted power generation obtaining subunit inputs the station predicted meteorological data into the simplified new energy-electricity conversion model to obtain predicted power generation under the condition of predicted meteorological resources;

and the third calculation subunit multiplies the predicted power generation amount under the condition of the predicted meteorological resources by the quotient of dividing the planned startup capacity by the rated installed capacity to obtain the equivalent predicted power under the condition of the predicted meteorological resources.

the error decoupling evaluation module comprises:

the numerical weather forecast link error evaluation unit is used for obtaining errors caused by the numerical weather forecast link of the new energy power generation station according to the equivalent forecast power under the forecast weather resource condition and the equivalent forecast power under the accurate weather resource condition;

the correction link error evaluation unit is used for obtaining errors caused by the correction link of the new energy power generation station according to the station predicted power and the equivalent predicted power under the accurate starting capacity condition;

and the model link error evaluation unit subtracts the error caused by the numerical weather forecast link and the error caused by the correction link from the total predicted error of the new energy power generation power to obtain the error caused by the model link of the new energy power generation station.

the new energy power generation power prediction total error obtaining module comprises:

and the new energy power generation power prediction total error obtaining unit is used for subtracting the actual power of the station from the station predicted power to obtain the new energy power generation power prediction total error.

In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the method for predicting and optimizing the generated power of the regional power grid new energy are implemented.

In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method for predictive optimization of new energy generated power of a regional power grid.

The invention provides a method, a device, electronic equipment and a computer readable storage medium for predicting and optimizing the generated power of new energy of a regional power grid, wherein the method comprises the following steps: acquiring historical operating data, station forecast meteorological data, station forecast data, station actual measurement data and operating data of a new energy power generation station in a regional power grid; establishing a simplified new energy-electricity conversion model of the new energy power station according to the historical operation data; acquiring a total predicted error of the new energy power generation power according to the station predicted data and the station measured data; obtaining equivalent prediction data according to the simplified new energy-electricity conversion model, the station prediction meteorological data, the station prediction data, the station actual measurement data and the operation data; obtaining errors caused by each key link of the new energy power generation power prediction of the new energy power generation station according to the total new energy power generation power prediction error and the equivalent prediction data; optimizing each key link of the new energy power generation power prediction according to errors caused by each key link, wherein the error of each key link of the new energy power generation station analysis power prediction is quantitatively refined on a scheduling side by using multi-source data such as a simplified new energy-electricity conversion model, station prediction data, station actual measurement data and operation data, and the like, so that weak links of the station with poor prediction level are accurately positioned, each key link of the new energy power generation power prediction is purposefully optimized according to the error caused by each key link, and corresponding optimization measures are pertinently developed to efficiently improve the power prediction level of the new energy power generation station.

In order to make the aforementioned and other objects, features and advantages of the invention comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. In the drawings:

fig. 1 is a first flowchart of a method for predicting and optimizing the generated power of the new energy of the regional power grid in the embodiment of the invention;

FIG. 2 shows a schematic representation of a NWP product production flow diagram in an embodiment of the present invention;

FIG. 3 illustrates a simplified new energy-to-electricity conversion model obtained by fitting in an embodiment of the present invention;

FIG. 4 illustrates a simplified new energy-to-electricity conversion model implemented using a statistical model in an embodiment of the present invention;

FIG. 5 shows the main steps of step S300 in the embodiment of the present invention;

fig. 6 is a flowchart illustrating a second method for predicting and optimizing the generated power of the new energy of the regional power grid in the embodiment of the present invention;

fig. 7 shows the detailed steps of step S150 in fig. 6;

fig. 8 is a third flowchart of a method for predicting and optimizing the generated power of the new energy of the regional power grid in the embodiment of the present invention;

fig. 9 shows the specific steps of step S300 in fig. 1, 6 and 8;

fig. 10 shows the specific steps of step S500 in fig. 1, 6 and 8;

FIG. 11 is a schematic diagram illustrating a new energy power generation power prediction error of a regional power grid dispatching side decoupling evaluation new energy power generation station according to an embodiment of the invention;

fig. 12 is a block diagram of a device for predicting and optimizing the generated power of the new energy of the regional power grid in the embodiment of the present invention;

fig. 13 is a block diagram of an electronic device according to an embodiment of the invention.

Detailed Description

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. 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 application.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

It should be noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of this application and the above-described drawings, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

In order to at least partially solve the technical problems in the prior art, the embodiment of the invention provides a method for predicting and optimizing the power of the new energy power generation of the regional power grid, which utilizes simplified new energy-electricity conversion model, station prediction data, station actual measurement data, operation data and other multi-source data to quantitatively and finely analyze errors of each key link of the power prediction of each new energy power generation station on a scheduling side, further accurately positions the weak prediction link of the station with poor prediction level, purposefully optimizes each key link of the power prediction of the new energy power generation according to the errors caused by each key link, and develops corresponding optimization measures in a targeted manner to efficiently improve the power prediction level of the new energy power generation station.

Fig. 1 is a schematic flow chart of a method for predicting and optimizing the generated power of the new energy of the regional power grid in the embodiment of the invention. As shown in fig. 1, the method for predicting and optimizing the generated power of the regional power grid new energy may include the following steps:

step S100: acquiring historical operating data, station forecast meteorological data, station forecast data, station actual measurement data and operating data of a new energy power generation station in a regional power grid;

wherein, new forms of energy electricity generation includes: the power generation by natural energy sources with intermittence, randomness and volatility, such as solar power generation, wind power generation, geothermal power generation, wave power generation, ocean current power generation, tidal power generation and the like.

The historical operating data includes measured meteorological factors of the station and power values corresponding to the time when the new energy power generation station operates at a plurality of times within a period of time (such as a month, a quarter, a year or several years), that is, time series data of the measured meteorological factors and power time series data corresponding to one.

The station forecast meteorological data are numerical value electric forecast data of the location of the station, and comprise various meteorological factors influencing the new energy power generation, such as station forecast total irradiance, station forecast wind direction, station forecast air temperature, station forecast air pressure, station forecast air speed and the like.

The Numerical Weather forecast (hereinafter referred to as NWP) is a method for predicting the atmospheric motion state and Weather phenomenon in a certain period of time by performing Numerical calculation through a large-scale computer under the condition of certain initial value and side value according to the actual atmospheric condition and solving the fluid mechanics and thermodynamics equation system describing the Weather evolution process.

Station NWP belongs to site refined forecast, the production flow is shown in figure 2, firstly, a global weather forecast field is downloaded from a Towei weather institution; then, carrying out data format standardization processing on the global atmospheric forecast field to enable the global atmospheric forecast field to have conditions for driving mesoscale numerical weather forecast mode software to run, and completing all preparation work before mode running; and finally, operating mesoscale numerical weather mode software by a power prediction service provider according to the prediction requirement of the specific geographic coordinate of the power prediction service provider to complete downscaling calculation of a local target area, and finally obtaining the atmospheric states of the geographic area where the new energy power generation station is located at different moments in the future.

The station measured data may include: and parameters such as station actual measurement weather data and actual measurement power values reported by the new energy power generation station.

The station actual measurement weather data comprises station actual measurement wind speed, station actual measurement wind direction, station actual measurement air temperature, station actual measurement air pressure, station actual measurement irradiance and the like, wherein the station actual measurement wind speed, the station actual measurement wind direction, the station actual measurement air temperature, the station actual measurement air pressure, the station actual measurement irradiance and the like are collected by a collecting device arranged in the station.

The operation data are related data in the operation process of the new energy power generation station, such as planned startup capacity, actual startup capacity, rated installed capacity and the like.

The station prediction data are station prediction power and the like, which are obtained by prediction of a prediction model at the station side or power prediction service providers, and the station terminal reports the station prediction power stored in the station prediction data to the scheduling side server.

Step S200: and establishing a simplified new energy-electricity conversion model of the new energy power generation station according to the historical operation data.

It is worth mentioning that since the power grid dispatching side cannot completely acquire and deploy the new energy-electricity conversion model provided by the power prediction service provider of each new energy power generation station, a large amount of historical data of the new energy power generation station needs to be used for modeling, so that a simplified new energy-electricity conversion model is obtained.

The simplified new energy-electricity conversion model is a mathematical model for describing the relationship between meteorological resources and active power of the new energy power generation equipment. In actual production, due to the influence of factors such as weather conditions and unit power generation performance, meteorological factors and electric power often present a complex mapping relationship, and in order to ensure power prediction accuracy, complex and variable unit operation conditions are generally considered to develop refined modeling, so that a simplified new energy-electricity conversion model with high applicability is obtained.

In an alternative embodiment, the historical operating data may be fitted to obtain a simplified new energy-to-electricity conversion model of the new energy power plant.

Specifically, historical operating data can be input into MATLAB software, a data fitting function in the MATLAB software is called, a quadratic polynomial function representing the corresponding relationship between meteorological factors and electric field power is obtained, referring to fig. 3, the relationship between the meteorological factors and power is shown, and in practical application, in order to accurately predict power, a functional relational expression between a plurality of factors (such as field station predicted wind speed, field station predicted wind direction, field station predicted air temperature, field station predicted air pressure, field station predicted irradiance and the like) and power is generally fitted for power prediction.

In another alternative embodiment, a statistical model can be used to implement a simplified new energy-electricity conversion model, which is essentially based on matching the physical causal relationship between the input (including NWP, historical data, etc.) of the system and the predicted power by using a statistical method, the principle of which is shown in fig. 4, and the meteorological elements of NWP, the meteorological elements of historical NWP and the operation data (such as measured power and measured meteorological elements) of the historical new energy power plant station are input into the statistical model to obtain the predicted power of the new energy power plant station, and the statistical model can be implemented by using techniques such as piecewise linear regression, kalman filtering, neural network, and support vector machine.

Step S300: acquiring a total predicted error of the new energy power generation power according to the station predicted data and the station measured data;

the station prediction data comprises station prediction power, and the station actual measurement data comprises: station actual power, etc.

Specifically, the total predicted error of the new energy power generation power is obtained by subtracting the actual station power from the predicted station power, that is, the calculation mode is as shown in formula (1).

E_total-i＝P_predict-i-P_actual-i＝E_NWP-i+E_model-i+E_revise-i(1)

E_total-iTotal error, P, is predicted for new energy generated power_predict-iPredicting power, P, for a station_actual-iFor station real power, E_NWP-i、E_model-iAnd E_revise-iThe unit is the NWP link error, the simplified new energy-electricity conversion model link error and the predicted result correction link error, and the unit is MW.

It should be noted that, referring to fig. 5, the prediction process of the station predicted power sequentially includes three key links of numerical weather forecast, new energy-electric power conversion, and prediction result correction according to the business process.

Firstly, generating a numerical weather forecast product by utilizing a meteorological mesoscale mode for a global initial field; then, weather elements related to weather resources in the numerical weather forecast product are input into a new energy-electricity conversion model on the site side, and the predicted power generation amount is obtained; and finally, correcting the prediction result according to operation information such as a unit maintenance plan and the like to obtain the station prediction power.

And the prediction result correction is to correct the predicted power calculated by the new energy-electricity conversion model according to the planned starting capacity of the new energy power generation station by the dispatching side to obtain a final prediction result. The prediction result correction is a management link closely related to artificial experience, and is mainly finished by the power prediction responsibility of the new energy power generation station: firstly, wind power prediction and scheduled maintenance work are linked, and the starting capacity, the starting time and the predicted output are reasonably corrected in a power prediction system in a manual input mode according to the number of units involved in the scheduled maintenance work; and secondly, considering the shutdown capacity of the new energy power generation unit possibly caused by special climate, and performing empirical correction on the prediction result in a manual input mode.

Step S400: obtaining equivalent prediction data according to the simplified new energy-electricity conversion model, the station prediction meteorological data, the station prediction data, the station actual measurement data and the operation data;

wherein equivalent prediction data is obtained by introducing weather and starting-up mode information and the like.

Specifically, by substituting the station actual measurement data and the station prediction meteorological data into the simplified new energy-electricity conversion model, equivalent prediction data under actual measurement conditions and prediction conditions can be obtained, and equivalent prediction data under actual working conditions can be obtained according to the operation data.

Step S500: and obtaining errors caused by each key link of the new energy power generation power prediction of the new energy power generation station according to the total new energy power generation power prediction error, the prediction data and the equivalent prediction data.

Specifically, errors caused by each key link are obtained according to equivalent prediction data under actual measurement conditions and prediction conditions, equivalent prediction data and prediction power under actual conditions and combined with the total prediction error of the new energy power generation power.

Step S600: and optimizing each key link of the new energy power generation power prediction according to the error caused by each key link.

Specifically, the key links of the new energy power generation power prediction include: a numerical weather forecasting link, a model link and a correction link. And optimizing each key link in a targeted manner according to errors caused by each link so as to improve the power prediction precision.

When the numerical weather forecast error is found to be high, the optimization of the numerical weather forecast link comprises the following steps:

(1) and carrying out atmospheric mode parametric tuning.

The atmospheric mode subgrid physical process parameterization scheme captures a physical process which cannot be analyzed by a numerical mode in an explicit mode, is the key for improving the medium-short term forecasting skills in a scale of 10-100km, and is also the key for customizing a numerical weather forecasting mode according to the characteristics of a forecasting object. The climate and meteorological features of different areas, and even the layout of observation points, all affect the mode optimization results. And aiming at main meteorological prediction indexes of the new energy convergence region, a customized grid encryption and parameterization scheme is realized based on a statistical regression and deep learning model, and then a new energy mesoscale forecasting mode is optimized.

(2) Establishing a multi-source meteorological value grid observation platform, and researching an attack and customs meteorological value ensemble forecasting technology.

Fusion access and management of full meteorological element multi-source observation information are achieved through a meteorological grid numerical observation platform, and the problems of few new energy meteorological observation data and poor quality are solved. The new energy weather ensemble forecasting of the power grid is developed, a plurality of weather forecasting modes and a plurality of initial values are adopted for forecasting respectively, the influence of uncertainty of model parameters and the initial values on a final result is reduced, and the probability of occurrence of extreme errors is reduced.

When the error ratio of the new energy-electricity conversion model is found to be high, the optimization of the new energy-electricity conversion model comprises the following steps:

(1) based on a new energy ubiquitous information platform, the sample size of the full-time-space scale of photoelectric operation is expanded, the model is continuously updated by utilizing on-line monitoring data, abnormal point interference is eliminated, and the data quality of a modeling sample is improved.

(2) And a statistical method such as deep learning is adopted, and the statistical relationship and the meteorological resource fluctuation physical cause-and-effect relationship are combined, so that the model precision is improved.

(3) And correcting the error of the new energy power generation power prediction result based on the non-stationary characteristic of the power prediction result in a period of time, and improving the prediction effect of the new energy power generation output climbing event.

When the error proportion of the error correction link is higher, the optimization of the error correction link comprises the following steps:

(1) and linking the maintenance plan with the power prediction, and inputting planned startup capacity and startup stop time information influenced by maintenance work into the power prediction system in advance.

(2) The power generation equipment is accurately operated and maintained, and unplanned outage is reduced.

(3) And developing the prediction of the unplanned shutdown of the equipment.

In summary, according to the method for predicting and optimizing the power of the new energy power generation of the regional power grid provided by the embodiment of the invention, the error of each key link of the power prediction of each new energy power generation station is quantitatively and finely analyzed at the scheduling side by using the simplified new energy-electricity conversion model, the station prediction data, the station actual measurement data, the operation data and other multi-source data, so that the weak link of the station with the poorer prediction level is accurately positioned, and the corresponding optimization measures can be conveniently and specifically developed in the follow-up process to efficiently improve the power prediction level of the new energy power generation station.

For example, aiming at the key deviation event, the error and the proportion of each link of the power prediction in the deviation time period are calculated, the cause of the predicted deviation is analyzed, and each key link of the power prediction is optimized in a targeted manner.

For example, the prediction deviation caused by the special weather event is often represented as large prediction deviation in a Model link, the early warning application of the special weather event is developed, the post analysis is changed into the early warning, an early warning Model of extreme weather such as cold tide, strong wind, icing and the like is established, and the early warning risk and the early warning level of various special weather events are given by combining NWP information.

In addition, by carrying out long-period error evaluation of years, seasons and months, the station with poor prediction level and weak prediction links thereof can be accurately positioned, targeted assistance guidance is carried out, and the new energy power generation power prediction level is improved.

In an optional embodiment, referring to fig. 6, the method for predicting and optimizing the generated power of the regional power grid new energy may further include:

step S150: and preprocessing the historical operating data.

The historical operating data is preprocessed, abnormal data are removed, and the like, so that the accuracy of a subsequently established simplified new energy-electricity conversion model is improved.

In an alternative embodiment, the historical operating data includes: the power values corresponding to a plurality of moments and the actually measured meteorological factors of a plurality of stations corresponding to the power values correspond to each other one by one (the meteorological factors refer to main meteorological parameters influencing the power values, for example, for a photovoltaic power station, the meteorological factors can be irradiance, and for wind power generation, the meteorological factors can be wind speed), for example: t is t_iWeather factor at the moment is D_iCorresponding power value is W_iReferring to fig. 7, this step S150 may include the following:

step S151: dividing the power values into a plurality of power intervals according to a preset power interval;

the preset power interval may be set by a user according to a user requirement, for example, may be set to 25kW, 20kW, 10kW, 5kW, and the like, which is not limited in this embodiment of the present invention.

Taking the preset power interval as 20kW as an example, dividing the power data of the new energy power station into a plurality of sections from 0 to the rated power, wherein the length of each section is 20kW, and each power section is [0,20], [20,40], …, [ p-20, p ], …, [ Pe-20, Pe ], wherein Pe is the rated power.

Step S152: and calculating the probability density of the actually measured meteorological factors of each station corresponding to the power value in a power interval by adopting a kernel density function.

Specifically, the actually measured meteorological factors of the station corresponding to the power value in the power interval are obtained from historical operating data; then, the probability density of the actually measured meteorological factors of each station is calculated by adopting a kernel density function.

Wherein the expression of the kernel density function is:

In addition, the kernel function can be implemented by a gaussian kernel function as shown in the following formula:

step S153: and rejecting abnormal data according to actually measured meteorological factors and probability density of each station corresponding to the power value in the power interval.

The method specifically comprises the following steps:

step I: sequencing the actually measured meteorological factors of the station corresponding to the power value in the power interval from small to large to obtain a meteorological factor sequence [ v [ [ v ])₁,v₂,…,v_n]And its corresponding probability density sequence [ P₁,P₂,…,P_n]；

Step II: traverse the probability density sequence [ P ]₁,P₂,…,P_n]Obtaining the maximum value P of the probability density_kAnd corresponding meteorological factor v_k。

Step III: from the maximum value of the probability density P_kAt the beginning, along P_k～P_nDirection of (D), judgment P_k+1-P_kIs less than P_k+2-P_k+1Whether the absolute value of (a) is true;

if not, then P is added_kCorresponding meteorological factor v_kMaximum meteorological factor v as power interval_max；

If yes, continue to judge P_k+2-P_k+1Whether or not the absolute value of (A) is less than P_k+3-P_k+2If not, then P is added_k+1Corresponding meteorological factor v_k+1Maximum meteorological factor v as power interval_maxIf yes, continue to judge P_k+3-P_k+2Whether or not the absolute value of (A) is less than P_k+4-P_k+3Until the absolute value of the judgment condition is not met;

from the maximum value of the probability density P_kAt the beginning, along P_k～P₁Direction of (D), judgment P_k-P_k-1Is less than P_k-1-P_k-2Whether the absolute value of (a) is true;

if not, then P is added_kCorresponding meteorological factor v_kMinimum meteorological factor v as power interval_min；

If yes, continue to judge P_k-1-P_k-2Whether or not the absolute value of (A) is less than P_k-2-P_k-3If not, then P is added_k+1Corresponding meteorological factor v_k+1Minimum meteorological factor v as power interval_minIf yes, continue judging P_k-2-P_k-3Whether or not the absolute value of (A) is less than P_k-3-P_k-4Until the absolute value of the absolute value does not meet the judgment condition;

the meteorological factor sequence [ v ]₁,v₂,…,v_n]The middle-jiao qi pattern factors are in v_min～v_maxAnd deleting the external meteorological factors and the corresponding power values.

In an optional embodiment, referring to fig. 8, the method for predicting and optimizing the generated power of the regional power grid new energy may further include:

step S550: and according to errors caused by each key link of the new energy power generation power prediction of each new energy power generation station in the regional power grid, decoupling and evaluating the new energy power generation power prediction errors of the regional power grid.

Specifically, errors of each link of the power prediction of the new energy power generation of the regional power grid depend on errors of each link of the power prediction of each new energy power generation station. The method for evaluating the prediction error condition of the new energy power generation power of the regional power grid mainly comprises two aspects: on one hand, the overall power prediction level of the regional power grid is the total error, the error of each sub-region and the error contribution ratio of each link; and on the other hand, the prediction level of each new energy power generation station comprises the whole prediction level and the prediction error condition of each link.

The indexes for evaluating the prediction level of each new energy power generation station in the regional power grid comprise:

1) and predicting errors of each new energy power generation station. Namely the prediction error (unit: MW) of the ith new energy power station, namely E_total-i。

2) Errors of each key link in the prediction process of each new energy power generation station, namely prediction error E of ith new energy power generation station caused by numerical weather forecast_NWP-iPrediction error E of ith new energy power generation station caused by new energy-electricity conversion model_model-iPrediction error E of ith new energy power generation station caused by result correction link_revise-i。

3) The ratio of the error contributions of all key links in the prediction process of each new energy power generation station, namely the prediction error E of the ith new energy power generation station caused by numerical weather forecast_NWP-iRatio of R_NWP-iPrediction error E of ith new energy power generation station caused by new energy-electricity conversion model_model-iRatio of R_model-iPrediction error E of ith new energy power generation station caused by result correction link_revise-iRatio of R_revise-i。

R_NWP-i＝|E_NWP-i|÷(|E_NWP-i|+|E_model-i|+|E_revise-i|)

R_model-i＝|E_NWP-i|÷(|E_NWP-i|+|E_model-i|+|E_revise-i|)

R_revise-i＝|E_NWP-i|÷(|E_NWP-i|+|E_model-i|+|E_revise-i|)

In addition, the indexes for evaluating the predicted level of the new energy power generation power of the regional power grid comprise:

1) the total prediction error, the error of misdetection of each sub-area and the power prediction error of each new energy power generation station.

The total prediction error of the regional power grid is equal to the sum of the prediction errors of all the new energy power generation stations in the regional power grid, and the prediction error of each sub-region is equal to the sum of the prediction errors of all the new energy power generation stations in the sub-region.

2) Error proportion of each key link of power prediction of all new energy power generation stations in regional power grid region, namely the proportion R of prediction errors caused by numerical weather forecast in regional power grid_NWPRatio R of prediction errors caused by new energy-electricity conversion model in regional power grid_modeiRatio R of prediction errors caused by result correction link in regional power grid_revise。

In an alternative embodiment, the station forecast data includes: station predicted power; the station measured data further comprises: station actual measurement meteorological data, etc., and the operational data includes: planned startup capacity, actual startup capacity, rated installed capacity, and the like; referring to fig. 9, this step S300 may include the following:

step S310: and calculating the equivalent predicted power under the condition of accurate starting capacity according to the station predicted power, the planned starting capacity and the actual starting capacity.

Specifically, the equivalent predicted power under the accurate startup capacity condition is obtained by multiplying the quotient of the actual startup capacity divided by the planned startup capacity by the station predicted power.

Wherein, the following formula is adopted to realize:

P_capacity-i＝P_predict-i×(C_actual-i÷C_scheduling-i)

P_capacity-irepresenting equivalent predicted power, P, under accurate boot-up capacity conditions_predict-iRepresenting the predicted power of the station, C_actual-iRepresenting the actual boot capacity, C_scheduling-iRepresenting the planned boot capacity.

Step S320: and obtaining equivalent prediction power under an accurate meteorological resource condition according to the actually measured meteorological data of the station, the simplified new energy-electricity conversion model, the planned starting capacity and the rated installed capacity.

Specifically, the actually measured meteorological data of the station are input into the simplified new energy-electricity conversion model to obtain the predicted generated energy under the accurate meteorological resource condition; and then, multiplying the quotient of the planned starting capacity divided by the rated installed capacity by the predicted power generation amount under the accurate meteorological resource condition to obtain the equivalent predicted power under the accurate meteorological resource condition.

Wherein, the following formula is adopted to realize:

P_sun-pc-i＝PC(S_actual-i)×(C_scheduling-i÷C_rated-i)

P_sun-pc-irepresenting the equivalent predicted power, S, under accurate meteorological resource conditions_actual-iRepresenting measured meteorological data at a site, PC () representing a simplified new energy-to-electricity conversion model, C_scheduling-iIndicating the planned boot capacity, C_rated-iIndicating the rated installed capacity.

Step S330: and obtaining equivalent predicted power under the condition of predicted meteorological resources according to the station predicted meteorological data, the simplified new energy-electricity conversion model, the planned starting capacity and the rated installed capacity.

Specifically, the station forecast meteorological data is input into a simplified new energy-electricity conversion model to obtain the forecast power generation amount under the condition of forecast meteorological resources; and then, multiplying the quotient of the planned starting capacity divided by the rated installed capacity by the predicted power generation amount under the condition of the predicted meteorological resources to obtain the equivalent predicted power under the condition of the predicted meteorological resources.

Wherein, the following formula is adopted to realize:

P_predict-pc-i＝PC(S_predict-i)×(C_scheduling-i÷C_rated-i)

P_predict-pc-irepresenting the equivalent predicted power under the condition of predicting meteorological resources, PC () representing a simplified new energy-to-electricity conversion model, S_predict-iIndicating predicted weather factors, C, of the station_scheduling-iIndicating the planned boot capacity, C_rated-iIndicating the rated installed capacity.

In an alternative embodiment, the key links include: a numerical weather forecasting link, a model conversion link and a correction link; referring to fig. 10, this step S500 may include the following:

step S510: and obtaining the error caused by the numerical weather forecast link of the new energy power generation station according to the equivalent predicted power under the predicted weather resource condition and the equivalent predicted power under the accurate weather resource condition.

Wherein, referring to fig. 11, the following is used:

E_NWP-i≈E_NWP-PC-i＝P_predict-pc-i-P_sun-pc-i

E_NWP-iindicating NWP link error, P_predict-pc-iRepresenting the equivalent predicted power, P, under the conditions of predicted meteorological resources_sun-pc-iRepresenting the equivalent predicted power under accurate meteorological resource conditions, E_NWP-PC-iAnd the prediction error of the NWP link under the condition of a simplified model is shown.

Step S420: and obtaining the error caused by the correction link according to the station predicted power and the equivalent predicted power under the accurate starting capacity condition.

Wherein, the following formula is adopted to realize:

E_revise-i＝P_predict-i-P_capacity-i

E_revise-icorrecting link errors for prediction results, P_predict-iPredicting power, P, for a station_capacity-iAnd the equivalent predicted power under the accurate starting capacity condition is represented.

Step S430: and subtracting the error caused by the numerical weather forecast link and the error caused by the correction link from the total predicted error of the new energy power to obtain the error caused by the model link.

Wherein, the following formula is adopted to realize:

E_model-i＝E_total-i-E_NWP-i-E_revise-i

E_total-itotal error prediction for new energy generated power, E_NWP-i、E_model-iAnd E_revise-iThe method comprises the steps of respectively correcting a NWP link error, a model link error and a prediction result error.

In summary, the method for predicting and optimizing the power of the new energy source of the regional power grid provided by the embodiment of the invention develops a fine evaluation of the power prediction error of any time period or moment or key link on the scheduling side, realizes an accurate monitoring of the power prediction operation condition of the new energy source, accurately positions the problem station and analyzes the composition of the prediction error by quantitatively analyzing the contribution ratio of each link error, namely, quantitatively evaluates a large deviation event of the new energy source power station, calculates the contribution ratio of each link error in the prediction deviation event, and accurately analyzes the cause of the prediction deviation event; by developing long-period error evaluation of years, seasons and months, weak prediction links of stations with poor prediction levels can be accurately positioned, the prediction method is purposefully improved, corresponding optimization measures are developed in a targeted mode, and the power prediction level of the new energy power generation station is effectively improved.

According to the refined evaluation index system for the regional power grid power prediction error, refined evaluation of the regional power grid new energy power generation power prediction operation is completed, a new energy power generation station and weak links with poor prediction level are pointed out, and targeted optimization suggestions and directions are given.

The power prediction optimization strategy developed according to the evaluation result mainly comprises three parts, namely, a new energy power station with a poor level can be locked according to the evaluation result, assistance guidance work can be pertinently developed, and the prediction level of the new energy power station is improved; secondly, according to the decoupling evaluation result, the specific link in the three main links of power prediction contributes to a larger prediction error, and optimization work such as regular or irregular model parameter correction is carried out in a targeted manner; if the prediction error of regularity is found in a continuous period of time, the substation prediction optimization cannot be performed in time, the correction of the prediction result can be performed at the dispatching main station end of the regional power grid, and the power prediction precision is improved.

In order to help those skilled in the art to better understand the embodiment of the present invention, the following takes a specific new energy power generation station as an example to describe in detail the steps of the method for predicting and optimizing the generated power of the new energy of the regional power grid according to the embodiment of the present invention:

(1) acquiring historical operating data, station forecast meteorological data, station actual measurement meteorological data, station forecast power, station actual power, planned startup capacity, actual startup capacity, rated installed capacity and the like of a new energy power generation station in a regional power grid;

(2) dividing a plurality of power values in the historical data into a plurality of power intervals according to a preset power interval;

(3) calculating the probability density of actually measured meteorological factors of each station corresponding to the power value in a power interval by adopting a kernel density function;

(4) sequencing the actually measured meteorological factors of the station corresponding to the power value in the power interval from small to large to obtain a meteorological factor sequence [ v [ [ v ])₁,v₂,…,v_n]And its corresponding probability density sequence [ P₁,P₂,…,P_n]；

(5) Traverse the probability density sequence [ P₁,P₂,…,P_n]Obtaining the maximum value P of the probability density_k；

(6) From the maximum value of the probability density P_kAt first, judge P_k+1-P_kIs absoluteValue less than P_k+2-P_k+1Whether the absolute value of (a) is true; if yes, continue to judge P_k+2-P_k+1Whether or not the absolute value of (A) is less than P_k+3-P_k+2Until the judgment condition is not satisfied, setting the meteorological factor corresponding to the probability density at the moment as the maximum meteorological factor v of the power interval_max；

(7) From the maximum value of the probability density P_kAt first, judge P_k-P_k-1Is less than P_k-1-P_k-2Whether the absolute value of (a) is true; if yes, continue to judge P_k-1-P_k-2Whether or not the absolute value of (A) is less than P_k-2-P_k-3Until the judgment condition is not satisfied, setting the meteorological factor corresponding to the probability density at the moment as the minimum meteorological factor v of the power interval_min；

(8) The meteorological factor sequence [ v ]₁,v₂,…,v_n]The middle-jiao qi pattern factors are in v_min～v_maxAnd deleting the external meteorological factors and the corresponding power values.

(9) Fitting the preprocessed historical operating data to obtain a simplified new energy-electricity conversion model of the new energy power generation station;

(10) subtracting the actual power of the station from the predicted power of the station to obtain a total predicted error of the power generation power of the new energy;

(11) the quotient of the actual starting capacity divided by the planned starting capacity is multiplied by the station predicted power to obtain the equivalent predicted power under the condition of accurate starting capacity;

(12) inputting actually measured meteorological data of the station into a simplified new energy-electricity conversion model to obtain predicted generated energy under the accurate meteorological resource condition;

(13) and multiplying the quotient of the planned starting capacity divided by the rated starting capacity by the predicted power generation amount under the accurate meteorological resource condition to obtain the equivalent predicted power under the accurate meteorological resource condition.

(14) Inputting the station prediction meteorological data into a simplified new energy-electricity conversion model to obtain the predicted generated energy under the condition of prediction meteorological resources;

(15) and multiplying the quotient of the planned starting capacity divided by the rated starting capacity by the predicted power generation amount under the condition of the predicted meteorological resources to obtain the equivalent predicted power under the condition of the predicted meteorological resources.

(16) Subtracting the equivalent prediction power under the accurate meteorological resource condition from the equivalent prediction power under the predicted meteorological resource condition to obtain an error caused by a numerical weather forecast link;

(17) the error caused by the correction link of the new energy power generation station is obtained by subtracting the equivalent predicted power under the condition of accurate starting capacity from the station predicted power

(18) And subtracting the error caused by the numerical weather forecast link and the error caused by the correction link from the total predicted error of the new energy power to obtain the error caused by the model link.

(19) According to error decoupling caused by each key link of new energy power generation power prediction of each new energy power generation station in the regional power grid, evaluating new energy power generation power prediction errors of the regional power grid;

(20) and optimizing each key link of the new energy power generation power prediction according to the error caused by each key link.

It should be noted that, in the embodiment of the present invention, when the new energy power generation station is a wind farm, since the most important meteorological factor affecting wind power is wind speed, the meteorological data includes wind speed, and certainly, in order to further improve the prediction accuracy, the meteorological data may further include auxiliary factors such as wind direction, air temperature, and air pressure; when the energy power generation station is a photovoltaic power station, the most important meteorological factor influencing the photoelectricity is irradiance, so the meteorological data comprises the irradiance, and of course, in order to further improve the prediction accuracy, the meteorological data can also comprise wind speed, wind direction, air temperature, air pressure, particulate matter concentration and the like.

The sequence of the method steps provided in the embodiment of the present invention is not the only sequence of the embodiment of the present invention, and the related steps may be exchanged according to the need or may be performed in parallel, so as to increase the flexibility and adaptability of the embodiment of the present invention.

Based on the same inventive concept, the embodiment of the present application further provides a device for predicting and optimizing the generated power of the new energy of the regional power grid, which can be used for implementing the method described in the above embodiment, as described in the following embodiments. Because the principle of solving the problems of the device for predicting and optimizing the generated power of the new energy of the regional power grid is similar to that of the method, the implementation of the device for predicting and optimizing the generated power of the new energy of the regional power grid can refer to the implementation of the method, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.

Fig. 12 is a block diagram of a device for predicting and optimizing the generated power of the new energy of the regional power grid in the embodiment of the present invention; as shown in fig. 12, the prediction optimization device for the generated power of the new energy of the regional power grid includes: the system comprises a data acquisition module 10, a modeling module 20, a new energy power generation total error acquisition module 30, an equivalent prediction data acquisition module 40, an error decoupling evaluation module 50 and a prediction optimization module 60.

The data acquisition module 10 acquires historical operating data, station forecast meteorological data, station forecast data, station actual measurement data and operating data of a new energy power generation station in a regional power grid.

The historical operating data includes field station actual measurement meteorological factors and power values corresponding to the moments when the new energy power generation field station operates at a plurality of moments within a period of time (such as a month, a quarter, a year or several years), that is, time series data of the actual measurement meteorological factors and power time series data corresponding to one.

The station forecast meteorological data are numerical value electric forecast data of the location of the station, and comprise various meteorological factors influencing the new energy power generation, such as station forecast wind speed, station forecast wind direction, station forecast air temperature, station forecast air pressure, forecast irradiance and the like.

The modeling module 20 builds a simplified new energy-to-electricity conversion model of the new energy power plant from the historical operating data.

The new energy power generation power prediction total error obtaining module 30 obtains a new energy power generation power prediction total error according to the station prediction data and the station actual measurement data;

wherein the station prediction data comprises: station predicted power, the station measured data includes: actual power of the station; the new energy power generation power prediction total error obtaining module 20 includes: and the new energy power generation power prediction total error obtaining unit is used for subtracting the actual power of the station from the station predicted power to obtain the new energy power generation power prediction total error.

Namely, the calculation mode is shown as formula (1).

E_total-i＝P_predict-i-P_actual-i＝E_NWP-i+E_model-i+E_revise-i(1)

The equivalent prediction data obtaining module 40 obtains equivalent prediction data according to the simplified new energy-electricity conversion model, the station prediction meteorological data, the station prediction data, the station actual measurement data and the operation data.

The error decoupling evaluation module 50 obtains errors caused by each key link of the new energy power generation power prediction of the new energy power generation station according to the total new energy power generation power prediction error and the equivalent prediction data.

The prediction optimization module 60 optimizes each key link of the new energy power generation power prediction according to the error caused by each key link.

(1) and carrying out atmospheric mode parametric tuning.

(3) And developing the prediction of the unplanned shutdown of the equipment.

In summary, the device for predicting and optimizing the power of the new energy power generation of the regional power grid provided by the embodiment of the invention utilizes the simplified new energy-electricity conversion model, the station prediction data, the station actual measurement data, the operation data and other multi-source data to quantitatively and finely analyze the error of each key link of the power prediction of each new energy power generation station on the scheduling side, so as to accurately locate the weak link of the power prediction of the station with the poorer prediction level, and facilitate the subsequent development of corresponding optimization measures to efficiently promote the power prediction level of the new energy power generation station.

In an optional embodiment, the device for predicting and optimizing the generated power of the regional grid new energy may further include: and the regional error evaluation module is used for decoupling and evaluating the new energy power generation power prediction error of the regional power grid according to the error caused by each key link of the new energy power generation power prediction of each new energy power generation station in the regional power grid.

R_NWP-i＝|E_NWP-i|÷(|E_NWP-i|+|E_model-i|+|E_revise-i|)

R_model-i＝|E_NWP-i|÷(|E_NWP-i|+|E_model-i|+|E_revise-i|)

R_revise-i＝|E_NWP-i|÷(|E_NWP-i|+|E_model-i|+|E_revise-i|)

In an optional embodiment, the device for predicting and optimizing the generated power of the regional grid new energy may further include: and the data preprocessing module is used for preprocessing the historical operating data.

In an alternative embodiment, the historical operating data includes: the power values corresponding to a plurality of moments and a plurality of actually measured meteorological factors of the stations corresponding to the power values correspond to the meteorological factors one to one, for example: t is t_iWeather factor at the moment is D_iCorresponding power value is W_iThe data preprocessing module comprises: the device comprises an interval dividing unit, a probability density acquiring unit and an abnormal data eliminating unit.

The probability density obtaining unit calculates the probability density of actually measured meteorological factors of each station corresponding to the power value in a power interval by adopting a kernel density function;

Wherein the expression of the kernel density function is:

representing the probability density of the measured meteorological factor x of the station, h representing the preset power interval, n representing the measured meteorological factor xThe number of actually measured meteorological factors, x, of the station corresponding to the power value in the power interval_iIndicating the actually measured meteorological factors of the ith station in the power interval; k () is a kernel function.

and the abnormal data removing unit removes abnormal data according to the actually measured meteorological factors and the probability density of the meteorological factors of each station corresponding to the power value in the power interval.

The method specifically comprises the following steps:

In an alternative embodiment, the modeling module 20 includes: and the data fitting unit is used for fitting the historical operating data to obtain a simplified new energy-electricity conversion model of the new energy power generation station.

Specifically, historical operating data can be input into MATLAB software, a data fitting function in the MATLAB software is called, a quadratic polynomial function representing the corresponding relation between meteorological factors and power station power is obtained, and in practical application, functional relational expressions between multiple factors (such as field station predicted wind speed, field station predicted wind direction, field station predicted air temperature, field station predicted air pressure and field station predicted irradiance) and power are generally fitted for power prediction in order to accurately predict power.

In another alternative embodiment, the simplified new energy-electricity conversion model may be implemented by using a statistical model, which is essentially implemented by matching a physical causal relationship between the input (including NWP, historical data, and the like) of the system and the predicted power by using a statistical method, and inputting the meteorological elements (such as wind speed, wind direction, irradiance, and the like) of the NWP, the meteorological elements of the historical NWP, and the operation data (such as measured power, measured wind speed, measured wind direction, measured irradiance, and the like) of the new energy power plant into the statistical model to obtain the predicted power of the new energy power plant, wherein the statistical model may be implemented by using piecewise linear regression, kalman filtering, a neural network, a support vector machine, and the like.

In an alternative embodiment, the station forecast data includes: station predicted power; the station measured data comprises: the station actually measures meteorological data, and the operation data comprises: planned startup capacity, actual startup capacity, and rated installed capacity; the equivalent prediction data obtaining module 40 includes: the power control device comprises a first equivalent prediction power acquisition unit, a second equivalent prediction power acquisition unit and a third equivalent prediction power acquisition unit.

The first equivalent prediction power obtaining unit calculates equivalent prediction power under the condition of accurate starting capacity according to the station prediction power, the planned starting capacity and the actual starting capacity;

wherein the first equivalent prediction power obtaining unit includes: and the first calculating subunit multiplies the station predicted power by the quotient of the actual boot capacity divided by the planned boot capacity to obtain the equivalent predicted power under the condition of accurate boot capacity.

The method is realized by adopting the following formula:

P_capacity-i＝P_predict-i×(C_actual-i÷C_scheduling-i)

The second equivalent prediction power obtaining unit obtains equivalent prediction power under the accurate meteorological resource condition according to the actually measured meteorological data of the station, the simplified new energy-electricity conversion model, the planned startup capacity and the rated installed capacity;

wherein the second equivalent prediction power obtaining unit includes: a first predicted power generation amount acquisition subunit and a second calculation subunit.

and the second calculating subunit multiplies the predicted power generation amount under the accurate meteorological resource condition by the quotient of dividing the planned starting capacity by the rated installed capacity to obtain the equivalent predicted power under the accurate meteorological resource condition.

The method is realized by adopting the following formula:

P_sun-pc-i＝PC(S_actual-i)×(C_scheduling-i÷C_rated-i)

And the third equivalent prediction power obtaining unit obtains equivalent prediction power under the condition of predicting meteorological resources according to the station prediction meteorological data, the simplified new energy-electricity conversion model, the planned startup capacity and the rated installed capacity.

Wherein the third equivalent prediction power obtaining unit includes: a second predicted power generation amount acquisition subunit and a third calculation subunit.

and the third calculating subunit multiplies the predicted power generation amount under the condition of the predicted meteorological resources by the quotient of dividing the planned starting capacity by the rated installed capacity to obtain the equivalent predicted power under the condition of the predicted meteorological resources.

The method is realized by adopting the following formula:

P_predict-pc-i＝PC(S_predict-i)×(C_scheduling-i÷C_rated-i)

P_predict-pc-irepresenting equivalent predictions under predicted meteorological resource conditionsPower, PC () represents a simplified New energy-to-Electrical conversion model, S_predict-iIndicating predicted weather factors, C, of the station_scheduling-iIndicating the planned boot capacity, C_rated-iIndicating the rated installed capacity.

In an alternative embodiment, the key links include: a numerical weather forecasting link, a model link and a correction link; the error decoupling evaluation module comprises: the system comprises a numerical weather forecast link error evaluation unit, a correction link error evaluation unit and a model link error evaluation unit.

The numerical weather forecast link error evaluation unit obtains an error caused by the numerical weather forecast link of the new energy power generation station according to the equivalent forecast power under the forecast weather resource condition and the equivalent forecast power under the accurate weather resource condition;

the method is realized by adopting the following formula:

E_NWP-i≈E_NWP-PC-i＝P_predict-pc-i-P_sun-pc-i

The error evaluation unit of the correction link obtains the error caused by the correction link of the new energy power generation station according to the station predicted power and the equivalent predicted power under the accurate starting capacity condition;

the method is realized by adopting the following formula:

E_revise-i＝P_predict-i-P_capacity-i

The method is realized by adopting the following formula:

E_model-i＝E_total-i-E_NWP-i-E_revise-i

In summary, the device for predicting and optimizing the power of the new energy source power generation of the regional power grid provided by the embodiment of the invention develops a fine evaluation of the power prediction error of any time period or moment or key link on the scheduling side, realizes an accurate monitoring of the power prediction operation condition of the new energy source, accurately positions the problem station and analyzes the composition of the prediction error by quantitatively analyzing the contribution ratio of each link error, namely, quantitatively evaluates a large deviation event of the new energy source power generation station, calculates the contribution ratio of each link error in the prediction deviation event, and accurately analyzes the cause of the prediction deviation event; by developing long-period error evaluation of years, seasons and months, weak prediction links of stations with poor prediction levels can be accurately positioned, the prediction method is purposefully improved, corresponding optimization measures are developed in a targeted mode, and the power prediction level of the new energy power generation station is effectively improved.

The apparatuses, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or implemented by a product with certain functions. A typical implementation device is an electronic device, which may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

In a typical example, the electronic device specifically includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the following steps when executing the program:

establishing a simplified new energy-electricity conversion model of the new energy power generation station according to the historical operation data;

From the above description, the electronic device provided by the embodiment of the invention can be used for quantitatively and finely analyzing errors of each key link of power prediction, so as to accurately position weak prediction links of stations with poor prediction levels, and correspondingly develop corresponding optimization measures to efficiently improve the power prediction levels of new energy power generation stations.

Referring now to FIG. 13, shown is a schematic diagram of an electronic device 600 suitable for use in implementing embodiments of the present application.

As shown in fig. 13, the electronic apparatus 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate works and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM)) 603. In the RAM603, various programs and data necessary for the operation of the system 600 are also stored. The CPU601, ROM602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted as necessary on the storage section 608.

In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, an embodiment of the invention includes a computer-readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of:

From the above description, the computer-readable storage medium provided by the embodiment of the invention can be used for quantitatively and finely analyzing errors of each key link of power prediction, so as to accurately locate a weak prediction link of a station with a poor prediction level, and correspondingly develop an optimization measure to efficiently improve the power prediction level of the new energy power generation station.

In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for predicting and optimizing the generated power of new energy of a regional power grid is characterized by comprising the following steps:

2. The method for predicting and optimizing the generated power of the regional power grid new energy according to claim 1, further comprising:

3. The method for predicting and optimizing the generated power of the regional power grid new energy according to claim 1, further comprising:

and preprocessing the historical operating data.

4. The method for predictive optimization of new energy generated power of a regional power grid according to claim 3, wherein the historical operating data comprises: a plurality of power values and a plurality of station actual measurement meteorological factors corresponding to the power values;

the preprocessing the historical operating data comprises the following steps:

5. The method for predicting and optimizing the generated power of the regional power grid new energy according to claim 4, wherein the expression of the kernel density function is as follows:

6. The method for predicting and optimizing the generated power of the regional power grid new energy according to claim 4, wherein the step of eliminating abnormal data according to actually measured meteorological factors and probability density thereof of each station corresponding to the power value in the power interval comprises the following steps:

Traverse the probability density sequence [ P ]₁,P₂,…,P_n]Obtaining the maximum value P of the probability density_k；

7. The method for predictive optimization of new energy generated power of a regional power grid according to claim 1, wherein the establishing of the simplified new energy-to-electricity conversion model of the new energy power generation station according to the historical operating data comprises:

and fitting the historical operation data to obtain a simplified new energy-electricity conversion model of the new energy power generation station.

8. The method for predicting and optimizing the power generation capacity of the regional power grid as claimed in claim 1, wherein the station prediction data comprises: station predicted power; the station measured data comprises: the station measured weather data, the operation data includes: planned startup capacity, actual startup capacity, and rated installed capacity;

the obtaining equivalent prediction data according to the simplified new energy-electricity conversion model, the station prediction meteorological data, the station prediction data, the station actual measurement data and the operation data comprises:

and obtaining equivalent predicted power under the condition of predicted meteorological resources according to the station predicted meteorological data, the simplified new energy-electricity conversion model, the planned starting capacity and the rated installed capacity.

9. The method for predicting and optimizing the power generation capacity of the regional power grid as claimed in claim 8, wherein the calculating the equivalent predicted power under the condition of the accurate startup capacity according to the station predicted power, the planned startup capacity and the actual startup capacity comprises:

and multiplying the quotient of dividing the actual starting capacity by the planned starting capacity by the station predicted power to obtain the equivalent predicted power under the condition of accurate starting capacity.

10. The method for predictive optimization of the generated power of the regional power grid new energy according to claim 8, wherein the obtaining of the equivalent predicted power under accurate meteorological resource conditions from the actually measured meteorological data of the station, the simplified new energy-to-electricity conversion model, the planned startup capacity, and the rated installed capacity comprises:

and multiplying the quotient of dividing the planned starting capacity by the rated installed capacity by the predicted power generation amount under the accurate meteorological resource condition to obtain the equivalent predicted power under the accurate meteorological resource condition.

11. The method for predictive optimization of power generation from new energy of regional power grid as claimed in claim 8, wherein said obtaining equivalent predicted power under predicted meteorological resource conditions from said station predicted meteorological data, said simplified new energy-to-electricity conversion model, said planned startup capacity, and said rated installed capacity comprises:

and multiplying the quotient of dividing the planned starting capacity by the rated installed capacity by the predicted power generation amount under the condition of the predicted meteorological resources to obtain the equivalent predicted power under the condition of the predicted meteorological resources.

12. The method for predicting and optimizing the generated power of the regional power grid new energy according to claim 8, wherein the key links comprise: a numerical weather forecasting link, a model link and a correction link;

the method for obtaining the errors caused by each key link of the new energy power generation power prediction of the new energy power generation station according to the total new energy power generation power prediction error and the equivalent prediction data comprises the following steps:

obtaining an error caused by the numerical weather forecast link of the new energy power generation station according to the equivalent predicted power under the predicted weather resource condition and the equivalent predicted power under the accurate weather resource condition;

13. The method for predicting and optimizing the power generation capacity of the regional power grid as claimed in claim 1, wherein the station prediction data comprises: station predicted power, the station measured data comprising: actual power of the station;

the step of obtaining the total predicted error of the new energy power generation power according to the station predicted data and the station measured data comprises the following steps:

14. The utility model provides a regional power grid new forms of energy generated power prediction optimization device which characterized in that includes:

the modeling module is used for establishing a simplified new energy-electricity conversion model of the new energy power generation station according to the historical operation data;

15. The device for predicting and optimizing the generated power of the regional power grid new energy according to claim 14, further comprising:

16. The device for predicting and optimizing the generated power of the regional power grid new energy according to claim 14, further comprising:

17. The device for predictive optimization of new energy generation power of a regional power grid according to claim 16, wherein the historical operating data comprises: a plurality of power values and a plurality of station actual measurement meteorological factors corresponding to the power values;

the data preprocessing module comprises:

18. The device for predictive optimization of new energy generation power of a regional power grid according to claim 14, wherein the modeling module comprises:

19. The device for predicting and optimizing the power generated by the regional power grid new energy according to claim 14, wherein the station prediction data comprises: station predicted power; the station measured data comprises: the station measured weather data, the operation data includes: planned startup capacity, actual startup capacity, and rated installed capacity;

the equivalent prediction data acquisition module comprises:

the first equivalent prediction power obtaining unit is used for calculating equivalent prediction power under the condition of accurate starting capacity according to the station prediction power, the planned starting capacity and the actual starting capacity;

the second equivalent prediction power obtaining unit is used for obtaining equivalent prediction power under the accurate meteorological resource condition according to the actually measured meteorological data of the station, the simplified new energy-electricity conversion model, the planned starting capacity and the rated installed capacity;

and the third equivalent prediction power obtaining unit is used for obtaining equivalent prediction power under the condition of prediction meteorological resources according to the station prediction meteorological data, the simplified new energy-electricity conversion model, the planned starting-up capacity and the rated installed capacity.

20. The device for predicting and optimizing the power generation capacity of the regional power grid as claimed in claim 19, wherein the first equivalent prediction power obtaining unit comprises:

and the first calculation subunit multiplies the station predicted power by the quotient of dividing the actual startup capacity by the planned startup capacity to obtain the equivalent predicted power under the condition of accurate startup capacity.

21. The device for predicting and optimizing the power generation capacity of the regional power grid as claimed in claim 19, wherein the second equivalent prediction power obtaining unit comprises:

the first predicted power generation obtaining subunit inputs the actually measured meteorological data of the station into the simplified new energy-electricity conversion model to obtain predicted power generation under the accurate meteorological resource condition;

22. The device for predictive optimization of power generation from new energy of regional power grid as claimed in claim 19, wherein the third equivalent predictive power obtaining unit comprises:

and the third calculation subunit multiplies the predicted power generation amount under the condition of predicting meteorological resources by the quotient of dividing the planned starting capacity by the rated installed capacity to obtain the equivalent predicted power under the condition of predicting meteorological resources.

23. The device for predicting and optimizing the power generated by the regional power grid new energy according to claim 19, wherein the key links comprise: a numerical weather forecasting link, a model link and a correction link;

the error decoupling evaluation module comprises:

the numerical weather forecast link error evaluation unit is used for obtaining errors caused by the numerical weather forecast link of the new energy power generation station according to the equivalent prediction power under the prediction weather resource condition and the equivalent prediction power under the accurate weather resource condition;

and the model link error evaluation unit is used for subtracting the error caused by the numerical weather forecast link and the error caused by the correction link from the total predicted error of the new energy power generation power to obtain the error caused by the model link of the new energy power generation station.

24. The device for predicting and optimizing the power generated by the regional power grid new energy according to claim 14, wherein the station prediction data comprises: station predicted power, the station measured data comprising: actual power of the station;

25. 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 the steps of the method for predictive optimization of new energy generated power for regional power grid according to any one of claims 1 to 13.

26. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method for predictive optimization of new energy generated power of a regional power grid according to any one of claims 1 to 13.