CN111860941A - Method and device for optimizing electric power big data intelligent decision platform - Google Patents
Method and device for optimizing electric power big data intelligent decision platform Download PDFInfo
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Abstract
The invention provides a method and a device for optimizing an intelligent decision platform of big electric power data, which relate to the technical field of electric power and are characterized in that a first fuel storage amount of a first purchasing base and a first fuel consumption amount of a first power plant are obtained; constructing a first fuel price prediction model according to the first fuel storage amount and the first fuel consumption amount, and obtaining a first power generation cost according to the first fuel prediction price; obtaining first power demand information; constructing a first power price prediction model according to the first power generation cost and the first power demand information; obtaining first power generation capacity information of a first power plant; obtaining a first benefit according to the first power generation capacity information and the first power forecast price; judging whether the first benefit reaches a first preset threshold value or not; when the first benefit reaches a first preset threshold value, the first purchasing base is determined to be a final first fuel purchasing base of the first power plant, the intelligent selection of the fuel purchasing base is achieved, and the technical effects of multi-energy complementary operation optimization and comprehensive benefit improvement are achieved.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a method and a device for optimizing an intelligent decision platform of electric power big data.
Background
Under the policy condition that the state greatly supports the implementation of 'Internet +' intelligent energy and multi-energy complementary projects, under the condition that the development requirements of energy industries such as safety, environmental protection, energy conservation, consumption reduction, environmental protection, emission reduction and the like are continuously improved under the condition that the power system reform is gradually deepened in the market environment, under the situation that the development requirements of energy industries such as safety, environmental protection, energy conservation, consumption reduction, environmental protection and the like are continuously improved, and under the background that the informatization and artificial intelligence technology is rapidly developed, for comprehensive power generation enterprises with various power supply types, in order to realize the energy utilization integration, improve the market competitiveness, improve the enterprise operation optimization level, and need to be combined with the actual conditions of the enterprises, the business requirements are taken as guidance, the specific business contents developed by the enterprises are determined, each application software system for realizing the intelligent decision-making business is built, the data governance work for ensuring the normal operation of the business application software systems is implemented, the data platform, and finally, the aims of optimal energy consumption, maximum benefit and strongest comprehensive competitiveness of enterprises are achieved.
At present, the business of the comprehensive power generation enterprise is divided mainly according to the industry types (such as water, electricity, thermal power and new energy), the business association between industries is not tight, the data of each business application system are more independent, the intelligent degree is not high, and most business work and data association work are finished mainly by means of manual analysis and decision. More importantly, most enterprises do not apply technologies such as cloud computing, big data and mobile collaboration to construct a data platform supporting development of various services, and corresponding data application and security guarantee systems are lacked, so that comprehensive power generation enterprises can be in new markets and technical environments
However, the applicant of the present invention finds that the prior art has at least the following technical problems:
the existing comprehensive power generation enterprises have low intelligent degree, the selection of a fuel purchasing base and the data association work are mainly completed by means of manual analysis and decision, and the corresponding big data application is lacked.
Disclosure of Invention
The embodiment of the invention provides a method and a device for optimizing an intelligent decision platform for big electric power data, which solve the technical problems that in the prior art, an integrated power generation enterprise has low intelligent degree, selection of a fuel purchasing base and data association work are mainly completed by means of manual analysis decision, and corresponding big data application is lacked, so that the technical effects of intelligently selecting the fuel purchasing base, realizing multi-energy complementary operation optimization and improving comprehensive benefits are achieved.
In view of the foregoing, embodiments of the present application are provided to provide a method and an apparatus for optimizing an intelligent power big data decision platform.
In a first aspect, the present invention provides a method for optimizing an intelligent decision platform for big data of electric power, where the method includes: obtaining a first fuel storage amount of a first purchasing base and a first fuel loss amount of a first power plant; constructing a first fuel price prediction model according to the first fuel storage amount and the first fuel loss amount, wherein the first fuel price prediction model outputs a first fuel predicted price; obtaining a first electricity generation cost from the first predicted fuel price; obtaining first power demand information; constructing a first power price prediction model according to the first power generation cost and the first power demand information, wherein the first power price prediction model outputs a first power prediction price; obtaining first power generation capacity information of a first power plant; obtaining a first benefit according to the first power generation capacity information and the first power forecast price; judging whether the first benefit reaches a first preset threshold value or not; and when the first benefit reaches a first preset threshold value, determining that the first purchasing base is a final first fuel purchasing base of the first power plant.
Preferably, the obtaining a first fuel consumption amount of the first power plant includes:
obtaining a first distance between the first purchasing base and the first power plant; obtaining a first procurement quantity of the first power plant; determining a first transportation loss amount according to the first distance and the first purchasing amount; obtaining first weather information of a first region; determining a second fuel consumption amount according to the first weather information and the first procurement amount; determining the first fuel consumption amount according to the first transportation consumption amount and the second fuel consumption amount.
Preferably, said constructing a first fuel price prediction model based on said first fuel storage amount and said first fuel consumption amount comprises:
obtaining the first fuel consumption; judging whether a first difference value between the first fuel storage amount and the first fuel consumption amount is larger than a second preset threshold value or not; when the first difference value is larger than a second preset threshold value, determining a first original fuel price; determining a first fuel price-to-difference based on the first fuel consumption amount; and constructing a first fuel price prediction model according to the first original fuel price and the first fuel differential price.
Preferably, said deriving a first electricity generation cost from said first predicted fuel price comprises:
obtaining a first shipping price based on the first distance and the first purchase amount; obtaining a first correlation factor coefficient of the first transportation price and the first predicted fuel price; obtaining a second correlation factor coefficient of the second fuel consumption and the first fuel predicted price; determining a first electricity generation cost according to the first correlation factor coefficient, the second correlation factor coefficient and the first fuel predicted price.
Preferably, the method further comprises:
obtaining a first procurement quantity of the first power plant; determining the time spent on a first fuel according to the first power generation capacity information and the first purchasing amount; determining a first transit time according to the first distance; obtaining a first procurement period according to the first fuel consumption and the first transportation time; judging whether the first purchasing period is smaller than a third preset threshold value or not; and when the first purchasing period is smaller than a third preset threshold value, determining that the first purchasing base is a final first fuel purchasing base of the first power plant.
In a second aspect, the present invention provides an apparatus for optimizing an intelligent decision platform for big data of electric power, the apparatus comprising:
A first obtaining unit, configured to obtain a first fuel storage amount of a first procurement repository and a first fuel consumption amount of a first power plant;
a first construction unit configured to construct a first fuel price prediction model based on the first fuel storage amount and the first fuel consumption amount, wherein the first fuel price prediction model outputs a first fuel predicted price;
a second obtaining unit for obtaining a first electricity generation cost from the first fuel predicted price;
a third obtaining unit configured to obtain first power demand information;
a second construction unit configured to construct a first power price prediction model according to the first power generation cost and the first power demand information, wherein the first power price prediction model outputs a first power predicted price;
a fourth obtaining unit for obtaining first power generation capacity information of the first power plant;
a fifth obtaining unit, configured to obtain a first benefit according to the first power generation capacity information and the first power forecast price;
The first judging unit is used for judging whether the first benefit reaches a first preset threshold value or not;
a first determination unit, configured to determine that the first procurement repository is a final first fuel procurement repository of the first power plant when the first benefit reaches a first preset threshold.
Preferably, the obtaining of the first fuel consumption amount of the first power plant in the first obtaining unit includes:
a sixth obtaining unit, configured to obtain a first distance from the first procurement repository to the first power plant;
a seventh obtaining unit, configured to obtain the first procurement amount of the first power plant;
a second determining unit, configured to determine a first transportation loss amount according to the first distance and the first purchasing amount;
an eighth obtaining unit, configured to obtain first weather information of the first region;
a third determination unit for determining a second fuel consumption amount according to the first weather information and the first procurement amount;
a fourth determination unit to determine the first fuel consumption amount according to the first transportation consumption amount and the second fuel consumption amount.
Preferably, the building a first fuel price prediction model according to the first fuel storage amount and the first fuel consumption amount in the first building unit includes:
a ninth obtaining unit for obtaining the first fuel consumption amount;
a second determination unit configured to determine whether a first difference between the first fuel storage amount and the first fuel consumption amount is greater than a second preset threshold;
a fifth determining unit, configured to determine a first raw fuel price when the first difference is greater than a second preset threshold;
a sixth determining unit for determining a first fuel price-difference based on the first fuel consumption amount;
a third construction unit for constructing a first fuel price prediction model from the first raw fuel price and the first differential fuel price.
Preferably, the obtaining of the first electricity generation cost from the first predicted fuel price in the second obtaining unit includes:
a tenth obtaining unit for obtaining a first shipping price according to the first distance and the first purchase amount;
An eleventh obtaining unit that obtains a first correlation factor coefficient of the first transportation price and the first predicted fuel price;
a twelfth obtaining unit configured to obtain a second correlation factor coefficient between the second fuel consumption amount and the predicted first fuel price;
a seventh determining unit for determining a first electricity generation cost based on the first correlation factor coefficient, the second correlation factor coefficient, and the first predicted fuel price.
Preferably, the apparatus further comprises:
a thirteenth obtaining unit for obtaining a first procurement amount of the first power plant;
an eighth determining unit, configured to determine, according to the first power generation information and the first purchase amount, when the first fuel is consumed;
a ninth determining unit for determining a first transit time according to the first distance;
a fourteenth obtaining unit for obtaining a first procurement period from the first fuel time and the first transportation time;
the third judging unit is used for judging whether the first purchasing period is smaller than a third preset threshold value or not;
A tenth determination unit, configured to determine that the first procurement repository is a final first fuel procurement repository of the first power plant when the first procurement period is less than a third preset threshold.
In a third aspect, the present invention provides an apparatus for optimizing an intelligent power big data decision platform, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of any one of the above methods when executing the program.
In a fourth aspect, the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of the methods described above.
One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:
according to the method and the device for optimizing the intelligent decision platform of the big electric power data, provided by the embodiment of the invention, the first fuel storage amount of a first purchasing base and the first fuel consumption amount of a first power plant are obtained; constructing a first fuel price prediction model according to the first fuel storage amount and the first fuel loss amount, wherein the first fuel price prediction model outputs a first fuel predicted price; obtaining a first electricity generation cost from the first predicted fuel price; obtaining first power demand information; constructing a first power price prediction model according to the first power generation cost and the first power demand information, wherein the first power price prediction model outputs a first power prediction price; obtaining first power generation capacity information of a first power plant; obtaining a first benefit according to the first power generation capacity information and the first power forecast price; judging whether the first benefit reaches a first preset threshold value or not; when the first benefit reaches a first preset threshold value, the first purchasing base is determined to be the final first fuel purchasing base of the first power plant, so that the technical problems that in the prior art, the intelligent degree of a comprehensive power generation enterprise is not high, the selection and data association work of the fuel purchasing base is mainly completed by means of manual analysis and decision making, and corresponding big data application is lacked are solved, the intelligent selection of the fuel purchasing base is achieved, and the technical effects of multi-energy complementary operation optimization and comprehensive benefit improvement are achieved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
FIG. 1 is a flowchart illustrating a method for optimizing an intelligent power big data decision platform according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an apparatus for optimizing an intelligent power big data decision platform according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of another apparatus for optimizing an intelligent power big data decision platform according to an embodiment of the present invention.
Description of reference numerals: a first obtaining unit 11, a first constructing unit 12, a second obtaining unit 13, a third obtaining unit 14, a second constructing unit 15, a fourth obtaining unit 16, a fifth obtaining unit 17, a first judging unit 18, a first determining unit 19, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 306.
Detailed Description
The embodiment of the invention provides a method and a device for optimizing an intelligent decision platform for big electric data, which are used for solving the technical problems that in the prior art, the intelligent degree of a comprehensive power generation enterprise is low, the selection of a fuel purchasing base and the data association work are mainly completed by means of manual analysis and decision, and the corresponding big data application is lacked.
The technical scheme provided by the invention has the following general idea: obtaining a first fuel storage amount of a first purchasing base and a first fuel loss amount of a first power plant; constructing a first fuel price prediction model according to the first fuel storage amount and the first fuel loss amount, wherein the first fuel price prediction model outputs a first fuel predicted price; obtaining a first electricity generation cost from the first predicted fuel price; obtaining first power demand information; constructing a first power price prediction model according to the first power generation cost and the first power demand information, wherein the first power price prediction model outputs a first power prediction price; obtaining first power generation capacity information of a first power plant; obtaining a first benefit according to the first power generation capacity information and the first power forecast price; judging whether the first benefit reaches a first preset threshold value or not; and when the first benefit reaches a first preset threshold value, determining that the first purchasing library is the final first fuel purchasing library of the first power plant, so that the technical effects of intelligently selecting the fuel purchasing library, realizing multi-energy complementary operation optimization and improving comprehensive benefits are achieved.
The technical solutions of the present invention are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present invention are described in detail in the technical solutions of the present application, and are not limited to the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
Example one
Fig. 1 is a flowchart illustrating a method for optimizing an intelligent power big data decision platform according to an embodiment of the present invention. As shown in fig. 1, an embodiment of the present invention provides a method for optimizing an intelligent decision platform for big power data, where the method includes:
step 110: a first fuel storage amount of a first procurement repository and a first fuel consumption amount of a first power plant are obtained.
Further, the obtaining a first fuel consumption amount of the first power plant includes: obtaining a first distance between the first purchasing base and the first power plant; obtaining a first procurement quantity of the first power plant; determining a first transportation loss amount according to the first distance and the first purchasing amount; obtaining first weather information of a first region; determining a second fuel consumption amount according to the first weather information and the first procurement amount; determining the first fuel consumption amount according to the first transportation consumption amount and the second fuel consumption amount.
Specifically, for comprehensive power generation enterprises, the fuel is mainly primary energy of coal, oil and gas meeting the requirements of thermal power production. In order to enable enterprises to comprehensively and timely master fuel market situations and improve bargaining capability and market competitiveness, information means is utilized to comprehensively and dynamically collect relevant policies of fuel, industry regulations, industry planning, new technology application conditions, main fuel market information, transportation market information, meteorological information, relevant fuel information of each thermal power plant of the enterprises and the like of each region, and the information is classified and sorted. According to the fuel market information of each region, the factors of fuel transportation, weather, fuel stock and consumption of thermal power plants of enterprises are comprehensively considered, a prediction model of the fuel supply and demand situation and the price trend of each region is constructed, the intelligent technology is adopted for analysis and prediction, support is provided for the optimization and formulation of the fuel purchasing inventory strategy of the enterprises, and the fuel market control capability of the enterprises is improved. First, a first fuel storage amount of a first purchasing base of a fuel market is obtained, and a first fuel consumption amount of a first power plant for transporting a first fuel to a destination is obtained, wherein the first fuel consumption amount of the first power plant is obtained by obtaining a first distance from the first purchasing base to the first power plant, and obtaining a first purchasing amount of the first power plant for purchasing in the first purchasing base. The first transportation loss amount may be determined from a first distance of the first fuel transportation and a first procurement amount of the first fuel procurement, and if the first distance is 200Km and the first procurement amount is 5 tons, the first transportation loss amount is 0.6% of the first procurement amount. And further acquiring first weather information of a first area where the first purchasing warehouse is located, and determining the second fuel consumption according to the first weather information and the first purchasing quantity, wherein if the first weather information is rainy weather, and the first purchasing quantity is 5 tons, the second transportation consumption is 1% of the first purchasing quantity. The first fuel consumption amount is determined based on a sum of the first transportation consumption amount and the second fuel consumption amount.
Step 120: and constructing a first fuel price prediction model according to the first fuel storage quantity and the first fuel loss quantity, wherein the first fuel price prediction model outputs a first fuel prediction price.
Further, the constructing a first fuel price prediction model according to the first fuel storage amount and the first fuel consumption amount comprises: obtaining the first fuel consumption; judging whether a first difference value between the first fuel storage amount and the first fuel consumption amount is larger than a second preset threshold value or not; when the first difference value is larger than a second preset threshold value, determining a first original fuel price; determining a first fuel price-to-difference based on the first fuel consumption amount; and constructing a first fuel price prediction model according to the first original fuel price and the first fuel differential price.
Specifically, by obtaining the first fuel consumption amount, a first difference between the first fuel storage amount and the first fuel consumption amount is determined, and it is determined whether the first difference is larger than a second preset threshold value, that is, a second preset threshold value of the first difference is set, wherein the second preset threshold value represents that the supply of the first fuel is larger than the demand. And when the first difference value between the first fuel storage quantity and the first fuel consumption is larger than a second preset threshold value, namely the first fuel storage quantity is far larger than the first fuel consumption, determining the original price of the first fuel. And determining a first fuel differential price according to the first fuel consumption, wherein the first fuel differential price is the price corresponding to the first fuel consumption. And constructing a first fuel price prediction model according to the first original fuel price, the first differential fuel price, the first fuel storage amount and the first fuel consumption amount.
Step 130: a first electricity generation cost is obtained from the first predicted fuel price.
Further, the obtaining a first power generation cost according to the first predicted fuel price includes: obtaining a first shipping price based on the first distance and the first purchase amount; obtaining a first correlation factor coefficient of the first transportation price and the first predicted fuel price; obtaining a second correlation factor coefficient of the second fuel consumption and the first fuel predicted price; determining a first electricity generation cost according to the first correlation factor coefficient, the second correlation factor coefficient and the first fuel predicted price.
Specifically, a first shipping price is determined in conjunction with a first procurement amount of the first power plant based on a first distance between the first procurement repository and the first power plant. A first correlation factor between the first shipping price and the first predicted fuel price is obtained, wherein a higher first shipping price increases the first predicted fuel price and the first correlation factor increases. And obtaining a second correlation factor coefficient of the second fuel consumption and the first fuel predicted price, wherein the second fuel consumption is large, the first fuel predicted price is increased, and the second correlation factor coefficient is increased. The first power generation cost is influenced by the first correlation factor coefficient, the second correlation factor coefficient and the first fuel forecast price, that is, the first power generation cost is influenced by the first transportation price, the second fuel consumption and the first fuel forecast price.
Step 140: first power demand information is obtained.
Step 150: and constructing a first power price prediction model according to the first power generation cost and the first power demand information, wherein the first power price prediction model outputs a first power prediction price.
Step 160: first power generation capacity information of a first power plant is obtained.
Step 170: and obtaining a first benefit according to the first power generation capacity information and the first power forecast price.
Specifically, first power demand information of customers of the first power plant is determined by obtaining power demand information of the first region. And constructing a first power price prediction model according to the first power generation cost and the first power demand information, namely acquiring multiple groups of first power generation cost data and the first power demand information to construct the first power price prediction model, wherein if the first power generation cost is low and the first power demand is large, the first power prediction price is high. The method comprises the steps of obtaining a first power generation capacity of a first power plant, wherein the first power generation capacity is the daily average power generation amount of the first power plant. The first benefit of the first power plant is calculated according to the first power generation capacity of the first power plant and the first power forecast price output by the first power price forecast model in combination with the first power demand information in step 140. The first benefit is the difference between the total electricity sales value and the total annual electricity production value of the first power plant.
Step 180: and judging whether the first benefit reaches a first preset threshold value.
Step 190: and when the first benefit reaches a first preset threshold value, determining that the first purchasing base is a final first fuel purchasing base of the first power plant.
Specifically, a first preset threshold value of the first benefit is set, and the first preset threshold value is a preset profit amount which is set by removing labor cost, cost and the like from the first power plant. And judging whether the first benefit reaches a first preset threshold value, and determining that the first purchasing base is a final first fuel purchasing base of the first power plant when the first benefit reaches the first preset threshold value. Therefore, by the method for optimizing the intelligent decision platform for big electric power data in the embodiment, the first benefit of the first power plant can be determined according to the fuel storage capacity of the first purchasing base, the distance from the first purchasing base to the first power plant, the meteorological information of the area where the first purchasing base is located, the customer demand information of the first power plant, the power generation capacity of the first power plant and other comprehensive factors, and the first purchasing base is determined to be the final first fuel purchasing base of the first power plant according to the first benefit, so that the technical effects of intelligently selecting the fuel purchasing base, realizing multi-energy complementary operation optimization and comprehensive benefit improvement are achieved, and the technical problems that in the prior art, the intelligent degree of a comprehensive power generation enterprise is low, the selection of the fuel purchasing base and the data association work are mainly completed by means of manual analysis decision and the corresponding big data application is lacked are solved.
Further, the method further comprises: obtaining a first procurement quantity of the first power plant; determining the time spent on a first fuel according to the first power generation capacity information and the first purchasing amount; determining a first transit time according to the first distance; obtaining a first procurement period according to the first fuel consumption and the first transportation time; judging whether the first purchasing period is smaller than a third preset threshold value or not; and when the first purchasing period is smaller than a third preset threshold value, determining that the first purchasing base is a final first fuel purchasing base of the first power plant.
Specifically, the using time of the first fuel at the first power plant, namely the time of the first fuel is determined by obtaining the first purchasing amount of the first power plant and the first generating capacity information and the first purchasing amount of the first power plant. A first procurement period of the first power plant is obtained according to the first fuel consumption and the first transportation time of the first fuel. And setting a third preset threshold value of the first purchasing period, wherein the third preset threshold value is the purchasing period with the minimum purchasing cost for purchasing the first fuel by the first power plant. And judging whether the first purchasing period is smaller than a third preset threshold value or not, and when the first purchasing period is smaller than the third preset threshold value, namely the purchasing cost of the first power plant for purchasing the first fuel in the first purchasing period is the lowest, and determining that the first purchasing base is the final first fuel purchasing base of the first power plant.
Example two
Based on the same inventive concept as the method for optimizing the intelligent decision platform for big electric data in the foregoing embodiment, the present invention further provides a method and an apparatus for optimizing the intelligent decision platform for big electric data, as shown in fig. 2, the apparatus includes:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain a first fuel storage amount of a first procurement warehouse and a first fuel consumption amount of a first power plant;
a first construction unit 12, the first construction unit 12 being configured to construct a first fuel price prediction model according to the first fuel storage amount and the first fuel consumption amount, wherein the first fuel price prediction model outputs a first fuel predicted price;
a second obtaining unit 13, the second obtaining unit 13 being configured to obtain a first electricity generation cost according to the first fuel predicted price;
a third obtaining unit 14, wherein the third obtaining unit 14 is configured to obtain the first power demand information;
a second constructing unit 15, where the second constructing unit 15 is configured to construct a first power price prediction model according to the first power generation cost and the first power demand information, where the first power price prediction model outputs a first power predicted price;
A fourth obtaining unit 16, wherein the fourth obtaining unit 16 is configured to obtain first power generation capacity information of the first power plant;
a fifth obtaining unit 17, where the fifth obtaining unit 17 is configured to obtain a first benefit according to the first power generation information and the first power forecast price;
a first judging unit 18, where the first judging unit 18 is configured to judge whether the first benefit reaches a first preset threshold;
a first determination unit 19, wherein the first determination unit 19 is configured to determine that the first procurement repository is a final first fuel procurement repository of the first power plant when the first benefit reaches a first preset threshold.
Further, the obtaining of the first fuel consumption amount of the first power plant in the first obtaining unit includes:
a sixth obtaining unit, configured to obtain a first distance from the first procurement repository to the first power plant;
a seventh obtaining unit, configured to obtain the first procurement amount of the first power plant;
a second determining unit, configured to determine a first transportation loss amount according to the first distance and the first purchasing amount;
an eighth obtaining unit, configured to obtain first weather information of the first region;
A third determination unit for determining a second fuel consumption amount according to the first weather information and the first procurement amount;
a fourth determination unit to determine the first fuel consumption amount according to the first transportation consumption amount and the second fuel consumption amount.
Further, the constructing, in the first constructing unit, a first fuel price prediction model according to the first fuel storage amount and the first fuel consumption amount includes:
a ninth obtaining unit for obtaining the first fuel consumption amount;
a second determination unit configured to determine whether a first difference between the first fuel storage amount and the first fuel consumption amount is greater than a second preset threshold;
a fifth determining unit, configured to determine a first raw fuel price when the first difference is greater than a second preset threshold;
a sixth determining unit for determining a first fuel price-difference based on the first fuel consumption amount;
a third construction unit for constructing a first fuel price prediction model from the first raw fuel price and the first differential fuel price.
Further, the obtaining, in the second obtaining unit, a first electricity generation cost according to the first predicted fuel price includes:
a tenth obtaining unit for obtaining a first shipping price according to the first distance and the first purchase amount;
an eleventh obtaining unit that obtains a first correlation factor coefficient of the first transportation price and the first predicted fuel price;
a twelfth obtaining unit configured to obtain a second correlation factor coefficient between the second fuel consumption amount and the predicted first fuel price;
a seventh determining unit for determining a first electricity generation cost based on the first correlation factor coefficient, the second correlation factor coefficient, and the first predicted fuel price.
Further, the apparatus further comprises:
a thirteenth obtaining unit for obtaining a first procurement amount of the first power plant;
an eighth determining unit, configured to determine, according to the first power generation information and the first purchase amount, when the first fuel is consumed;
a ninth determining unit for determining a first transit time according to the first distance;
A fourteenth obtaining unit for obtaining a first procurement period from the first fuel time and the first transportation time;
the third judging unit is used for judging whether the first purchasing period is smaller than a third preset threshold value or not;
a tenth determination unit, configured to determine that the first procurement repository is a final first fuel procurement repository of the first power plant when the first procurement period is less than a third preset threshold.
Various variations and embodiments of the method for optimizing an electric power big data intelligent decision platform in the first embodiment of fig. 1 are also applicable to the apparatus for optimizing an electric power big data intelligent decision platform in the present embodiment, and through the foregoing detailed description of the method for optimizing an electric power big data intelligent decision platform, those skilled in the art can clearly understand that the method for implementing the apparatus for optimizing an electric power big data intelligent decision platform in the present embodiment is not described in detail herein for the sake of brevity of the description.
EXAMPLE III
Based on the same inventive concept as the method for optimizing the electric big data intelligent decision platform in the foregoing embodiment, the present invention further provides an apparatus for optimizing the electric big data intelligent decision platform, as shown in fig. 3, including a memory 304, a processor 302, and a computer program stored on the memory 304 and executable on the processor 302, wherein the processor 302 implements the steps of any one of the methods for optimizing the electric big data intelligent decision platform when executing the program.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
Example four
Based on the same inventive concept as the method for optimizing the intelligent decision platform for big power data in the foregoing embodiments, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which when executed by a processor, implements the following steps: obtaining a first fuel storage amount of a first purchasing base and a first fuel loss amount of a first power plant; constructing a first fuel price prediction model according to the first fuel storage amount and the first fuel loss amount, wherein the first fuel price prediction model outputs a first fuel predicted price; obtaining a first electricity generation cost from the first predicted fuel price; obtaining first power demand information; constructing a first power price prediction model according to the first power generation cost and the first power demand information, wherein the first power price prediction model outputs a first power prediction price; obtaining first power generation capacity information of a first power plant; obtaining a first benefit according to the first power generation capacity information and the first power forecast price; judging whether the first benefit reaches a first preset threshold value or not; and when the first benefit reaches a first preset threshold value, determining that the first purchasing base is a final first fuel purchasing base of the first power plant.
In a specific implementation, when the program is executed by a processor, any method step in the first embodiment may be further implemented.
One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:
according to the method and the device for optimizing the intelligent decision platform of the big electric power data, provided by the embodiment of the invention, the first fuel storage amount of a first purchasing base and the first fuel consumption amount of a first power plant are obtained; constructing a first fuel price prediction model according to the first fuel storage amount and the first fuel loss amount, wherein the first fuel price prediction model outputs a first fuel predicted price; obtaining a first electricity generation cost from the first predicted fuel price; obtaining first power demand information; constructing a first power price prediction model according to the first power generation cost and the first power demand information, wherein the first power price prediction model outputs a first power prediction price; obtaining first power generation capacity information of a first power plant; obtaining a first benefit according to the first power generation capacity information and the first power forecast price; judging whether the first benefit reaches a first preset threshold value or not; when the first benefit reaches a first preset threshold value, the first purchasing base is determined to be the final first fuel purchasing base of the first power plant, so that the technical problems that in the prior art, the intelligent degree of a comprehensive power generation enterprise is not high, the selection and data association work of the fuel purchasing base is mainly completed by means of manual analysis and decision making, and corresponding big data application is lacked are solved, the intelligent selection of the fuel purchasing base is achieved, and the technical effects of multi-energy complementary operation optimization and comprehensive benefit improvement are achieved.
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.
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 will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. A method for optimizing an intelligent power big data decision platform is characterized by comprising the following steps:
obtaining a first fuel storage amount of a first purchasing base and a first fuel loss amount of a first power plant;
constructing a first fuel price prediction model according to the first fuel storage amount and the first fuel loss amount, wherein the first fuel price prediction model outputs a first fuel predicted price;
obtaining a first electricity generation cost from the first predicted fuel price;
obtaining first power demand information;
constructing a first power price prediction model according to the first power generation cost and the first power demand information, wherein the first power price prediction model outputs a first power prediction price;
obtaining first power generation capacity information of a first power plant;
obtaining a first benefit according to the first power generation capacity information and the first power forecast price;
judging whether the first benefit reaches a first preset threshold value or not;
and when the first benefit reaches a first preset threshold value, determining that the first purchasing base is a final first fuel purchasing base of the first power plant.
2. The method of claim 1, wherein the obtaining a first amount of fuel consumption of a first power plant comprises:
Obtaining a first distance between the first purchasing base and the first power plant;
obtaining a first procurement quantity of the first power plant;
determining a first transportation loss amount according to the first distance and the first purchasing amount;
obtaining first weather information of a first region;
determining a second fuel consumption amount according to the first weather information and the first procurement amount;
determining the first fuel consumption amount according to the first transportation consumption amount and the second fuel consumption amount.
3. The method of claim 1, wherein constructing a first fuel price prediction model based on the first fuel storage amount and the first fuel consumption amount comprises:
obtaining the first fuel consumption;
judging whether a first difference value between the first fuel storage amount and the first fuel consumption amount is larger than a second preset threshold value or not;
when the first difference value is larger than a second preset threshold value, determining a first original fuel price;
determining a first fuel price-to-difference based on the first fuel consumption amount;
and constructing a first fuel price prediction model according to the first original fuel price and the first fuel differential price.
4. The method of claim 2, wherein said deriving a first electricity generation cost based on said first predicted fuel price comprises:
Obtaining a first shipping price based on the first distance and the first purchase amount;
obtaining a first correlation factor coefficient of the first transportation price and the first predicted fuel price;
obtaining a second correlation factor coefficient of the second fuel consumption and the first fuel predicted price;
determining a first electricity generation cost according to the first correlation factor coefficient, the second correlation factor coefficient and the first fuel predicted price.
5. The method of claim 2, wherein the method further comprises:
obtaining a first procurement quantity of the first power plant;
determining the time spent on a first fuel according to the first power generation capacity information and the first purchasing amount;
determining a first transit time according to the first distance;
obtaining a first procurement period according to the first fuel consumption and the first transportation time;
judging whether the first purchasing period is smaller than a third preset threshold value or not;
and when the first purchasing period is smaller than a third preset threshold value, determining that the first purchasing base is a final first fuel purchasing base of the first power plant.
6. A method for optimizing an intelligent power big data decision platform is characterized by comprising the following steps:
A first obtaining unit, configured to obtain a first fuel storage amount of a first procurement repository and a first fuel consumption amount of a first power plant;
a first construction unit configured to construct a first fuel price prediction model based on the first fuel storage amount and the first fuel consumption amount, wherein the first fuel price prediction model outputs a first fuel predicted price;
a second obtaining unit for obtaining a first electricity generation cost from the first fuel predicted price;
a third obtaining unit configured to obtain first power demand information;
a second construction unit configured to construct a first power price prediction model according to the first power generation cost and the first power demand information, wherein the first power price prediction model outputs a first power predicted price;
a fourth obtaining unit for obtaining first power generation capacity information of the first power plant;
a fifth obtaining unit, configured to obtain a first benefit according to the first power generation capacity information and the first power forecast price;
The first judging unit is used for judging whether the first benefit reaches a first preset threshold value or not;
a first determination unit, configured to determine that the first procurement repository is a final first fuel procurement repository of the first power plant when the first benefit reaches a first preset threshold.
7. An apparatus for optimizing an intelligent power big data decision platform, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 5 when executing the program.
8. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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