CN111738854A - New energy spot transaction decision-making data service system based on cloud computing - Google Patents
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Abstract
The invention provides a new energy spot transaction decision-making data service system based on cloud computing, which comprises a cloud computing platform, wherein the cloud computing platform mainly comprises a basic data management module and is used for collecting real-time data; the transaction data analysis module is used for providing analysis of transaction data; the power grid model simulation module is used for providing related power grid section and current information through power grid simulation; and the spot transaction decision module gives out a corresponding spot market optimal declaration strategy through data analysis, algorithm calling, simulation and the like. The cloud computing model system building method has the advantages that the cloud computing model system building is provided, the shared resources with abundant technologies, the strong computing capability and the distributed processing mode are provided, and the accurate and rapid data service support and the decision analysis support can be provided for the spot transaction decision of new energy in the face of a large amount of market information and a network blocking node model with instant change.
Description
Technical Field
The invention belongs to the field of computer systems, and particularly relates to a data service system for new energy spot transaction decision based on cloud computing.
Background
28 th month in 2017, the national development reform committee and the national energy agency issue a notice about the development of the pilot work of the electric power spot market construction, eight provinces of southern (starting from Guangdong), Mongxi, Zhejiang, Shanxi, Shandong, Fujian, Sichuan and Gansu are selected as the first pilot, day-ahead, day-by-day and real-time electric energy transactions are carried out, and the electric energy commodity price which embodies the time and position characteristics is formed.
The new energy is taken as a new electricity change entrant, the experience in market condition prejudgment and the trade bidding strategy is insufficient, the data accumulation is insufficient, the real-time information is not timely acquired, and the decision accuracy has no scientific support basis.
The decision making difficulty of new energy trading is increased due to the shortened quotation response time and the complex market rules, and the market disclosure information, the market supply and demand relationship, the policy influence and the blocking condition of a power grid channel need to be simulated, analyzed and calculated in real time, and trade benchmarking analysis, profit and income analysis, trade evaluation and optimization are carried out by combining the real-time production data and the real-time trade results.
Meanwhile, the current spot transaction needs the reporting of power station operators, group marketing personnel cannot see production and transaction data in real time, and decision information is not shared.
In addition, market subjects related to new energy participate in transactions, weather in the whole area needs to be acquired in real time to judge supply and demand relations and price trends, an integral platform does not provide information in real time at present, the data volume of the whole network is large, and the computing capacity is insufficient;
in addition, the spot transaction decision of new energy needs to acquire market information disclosed by a transaction center in time and analyze and calculate the market information, and the disclosure content of the transaction center is in an external network and needs to be integrated and collected;
in summary, the existing new energy stock-in-stock transaction lacks necessary data services, and how to design a data service system to provide necessary data service support such as data processing and data mining for the new energy stock-in-stock transaction is a problem that needs to be solved at present.
Disclosure of Invention
The invention provides a cloud computing-based data service system for new energy spot transaction decisions, which is used for providing centralized data service support for the new energy spot transaction decisions.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a data service system for new energy spot transaction decision based on cloud computing comprises a cloud computing platform, wherein the cloud computing platform mainly comprises:
the basic data management module is used for collecting real-time data, including power station real-time production data, market quotation data, policy change data, rule change data and meteorological condition data;
the power grid model simulation module is used for performing simulation analysis on the power grid operation condition by using historical data and real-time operation data, performing real simulation on the power grid operation condition, and providing related power grid section and flow information for transaction data analysis to be used as input of decision analysis;
the transaction data analysis module is used for providing analysis of transaction data, giving final profit and loss analysis through analysis of transaction conditions and further serving as a basis of an optimization algorithm of the transaction decision module;
and the spot transaction decision module analyzes and provides a corresponding spot market optimal declaration strategy through input data provided by the basic data management module, the power grid model simulation module and the transaction data analysis module, and achieves artificial intelligent analysis based on big data through online real-time data of the cloud computing platform.
Furthermore, the cloud computing platform adopts a B/S structure, system programs and data are stored in a server side, a user interacts through terminal equipment which is provided with a browser and can be connected to the server, and the system operation server adopts an open-source linux operation system.
Further, in the real-time data collected by the basic data management module, the real-time production data of the power station comprises real-time power and real-time wind speed data with the resolution of 15 minutes; the market quotation data comprises the next day 15-minute whole-network supply-demand ratio data and 15-minute power generation space data of various units; the policy change data includes change data caused by a real-time policy affecting the power generation load; the rule change data comprises change data caused by rule adjustment of each trial settlement; the meteorological condition data comprises wind and light resource data of the next day in the whole network for 15 minutes.
Further, the simulation process of the power grid model simulation module is as follows:
1) establishing a typical IEEE 3 node model for a power grid;
2) simulating historical operating data, and combining real-time operating data to give a corresponding simulation result;
3) and acquiring the current power grid section tide information condition through a simulation result.
Further, the analysis method of the transaction data analysis module for the transaction data (daily clearing data, mainly including TMR electric quantity, medium and long term settlement electric quantity, spot positive electric quantity, spot negative electric quantity, medium and long term average price, day-ahead settlement price, real-time settlement price) is as follows:
1) the medium and long term settlement electric quantity + the spot positive electric quantity + the spot negative electric quantity + the metering deviation correction electric quantity is TMR electric quantity;
2) the spot-goods positive electricity charge/the spot-goods positive electricity quantity is equal to the spot-goods positive average price (the price is more than the price of the electricity quantity in the middle and long term);
3) the spot-shipment negative electricity charge is equal to the average spot-shipment price (the price of the power generation right is transferred, and the difference is calculated to be the income);
4) the medium and long term equity electric charge + spot positive electric charge + spot negative electric charge + (assessment, apportionment, etc.) is the actual income electric charge;
5) actual income electricity charge/actual generated energy amount is the actual average electricity price;
6) the actual income electric charge- (TMR electric quantity medium and long term estimation average price) is the actual profit and loss;
7) actual profit and loss + (TMR electric quantity-medium and long-term electric quantity) and subsidy electricity price are actual profit and loss of new energy;
finally, displaying and forming various indexes and profit and loss data on a cloud platform in an analysis table form; based on the large accumulation of daily transaction results, typical transaction day conditions are extracted and provided to a spot transaction decision module, and a subsequent transaction decision module provides a corresponding transaction strategy based on the income conditions of the typical day.
Furthermore, the spot transaction decision module comprises a medium-long term curve decomposition strategy analysis and a five-section quotation strategy analysis, wherein the medium-long term curve decomposition strategy analysis outputs a medium-long term decomposition strategy curve and a total income, and the five-section quotation strategy analysis outputs a five-section volume price curve.
1. And (3) medium-long term curve decomposition strategy:
the first step is as follows: preparing and inputting: the method comprises the following steps of planning electric quantity in a monthly medium-long term, sending medium-long term electric quantity, predicting short-term prediction and the lower limit of the probability thereof (0-1, 0.05 is an interval), obtaining the relation results of medium-long term and day-ahead, day-ahead and real-time electricity prices, and obtaining clear monitoring and settlement data of transaction data, subsidy prices, medium-long term prices and field electricity utilization rate;
the second step is that: analyzing parameters such as historical clearing results, historical electricity prices, market disclosure data, subsidy prices, medium and long-term prices, field power consumption rate and the like;
when the electricity is not limited:
if the medium and long term price is more than the daily price:
if the day-ahead price is greater than the day-in price:
the selection probability is a1
Otherwise:
the selection probability is a2
If the medium-long term price is less than the daily price:
if the day-ahead price is less than the day-in price:
the selection probability is a3
Otherwise:
the selection probability is a4
When the power is limited, the power supply is started,
similarly, the coefficients a5, a6, a7, a 8;
the third step: outputting a medium-long term decomposition strategy curve and total income according to the parameters;
2. five-section quotation strategy:
the first step is as follows: preparing and inputting: the system comprises power grid model simulation data, market supply and demand analysis data, power generation space data and actual power generation power for 15 minutes;
the second step is that: according to the market supply and demand, the power generation space and the historical clearing marginal price, adopting the similar day condition searching, giving the forecast of clearing price at each moment, giving the declaration price at each moment of 96 points, combining, and forming 5 sections according to the price sections;
the third step: outputting n rows and 3 columns of groups and states, and outputting five sections of volume price curves;
when the input data is more than 1 day (96 points), every 5 rows are grouped (namely, five declaration results of one day), the first column is a power starting point, the second column is a power ending point, and the third column is a price of electricity.
Compared with the prior art, the invention has the following beneficial effects:
the cloud computing model system building method has the advantages that the cloud computing model system building is provided, the shared resources with abundant technologies, the strong computing capability and the distributed processing mode are provided, and the accurate and rapid data service support and the decision analysis support can be provided for the spot transaction decision of new energy in the face of a large amount of market information and a network blocking node model with instant change.
Drawings
FIG. 1 is a system architecture diagram according to an embodiment of the present invention;
fig. 2 is a flowchart of cloud data interaction according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
In order to make the objects and features of the present invention more comprehensible, embodiments accompanying the present invention are further described below. It is noted that the drawings are in greatly simplified form and employ non-precise ratios for the purpose of facilitating and distinctly aiding in the description of the patented embodiments of the invention.
The system design of the invention adopts a cloud computing mode, transaction data, market information disclosed by a transaction center, factors influencing transaction decision, such as external policies of transaction, power grid node simulation results, climate and the like, are obtained in real time through a cloud platform, and an auxiliary decision strategy required by transaction is provided through a large amount of computation and analysis. Each power station can log in through a cloud account, check strategies related to auxiliary decision-making, transaction data analysis of the power station, and the like, on one hand, data sharing can be achieved, on the other hand, more cloud data are accumulated, more value-added effect is achieved on data mining analysis, and based on a large amount of historical data, the optimal transaction decision-making strategy is given through extracting transaction conditions of typical days.
The system adopts a B/S structure, and a user interacts with the cloud computing platform through terminal equipment which is provided with a browser and can be connected to a server. As shown in fig. 1, the system program and data of the cloud computing platform are stored in a server side of the cloud platform, and the system operating server adopts an open-source linux operating system.
As shown in fig. 1, the system program of the cloud computing platform mainly includes a basic data management module, a power grid model simulation module, a transaction data analysis module, and a spot transaction decision module. The basic data management module, the power grid model simulation module and the transaction data analysis module are in data connection with the spot transaction decision module and provide data input for the transaction decision module;
the basic information management module is mainly used for collecting real-time data and serving a transaction decision module; providing real-time production data, market quotations, policy changes, rule changes, meteorological conditions and other data of the power station required by decision making for a transaction decision module; the real-time production data of the power station comprises real-time power and real-time wind speed data with the resolution of 15 minutes; the market quotation data comprises the next day 15-minute whole-network supply-demand ratio data and 15-minute power generation space data of various units; the policy change data includes change data caused by a real-time policy affecting the power generation load; the rule change data comprises change data caused by rule adjustment of each trial settlement; the meteorological condition data comprises wind and light resource data of the next day in the whole network for 15 minutes.
The simulation of the power grid model is mainly to simulate the power grid architecture and node conditions and better provide analysis basis for a transaction decision module; the simulation method is that a typical IEEE 3 node model is established for a power grid; simulating historical operating data, and combining real-time operating data to give a corresponding simulation result; and acquiring the current power grid section and current information conditions through the simulation result.
The transaction data analysis module gives out final profit and loss analysis through analysis of transaction conditions and further serves as the basis of an optimization algorithm of the transaction decision module;
the transaction situation described herein is represented by transaction data, which is mainly daily clearing data, including TMR electric quantity, medium and long term settlement electric quantity, spot positive electric quantity, spot negative electric quantity, medium and long term average price, day-ahead settlement price, real-time settlement price, and the like;
the analysis method of the transaction data is as follows:
1) the medium and long term settlement electric quantity + the spot positive electric quantity + the spot negative electric quantity + the metering deviation correction electric quantity is TMR electric quantity;
2) the spot-goods positive electricity charge/the spot-goods positive electricity quantity is equal to the spot-goods positive average price (the price is more than the price of the electricity quantity in the middle and long term);
3) the spot-shipment negative electricity charge is equal to the average spot-shipment price (the price of the power generation right is transferred, and the difference is calculated to be the income);
4) the medium and long term equity electric charge + spot positive electric charge + spot negative electric charge + (assessment, apportionment, etc.) is the actual income electric charge;
5) actual income electricity charge/actual generated energy amount is the actual average electricity price;
6) the actual income electric charge- (TMR electric quantity medium and long term estimation average price) is the actual profit and loss;
7) actual profit and loss + (TMR electric quantity-medium and long term electric quantity) and subsidy electricity price are the actual profit and loss of new energy.
The transaction decision module gives out a corresponding spot market optimal declaration strategy through a large amount of data analysis, algorithm calling, simulation and the like, and achieves artificial intelligence analysis based on big data through online real-time data of a cloud computing platform.
The trading decision module mainly applies a medium-long term curve decomposition strategy and a five-section quotation strategy, and the description is as follows:
1. and (3) medium-long term curve decomposition strategy:
the first step is as follows: preparing and inputting: the method comprises the following steps of planning electric quantity in a monthly medium-long term, sending medium-long term electric quantity, predicting short-term prediction and the lower limit of the probability thereof (0-1, 0.05 is an interval), obtaining the relation results of medium-long term and day-ahead, day-ahead and real-time electricity prices, and obtaining clear monitoring and settlement data of transaction data, subsidy prices, medium-long term prices and field electricity utilization rate;
the second step is that: analyzing parameters such as historical clearing results, historical electricity prices, market disclosure data, subsidy prices, medium and long-term prices, field power consumption rate and the like;
when the electricity is not limited:
if the medium and long term price is more than the daily price:
if the day-ahead price is greater than the day-in price:
the selection probability is a1
Otherwise:
the selection probability is a2
If the medium-long term price is less than the daily price:
if the day-ahead price is less than the day-in price:
the selection probability is a3
Otherwise:
the selection probability is a4
When the power is limited, the power supply is started,
similarly, the coefficients a5, a6, a7, a 8;
the third step: outputting a medium-long term decomposition strategy curve and total income according to the parameters;
2. five-section quotation strategy:
the first step is as follows: preparing and inputting: the system comprises power grid model simulation data, market supply and demand analysis data, power generation space data and actual power generation power for 15 minutes;
the second step is that: according to the market supply and demand, the power generation space and the historical clearing marginal price, adopting the similar day condition searching, giving the forecast of clearing price at each moment, giving the declaration price at each moment of 96 points, combining, and forming 5 sections according to the price sections;
the third step: outputting n rows and 3 columns of groups and states, and outputting five sections of volume price curves;
when the input data is more than 1 day (96 points), every 5 rows are grouped (namely, five declaration results of one day), the first column is a power starting point, the second column is a power ending point, and the third column is a price of electricity.
The trading decision module can provide support in the aspect of trading data analysis for trading decision through a medium-long term curve decomposition strategy and a five-section quotation strategy.
As shown in fig. 2, the real data of the cloud computing platform in the invention is mainly acquired from the power station side; market information and power grid blocking information are mainly obtained by dispatching from a trading center; weather prediction data required by new energy decision making are mainly obtained from a third-party weather service platform. Transaction data of the power station is mainly obtained through maintenance, entry and acquisition of WEB interface operators;
the transaction data server is mainly responsible for data acquisition and acquisition in the basic data management module, the business calculation server is mainly responsible for core calculation of the transaction decision module, and the transaction web server is mainly responsible for statistical analysis and display of transaction analysis results.
The cloud platform computing mainly models and simulates the whole market through an electric power market economics model, gives a corresponding trading strategy, and carries out deep data mining and self-learning optimization based on a real-time online big data analysis technology.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. The utility model provides a data service system of new forms of energy spot-shipment transaction decision based on cloud, which is characterized in that, includes the cloud computing platform, the cloud computing platform mainly includes:
the basic data management module is used for collecting real-time data, including power station real-time production data, market quotation data, policy change data, rule change data and meteorological condition data;
the power grid model simulation module is used for performing simulation analysis on the power grid operation condition by using historical data and real-time operation data, performing real simulation on the power grid operation condition, and providing related power grid section and flow information for transaction data analysis to be used as input of decision analysis;
the transaction data analysis module is used for providing analysis of transaction data, giving final profit and loss analysis through analysis of transaction conditions and further serving as a basis of an optimization algorithm of the transaction decision module;
and the spot-shipment transaction decision module gives out corresponding optimum declaration strategy analysis of the spot-shipment market through input data provided by the basic data management module, the power grid model simulation module and the transaction data analysis module.
2. The cloud-computing-based data service system for new energy spot transaction decision making according to claim 1, wherein the cloud computing platform adopts a B/S structure, system programs and data are stored in a server, a user interacts through a terminal device which is provided with a browser and can be connected to the server, and the system operation server adopts an open-source linux operation system.
3. The cloud-computing-based data service system for new energy spot transaction decision making according to claim 1, wherein the real-time data collected by the basic data management module includes real-time power generation and real-time wind speed data with 15-minute resolution; the market quotation data comprises the next day 15-minute whole-network supply-demand ratio data and 15-minute power generation space data of various units; the policy change data includes change data caused by a real-time policy affecting the power generation load; the rule change data comprises change data caused by rule adjustment of each trial settlement; the meteorological condition data comprises wind and light resource data of the next day in the whole network for 15 minutes.
4. The cloud-computing-based data service system for new energy spot transaction decision making according to claim 1, wherein the grid model simulation module simulation process is as follows:
1) establishing a typical IEEE 3 node model for a power grid;
2) simulating historical operating data, and combining real-time operating data to give a corresponding simulation result;
3) and acquiring the current power grid section tide information condition through a simulation result.
5. The cloud-computing-based data service system for new energy spot transaction decision making according to claim 1, wherein the transaction data mainly comprises daily clearing data, and the daily clearing data mainly comprises TMR electric quantity, medium and long term settlement electric quantity, spot positive electric quantity, spot negative electric quantity, medium and long term average price, day-ahead settlement price and real-time settlement price.
6. The cloud-computing-based data service system for new energy spot trading decision making according to claim 1, wherein the spot trading decision module comprises a medium-long term curve decomposition strategy analysis and a five-segment quotation strategy analysis, the medium-long term curve decomposition strategy analysis outputs a medium-long term decomposition strategy curve and total income, and the five-segment quotation strategy analysis outputs a five-segment quantitative price curve.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113592507A (en) * | 2021-09-28 | 2021-11-02 | 国能日新科技股份有限公司 | Electric power spot transaction monthly income simulation analysis method and device |
| CN113887800A (en) * | 2021-09-29 | 2022-01-04 | 西安峰频能源科技有限公司 | Monthly or ten-day time period transaction auxiliary decision making method and system |
| CN113917978A (en) * | 2021-10-19 | 2022-01-11 | 华能浙江能源销售有限责任公司 | Electric power spot market auxiliary quotation system |
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