CN111738854A - New energy spot transaction decision-making data service system based on cloud computing - Google Patents
New energy spot transaction decision-making data service system based on cloud computing Download PDFInfo
- Publication number
- CN111738854A CN111738854A CN202010571605.1A CN202010571605A CN111738854A CN 111738854 A CN111738854 A CN 111738854A CN 202010571605 A CN202010571605 A CN 202010571605A CN 111738854 A CN111738854 A CN 111738854A
- Authority
- CN
- China
- Prior art keywords
- data
- transaction
- analysis
- spot
- real
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 claims abstract description 37
- 238000004088 simulation Methods 0.000 claims abstract description 29
- 230000008859 change Effects 0.000 claims abstract description 19
- 238000007405 data analysis Methods 0.000 claims abstract description 16
- 238000013523 data management Methods 0.000 claims abstract description 10
- 238000000034 method Methods 0.000 claims abstract description 7
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 5
- 230000007774 longterm Effects 0.000 claims description 20
- 238000010248 power generation Methods 0.000 claims description 15
- 238000000354 decomposition reaction Methods 0.000 claims description 12
- 238000004519 manufacturing process Methods 0.000 claims description 7
- 238000005457 optimization Methods 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 2
- 230000000903 blocking effect Effects 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000012545 processing Methods 0.000 abstract description 3
- 230000005611 electricity Effects 0.000 description 25
- 238000007418 data mining Methods 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000013439 planning Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Databases & Information Systems (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Entrepreneurship & Innovation (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Fuzzy Systems (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Technology Law (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Mathematical Physics (AREA)
- Probability & Statistics with Applications (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
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.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010571605.1A CN111738854A (en) | 2020-06-22 | 2020-06-22 | New energy spot transaction decision-making data service system based on cloud computing |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010571605.1A CN111738854A (en) | 2020-06-22 | 2020-06-22 | New energy spot transaction decision-making data service system based on cloud computing |
Publications (1)
Publication Number | Publication Date |
---|---|
CN111738854A true CN111738854A (en) | 2020-10-02 |
Family
ID=72652019
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010571605.1A Pending CN111738854A (en) | 2020-06-22 | 2020-06-22 | New energy spot transaction decision-making data service system based on cloud computing |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN111738854A (en) |
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 |
Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20140081704A1 (en) * | 2012-09-15 | 2014-03-20 | Honeywell International Inc. | Decision support system based on energy markets |
WO2016065092A1 (en) * | 2014-10-22 | 2016-04-28 | Causam Energy, Inc. | Systems and methods for advanced energy settlements, network-based messaging, and applications supporting the same |
CN107133870A (en) * | 2017-03-27 | 2017-09-05 | 国网辽宁省电力有限公司电力科学研究院 | Electric power spot exchange Security Checking method based on cycle static analysis in 30 second |
CN109858783A (en) * | 2019-01-16 | 2019-06-07 | 国能日新科技股份有限公司 | Wind power plant electricity transaction auxiliary decision-making support system and aid decision support method |
CN109948209A (en) * | 2019-03-07 | 2019-06-28 | 广东电力交易中心有限责任公司 | Operation simulation method, device and equipment suitable for power spot market |
CN110363452A (en) * | 2019-08-19 | 2019-10-22 | 深圳市深电能售电有限公司 | A kind of power spot market transaction command system and working method |
CN110503271A (en) * | 2019-08-30 | 2019-11-26 | 南京工业大学 | Multi-type energy storage configuration method of comprehensive energy system |
CN110543697A (en) * | 2019-08-15 | 2019-12-06 | 南方电网科学研究院有限责任公司 | electric power market simulation operation system |
CN110633889A (en) * | 2019-07-31 | 2019-12-31 | 中国电力科学研究院有限公司 | Regional spot market technical support system |
CN110689239A (en) * | 2019-09-11 | 2020-01-14 | 新奥数能科技有限公司 | Auxiliary decision-making method and system for realizing income maximization by participation of power users in market |
CN111224705A (en) * | 2019-12-18 | 2020-06-02 | 西安交通大学 | Index modulation orthogonal frequency division multiplexing safe transmission method based on random mapping |
-
2020
- 2020-06-22 CN CN202010571605.1A patent/CN111738854A/en active Pending
Patent Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20140081704A1 (en) * | 2012-09-15 | 2014-03-20 | Honeywell International Inc. | Decision support system based on energy markets |
WO2016065092A1 (en) * | 2014-10-22 | 2016-04-28 | Causam Energy, Inc. | Systems and methods for advanced energy settlements, network-based messaging, and applications supporting the same |
CN107133870A (en) * | 2017-03-27 | 2017-09-05 | 国网辽宁省电力有限公司电力科学研究院 | Electric power spot exchange Security Checking method based on cycle static analysis in 30 second |
CN109858783A (en) * | 2019-01-16 | 2019-06-07 | 国能日新科技股份有限公司 | Wind power plant electricity transaction auxiliary decision-making support system and aid decision support method |
CN109948209A (en) * | 2019-03-07 | 2019-06-28 | 广东电力交易中心有限责任公司 | Operation simulation method, device and equipment suitable for power spot market |
CN110633889A (en) * | 2019-07-31 | 2019-12-31 | 中国电力科学研究院有限公司 | Regional spot market technical support system |
CN110543697A (en) * | 2019-08-15 | 2019-12-06 | 南方电网科学研究院有限责任公司 | electric power market simulation operation system |
CN110363452A (en) * | 2019-08-19 | 2019-10-22 | 深圳市深电能售电有限公司 | A kind of power spot market transaction command system and working method |
CN110503271A (en) * | 2019-08-30 | 2019-11-26 | 南京工业大学 | Multi-type energy storage configuration method of comprehensive energy system |
CN110689239A (en) * | 2019-09-11 | 2020-01-14 | 新奥数能科技有限公司 | Auxiliary decision-making method and system for realizing income maximization by participation of power users in market |
CN111224705A (en) * | 2019-12-18 | 2020-06-02 | 西安交通大学 | Index modulation orthogonal frequency division multiplexing safe transmission method based on random mapping |
Non-Patent Citations (4)
Title |
---|
刘瑞丰;陈天恩;李焰;贺元康;: "基于能源转型的电力市场建设分析与思考", 中国电力企业管理, no. 07 * |
昌力;庞伟;严兵;杨春祥;司晓峰;刘一峰;: "可再生能源跨区现货市场技术支持系统设计", 电力系统保护与控制, no. 09 * |
董全学;张信;李晨;王恩琦;曾鸣;田廓;: "计及排放约束与输电约束的电力市场均衡分析", 水电能源科学, no. 05 * |
陈雨果;张轩;罗钢;白杨;谭振飞;钟海旺;: "用户报量不报价模式下电力现货市场需求响应机制与方法", 电力系统自动化, no. 09 * |
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 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN107845022B (en) | Electric power market aid decision-making system | |
Papavasiliou et al. | Reserve requirements for wind power integration: A scenario-based stochastic programming framework | |
CN111478312A (en) | Comprehensive energy cluster coordination control method for improving power grid stability | |
CN111738854A (en) | New energy spot transaction decision-making data service system based on cloud computing | |
Khan et al. | Genetic algorithm based optimized feature engineering and hybrid machine learning for effective energy consumption prediction | |
CN108388962B (en) | Wind power prediction system and method | |
CN109858783B (en) | Wind power plant electric power transaction assistant decision support system and assistant decision support method | |
CN105740975A (en) | Data association relationship-based equipment defect assessment and prediction method | |
CN103646329B (en) | Interregional and interprovincial electricity trading operational control method | |
CN107818386A (en) | Power grid enterprises' Operating profit Forecasting Methodology | |
CN106651636A (en) | Multi-energy resource optimum allocation method for global energy internet | |
CN114140176B (en) | Adjustable capacity prediction method and device for load aggregation platform | |
CN114596693A (en) | Method, system, medium, and program product for energy monitoring and management | |
CN114943565A (en) | Electric power spot price prediction method and device based on intelligent algorithm | |
CN117010946A (en) | Thermal power plant production and operation cost accounting system and application method thereof | |
Cai et al. | Gray wolf optimization-based wind power load mid-long term forecasting algorithm | |
CN108960522A (en) | A kind of photovoltaic power generation quantity prediction analysis method | |
Mena et al. | Multi-objective two-stage stochastic unit commitment model for wind-integrated power systems: A compromise programming approach | |
Leiva et al. | Data-driven flexibility prediction in low voltage power networks | |
CN116777616A (en) | Probability density distribution-based future market new energy daily transaction decision method | |
CN108345996B (en) | System and method for reducing wind power assessment electric quantity | |
CN115882451A (en) | Method, device and equipment for predicting generated power of new energy power station | |
Xueliang et al. | Study of power grid planning integrated information platform based on big-data technology | |
CN114240070A (en) | Intelligent assessment system for project cost of power distribution network | |
Ji | Sensitivity Analysis Model of Wind Power Project Cost Influencing Factors Based on Improved LCOE |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination |