CN115511634A - New energy day-ahead transaction decision-making method and device for electricity market based on settlement income - Google Patents
New energy day-ahead transaction decision-making method and device for electricity market based on settlement income Download PDFInfo
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Abstract
The application provides a settlement income-based new energy day-ahead trading decision method for an electric power market, and relates to the technical field of electric field electric power trading auxiliary decision, wherein the method comprises the following steps: acquiring data to be processed, wherein the data to be processed comprises historical data, contract data and market environment information, and the historical data comprises comprehensive historical trading price data; predicting the new energy trading price of the electric power market based on historical data to obtain predicted data; and constructing a settlement income objective function and constraint conditions, and solving based on the constraint conditions by taking the settlement income objective function as a solving target according to the prediction data, contract data and market environment information to obtain an optimal day-ahead declaration scheme. By adopting the scheme, the invention comprehensively considers factors such as market rule information, medium and long-term transaction information, electricity price prediction information, power prediction information and the like, improves the settlement income as a declaration target to carry out quantitative processing, and has stronger objectivity.
Description
Technical Field
The application relates to the technical field of electric field electric power transaction auxiliary decision-making, in particular to a settlement income-based electric power market new energy day-ahead transaction decision-making method and device.
Background
Under the scene of the electric power spot market, new energy power generation enterprises participate in electric power spot transaction and need to make a future declaration according to the requirements of current market rules. The future declaration scenario is a 96-point power curve. Therefore, how to make a reasonable and effective day-ahead declaration scheme and realize the maximization of the income is a core problem which needs to be solved urgently.
At present, most new energy power generation enterprises have three main day-ahead declaration modes: the first method is to directly use the original power prediction data of a wind power prediction system as a market declaration scheme; secondly, the original power prediction data is adjusted in a manual mode to form a market declaration scheme; and thirdly, generating a market declaration scheme by means of a software system. However, the first method completely depends on the original power prediction data, does not comprehensively consider various factors such as medium and long term contract conditions, market supply and demand conditions, market assessment rules and the like, and has the advantages that the power prediction result generally has large deviation, and assessment cost is generated in settlement generally. In the second mode, although the original power prediction data is adjusted to a certain extent, the adjustment operation depends on personal ability and experience excessively, an explicit intermediate process of quantitative processing is not provided, part of factors in a practically generated market declaration scheme have artificial randomness, and the working efficiency is low. The third mode is based on informatization means, but usually based on historical transaction data, a statistical analysis method is adopted for carrying out price trend analysis, error analysis is carried out on short-term power prediction data and actual power data, and an error result is used as a basis for short-term power adjustment to generate a market declaration scheme.
Disclosure of Invention
The present application is directed to solving, at least in part, one of the technical problems in the related art.
Therefore, the first purpose of the application is to provide a settlement income-based new energy day-ahead trading decision method for the power market, solve the technical problems that the generated market declaration scheme is difficult to reduce loss and increase settlement income to the maximum extent due to incomplete consideration factors and lack of deep analysis and mining of full data in the existing method, realize comprehensive consideration of factors such as market rule information, medium and long-term trading information, electricity price prediction information and power prediction information, and improve the settlement income to serve as a declaration target for quantitative processing, so that the objectivity is stronger.
The second purpose of the application is to provide a new energy day-ahead transaction decision device for the electric power market based on the income settlement.
A third object of the present application is to propose a computer device.
A fourth object of the present application is to propose a non-transitory computer-readable storage medium.
In order to achieve the above object, an embodiment of a first aspect of the present application provides a method for making a decision on a new energy day-ahead transaction in an electric power market based on settlement revenue, including: acquiring data to be processed, wherein the data to be processed comprises historical data, contract data and market environment information, and the historical data comprises comprehensive historical trading price data; predicting the new energy trading price of the power market based on historical data to obtain predicted data; constructing a settlement income objective function and constraint conditions, and solving based on the constraint conditions by taking the settlement income objective function as a solving target according to the prediction data, contract data and market environment information to obtain an optimal day-ahead declaration scheme, wherein the settlement income objective function is expressed as:
J_jiesuan t ＝P_zhong t ×Q_zhong t
+(Q_riqian t -Q_zhong t )×P_riqian t
+(Q_shishi t -Q_riqian t )×P_shishi t
wherein, J jiesuan Represents settlement income, J _ jiesuan t Indicating spot settlement revenue at time t, P _ zhong t Represents the spot-held medium-and long-term weighted electricity rate at time t, Q _ zhong t Indicating that the current good at time t has a medium or long term charge, Q _ riqian t Indicating the quantity of current-in-stock-day-ahead electricity at time t, P _ riqian t Indicating the spot-day-ahead electricity price at time t, Q _ shishi t Represents the real-time electric quantity of the spot goods at the moment t, P _ shishi t Represents the spot real-time electricity price at time T, T represents the total number of times of the day-ahead declared power curve, T represents the tth time, T =1, 2.
According to the settlement income-based new energy day-ahead transaction decision method for the power market, medium-and-long-term transactions and day-ahead transactions are organically combined, factors such as market rule information, medium-and-long-term transaction information, electricity price prediction information and power prediction information are comprehensively considered, settlement income is improved, day-ahead declaration problems are subjected to quantitative processing, a day-ahead declaration auxiliary decision model is constructed, a day-ahead declaration scheme is formed, and the day-ahead declaration problems are subjected to quantitative processing, so that the generated declaration scheme is higher in objectivity and more comprehensive in consideration of the factors.
Optionally, in an embodiment of the present application, the historical data further includes a system load rate curve, and the predicting the new energy trading price of the electric power market based on the historical data to obtain predicted data includes:
constructing a characteristic project based on comprehensive historical transaction price data and a system load rate curve, and extracting key characteristics;
predicting the day-ahead price and the real-time price in a preset time period based on the key characteristics to obtain prediction data,
after the data to be processed is obtained, the method further comprises the following steps:
and classifying and aggregating the contract data to obtain processed contract data.
Optionally, in one embodiment of the present application, the constraint condition includes a settlement income constraint expressed as:
J chaoe,h ＝min(J 11,h +J 12,h ,J jiesuan,h )
wherein, J jiesuan，h Denotes the spot settlement income at h, Q zhong，4h+i Represents the medium-and long-term electric quantity at h-hour i, P zhong，4h+i Represents the medium-and long-term weighted electric quantity at h, i and Q riqian，4h+i Indicating the current quantity of electricity at time h and i, P riqian，4h+i Indicating spot-time day-ahead electricity price at h hour i, Q shishi，4h+i Real-time electric quantity of stock in h hour i moment, P shishi，4h+i Representing the spot real-time electricity price at h hour i time, J chaoe,h RepresentExcess profit recovery loss at h, J 11,h Represents a deviation recovery loss in which the amount of generated electric power at h is lower than the lower limit of the deviation allowable for the electric power before the actual date, J 22,h And represents the deviation recovery loss of the generated power amount at h time which is higher than the allowable deviation upper limit of the power amount before the current goods.
Optionally, in an embodiment of the present application, the obtaining an optimal future declaration scheme by solving based on constraint conditions with settlement revenue as a solution target according to the prediction data, the contract data, and the market environment information includes:
according to medium-long term contract historical data, market disclosure data, power prediction data and prediction data contained in the contract data, a settlement income objective function is taken as a solving target, a constructed constraint condition is taken as a limit, and an ant colony algorithm is adopted for solving to obtain an optimal future declaration scheme.
In order to achieve the above object, a second aspect of the present invention provides a settlement income-based new energy day-ahead transaction decision device for an electric power market, comprising an obtaining module, a forecasting module, and a scheme generating module, wherein,
the acquisition module is used for acquiring data to be processed, wherein the data to be processed comprises historical data, contract data and market environment information, and the historical data comprises comprehensive historical trading price data;
the prediction module is used for predicting the new energy trading price of the electric power market based on historical data to obtain prediction data;
a scheme generation module for constructing a settlement income objective function and constraint conditions, solving based on the constraint conditions by taking the settlement income objective function as a solving target according to the prediction data, contract data and market environment information to obtain an optimal future declaration scheme,
wherein, the settlement income objective function is expressed as:
J_jiesuan t ＝P_zhong t ×Q_zhong t
+(Q_riqian t -Q_zhong t )×P_riqian t
+(Q_shishi t -Q_riqian t )×P_shishi t
wherein, J jiesuan Denotes settlement income, J jiesuan t Indicating the spot settlement income at time t, P _ zhong t Represents the current cargo hold medium and long-term weighted electricity price at time t, Q _ zhong t Denotes the current capacity of the stock at time t, Q _ riqian t Indicating the quantity of current-in-stock-day-ahead electricity at time t, P _ riqian t Indicating the spot-stock day-ahead electricity price at time t, Q _ shishi t Represents the real-time electric quantity of the spot goods at the time t, P _ shishi t Represents the spot real-time electricity price at time T, T represents the total number of times of the day-ahead declared power curve, T represents the tth time, T =1, 2.
Optionally, in an embodiment of the present application, the historical data further includes a system load rate curve, and the prediction module is specifically configured to:
constructing a characteristic project based on comprehensive historical transaction price data and a system load rate curve, and extracting key characteristics;
predicting the day-ahead price and the real-time price in a preset time period based on the key characteristics to obtain prediction data,
after acquiring the data to be processed, the method further comprises the following steps:
and classifying and aggregating the contract data to obtain the processed contract data.
Optionally, in one embodiment of the present application, the constraint condition includes a settlement income constraint expressed as:
J chaoe,h ＝min(J 11,h +J 12,h ,J jiesuan,h )
wherein, J jiesuan，h Denotes the spot-settlement income at h, Q zhong，4h+i Represents h timeMedium and long term power at time i, P zhong，4h+i Represents the medium-and long-term weighted electric quantity at h-hour i, Q riqian，4h+i Indicating the current quantity of electricity at time h and i, P riqian，4h+i Indicating the spot-time day-ahead electricity price at time h, i, Q shishi，4h+i Real-time electric quantity of stock in h hour i hour, P shishi，4h+i Express the spot real-time electricity price at time h, i, J chaoe,h Represents the excess profit recovery loss at h, J 11,h Represents a deviation recovery loss in which the amount of generated electric power at h is lower than the lower limit of the deviation allowable for the electric power before the actual date, J 22,h And a deviation recovery loss indicating that the generated power at h is higher than the allowable deviation upper limit of the actual-date power.
Optionally, in an embodiment of the present application, solving based on constraint conditions and with the settlement income as a solution objective according to the prediction data, the contract data, and the market environment information to obtain an optimal future declaration scheme includes:
according to medium-long term contract historical data, market disclosure data, power prediction data and prediction data contained in the contract data, a settlement income objective function is taken as a solving target, a constructed constraint condition is taken as a limit, and an ant colony algorithm is adopted for solving to obtain an optimal future declaration scheme.
In order to achieve the above object, a third embodiment of the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for making the new energy day-ahead trading decision for the electricity market based on the settlement revenue when executing the computer program.
To achieve the above object, a fourth aspect of the present invention provides a non-transitory computer-readable storage medium, wherein instructions of the storage medium, when executed by a processor, are capable of executing the above method for accounting income based new energy source day-ahead trading decision of an electric power market.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The above and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flow chart of a settlement revenue-based electric power market new energy day-ahead transaction decision method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a settlement income-based electric power market new energy day-ahead transaction decision making device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present application and should not be construed as limiting the present application.
The method and apparatus for decision making of new energy day-ahead trading in the electricity market based on settlement revenue according to the embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart illustrating a settlement revenue-based electric power market new energy day-ahead transaction decision method according to an embodiment of the present application.
As shown in fig. 1, the settlement income-based new energy day-ahead transaction decision method for the power market comprises the following steps:
step 101, acquiring data to be processed, wherein the data to be processed comprises historical data, contract data and market environment information, and the historical data comprises comprehensive historical trading price data;
103, constructing a settlement income objective function and constraint conditions, solving by taking the settlement income objective function as a solving target and based on the constraint conditions according to the prediction data, the contract data and the market environment information to obtain an optimal daily declaration scheme,
wherein, the settlement income objective function is expressed as:
J_jiesuan t ＝P_zhong t ×Q_zhong t
+(Q_riqian t -Q_zhong t )×P_riqian t
+(Q_shishi t -Q_riqian t )×P_shishi t
wherein, J jiesuan Represents settlement income, J _ jiesuan t Indicating spot settlement revenue at time t, P _ zhong t Represents the spot-held medium-and long-term weighted electricity rate at time t, Q _ zhong t Denotes the current capacity of the stock at time t, Q _ riqian t Indicating the quantity of current-in-stock-day-ahead electricity at time t, P _ riqian t Indicating the spot-stock day-ahead electricity price at time t, Q _ shishi t Represents the real-time electric quantity of the spot goods at the moment t, P _ shishi t Represents the spot real-time electricity price at time T, T represents the total number of times of the day-ahead declared power curve, T represents the tth time, T =1, 2.. T,
according to the settlement income-based new energy day-ahead transaction decision method for the power market, medium-and-long-term transactions and day-ahead transactions are organically combined, factors such as market rule information, medium-and-long-term transaction information, electricity price prediction information and power prediction information are comprehensively considered, settlement income is improved, day-ahead declaration problems are subjected to quantitative processing, a day-ahead declaration auxiliary decision model is constructed, a day-ahead declaration scheme is formed, and the day-ahead declaration problems are subjected to quantitative processing, so that the generated declaration scheme is higher in objectivity and more comprehensive in consideration of the factors.
Optionally, in an embodiment of the present application, the historical data further includes a system load rate curve, and the predicting the new energy trading price of the power market based on the historical data to obtain predicted data includes:
constructing a characteristic project based on comprehensive historical transaction price data and a system load rate curve, and extracting key characteristics;
predicting the day-ahead price and the real-time price in a preset time period based on the key characteristics to obtain prediction data,
after the data to be processed is obtained, the method further comprises the following steps:
and classifying and aggregating the contract data to obtain processed contract data.
Optionally, in an embodiment of the present application, the constraint condition includes a settlement income constraint, where the settlement income constraint is that the excess recovery loss per hour is not greater than the settlement income, specifically:
for each h hour, the fee deducted cannot be greater than the profitability revenue of the station itself, namely:
J chaoe,h ＝min(J 11,h +J 12,h ,J jiesuan,h )
wherein, J jiesuan，h Denotes the spot-settlement income at h, Q zhong，4h+i Represents the medium-and long-term electric quantity at h, i, P zhong，4h+i Represents the medium-and long-term weighted electric quantity at h-hour i, Q riqian，4h+i Current day-ahead power, P, representing h hours, i time riqian，4h+i Indicating the spot-time day-ahead electricity price at time h, i, Q shishi，4h+i Real-time electric quantity of stock in h hour i moment, P shishi，4h+i Representing the spot real-time electricity price at h hour i time, J chaoe,h Represents the excess profit recovery loss at h, J 11,h Represents a deviation recovery loss in which the amount of generated electric power at h is lower than the lower limit of the deviation allowable for the electric power before the actual date, J 22,h And a deviation recovery loss indicating that the generated power at h is higher than the allowable deviation upper limit of the actual-date power.
Optionally, in an embodiment of the present application, after determining the settlement income objective function and the constraint equation, according to the existing medium-and-long term contract historical data, the market disclosure data, the power prediction data of the wind farm, and the data of the day-ahead price, the real-time price prediction data and the like obtained through the price prediction model, an advanced bionic intelligent optimization algorithm — an ant colony optimization algorithm is adopted, during the day-ahead transaction declaration of the electric power market, the declaration risk and the profit are dynamically balanced, factors to be considered in the day-ahead declaration are taken as constraint conditions, and finally, an optimal day-ahead declaration scheme is obtained through an ant colony algorithm.
In order to realize the embodiment, the application also provides a new energy day-ahead transaction decision-making device for the electric power market based on the settlement income.
Fig. 2 is a schematic structural diagram of a settlement income-based new energy day-ahead transaction decision making device for an electric power market according to an embodiment of the present application.
As shown in fig. 2, the settlement income-based new energy day-ahead transaction decision making apparatus for an electric power market includes: . . .
An acquisition module, a prediction module, and a solution generation module, wherein,
the acquisition module is used for acquiring data to be processed, wherein the data to be processed comprises historical data, contract data and market environment information, and the historical data comprises comprehensive historical trading price data;
the prediction module is used for predicting the new energy trading price of the electric power market based on historical data to obtain prediction data;
a scheme generation module for constructing a settlement income objective function and constraint conditions, solving based on the constraint conditions by taking the settlement income objective function as a solving target according to the prediction data, contract data and market environment information to obtain an optimal future declaration scheme,
wherein the settlement revenue objective function is expressed as:
J_jiesuan t ＝P_zhong t ×Q_zhong t
+(Q_riqian t -Q_zhong t )×P_riqian t
+(Q_shishi t -Q_riqian t )×P_shishi t
wherein, J jiesuan Represents settlement income, J _ jiesuan t Indicating the spot settlement income at time t, P _ zhong t Represents the spot-held medium-and long-term weighted electricity rate at time t, Q _ zhong t Indicating that the current good at time t has a medium or long term charge, Q _ riqian t Indicating the quantity of current day power at time t, P _ riqian t Indicating the spot-stock day-ahead electricity price at time t, Q _ shishi t Represents the real-time electric quantity of the spot goods at the time t, P _ shishi t Represents the spot real-time electricity price at time T, T represents the total number of times of the day-ahead declared power curve, T represents the tth time, T =1, 2.
Optionally, in an embodiment of the present application, the historical data further includes a system load rate curve, and the prediction module is specifically configured to:
constructing a characteristic project based on comprehensive historical transaction price data and a system load rate curve, and extracting key characteristics;
predicting the day-ahead price and the real-time price in a preset time period based on the key characteristics to obtain prediction data,
after the data to be processed is obtained, the method further comprises the following steps:
and classifying and aggregating the contract data to obtain the processed contract data.
Optionally, in one embodiment of the present application, the constraint condition comprises a settlement income constraint expressed as:
J chaoe,h ＝min(J 11,h +J 12,h ,J jiesuan,h )
wherein, J jiesuan，h Denotes the spot-settlement income at h, Q zhong，4h+i Represents the medium-and long-term electric quantity at h-hour i, P zhong，4h+i Represents the medium-and long-term weighted electric quantity at h, i and Q riqian，4h+i Indicating the current quantity of electricity at time h and i, P riqian，4h+i Indicating spot-time day-ahead electricity price at h hour i, Q shishi，4h+i Real-time electric quantity of stock in h hour i moment, P shishi，4h+i Representing the spot real-time electricity price at h hour i time, J chaoe,h Represents the excess profit recovery loss at h, J 11,h A deviation recovery loss representing that the generated electric power at h is lower than the allowable deviation lower limit of the electric power before the actual date, J 22,h And a deviation recovery loss indicating that the generated power at h is higher than the allowable deviation upper limit of the actual-date power.
Optionally, in an embodiment of the present application, the obtaining an optimal future declaration scheme by solving based on constraint conditions with settlement revenue as a solution target according to the prediction data, the contract data, and the market environment information includes:
according to medium-long term contract historical data, market disclosure data, power prediction data and prediction data contained in the contract data, a settlement income objective function is taken as a solving target, a constructed constraint condition is taken as a limit, and an ant colony algorithm is adopted for solving to obtain an optimal future declaration scheme.
It should be noted that the explanation of the embodiment of the settlement income-based new energy day-ahead transaction decision method for the electric power market is also applicable to the settlement income-based new energy day-ahead transaction decision device of the embodiment, and details are not repeated here.
In order to implement the foregoing embodiments, the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method described in the foregoing embodiments is implemented.
In order to implement the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the method of the above embodiments.
In the description of the present specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are exemplary and should not be construed as limiting the present application and that changes, modifications, substitutions and alterations in the above embodiments may be made by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A settlement income-based new energy day-ahead transaction decision method for an electric power market is characterized by comprising the following steps:
acquiring data to be processed, wherein the data to be processed comprises historical data, contract data and market environment information, and the historical data comprises comprehensive historical trading price data;
predicting the new energy trading price of the power market based on the historical data to obtain predicted data;
constructing a settlement income objective function and constraint conditions, taking the settlement income objective function as a solving target according to the prediction data, the contract data and the market environment information, and solving based on the constraint conditions to obtain an optimal day-ahead declaration scheme,
wherein the settlement revenue objective function is expressed as:
J_jiesuan t ＝P_zhong t ×Q_zhong t +(Q_riqian t -Q_zhong t )×P_riqian t +(Q_shishi t -Q_riqian t )×P_shishi t
wherein, J jiesuan Represents settlement income, J-jiesuan t Representing the spot settlement income at time t, P-zhong t Representing a point of sale held mid-to-long-term weighted electricity price at time t, Q-zhong t Indicating that the current cargo at time t holds a medium or long term electricity quantity, Q-riqian t Indicating the quantity of current-in-stock-day-ahead electricity at time t, P-riqian t Showing the spot-stock day-ahead electricity price at time t, Q-shishi t Representing the real-time electric quantity of the spot goods at time t, P-shishi t Represents the spot real-time electricity price at time T, T represents the total number of times of the day-ahead declared power curve, T represents the tth time, T =1, 2.
2. The method of claim 1, wherein the historical data further comprises a system load rate curve, and wherein predicting new energy trading prices for the electricity market based on the historical data results in predicted data comprising:
constructing a characteristic project based on the comprehensive historical transaction price data and the system load rate curve, and extracting key characteristics;
predicting a day-ahead price and a real-time price within a preset time period based on the key features to obtain the prediction data,
after the data to be processed is obtained, the method further comprises the following steps:
and classifying and aggregating the contract data to obtain the processed contract data.
3. The method of claim 1, wherein the constraint condition comprises a settlement revenue constraint expressed as:
J chaoe,h ＝min(J 11,h +J 12,h ,J jiesuan,h )
wherein, J jiesuan，h Denotes the spot-settlement income at h, Q zhong，4h+i Represents the medium-and long-term electric quantity at h, i, P zhong，4h+i Represents the medium-and long-term weighted electric quantity at h-hour i, Q riqian，4h+i Current day-ahead power, P, representing h hours, i time riqian，4h+i Indicating spot-time day-ahead electricity price at h hour i, Q shishi，4h+i Real-time electric quantity of stock in h hour i moment, P shishi，4h+i Express the spot real-time electricity price at time h, i, J chaoe,h Represents the excess profit recovery loss at h, J 11,h Represents a deviation recovery loss in which the amount of generated electric power at h is lower than the lower limit of the deviation allowable for the electric power before the actual date, J 22,h And represents the deviation recovery loss of the generated power amount at h time which is higher than the allowable deviation upper limit of the power amount before the current goods.
4. The method of claim 1, wherein said solving based on said constraint on said settlement revenue as a solution objective based on said forecast data, said contract data, and said market environment information to obtain an optimal future declaration scenario comprises:
and according to the medium-long term contract historical data, the market disclosure data, the power prediction data and the prediction data contained in the contract data, taking the settlement income objective function as a solving target, taking the constructed constraint condition as a limit, and solving by adopting an ant colony algorithm to obtain an optimal future declaration scheme.
5. A settlement income-based new energy day-ahead transaction decision making device for an electric power market is characterized by comprising an acquisition module, a prediction module and a scheme generation module,
the acquisition module is used for acquiring data to be processed, wherein the data to be processed comprises historical data, contract data and market environment information, and the historical data comprises comprehensive historical trading price data;
the prediction module is used for predicting the new energy trading price of the electric power market based on the historical data to obtain prediction data;
the scheme generating module is used for constructing a settlement income objective function and a constraint condition, solving based on the constraint condition by taking the settlement income objective function as a solving target according to the prediction data, the contract data and the market environment information to obtain an optimal future declaration scheme,
wherein the settlement revenue objective function is expressed as:
J_jiesuan t ＝P_zhong t ×Q_zhong t
+(Q_riqian t -Q_zhong t )×P_riqian t
+(Q_shishi t -Q_riqian t )×P_shishi t
wherein, J jiesuan Denotes settlement income, J-jiesuan t Representing the spot settlement income at time t, P-zhong t Q-zhong representing a spot-held medium-and long-term weighted electricity price at time t t Indicating the current good-hold medium and long term electricity quantity at time t, Q-riqian t Indicating the quantity of current day-ahead electricity at time t, P-riqian t Showing the spot-stock day-ahead electricity price at time t, Q-shishi t Real-time electric quantity of stock at time t, P-shishi t Represents the spot real-time electricity price at time T, T represents the total number of times of the day-ahead declared power curve, T represents the tth time, T =1, 2.
6. The apparatus of claim 5, wherein the historical data further comprises a system load rate curve, and wherein the prediction module is specifically configured to:
constructing a characteristic project based on the comprehensive historical transaction price data and the system load rate curve, and extracting key characteristics;
predicting a day-ahead price and a real-time price within a preset time period based on the key features to obtain the prediction data,
after the data to be processed is obtained, the method further comprises the following steps:
and classifying and aggregating the contract data to obtain processed contract data.
7. The apparatus of claim 5, wherein the constraint condition comprises a settlement revenue constraint expressed as:
J chaoe,h ＝min(J 11,h +J 12,h ,J jiesuan,h )
wherein, J jiesuan，h Denotes the spot settlement income at h, Q zhong，4h+i Represents the medium-and long-term electric quantity at h-hour i, P zhong，4h+i Represents the medium-and long-term weighted electric quantity at h-hour i, Q riqian，4h+i Indicating the current quantity of electricity at time h and i, P riqian，4h+i Indicating the spot-time day-ahead electricity price at time h, i, Q shishi，4h+i Real-time electric quantity of stock in h hour i hour, P shishi，4h+i Representing the spot real-time electricity price at h hour i time, J chaoe,h Represents the excess profit recovery loss at h, J 11,h A deviation recovery loss representing that the generated electric power at h is lower than the allowable deviation lower limit of the electric power before the actual date, J 22,h And a deviation recovery loss indicating that the generated power at h is higher than the allowable deviation upper limit of the actual-date power.
8. The apparatus of claim 5, wherein said solving based on said constraint on said settlement revenue as a solution objective based on said forecast data, said contract data, and said market environment information to obtain an optimal future declaration scenario comprises:
and according to the medium-long term contract historical data, the market disclosure data, the power prediction data and the prediction data contained in the contract data, taking the settlement income objective function as a solving target, taking the constructed constraint condition as a limit, and adopting an ant colony algorithm to solve to obtain an optimal future declaration scheme.
9. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to any of claims 1-4 when executing the computer program.
10. A non-transitory computer-readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method of any one of claims 1-4.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116128323A (en) * | 2023-04-07 | 2023-05-16 | 阿里巴巴达摩院(杭州)科技有限公司 | Power transaction decision processing method, storage medium and electronic equipment |
| CN116362828A (en) * | 2023-03-06 | 2023-06-30 | 中能智新科技产业发展有限公司 | Thermal power generating unit quotation decision method based on similarity scene and particle swarm algorithm |
| CN116957635A (en) * | 2023-09-20 | 2023-10-27 | 中国华能集团清洁能源技术研究院有限公司 | Power price acquisition method, device, electronic equipment and storage medium |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116362828A (en) * | 2023-03-06 | 2023-06-30 | 中能智新科技产业发展有限公司 | Thermal power generating unit quotation decision method based on similarity scene and particle swarm algorithm |
| CN116362828B (en) * | 2023-03-06 | 2023-10-20 | 中能智新科技产业发展有限公司 | Thermal power generating unit quotation decision method based on similarity scene and particle swarm algorithm |
| CN116128323A (en) * | 2023-04-07 | 2023-05-16 | 阿里巴巴达摩院(杭州)科技有限公司 | Power transaction decision processing method, storage medium and electronic equipment |
| CN116128323B (en) * | 2023-04-07 | 2023-07-21 | 阿里巴巴达摩院(杭州)科技有限公司 | Power transaction decision processing method, storage medium and electronic equipment |
| CN116957635A (en) * | 2023-09-20 | 2023-10-27 | 中国华能集团清洁能源技术研究院有限公司 | Power price acquisition method, device, electronic equipment and storage medium |
| CN116957635B (en) * | 2023-09-20 | 2023-12-26 | 中国华能集团清洁能源技术研究院有限公司 | Power price acquisition method, device, electronic equipment and storage medium |
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