CN115601067A - New energy day-ahead transaction decision-making method and device for electric power market - Google Patents

New energy day-ahead transaction decision-making method and device for electric power market Download PDF

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CN115601067A
CN115601067A CN202211292140.1A CN202211292140A CN115601067A CN 115601067 A CN115601067 A CN 115601067A CN 202211292140 A CN202211292140 A CN 202211292140A CN 115601067 A CN115601067 A CN 115601067A
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初瑾珠
潘霄峰
易伟峰
申旭辉
王强
孙财新
李志文
关何格格
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Huaneng Clean Energy Research Institute
Huaneng New Energy Co Ltd Shanxi Branch
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Abstract

The application provides a method for making a decision on new energy day-ahead transaction in an electric power market, which 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 power market based on historical data to obtain predicted data; and constructing an excess profit recovery loss objective function and constraint conditions, and solving by taking the excess profit recovery loss objective function as a solving target and based on the constraint conditions according to the prediction data, contract data and market environment information to obtain an optimal daily declaration scheme. The invention adopting the scheme comprehensively considers factors such as market rule information, medium and long term transaction information, electricity price prediction information, power prediction information and the like, aims to reduce excess profit recovery loss, carries out quantitative processing on the problem declared in the future and has stronger objectivity.

Description

New energy day-ahead transaction decision-making method and device for electric power market
Technical Field
The application relates to the technical field of electric field electric power transaction auxiliary decision-making, in particular to a new energy day-ahead transaction decision-making method and device in an electric power market.
Background
Under the scene of the electric power spot market, new energy power generation enterprises participate in electric power spot trade and need to make 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, a first objective of the application is to provide a new energy day-ahead trading decision-making method for an electric power market, which solves the technical problems that the existing method is incomplete in consideration factors, lacks deep analysis and mining of full data, causes the generated market declaration scheme to be insufficient in comprehensiveness, is difficult to reduce the excess profit recovery loss to the maximum extent and increases the profit, achieves comprehensive consideration of factors such as market rule information, medium-and-long-term trading information, electricity price prediction information and power prediction information, aims at reducing the excess profit recovery loss, carries out quantitative processing on day-ahead declaration problems, and is higher in objectivity and more comprehensive in consideration factors.
The second purpose of the present application is to provide a new energy day-ahead transaction decision method device for an electric power market.
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 trade in an electric power market, 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 an excess profit and recovery loss objective function and constraint conditions, solving by taking the excess profit and recovery loss objective function as a solving target and based on the constraint conditions according to the prediction data, contract data and market environment information to obtain an optimal future declaration scheme,
wherein the excess profit recovery loss objective function is expressed as:
Figure BDA0003900916030000021
wherein, J 11，t Represents the bias recovery loss, J 12，t And a deviation recovery loss indicating that the actual power generation amount at time t is higher than the allowable deviation upper limit of the power amount before the spot date.
According to the method for deciding the day-ahead transaction of the new energy in the power market, the medium-long term transaction and the day-ahead transaction are organically combined, factors such as market rule information, medium-long term transaction information, price prediction information and power prediction information are comprehensively considered, the purpose of reducing excess profit recovery loss is achieved, the day-ahead declaration problem is subjected to quantitative processing, a day-ahead declaration auxiliary decision-making model is built, and a day-ahead declaration scheme is formed.
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 comprises an excess profit recovery loss constraint expressed as:
if the actual power generation amount is lower than the allowable deviation lower limit of the electric quantity before the spot date, the deviation recovery loss is expressed as:
J 11,t ＝max(Q_riqian t -(1+λ)×Q_shishi t ,0)×max(Pzr t -P_shishi t ,0)
wherein, Q _ riqian t Represents the quantity of electricity before the current date of stock at time t, lambda represents the excess profit recovery ratio, pzr t Weighted electricity prices Q _ shishi, representing the post electricity prices and day-ahead electricity prices at time t t Represents the real-time electric quantity of the spot goods at the time t, P _ shishi t Represents the spot real-time electricity rate at time t,
if the actual power generation amount is lower than the allowable deviation lower limit of the power amount before the spot date, the deviation recovery loss is expressed as:
J 11,t ＝max(Q_riqian t -(1+λ)×Q_shishi t ,0)×max(Pzr t -P_shishi t ,0)
wherein Q _ riqian t Indicating the quantity of current day electricity at time t, λ indicating the proportion of excess profit recovery, pzr t Weighted electricity prices, Q _ shishi, representing the benchmarking electricity prices at time t and the day-ahead electricity prices t Represents the real-time electric quantity of the spot goods at the time t, P _ shishi t And the spot real-time electricity price at the time t is shown.
Optionally, in an embodiment of the present application, according to the forecast data, the contract data, and the market environment information, the excess profit-recovery-loss objective function is taken as a solution objective, and the solution is performed based on a constraint condition, so as to obtain an optimal future declaration scheme, including:
according to medium-long term contract historical data, market disclosure data, power prediction data and prediction data contained in the contract data, an excess profit recovery loss 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 objects, a second aspect of the present invention provides a method and an apparatus for making a decision on a new energy day-ahead trade in an electric power market, including an obtaining module, a predicting 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;
the scheme generation module is used for constructing an excess profit recovery loss objective function and constraint conditions, solving the excess profit recovery loss objective function as a solving target based on the constraint conditions according to the prediction data, the contract data and the market environment information to obtain an optimal future declaration scheme,
wherein the excess profit recovery loss objective function is expressed as:
Figure BDA0003900916030000031
wherein, J 11，t Represents the bias recovery loss, J 12，t And the deviation recovery loss is represented by the deviation recovery loss of the real power generation amount at the time t, which is higher than the allowable deviation upper limit of the power generation amount before the current date.
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 application, the constraint condition comprises an excess profit-recovery-loss constraint expressed as:
if the actual power generation amount is lower than the allowable deviation lower limit of the power amount before the spot date, the deviation recovery loss is expressed as:
J 11,t ＝max(Q_riqian t -(1+λ)×Q_shishi t ,0)×max(Pzr t -P_shishi t ,0)
wherein, Q _ riqian t Indicating the quantity of current day electricity at time t, λ indicating the proportion of excess profit recovery, pzr t Weighted electricity prices, Q _ shishi, representing the benchmarking electricity prices at time t and the day-ahead electricity prices t Represents the real-time electric quantity of the spot goods at the time t, P _ shishi t Represents the spot real-time electricity rate at time t,
if the actual power generation amount is lower than the allowable deviation lower limit of the power amount before the spot date, the deviation recovery loss is expressed as:
J 11,t ＝max(Q_riqian t -(1+λ)×Q_shishi t ,0)×max(Pzr t -P_shishi t ,0)
wherein, Q _ riqian t Indicating the quantity of current day electricity at time t, λ indicating the proportion of excess profit recovery, pzr t Weighted electricity prices, Q _ shishi, representing the benchmarking electricity prices at time t and the day-ahead electricity prices t Represents the real-time electric quantity of the spot goods at the moment t, P _ shishi t And the spot real-time electricity price at the time t is shown.
Optionally, in an embodiment of the present application, according to the prediction data, the contract data, and the market environment information, solving based on a constraint condition with an excess profit recovery loss objective function as a solving objective, 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, an excess profit recovery loss 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 device computer apparatus, 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 for making a decision on a new energy day-ahead trade in an electric power market is implemented.
In order to achieve the above object, a fourth aspect of the present invention provides a non-transitory computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor, can execute the above method for making a decision on a new energy day-ahead trade in 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.
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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 flowchart of a method for making a decision on a new energy future trade in an electric power market according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a new energy day-ahead transaction decision method and device in an electric power market according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function 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 the device for determining the day-ahead new energy trading in the power market according to the embodiment of the application are described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for making a decision on a new energy future trade in an electric power market according to an embodiment of the present application.
As shown in fig. 1, the method for making a decision on a new energy day-ahead transaction in an electric power market includes 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;
step 102, predicting the new energy trading price of the electric power market based on historical data to obtain predicted data;
and 103, constructing an excess profit recovery loss objective function and constraint conditions, solving by taking the excess profit recovery loss 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, and obtaining an optimal future declaration scheme.
Wherein, the excess profit-recovery loss objective function is expressed as:
Figure BDA0003900916030000051
wherein, J 11，t Represents the bias recovery loss, J 12，t And a deviation recovery loss indicating that the actual power generation amount at time t is higher than the allowable deviation upper limit of the power amount before the spot date.
According to the method for making the decision on the day-ahead transaction of the new energy in the power market, the medium-and-long-term transaction and the day-ahead transaction are organically combined, factors such as market rule information, medium-and-long-term transaction information, price prediction information and power prediction information are comprehensively considered, the purpose of reducing excess profit recovery loss is achieved, the day-ahead declaration problem is subjected to quantitative processing, a day-ahead declaration auxiliary decision-making model is constructed, and a day-ahead declaration scheme is formed.
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 the processed contract data.
Optionally, in one embodiment of the present application, the constraint condition comprises an excess profit recovery loss constraint expressed as:
if Q _ riqian t >(1+λ)×Q_shishi t I.e. the real power generation amount Q _ shishi t Lower than the amount of the daily output power Q _ riqian t The lower deviation limit allowed, the deviation recovery loss is:
J 11,t ＝max(Q_riqian t -(1+λ)×Q_shishi t ,0)×max(Pzr t -P_shishi t ,0)
wherein, Q-riqian t Represents the quantity of electricity before the current date of stock at time t, lambda represents the excess profit recovery ratio, pzr t Weighted price of post and day-ahead price of electricity 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 rate at time t,
if Q _ riqian t <(1-λ)×Q_shishi t That is, the amount of actual power generation Q _ shishi t Higher than the day-ahead clear electricity Q _ riqian t The upper deviation limit allowed, the deviation recovery loss is:
J 11,t ＝max(Q_riqian t -(1+λ)×Q_shishi t ,0)×max(Pzr t -P_shishi t ,0)
wherein, Q _ riqian t Showing the spot day at time tFront capacity, λ represents the proportion of excess profit recovery, pzr t Weighted electricity prices Q _ shishi, representing the post electricity prices and day-ahead electricity prices at time t t Represents the real-time electric quantity of the spot goods at the moment t, P _ shishi t And the spot real-time electricity price at the time t is shown.
Optionally, in an embodiment of the present application, after determining the excess profit recovery loss 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, and using factors to be considered in the day-ahead declaration as constraint conditions, and finally, an optimal day-ahead declaration scheme is obtained through the ant colony algorithm.
In order to implement the embodiment, the application further provides a new energy day-ahead transaction decision method and device for the power market.
Fig. 2 is a schematic structural diagram of a new energy day-ahead transaction decision making method and device in an electric power market according to an embodiment of the present application.
As shown in fig. 2, the device for determining the future new energy trade in the power market includes: an acquisition module, a prediction module, and a scheme 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 forecasting module is used for forecasting the new energy trading price of the electric power market based on historical data to obtain forecasting data;
the scheme generation module is used for constructing an excess profit recovery loss objective function and constraint conditions, solving the excess profit recovery loss objective function as a solving target based on the constraint conditions according to the prediction data, the contract data and the market environment information to obtain an optimal future declaration scheme,
wherein the excess profit recovery loss objective function is expressed as:
Figure BDA0003900916030000061
wherein, J 11，t Represents the bias recovery loss, J 12，t And a deviation recovery loss indicating that the actual power generation amount at time t is higher than the allowable deviation upper limit of the power amount before the spot date.
Optionally, in an embodiment of the 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 processed contract data.
Optionally, in one embodiment of the application, the constraint condition comprises an excess profit-recovery-loss constraint expressed as:
if the actual power generation amount is lower than the allowable deviation lower limit of the electric quantity before the spot date, the deviation recovery loss is expressed as:
J 11,t ＝max(Q_riqian t -(1+λ)×Q_shishi t ,0)×max(Pzr t -P_shishi t ,0)
wherein, Q _ riqian t Represents the quantity of electricity before the current date of stock at time t, lambda represents the excess profit recovery ratio, pzr t Weighted electricity prices, Q _ shishi, representing the benchmarking electricity prices at time t and the day-ahead electricity prices t Represents the real-time electric quantity of the spot goods at the time t, P _ shishi t Represents the spot real-time electricity rate at time t,
if the actual power generation amount is lower than the allowable deviation lower limit of the power amount before the spot date, the deviation recovery loss is expressed as:
J 11,t ＝max(Q_riqian t -(1+λ)×Q_shishi t ,0)×max(Pzr t -P_shishi t ,0)
wherein Q _ riqian t Represents the quantity of electricity before the current date of stock at time t, lambda represents the excess profit recovery ratio, pzr t Weighted electricity prices, Q _ shishi, representing the benchmarking electricity prices at time t and the day-ahead electricity prices t Represents the real-time electric quantity of the spot goods at the time t, P _ shishi t And the spot real-time electricity price at the time t is shown.
Optionally, in an embodiment of the present application, according to the prediction data, the contract data, and the market environment information, solving based on a constraint condition with an excess profit recovery loss objective function as a solving objective, 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, an excess profit recovery loss 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 new energy day-ahead transaction decision method in the power market is also applicable to the new energy day-ahead transaction decision method device in the power market in 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 running 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 herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., 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 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 well 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 separate 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. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.

Claims (10)

1. A decision-making method for day-ahead trading of new energy in 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 electric power market based on the historical data to obtain predicted data;
constructing an excess profit recovery loss objective function and constraint conditions, solving by taking the excess profit recovery loss 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 future declaration scheme,
wherein the excess profit recovery loss objective function is expressed as:
Figure FDA0003900916020000011
wherein, J 11，t Represents the bias recovery loss, J 12，t And the deviation recovery loss is represented by the deviation recovery loss of the real power generation amount at the time t, which is higher than the allowable deviation upper limit of the power generation amount before the current date.
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 an excess profitability recovery loss constraint represented as:
if the actual power generation amount is lower than the allowable deviation lower limit of the power amount before the spot date, the deviation recovery loss is expressed as:
J 11,t ＝max(Q_riqian t -(1+λ)×Q_shishi t ,0)×max(Pzr t -P_shishi t ,0)
wherein Q _ riqian t Represents the quantity of electricity before the current date of stock at time t, lambda represents the excess profit recovery ratio, pzr t Weighted electricity prices Q _ shishi, representing the post electricity prices and day-ahead electricity prices at time t t Represents the real-time electric quantity of the spot goods at the time t, P _ shishi t Represents the spot real-time electricity rate at time t,
if the actual power generation amount is lower than the allowable deviation lower limit of the power amount before the spot date, the deviation recovery loss is expressed as:
J 11,t ＝max(Q_riqian t -(1+λ)×Q_shishi t ,0)×max(Pzr t -P_shishi t ,0)
wherein Q _ riqian t Represents the quantity of electricity before the spot date at time t, lambda represents the excess profit recovery ratio,Pzr t weighted electricity prices Q _ shishi, representing the post electricity prices and day-ahead electricity prices at time t t Represents the real-time electric quantity of the spot goods at the time t, P _ shishi t Indicating the spot real-time electricity prices at time t.
4. The method of claim 1, wherein said solving based on said constraints with said excess profit-and-recovery loss objective function as a solution objective based on said forecast data, said contract data, and said market environment information to obtain an optimal ante-date declaration scheme 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 excess profit recovery loss 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 day-ahead declaration scheme.
5. A decision-making method device for day-ahead trading of new energy in an electric power market is characterized by comprising an acquisition module, a prediction module and a scheme 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 the historical data to obtain prediction data;
the scheme generation module is used for constructing an excess profit and recovery loss objective function and constraint conditions, solving by taking the excess profit and recovery loss 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 future declaration scheme,
wherein the excess profit recovery loss objective function is expressed as:
Figure FDA0003900916020000021
wherein, J 11，t Represents the bias recovery loss, J 12，t And a deviation recovery loss indicating that the actual power generation amount at time t is higher than the allowable deviation upper limit of the power amount before the spot date.
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 the processed contract data.
7. The apparatus of claim 5, wherein the constraint condition comprises an excess profit-recovery-loss constraint expressed as:
if the actual power generation amount is lower than the allowable deviation lower limit of the electric quantity before the spot date, the deviation recovery loss is expressed as:
J 11,t ＝max(Q_riqian t -(1+λ)×Q_shishi t ,0)×max(Pzr t -P_shishi t ,0)
wherein Q _ riqian t Represents the quantity of electricity before the current date of stock at time t, lambda represents the excess profit recovery ratio, pzr t Weighted electricity prices, Q _ shishi, representing the benchmarking electricity prices at time t and the day-ahead electricity prices t Represents the real-time electric quantity of the spot goods at the time t, P _ shishi t Represents the spot real-time electricity rate at time t,
if the actual power generation amount is lower than the allowable deviation lower limit of the power amount before the spot date, the deviation recovery loss is expressed as:
J 11,t ＝max(Q_riqian t -(1+λ)×Q_shishi t ,0)×max(Pzr t -P_shishi t ,0)
wherein Q _ riqian t Represents the quantity of electricity before the current date of stock at time t, lambda represents the excess profit recovery ratio, pzr t Weighted electricity prices, Q _ shishi, representing the benchmarking electricity prices at time t and the day-ahead electricity prices t Represents the real-time electric quantity of the spot goods at the time t, P _ shishi t Indicating the spot real-time electricity prices at time t.
8. The apparatus of claim 5, wherein said solving based on said constraint on said excess profit-and-recovery loss objective function as a solution objective based on said forecast data, said contract data, and said market environment information to obtain an optimal ante-date declaration scheme 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 excess profit recovery loss 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 day-ahead 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.
CN202211292140.1A 2022-10-20 2022-10-20 New energy day-ahead transaction decision-making method and device for electric power market Pending CN115601067A (en)

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