Auxiliary decision-making method for new energy stations based on trading market and auxiliary service market

Abstract

The present invention belongs to the field of new energy frequency regulation technology and discloses a new energy station auxiliary decision-making method based on a trading market and ancillary service market. The method includes: obtaining market load forecast values ​​and power generation forecast values, and determining frequency regulation demand capacity based on the market load forecast values ​​and power generation forecast values; publishing frequency regulation demand capacity to each new energy station, and each new energy station determines whether to participate in frequency regulation auxiliary services based on its own operating data. If so, the new energy station is marked as a frequency regulation participant, and each frequency regulation participant generates participation feedback information, which includes: declared quantity, price and frequency regulation performance indicators; obtaining participation feedback information of each frequency regulation participant; and determining the frequency regulation capacity and frequency regulation mileage of each frequency regulation participant based on the participation feedback information of each frequency regulation participant. The present invention has a better frequency regulation effect, realizes differentiated compensation for fast and slow frequency regulation resources, and is conducive to reducing system frequency regulation costs while ensuring energy storage benefits.

Description

Technical Field

[0001] The present invention belongs to the field of new energy frequency modulation technology, and specifically relates to a new energy station auxiliary decision-making method based on a trading market and ancillary service market. Background Art

[0002] The electric energy market and ancillary service market are important components of the power market system and have been significantly developed and improved in China in recent years.

[0003] The electricity market primarily involves the production, transmission, and use of electricity. Under a market-based mechanism, electricity prices in the energy market are divided into medium- and long-term electricity prices and spot electricity prices, both of which are determined through market-based methods. This mechanism aims to combine the stability and flexibility of electricity demand prices, thereby more effectively regulating electricity resources.

[0004] The ancillary services market refers to services other than normal electricity production, transmission, and use, which are provided to maintain the safe and stable operation of the power system, ensure the quality of electricity, and promote the consumption of clean energy. These services include peak shaving, frequency regulation, and standby.

[0005] With the wide-area construction of new power systems, new energy units have begun to be integrated into the power grid on a large scale, which not only gives green attributes to the "commodity" of electricity, but also significantly improves the flexibility of the system and effectively implements the country's dual carbon goals.

[0006] However, as renewable energy sources rapidly replace traditional generating capacity, the inertia of the power system is declining significantly, as new energy generators are unable to provide effective inertia support. This has reduced the system's ability to resist disturbances and led to more serious frequency security issues. Furthermore, the uncertainty of renewable energy output has further exacerbated power system frequency fluctuations, and the system's frequency regulation deficit continues to widen. Traditional generators are unable to effectively support the more frequent frequency fluctuations caused by the reduced inertia of the new power system, necessitating the inclusion of more efficient and reliable frequency regulation resources.

[0007] Furthermore, system frequency regulation demands vary significantly during different frequency regulation periods. Therefore, a frequency regulation ancillary services market mechanism suitable for energy storage participation should be able to reflect these changes in system frequency regulation demands and guide frequency regulation resource participation by optimizing the frequency regulation market pricing mechanism. Existing frequency regulation mechanisms mostly determine the winning bid capacity and clearing price of frequency regulation resources through marginal clearing, but this approach is not conducive to guiding the rational participation of new energy sources in the frequency regulation ancillary services market. Summary of the Invention

[0008] The purpose of the present invention is to provide a new energy station auxiliary decision-making method based on the trading market and the auxiliary service market, so as to solve the problem that most of the existing frequency regulation mechanisms determine the winning capacity and clearing price of frequency regulation resources through marginal clearing, but this method is not conducive to guiding frequency regulation resources to rationally participate in the frequency regulation auxiliary service market.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] In a first aspect, the present invention provides a new energy station auxiliary decision-making method based on a trading market and ancillary service market, the method comprising:

[0011] Obtain market load forecast values ​​and power generation forecast values, and determine frequency regulation demand capacity based on the market load forecast values ​​and power generation forecast values;

[0012] Publish frequency regulation demand capacity to each renewable energy station. Each renewable energy station determines whether to participate in the frequency regulation auxiliary service based on its own operating data. If so, the renewable energy station is marked as a frequency regulation participant. Each frequency regulation participant generates participation feedback information, which includes the declared quantity, price, and frequency regulation performance indicators.

[0013] Obtain participation feedback information from each FM participant;

[0014] The frequency regulation capacity and frequency regulation mileage of each frequency regulation participant are determined based on the participation feedback information of each frequency regulation participant.

[0015] Preferably, the method further comprises: each frequency modulation participant generates a frequency modulation performance indicator, including:

[0016] During the frequency regulation scheduling cycle, calculate the response time, regulation speed and regulation accuracy of each frequency regulation participant;

[0017] Determine the time weight corresponding to the response time, the speed weight corresponding to the adjustment speed, and the accuracy weight corresponding to the adjustment accuracy based on the weighting method;

[0018] The frequency regulation performance index is determined based on the response time and time weight of each frequency regulation participant, the adjustment speed and speed weight of each frequency regulation participant, and the adjustment accuracy and accuracy weight of each frequency regulation participant.

[0019] Preferably, within the frequency modulation scheduling period, the response time, modulation speed, and modulation accuracy of each frequency modulation participant are calculated, including:

[0020] Obtain the adjustment delay duration, response start time, response end time, starting output, ending output, actual output and required output of each frequency modulation participant when responding to the frequency modulation instruction;

[0021] Determine the response time of each frequency modulation participant based on the adjustment delay time, response start time and response end time;

[0022] Determine the adjustment speed of each frequency regulation participant based on the starting output and ending output;

[0023] Determine the regulation accuracy of each frequency regulation participant based on actual output and required output.

[0024] Preferably, determining the frequency modulation capacity and frequency modulation mileage of each frequency modulation participant based on the participation feedback information of each frequency modulation participant includes:

[0025] Determine the initial frequency regulation capacity and initial frequency regulation mileage of each frequency regulation participant based on the quantity and price declared by each frequency regulation participant;

[0026] Correcting the initial frequency regulation capacity of each frequency regulation participant to obtain the frequency regulation capacity of each frequency regulation participant;

[0027] The initial frequency modulation mileage of each frequency modulation participant is corrected to obtain the frequency modulation mileage of each frequency modulation participant.

[0028] Preferably, the initial frequency modulation capacity of each frequency modulation participant is corrected to obtain the frequency modulation capacity of each frequency modulation participant, including:

[0029] Obtain the installed capacity and replacement capacity of each frequency regulation participant;

[0030] Determine the capacity ratio of each frequency regulation participant based on their installed capacity and replacement capacity;

[0031] Multiply the capacity ratio of each frequency regulation participant by the corresponding initial frequency regulation capacity to obtain the frequency regulation capacity of each frequency regulation participant.

[0032] Preferably, the initial frequency modulation mileage of each frequency modulation participant is corrected to obtain the frequency modulation mileage of each frequency modulation participant, including:

[0033] Obtain the frequency regulation demand capacity within two consecutive frequency regulation scheduling cycles;

[0034] Determining a capacity demand ratio based on the frequency regulation demand capacities within two consecutive frequency regulation scheduling periods;

[0035] determining a first correction coefficient according to the capacity demand ratio;

[0036] The product of the first correction coefficient and the initial frequency modulation mileage of each frequency modulation participant is calculated respectively to obtain the frequency modulation mileage of each frequency modulation participant.

[0037] Preferably, based on the declared quantity and price of each frequency regulation participant, the initial frequency regulation capacity and initial frequency regulation mileage of each frequency regulation participant are determined, including:

[0038] Taking frequency regulation capacity and frequency regulation mileage as decision variables, the total cost function of frequency regulation cost is constructed according to the declared quantity and price of each frequency regulation participant and the decision variables;

[0039] Based on the preset optimization algorithm, the total cost function of the frequency modulation cost is solved to obtain the optimal frequency modulation capacity and optimal frequency modulation mileage of each frequency modulation participant. The optimal frequency modulation capacity of each frequency modulation participant is used as the initial frequency modulation capacity of each frequency modulation participant, and the optimal frequency modulation mileage of each frequency modulation participant is used as the initial frequency modulation mileage of each frequency modulation participant.

[0040] In a second aspect, the present invention provides a new energy station decision-making assistance system based on a trading market and ancillary service market. The system is used to implement the above-mentioned new energy station decision-making assistance method based on a trading market and ancillary service market. The system includes:

[0041] The capacity forecast module is used to obtain the market load forecast value and the power generation forecast value, and determine the frequency regulation demand capacity based on the market load forecast value and the power generation forecast value;

[0042] The capacity publishing module is used to publish the frequency regulation capacity requirements to each new energy station. Each new energy station determines whether to participate in the frequency regulation auxiliary service based on its own operating data. If so, the new energy station is marked as a frequency regulation participant. Each frequency regulation participant generates participation feedback information, which includes the declared quantity, price, and frequency regulation performance indicators.

[0043] The data acquisition module is used to obtain the participation feedback information of each frequency modulation participant;

[0044] The frequency modulation determination module is used to determine the frequency modulation capacity and frequency modulation mileage of each frequency modulation participant based on the participation feedback information of each frequency modulation participant.

[0045] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned new energy station auxiliary decision-making method based on the trading market and the auxiliary service market is implemented.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned new energy station auxiliary decision-making method based on the trading market and the auxiliary service market.

[0047] Beneficial effects:

[0048] The present invention determines the frequency regulation demand capacity through the market load forecast value and the power generation forecast value, and then publishes the frequency regulation demand capacity to all frequency regulation participants. Then, each new energy station determines whether to participate in the frequency regulation auxiliary service based on its own operating data. If so, the frequency regulation participant that wants to participate in the frequency regulation auxiliary service generates corresponding participation feedback information such as the declared quantity, price and frequency regulation performance index. Finally, comprehensive allocation is carried out based on the declared quantity, price and frequency regulation performance index of all frequency regulation participants that want to participate in the frequency regulation auxiliary service to determine the frequency regulation capacity and frequency regulation mileage of each frequency regulation participant. The above method enables the present invention to have a better frequency regulation effect, realize differentiated compensation of fast and slow frequency regulation resources, which is beneficial to reducing the system frequency regulation cost while ensuring energy storage benefits, and is beneficial to guiding new energy to rationally participate in the frequency regulation auxiliary service market. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings:

[0050] Figure 1 This is a flow chart of a new energy station auxiliary decision-making method based on a trading market and ancillary service market provided by an embodiment of the present invention;

[0051] Figure 2 It is a block diagram of a new energy station decision support system based on a trading market and ancillary service market provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly introduced below in conjunction with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0053] Example 1

[0054] Figure 1It is a flowchart of a new energy station decision-making assistance method based on a trading market and ancillary service market provided by an embodiment of the present invention. The application scenarios of the method of this embodiment include: frequency regulation party, dispatching party and frequency regulation participants. The frequency regulation party is a frequency regulation agency, and the frequency regulation party is responsible for publishing the frequency regulation demand capacity. The frequency regulation participant is a new energy station, and the dispatching party is an electric power dispatching agency. The dispatching instruction is generated according to the frequency regulation capacity and frequency regulation mileage finally generated by the frequency regulation party, and the dispatching instruction is sent to the frequency regulation participants to enable each frequency regulation participant to participate in the frequency regulation of the power grid. Based on the above scenario, this embodiment provides a new energy station decision-making assistance method based on a trading market and ancillary service market. As Figure 1 As shown, the method includes:

[0055] Step S10: Obtain the market load forecast value and the power generation forecast value, and determine the frequency regulation demand capacity according to the market load forecast value and the power generation forecast value.

[0056] In this embodiment, the market load forecast value is obtained in the following manner:

[0057] 1. Data collection: 1. Collect historical load data: including electricity demand at different time scales (such as hourly, daily, and monthly); 2. Collect data on relevant influencing factors: such as weather conditions (temperature, humidity, wind speed), holiday arrangements, economic development indicators, etc.

[0058] 2. Data processing: 1. Data cleaning: handling missing values, outliers, etc.; 2. Feature engineering: extracting and selecting features that are helpful for prediction, such as timestamps, seasonal indexes, weather factors, etc.

[0059] 3. Model selection: Choose an appropriate forecasting model, such as time series analysis (ARIMA model), regression analysis, machine learning methods (such as random forest, support vector machine, neural network), etc.

[0060] 4. Model training and validation: 1. Use historical data to train the model; 2. Verify the accuracy of the model through methods such as cross-validation.

[0061] 5. Forecasting: Use the trained model to forecast the load for a period of time in the future.

[0062] In this embodiment, the method for obtaining the power generation prediction value is as follows:

[0063] 1. Data collection: Collect data related to power generation, including historical power generation, equipment parameters, fuel costs, environmental conditions, etc.

[0064] 2. Data processing: Data cleaning and feature engineering are also required.

[0065] 3. Model selection: Select an appropriate prediction model based on the type of power generation (such as thermal, hydro, wind, solar, etc.); for example, for photovoltaic power generation, use a model based on weather forecasts and photovoltaic cell characteristics.

[0066] 4. Model training and validation: Use historical power generation data to train and validate the model.

[0067] 5. Forecast: Use the model to predict the power generation capacity in the future.

[0068] Step S20: Publish the frequency regulation demand capacity to each new energy station. Each new energy station determines whether to participate in the frequency regulation auxiliary service based on its own operating data. If so, the new energy station is marked as a frequency regulation participant. Each frequency regulation participant generates participation feedback information, which includes: declared quantity, price and frequency regulation performance indicators.

[0069] Among them, the operating data includes: power generation power, wind speed, solar radiation intensity, equipment status and other data. The above operating data are first collected from each new energy station and evaluated, that is, the key performance indicators such as the reliability, response speed, and regulation capability of the station are evaluated. Based on the above key performance indicators, it is determined whether it can participate in the frequency regulation auxiliary service. If it can participate in the frequency regulation auxiliary service, it will mark itself as a frequency regulation participant, and generate data such as declared quantity and price and frequency regulation performance indicators, and then report them to the frequency regulation party.

[0070] In this embodiment, the method further includes: each frequency modulation participant generating a frequency modulation performance indicator, including:

[0071] Step S201: within the frequency modulation scheduling period, the response time, modulation speed and modulation accuracy of each frequency modulation participant are calculated.

[0072] Specifically, during the frequency modulation scheduling cycle, the response time, modulation speed, and modulation accuracy of each frequency modulation participant are calculated, including:

[0073] Step a10: Obtain the adjustment delay duration, response start time, response end time, starting output, ending output, actual output, and required output of each frequency modulation participant when responding to the frequency modulation instruction;

[0074] Step a20: Determine the response time of each frequency modulation participant according to the adjustment delay time, the response start time, and the response end time;

[0075] Step a30: Determine the adjustment speed of each frequency modulation participant based on the starting output and the ending output;

[0076] Step a40: Determine the regulation accuracy of each frequency regulation participant based on the actual output and the required output.

[0077] Among them, the response time of each frequency modulation participant refers to the delay time for each frequency modulation participant to reach the output direction consistent with the instruction requirement after receiving the frequency modulation instruction; in this embodiment, multiple frequency modulation instructions will be generated within a frequency modulation scheduling cycle. When the frequency modulation participant executes multiple frequency modulation instructions, each frequency modulation participant may determine multiple response times. This embodiment sums all response times determined by the same frequency modulation participant, and then averages them, and uses the average value of all response times as the final response time of the frequency modulation participant.

[0078] Among them, the adjustment speed of each frequency regulation participant refers to the rate at which each frequency regulation participant reaches the output required by the instruction after receiving the frequency regulation instruction, and the adjustment accuracy of each frequency regulation participant refers to the degree of deviation from the output required by the instruction after receiving the frequency regulation instruction.

[0079] Step S201: Determine the time weight corresponding to the response time, the speed weight corresponding to the adjustment speed, and the precision weight of the adjustment precision based on a weighting method.

[0080] The weighting method includes: subjective weighting method and objective weighting method, and the time weight corresponding to the response time, the speed weight corresponding to the adjustment speed and the precision weight corresponding to the adjustment precision are determined based on the weighting method;

[0081] The subjective weight of each indicator is determined based on the subjective weighting method, and each indicator is response time, adjustment speed and adjustment accuracy;

[0082] Determine the objective weight of each indicator based on the objective weighting method;

[0083] According to the subjective weight and objective weight of each indicator, the final weight of each indicator is determined. The final weight is the time weight corresponding to the response time, the speed weight corresponding to the adjustment speed, and the accuracy weight corresponding to the adjustment accuracy.

[0084] In the process of determining the final weight of each indicator according to the subjective weight and the objective weight of each indicator, the subjective weight and the objective weight are used to solve the first solution coefficient of the subjective weight and the second solution coefficient of the objective weight;

[0085] The calculation expression of the final weight of each indicator is:

[0086] W i =k1*w 1,i +k2* w 2,i ;

[0087] Where W i is the final weight of the i-th indicator, k1 is the first solution coefficient, k2 is the second solution coefficient, w 1,i is the subjective weight of the i-th indicator, w 2,iis the objective weight of the i-th indicator, where i=1,2,3.

[0088] In this embodiment, the subjective weighting method mainly relies on the experience, knowledge, intuition and preferences of experts or decision makers to assign different weights to different indicators. The main steps are:

[0089] Determine the evaluation indicators: First, you need to determine all the indicators required to evaluate a decision problem;

[0090] Select experts: Choose experts with relevant field knowledge and experience;

[0091] Obtain weight information: Obtain weight information from experts through questionnaires, interviews, Delphi method, etc.;

[0092] Processing and integrating data: Processing the collected data, such as consistency checking and correction, to integrate the final subjective weight.

[0093] In this embodiment, objective weighting is used, as opposed to subjective weighting. Objective weighting is a method for determining indicator weights based on the statistical properties of the data itself. It does not rely on the decision maker's subjective judgment, but rather uses mathematical methods to calculate weights, resulting in greater objectivity. This embodiment's objective weighting primarily employs the entropy weighting method, which utilizes the concept of information entropy. Lower information entropy indicates greater variability and information content, and therefore a higher weight should be assigned.

[0094] Then the calculation expression of the first solution coefficient is:

[0095] ;

[0096] Where, , .

[0097] Then the calculation expression of the second solution coefficient is:

[0098] .

[0099] The final weights of the indicators in this embodiment can improve the fairness and rationality of the scheduling market and enhance market transparency.

[0100] Step S202: determining a frequency modulation performance index according to the response time and time weight of each frequency modulation participant, the adjustment speed and speed weight of each frequency modulation participant, and the adjustment accuracy and accuracy weight of each frequency modulation participant.

[0101] In this embodiment, W1 is the time weight, W2 is the speed weight, and W3 is the accuracy weight. The frequency modulation performance index of the nth frequency modulation participant is The calculation expression is:

[0102] ;

[0103] Where, is the time weight of the nth FM participant, is the speed weight of the nth FM participant, is the precision weight of the nth FM participant, is the response time of the nth FM participant, is the adjustment speed of the nth FM participant, is the regulation accuracy of the nth frequency modulation participant, where n is a positive integer.

[0104] Step S30: Obtain participation feedback information of each frequency modulation participant, that is, obtain the declared quantity, price and frequency modulation performance indicators obtained in step S20.

[0105] Step S40: determining the frequency modulation capacity and frequency modulation mileage of each frequency modulation participant based on the participation feedback information of each frequency modulation participant.

[0106] Specifically, the frequency modulation capacity and frequency modulation mileage of each frequency modulation participant are determined based on the participation feedback information of each frequency modulation participant, including:

[0107] Step S401: Determine the initial frequency regulation capacity and initial frequency regulation mileage of each frequency regulation participant based on the declared quantity and price of each frequency regulation participant;

[0108] Step S402: correcting the initial frequency modulation capacity of each frequency modulation participant to obtain the frequency modulation capacity of each frequency modulation participant;

[0109] Step S403: correcting the initial FM mileage of each FM participant to obtain the FM mileage of each FM participant.

[0110] In this embodiment, due to the uncertainty on the power consumption side and the power generation side, the frequency of the power grid will fluctuate randomly, and the frequency regulation capacity will also change dynamically over time. When the frequency regulation demand capacity increases rapidly, in order to ensure the frequency stability of the power grid, it is necessary to dynamically correct the initial frequency regulation capacity and initial frequency regulation mileage of each frequency regulation participant. This can effectively reduce the system's frequency regulation cost and increase energy storage benefits while ensuring the stability of the power grid.

[0111] The initial frequency modulation capacity of each frequency modulation participant is corrected to obtain the frequency modulation capacity of each frequency modulation participant, including:

[0112] Step b10: Obtain the installed capacity and replacement capacity of each frequency regulation participant. The installed capacity of each frequency regulation participant refers to its inherent capacity, such as thermal power installed capacity and energy storage adjustable capacity. The replacement capacity of each frequency regulation participant refers to the installed capacity of traditional frequency regulation resources (thermal power units) required to achieve the same frequency regulation effect (consistent frequency regulation performance indicators) per unit installed capacity (1MW) of any frequency regulation resource under the same environment.

[0113] Step b20: Determine the capacity ratio of each frequency regulation participant based on the installed capacity and replacement capacity of each frequency regulation participant, where the capacity ratio of each frequency regulation participant = replacement capacity of each frequency regulation participant / installed capacity of each frequency regulation participant.

[0114] Step b30: Multiply the capacity ratio of each frequency modulation participant by the corresponding initial frequency modulation capacity to obtain the frequency modulation capacity of each frequency modulation participant.

[0115] Among them, the initial frequency modulation mileage of each frequency modulation participant is corrected to obtain the frequency modulation mileage of each frequency modulation participant, including:

[0116] Step c10: Obtain the frequency modulation required capacity in two consecutive frequency modulation scheduling periods.

[0117] Step c20: Determine a capacity demand ratio based on the frequency modulation demand capacities in two consecutive frequency modulation scheduling periods, where the capacity demand ratio = the frequency modulation demand capacity in the t frequency modulation scheduling period / the frequency modulation demand capacity in the (t-1) frequency modulation scheduling period.

[0118] Step c30: Determine a first correction coefficient based on the capacity demand ratio. When the capacity demand ratio is less than or equal to 1, the first correction coefficient is the capacity demand ratio (i.e., the first correction coefficient = the capacity demand ratio). When the capacity demand ratio is greater than 1, the capacity demand ratio is mapped to the interval [0, 1] using the tanh function. In this case, the calculation expression for the first correction coefficient is:

[0119] α=|tanh(β)|;

[0120] Where α is the first correction coefficient and β is the capacity demand ratio.

[0121] Step c40: Calculate the product of the first correction coefficient and the initial frequency modulation mileage of each frequency modulation participant to obtain the frequency modulation mileage of each frequency modulation participant.

[0122] As a further optimization of this embodiment, based on the declared quantity and price of each frequency regulation participant, the initial frequency regulation capacity and initial frequency regulation mileage of each frequency regulation participant are determined, including:

[0123] Step d10: Using frequency regulation capacity and frequency regulation mileage as decision variables, construct a total frequency regulation cost function based on the declared quantity and price of each frequency regulation participant and the decision variables;

[0124] Step d20: Solve the total cost function of the frequency modulation cost based on the preset optimization algorithm to obtain the optimal frequency modulation capacity and optimal frequency modulation mileage of each frequency modulation participant, and use the optimal frequency modulation capacity of each frequency modulation participant as the initial frequency modulation capacity of each frequency modulation participant, and use the optimal frequency modulation mileage of each frequency modulation participant as the initial frequency modulation mileage of each frequency modulation participant; in this embodiment, the preset optimization algorithm can be a particle swarm algorithm or a genetic algorithm.

[0125] The present invention determines the frequency regulation demand capacity through the market load forecast value and the power generation forecast value, and then publishes the frequency regulation demand capacity to all frequency regulation participants. Then, each new energy station determines whether to participate in the frequency regulation auxiliary service based on its own operating data. If so, the frequency regulation participant that wants to participate in the frequency regulation auxiliary service generates corresponding participation feedback information such as the declared quantity, price and frequency regulation performance index. Finally, comprehensive allocation is carried out based on the declared quantity, price and frequency regulation performance index of all frequency regulation participants that want to participate in the frequency regulation auxiliary service to determine the frequency regulation capacity and frequency regulation mileage of each frequency regulation participant. The above method enables the present invention to have a better frequency regulation effect, realize differentiated compensation of fast and slow frequency regulation resources, which is beneficial to reducing the system frequency regulation cost while ensuring energy storage benefits, and is beneficial to guiding new energy to rationally participate in the frequency regulation auxiliary service market.

[0126] Example 2

[0127] Figure 2 This is a block diagram of a new energy station decision support system based on a trading market and ancillary service market provided by an embodiment of the present invention. Figure 2 As shown, this embodiment provides a new energy station decision-making assistance system based on a trading market and ancillary service market. The system is used to implement the new energy station decision-making assistance method based on a trading market and ancillary service market in Example 1. The system includes:

[0128] The capacity forecast module is used to obtain the market load forecast value and the power generation forecast value, and determine the frequency regulation demand capacity based on the market load forecast value and the power generation forecast value;

[0129] The capacity publishing module is used to publish the frequency regulation capacity requirements to each new energy station. Each new energy station determines whether to participate in the frequency regulation auxiliary service based on its own operating data. If so, the new energy station is marked as a frequency regulation participant. Each frequency regulation participant generates participation feedback information, which includes the declared quantity, price, and frequency regulation performance indicators.

[0130] The data acquisition module is used to obtain the participation feedback information of each frequency modulation participant;

[0131] The frequency modulation determination module is used to determine the frequency modulation capacity and frequency modulation mileage of each frequency modulation participant based on the participation feedback information of each frequency modulation participant.

[0132] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the new energy station auxiliary decision-making method based on the trading market and the auxiliary service market of the first embodiment is implemented.

[0133] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the new energy station auxiliary decision-making method based on the trading market and the auxiliary service market of the first embodiment is implemented.

[0134] The present invention determines the frequency regulation demand capacity through the market load forecast value and the power generation forecast value, and then publishes the frequency regulation demand capacity to all frequency regulation participants. Then, each new energy station determines whether to participate in the frequency regulation auxiliary service based on its own operating data. If so, the frequency regulation participant that wants to participate in the frequency regulation auxiliary service generates corresponding participation feedback information such as the declared quantity, price and frequency regulation performance index. Finally, comprehensive allocation is carried out based on the declared quantity, price and frequency regulation performance index of all frequency regulation participants that want to participate in the frequency regulation auxiliary service to determine the frequency regulation capacity and frequency regulation mileage of each frequency regulation participant. The above method enables the present invention to have a better frequency regulation effect, realize differentiated compensation of fast and slow frequency regulation resources, which is beneficial to reducing the system frequency regulation cost while ensuring energy storage benefits, and is beneficial to guiding new energy to rationally participate in the frequency regulation auxiliary service market.

[0135] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0137] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A new energy station auxiliary decision-making method based on the trading market and auxiliary service market, characterized in that: The method comprises: Obtain market load forecast values ​​and power generation forecast values, and determine frequency regulation demand capacity based on the market load forecast values ​​and power generation forecast values; Publish the frequency regulation demand capacity to each renewable energy station. Each renewable energy station will determine whether to participate in the frequency regulation auxiliary service based on its own operating data. If so, the renewable energy station will be marked as a frequency regulation participant, and each frequency regulation participant will generate participation feedback information. Obtain participation feedback information from each FM participant; Determine the frequency regulation capacity and frequency regulation mileage of each frequency regulation participant based on the participation feedback information of each frequency regulation participant; The method further includes: each frequency modulation participant generating a frequency modulation performance indicator, including: During the frequency regulation scheduling cycle, calculate the response time, regulation speed and regulation accuracy of each frequency regulation participant; Determine the time weight corresponding to the response time, the speed weight corresponding to the adjustment speed, and the accuracy weight corresponding to the adjustment accuracy based on the weighting method; Determine the frequency regulation performance index based on the response time and time weight of each frequency regulation participant, the regulation speed and speed weight of each frequency regulation participant, and the regulation accuracy and accuracy weight of each frequency regulation participant; During the frequency regulation scheduling cycle, the response time, regulation speed, and regulation accuracy of each frequency regulation participant are calculated, including: Obtain the adjustment delay duration, response start time, response end time, starting output, ending output, actual output and required output of each frequency modulation participant when responding to the frequency modulation instruction; Determine the response time of each frequency modulation participant based on the adjustment delay time, response start time and response end time; Determine the adjustment speed of each frequency regulation participant based on the starting output and ending output; Determine the regulation accuracy of each frequency regulation participant based on actual output and required output; The participation feedback information includes: declared quantity, price and frequency regulation performance indicators. The frequency regulation capacity and frequency regulation mileage of each frequency regulation participant are determined based on the participation feedback information of each frequency regulation participant, including: Determine the initial frequency regulation capacity and initial frequency regulation mileage of each frequency regulation participant based on the quantity and price declared by each frequency regulation participant; Correcting the initial frequency regulation capacity of each frequency regulation participant to obtain the frequency regulation capacity of each frequency regulation participant; Correct the initial frequency modulation mileage of each frequency modulation participant to obtain the frequency modulation mileage of each frequency modulation participant; The initial frequency regulation capacity of each frequency regulation participant is corrected to obtain the frequency regulation capacity of each frequency regulation participant, including: Obtain the installed capacity and replacement capacity of each frequency regulation participant; Determine the capacity ratio of each frequency regulation participant based on their installed capacity and replacement capacity; Multiply the capacity ratio of each frequency regulation participant by the corresponding initial frequency regulation capacity to obtain the frequency regulation capacity of each frequency regulation participant; The initial frequency modulation mileage of each frequency modulation participant is corrected to obtain the frequency modulation mileage of each frequency modulation participant, including: Obtain the frequency regulation demand capacity within two consecutive frequency regulation scheduling cycles; Determining a capacity demand ratio based on the frequency regulation demand capacities within two consecutive frequency regulation scheduling periods; determining a first correction coefficient according to the capacity demand ratio; Calculate the product of the first correction coefficient and the initial frequency modulation mileage of each frequency modulation participant respectively to obtain the frequency modulation mileage of each frequency modulation participant; Based on the declared quantity and price of each frequency regulation participant, the initial frequency regulation capacity and initial frequency regulation mileage of each frequency regulation participant are determined, including: Taking frequency regulation capacity and frequency regulation mileage as decision variables, the total cost function of frequency regulation cost is constructed according to the declared quantity and price of each frequency regulation participant and the decision variables; Based on the preset optimization algorithm, the total cost function of the frequency modulation cost is solved to obtain the optimal frequency modulation capacity and optimal frequency modulation mileage of each frequency modulation participant. The optimal frequency modulation capacity of each frequency modulation participant is used as the initial frequency modulation capacity of each frequency modulation participant, and the optimal frequency modulation mileage of each frequency modulation participant is used as the initial frequency modulation mileage of each frequency modulation participant.

2. A new energy station auxiliary decision-making system based on a trading market and ancillary service market, the system is used to implement the new energy station auxiliary decision-making method based on a trading market and ancillary service market according to claim 1, characterized in that: The system comprises: The capacity forecast module is used to obtain the market load forecast value and the power generation forecast value, and determine the frequency regulation demand capacity based on the market load forecast value and the power generation forecast value; The capacity publishing module is used to publish the frequency regulation demand capacity to each new energy station. Each new energy station determines whether to participate in the frequency regulation auxiliary service based on its own operating data. If so, the new energy station is marked as a frequency regulation participant, and each frequency regulation participant generates participation feedback information; The data acquisition module is used to obtain the participation feedback information of each frequency modulation participant; The frequency modulation determination module is used to determine the frequency modulation capacity and frequency modulation mileage of each frequency modulation participant based on the participation feedback information of each frequency modulation participant.

3. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the new energy station auxiliary decision-making method based on the trading market and the auxiliary service market as described in claim 1 is implemented.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the new energy station auxiliary decision-making method based on the trading market and the auxiliary service market as described in claim 1 is implemented.

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