New energy station auxiliary decision-making method based on transaction market and auxiliary service market

Abstract

The invention belongs to the technical field of new energy frequency modulation, and discloses a new energy station auxiliary decision-making method based on a transaction market and an auxiliary service market, and the method comprises the steps: obtaining a market load prediction value and a generation power prediction value, and determining the frequency modulation demand capacity according to the market load prediction value and the generation power prediction value; the frequency modulation demand capacity is published to each new energy station, each new energy station judges whether to participate in the frequency modulation auxiliary service according to own operation data, if so, the new energy station is marked as a frequency modulation participant, each frequency modulation participant generates participation feedback information, and the participation feedback information comprises a declaration quantity price and a frequency modulation performance index; obtaining participation feedback information of each frequency modulation participant; and determining the frequency modulation capacity and the frequency modulation mileage of each frequency modulation participant based on the participation feedback information of each frequency modulation participant. The method has a better frequency modulation effect, differential compensation of fast and slow frequency modulation resources is realized, and the energy storage benefit is guaranteed while the frequency modulation cost of the system is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy frequency modulation, and specifically relates to a new energy station auxiliary decision-making method based on a trading market and an auxiliary 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 electric energy market mainly involves the production, transmission and use of electric energy. Under the market mechanism, the electricity price formation in the electric energy market is 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 integrate the stability and flexibility of electricity demand prices, so as to more effectively regulate power resources.

[0004] The ancillary service market refers to services other than normal electricity production, transmission, and use that are provided to maintain safe and stable operation of the power system, ensure electricity quality, 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 connected to the power grid on a large scale, which not only gives green attributes to the electricity "commodity", but also significantly improves the system flexibility and effectively implements the country's dual carbon goals.

[0006] However, in the process of new energy rapidly replacing the power generation capacity of traditional units, since new energy units cannot provide effective inertia support for the system, the inertia of the power system has shown a clear downward trend, and the system's anti-disturbance ability has been reduced, facing more serious frequency safety issues. In addition, the uncertainty of new energy output has further aggravated the frequency fluctuations of the power system, and the system frequency regulation shortage has continued to widen. Traditional units are unable to effectively support the more frequent frequency fluctuations caused by the reduction in inertia of the new power system, and there is an urgent need to incorporate more efficient and reliable frequency regulation resources.

[0007] In addition, the system frequency regulation demand varies significantly in different frequency regulation periods. Therefore, the frequency regulation auxiliary service market mechanism that adapts to the participation of energy storage should have the ability to map the changes in system frequency regulation demand and guide the participation of frequency regulation resources by optimizing the frequency regulation market pricing mechanism. 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 new energy to rationally participate in the frequency regulation auxiliary service 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: In a first aspect, the present invention provides a new energy station auxiliary decision-making method based on a trading market and an auxiliary service market, the method comprising: 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; Release 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 operation 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; Obtain participation feedback information from each frequency modulation participant; 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.

[0010] Preferably, the method further comprises: each frequency modulation participant generates 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; The frequency modulation performance index is determined based on 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.

[0011] Preferably, during the frequency modulation scheduling cycle, the response time, the adjustment speed and the adjustment accuracy of each frequency modulation 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 according to the adjustment delay time, response start time and response end time; Determine the adjustment speed of each frequency regulation participant according to the starting output and ending output; Determine the regulation accuracy of each frequency regulation participant based on actual output and required output.

[0012] 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: 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; The initial frequency regulation mileage of each frequency regulation participant is corrected to obtain the frequency regulation mileage of each frequency regulation participant.

[0013] 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: 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; The capacity ratio of each frequency regulation participant is multiplied by the corresponding initial frequency regulation capacity to obtain the frequency regulation capacity of each frequency regulation participant.

[0014] 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: 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 in two consecutive frequency regulation dispatch cycles; determining a first correction coefficient according to the capacity demand ratio; 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.

[0015] 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: 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.

[0016] In a second aspect, the present invention provides a new energy station auxiliary decision-making system based on a trading market and an auxiliary service market, the system is used to implement the above-mentioned new energy station auxiliary decision-making method based on a trading market and an auxiliary service market, and the system includes: The capacity prediction module is used to obtain the market load prediction value and the power generation prediction value, and determine the frequency regulation demand capacity according to the market load prediction value and the power generation prediction value; The capacity release module is used to release 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 operation 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; A data acquisition module is used to obtain participation feedback information from 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.

[0017] 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, wherein the processor implements the above-mentioned new energy station auxiliary decision-making method based on a trading market and ancillary service market when executing the computer program.

[0018] 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 a trading market and ancillary service market.

[0019] Beneficial effects: 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 according to its own operation data. If so, the frequency regulation participants who want to participate in the frequency regulation auxiliary service generate corresponding participation feedback information such as the declared quantity, price and frequency regulation performance index. Finally, comprehensive allocation is carried out according to the declared quantity, price and frequency regulation performance index of all frequency regulation participants who 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 income, and is beneficial to guiding new energy to rationally participate in the frequency regulation auxiliary service market. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] 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 specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings: Figure 1 It 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; 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

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination 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 ordinary technicians in this field, 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.

[0022] Embodiment 1 Figure 1 It is a flow chart 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, dispatcher 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 participants are new energy stations. The dispatcher is an electric power dispatching agency. The dispatching instructions are generated according to the frequency regulation capacity and frequency regulation mileage finally generated by the frequency regulation party, and the dispatching instructions are sent to the frequency regulation participants so that each frequency regulation participant can participate in the frequency regulation of the power grid. Based on the above scenarios, 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: 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.

[0023] In this embodiment, the market load forecast value is obtained in the following manner: 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.

[0024] 2. Data processing: 1. Cleaning data: 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.

[0025] 3. Model selection: Select a suitable forecasting model, such as time series analysis (ARIMA model), regression analysis, machine learning methods (such as random forest, support vector machine, neural network), etc.

[0026] 4. Model training and verification: 1. Use historical data to train the model; 2. Verify the accuracy of the model through methods such as cross-validation.

[0027] 5. Forecast: Use the trained model to forecast the load for a period of time in the future.

[0028] In this embodiment, the method for obtaining the power generation prediction value is as follows: 1. Data collection: Collect data related to power generation, including historical power generation, equipment parameters, fuel costs, environmental conditions, etc.

[0029] 2. Data processing: Data cleaning and feature engineering are also required.

[0030] 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.

[0031] 4. Model training and verification: Use historical power generation data to train and verify the model.

[0032] 5. Forecast: Use the model to predict the power generation in the future.

[0033] 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.

[0034] Among them, the operating data include: 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 the above operating data are evaluated, that is, the key performance indicators such as the reliability, response speed, and regulation capability of the station are evaluated. According to 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.

[0035] In this embodiment, the method further includes: each frequency modulation participant generates a frequency modulation performance indicator, including: Step S201: within the frequency modulation scheduling cycle, the response time, adjustment speed and adjustment accuracy of each frequency modulation participant are calculated.

[0036] Specifically, during the frequency modulation scheduling cycle, the response time, modulation speed and modulation accuracy of each frequency modulation participant are calculated, including: Step a10: Obtain the adjustment delay time, 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; Step a20: determining the response time of each frequency modulation participant according to the adjustment delay time, the response start time and the response end time; Step a30: Determine the adjustment speed of each frequency modulation participant according to the starting output and the ending output; Step a40: Determine the regulation accuracy of each frequency regulation participant based on the actual output and the required output.

[0037] 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 up all response times determined by the same frequency modulation participant, and then averages them, and takes the average value of all response times as the final response time of the frequency modulation participant.

[0038] Among them, the adjustment speed of each frequency modulation participant refers to the rate at which each frequency modulation participant reaches the output required by the instruction after receiving the frequency modulation instruction, and the adjustment accuracy of each frequency modulation participant refers to the degree of deviation from the output required by the instruction after receiving the frequency modulation instruction.

[0039] 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 the weighting method.

[0040] Wherein, 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; 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; Determine the objective weight of each indicator based on the objective weighting method; 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.

[0041] 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; The calculation expression of the final weight of each indicator is: W i =k1*w 1,i +k2* w 2,i ; Where Wi is the final weight of the ith indicator, k1 is the first solution coefficient, k2 is the second solution coefficient, w 1,i is the subjective weight of the ith indicator, w 2,i is the objective weight of the i-th indicator, where i=1,2,3.

[0042] In this embodiment, the subjective weighting method mainly relies on the experience, knowledge, intuition and preference of experts or decision makers to assign different weights to different indicators. The main steps are: Determine the evaluation indicators: First, you need to determine all the indicators required to evaluate a decision problem; Select experts: Select experts with relevant field knowledge and experience; Obtain weight information: Obtain weight information from experts through questionnaires, interviews, Delphi method, etc.; Processing and integrating data: Processing the collected data, such as consistency checking and correction, to integrate the final subjective weight.

[0043] In this embodiment, the objective weighting method is opposite to the subjective weighting method. The objective weighting method is a method for determining the weight of indicators based on the statistical characteristics of the data itself. It does not rely on the subjective judgment of the decision maker, but calculates the weight through a certain mathematical method, so it has strong objectivity; the objective weighting method of this embodiment mainly adopts the entropy weight method, using the concept of information entropy. The smaller the information entropy of the indicator, the greater the degree of variation of the indicator, the more information it contains, and therefore the weight should be greater.

[0044] Then the calculation expression of the first solution coefficient is: ; In the formula, , .

[0045] Then the calculation expression of the second solution coefficient is: .

[0046] The final weights of the indicators in this embodiment can improve the fairness and rationality of the scheduling market and enhance market transparency.

[0047] Step S202: determining the 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.

[0048] 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: ; In the formula, 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 frequency modulation participant, is the response time of the nth FM participant, is the adjustment speed of the nth frequency modulation participant, is the regulation accuracy of the nth frequency modulation participant, where n is a positive integer.

[0049] Step S30: Obtain the participation feedback information of each frequency modulation participant, that is, obtain the declared quantity, price and frequency modulation performance index obtained in step S20.

[0050] Step S40: 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.

[0051] Specifically, 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: Step S401: determining 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; Step S402: correcting the initial frequency modulation capacity of each frequency modulation participant to obtain the frequency modulation capacity of each frequency modulation participant; Step S403: correct the initial frequency modulation mileage of each frequency modulation participant to obtain the frequency modulation mileage of each frequency modulation participant.

[0052] In this embodiment, due to the uncertainty on the electricity 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, so as to effectively reduce the frequency regulation cost of the system and increase the energy storage benefit while ensuring the stability of the power grid.

[0053] The initial frequency modulation capacity of each frequency modulation participant is corrected to obtain the frequency modulation capacity of each frequency modulation participant, including: Step b10: Obtain the installed capacity and replacement capacity of each frequency regulation participant, where the installed capacity of each frequency regulation participant refers to the inherent capacity, such as thermal power installed capacity, energy storage adjustable capacity, etc.; 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) of any frequency regulation resource per unit installed capacity (1MW) under the same environment.

[0054] Step b20: Determine the capacity ratio of each frequency regulation participant based on the installed capacity and replacement capacity of each frequency regulation participant, wherein the capacity ratio of each frequency regulation participant = replacement capacity of each frequency regulation participant / installed capacity of each frequency regulation participant.

[0055] 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.

[0056] 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: Step c10: Obtain the frequency modulation demand capacity in two consecutive frequency modulation scheduling cycles.

[0057] Step c20: Determine the capacity demand ratio based on the frequency modulation demand capacity in two consecutive frequency modulation scheduling cycles, where the capacity demand ratio = the frequency modulation demand capacity in t frequency modulation scheduling cycle / the frequency modulation demand capacity in (t-1) frequency modulation scheduling cycle.

[0058] Step c30: Determine the first correction coefficient according to 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, and the calculation expression of the first correction coefficient is: α = |tanh(β)|; Where α is the first correction coefficient and β is the capacity demand ratio.

[0059] Step c40: 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.

[0060] 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: Step d10: 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; 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.

[0061] 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 according to its own operation data. If so, the frequency regulation participants who want to participate in the frequency regulation auxiliary service generate corresponding participation feedback information such as the declared quantity, price and frequency regulation performance index. Finally, comprehensive allocation is carried out according to the declared quantity, price and frequency regulation performance index of all frequency regulation participants who 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 income, and is beneficial to guiding new energy to rationally participate in the frequency regulation auxiliary service market.

[0062] Embodiment 2 Figure 2 is a block diagram of a new energy station auxiliary decision-making system based on a trading market and ancillary service market provided by an embodiment of the present invention, such as Figure 2 As shown, this embodiment provides a new energy station auxiliary decision-making system based on a trading market and an auxiliary service market, and the system is used to implement the new energy station auxiliary decision-making method based on a trading market and an auxiliary service market in embodiment 1. The system includes: The capacity prediction module is used to obtain the market load prediction value and the power generation prediction value, and determine the frequency regulation demand capacity according to the market load prediction value and the power generation prediction value; The capacity release module is used to release 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 operation 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; A data acquisition module is used to obtain participation feedback information from 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.

[0063] 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.

[0064] 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.

[0065] 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 according to its own operation data. If so, the frequency regulation participants who want to participate in the frequency regulation auxiliary service generate corresponding participation feedback information such as the declared quantity, price and frequency regulation performance index. Finally, comprehensive allocation is carried out according to the declared quantity, price and frequency regulation performance index of all frequency regulation participants who 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 income, and is beneficial to guiding new energy to rationally participate in the frequency regulation auxiliary service market.

[0066] 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 adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0067] 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 box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate 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 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0068] The above are only 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 modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A new energy station auxiliary decision-making method based on a trading market and ancillary 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; Release the 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 operation data. If so, the renewable energy station is marked as a frequency regulation participant. Each frequency regulation participant generates participation feedback information. Obtain participation feedback information from each frequency modulation participant; 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.

2. The new energy station auxiliary decision-making method based on the trading market and auxiliary service market according to claim 1 is characterized in that: The method further includes: each frequency modulation participant generates 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; The frequency modulation performance index is determined based on 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.

3. The new energy station auxiliary decision-making method based on the trading market and auxiliary service market according to claim 2 is characterized in that: 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 according to the adjustment delay time, response start time and response end time; Determine the adjustment speed of each frequency regulation participant according to the starting output and ending output; Determine the regulation accuracy of each frequency regulation participant based on actual output and required output.

4. The new energy station auxiliary decision-making method based on the trading market and auxiliary service market according to claim 3 is characterized in that: The participation feedback information includes: declared quantity, price and frequency regulation performance index, and 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; The initial frequency regulation mileage of each frequency regulation participant is corrected to obtain the frequency regulation mileage of each frequency regulation participant.

5. The new energy station auxiliary decision-making method based on the trading market and auxiliary service market according to claim 4 is characterized in that: 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; The capacity ratio of each frequency regulation participant is multiplied by the corresponding initial frequency regulation capacity to obtain the frequency regulation capacity of each frequency regulation participant.

6. The new energy station auxiliary decision-making method based on the trading market and auxiliary service market according to claim 4 is characterized in that: 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 in two consecutive frequency regulation dispatch cycles; determining a first correction coefficient according to the capacity demand ratio; 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.

7. The new energy station auxiliary decision-making method based on the trading market and auxiliary service market according to claim 4 is characterized in that: 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.

8. 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 as described in any one of claims 1 to 7, characterized in that: The system comprises: The capacity prediction module is used to obtain the market load prediction value and the power generation prediction value, and determine the frequency regulation demand capacity according to the market load prediction value and the power generation prediction value; The capacity release module is used to release 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 operation data. If so, the new energy station is marked as a frequency regulation participant, and each frequency regulation participant generates participation feedback information; A data acquisition module is used to obtain participation feedback information from 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.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: 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 described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements the new energy station auxiliary decision-making method based on the trading market and the auxiliary service market as described in any one of claims 1 to 7.

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