Circuit auxiliary market operation strategy optimization method and device based on model analysis

Abstract

The invention provides a circuit-assisted market operation strategy optimization method and device based on model analysis, relates to the field of power markets, and solves the problem that in the prior art, when a market is optimized, due to unstable power supply of renewable resources, excessive production capacity of an adjusted market operation strategy is possibly caused, and the market operation strategy optimization efficiency is improved. And the development of a power system is not facilitated. The method comprises the following steps: acquiring operation parameter data of a power system and market transaction data of a power market; analyzing the operation parameter data of the power system to obtain market demand data; adjusting a transaction strategy based on the market demand data and the market transaction; predicting the market demand data according to the operation parameter data of the power system to obtain predicted demand data; evaluating the market risk according to the predicted demand data to obtain a risk level; and implementing risk management and control measures according to the risk level. The method and the device are used in a circuit-assisted market operation strategy optimization process.

Description

Technical Field

[0001] This application relates to the field of electricity markets, and in particular to a method and apparatus for optimizing circuit-assisted market operation strategies based on model analysis. Background Technology

[0002] The power ancillary services market is a market-based trading platform for ancillary services other than electricity trading, which are essential for maintaining the safe, stable, and high-quality operation of the power system. The power system needs to be balanced in real time, and instantaneous imbalances can cause frequency fluctuations, which may lead to large-scale power outages in severe cases. The ancillary services market uses economic means to incentivize and ensure that there are sufficient and reliable regulatory resources to cope with various emergencies.

[0003] Existing circuit-assisted market operation strategy optimization involves collecting supply and demand data from the electricity market and constructing an optimization model. The optimization model is then used to analyze the supply and demand data to obtain optimization measures for the market operation strategy. The market operation is then adjusted based on these optimization measures. However, with the integration of renewable resources, the existing technology for market optimization may lead to overcapacity in the adjusted market operation strategy due to the instability of renewable resource power supply, which is detrimental to the development of the power system. Summary of the Invention

[0004] This application provides a circuit-assisted market operation strategy optimization method and apparatus based on model analysis, which solves the technical problem that when optimizing the market in the prior art, the unstable power supply of renewable resources may lead to overcapacity in the adjusted market operation strategy, which is detrimental to the development of the power system.

[0005] To achieve the above objectives, this application adopts the following technical solution: Firstly, it provides a model-based approach to optimize circuit-assisted market operation strategies, including: To acquire power system operating parameter data and electricity market transaction data; By analyzing the operating parameter data of the power system, market demand data can be obtained; Adjust trading strategies based on market demand data and market transactions; Market demand data is predicted based on the operating parameters of the power system to obtain the predicted demand data; Market risks are assessed based on forecasted demand data to determine risk levels; Implement risk control measures based on the risk level.

[0006] Based on the above technical solutions, the circuit-assisted market operation strategy optimization method based on model analysis provided in this application obtains power system operation parameter data and analyzes it to derive market demand data. This allows for timely and accurate understanding of the current actual demand situation in the power market, providing a solid basis for subsequent decision-making. Adjusting trading strategies based on market demand data and market transaction conditions makes these strategies more aligned with market realities, helping to improve trading efficiency, increase revenue or reduce costs, and enhance competitiveness in the power market. Predicting market demand based on power system operation parameter data provides advance knowledge of future market demand trends, enabling power companies and relevant market participants to plan and prepare for production, procurement, and reserves in advance. Assessing market risks and determining risk levels based on predicted demand data allows market participants to clearly understand the degree of risk they face, providing a clear direction for risk management. Implementing risk control measures based on risk levels allows for timely responses to potential risks, reducing the impact of risks on the power system and power market, ensuring the stable and reliable operation of the power market, and maintaining market order and the interests of all parties.

[0007] In conjunction with the first aspect above, in one possible implementation, the analysis of power system operating parameter data to obtain market demand data includes: Retrieve power system operating parameter data for a specified time period; the operating parameter data includes: unit ratio, load demand, and renewable energy ratio; The net load of the power system is obtained by calculating the difference between load demand and the proportion of renewable energy. The net load is then matched with the corresponding demand market database to obtain the corresponding market demand data. The market demand data includes frequency regulation capacity demand and reserve capacity demand.

[0008] In conjunction with the first aspect above, in one possible implementation, adjusting the trading strategy based on market demand data and market transactions includes: Retrieve market transaction data, including frequency regulation quotations, reserve quotations, and electricity prices; allocate the power generation of several generating units according to the net load; and determine the power generation of several generating units based on the allocation results. Construct the objective function: ;in, This represents the power generation of the i-th generating unit; This represents the frequency regulation capacity requirement of the i-th generating unit; This represents the reserve capacity requirement of the i-th unit; TX is the error adjustment coefficient. The total market transaction amount is calculated based on the objective function. If the total market transaction amount is less than the preset cost threshold, power generation is carried out according to the allocation results. Otherwise, the power generation ratio of the generating units is reallocated.

[0009] In conjunction with the first aspect above, in one possible implementation, the allocation of power generation from several generating units according to net load includes: Construct the cost function for each unit: ;in, Let i be the total cost of the i-th unit; Let i be the power generation capacity of the i-th unit; The cost of the i-th unit under no-load conditions; , This is the cost coefficient; >0; Solving for the first derivative of the cost function for each unit yields the incremental rate cost function; its expression is: ; Establish constraints: ;in, The total power consumption of the power system; The total power generation capacity of renewable energy sources; This is the minimum generating capacity of the unit; This is the maximum generating capacity of the unit; Construct the power balance equation: The incremental rate of the unit is obtained by solving the power balance equation: The theoretical output of each unit is calculated based on the incremental rate of change of the unit: When the calculated theoretical output is less than the minimum power generation, the theoretical output is adjusted to the minimum power generation; when the calculated theoretical output is greater than the maximum power generation, the theoretical output is adjusted to the maximum power generation. Through formula The power generation ratio of each unit is calculated, and the power generation of each unit is allocated according to the power generation ratio.

[0010] In conjunction with the first aspect above, in one possible implementation, the step of forecasting market demand data based on power system operating parameter data to obtain forecasted demand data includes: Retrieve the net load within the set time period; calculate the rate of change of net load in adjacent time periods; calculate the standard deviation of the net load rate based on the rate of change; integrate the rate of change and standard deviation of net load into a demand forecast sequence in chronological order; The demand forecasting model is invoked by inputting the demand forecasting sequence into the demand forecasting model to obtain the corresponding predicted rate of change of net load; the demand forecasting model is constructed based on an artificial intelligence model. The average net load over a set time period is calculated; the predicted load demand is calculated based on the average value and the predicted rate of change of net load; the predicted load demand is matched with the demand market database to obtain the corresponding predicted demand data; the data type of the predicted demand data is consistent with the market demand data.

[0011] In conjunction with the first aspect above, in one possible implementation, the demand forecasting model is constructed based on an artificial intelligence model, including: Select appropriate models and deep learning frameworks from the artificial intelligence model library; use the deep learning framework to build the model to obtain the constructed model; Obtain the standard dataset; the standard dataset includes standard input data consistent with the content attributes of the demand forecast sequence; and standard output data consistent with the content attributes of the predicted rate of change. The standard dataset is divided into training, validation, and test sets according to a set ratio; the model is trained using the training set; the internal parameters of the model are adjusted using the validation set; and the trained model is tested using the test set to obtain test metrics. Obtain the indicator threshold and compare the test indicator with the indicator threshold; if all test indicators are greater than the indicator threshold, mark the trained model as the demand prediction model; otherwise, retrain the demand prediction model.

[0012] In conjunction with the first aspect above, in one possible implementation, the assessment of market risk based on predicted demand data to obtain a risk level includes: Obtain historical forecast demand data and corresponding actual demand data; calculate the difference between forecast demand data and actual demand data to obtain the forecast error; obtain market volatility factor data; among which, volatility factor data includes: seasonal trend data and price fluctuation data; A comprehensive analysis of market risk is conducted based on forecast errors and volatility factors to obtain a risk coefficient. The risk coefficient is then compared with a risk threshold to obtain a coefficient range. The risk level of the market risk is determined based on the coefficient range. The risk levels include: low, medium, and high.

[0013] In conjunction with the first aspect above, in one possible implementation, the comprehensive analysis of market risk based on prediction errors and volatility factor data includes: Retrieve data on forecast error and market volatility factors; label forecast error, seasonal trend data, and price volatility data as YW, JQ, and JB, respectively. Construct a risk assessment function: ;in, , and It is a proportionality coefficient greater than 0; DL is the dimensionless coefficient. The market risk coefficient is calculated based on the risk assessment function.

[0014] In conjunction with the first aspect above, in one possible implementation, the risk control measures based on risk levels include: When the risk level is low, market demand data and market transaction data are monitored and adjusted in real time; when the risk level is medium, the operation of the power system is adjusted and evaluated based on the forecast error. When the risk level is high, the market demand data is re-forecasted, the market fluctuation factor data is matched with the corresponding factor processing library to obtain adjustment parameters, and the operation of the power system is adjusted according to the adjustment parameters.

[0015] Secondly, a circuit-assisted market operation strategy optimization device based on model analysis is provided, comprising: a communication unit and a processing unit; the communication unit is used to acquire power system operation parameter data and power market transaction data; the processing unit is used to analyze the power system operation parameter data to obtain market demand data; adjust the transaction strategy based on the market demand data and market transactions; predict the market demand data based on the power system operation parameter data to obtain predicted demand data; assess market risk based on the predicted demand data to obtain a risk level; and implement risk control measures based on the risk level.

[0016] Thirdly, this application provides a circuit-aided market operation strategy optimization apparatus based on model analysis, comprising: a processor and a storage medium; the storage medium includes instructions, and the processor is used to execute the instructions to implement the method described in the first aspect and any possible implementation thereof. This circuit-aided market operation strategy optimization apparatus based on model analysis can be an electronic device or a chip within an electronic device.

[0017] Fourthly, this application provides a circuit-assisted market operation strategy optimization system based on model analysis, comprising: a data acquisition module and a processing module; wherein, the data acquisition module is used to acquire power system operating parameter data and power market transaction data; the processing module is used to analyze the power system operating parameter data to obtain market demand data; adjust the trading strategy based on the market demand data and market transactions; predict the market demand data based on the power system operating parameter data to obtain predicted demand data; assess market risk based on the predicted demand data to obtain a risk level; and implement risk control measures based on the risk level.

[0018] Fifthly, this application provides a computer-readable storage medium storing instructions that, when executed on a circuit-assisted market operation strategy optimization device based on model analysis, cause the circuit-assisted market operation strategy optimization device based on model analysis to perform the method described in the first aspect and any possible implementation thereof.

[0019] In a sixth aspect, this application provides a computer program product containing instructions that, when run on a circuit-aided market operation strategy optimization device based on model analysis, cause the circuit-aided market operation strategy optimization device based on model analysis to perform the method described in the first aspect and any possible implementation thereof.

[0020] This application provides a model-based method and apparatus for optimizing circuit-assisted market operation strategies. It retrieves power system operating parameter data for a set time period, calculates the net load by the difference between load demand and the proportion of renewable energy, and matches this net load with a demand market database to obtain market demand data. This accurately reflects the real demand of the power system for various ancillary services at different times, providing a reliable basis for subsequent unit output scheduling and market transactions. The method constructs a cost function for each unit and solves its first derivative to obtain the incremental rate cost function. Combining the power balance equation and constraints, it solves for the incremental rate of each unit, and then calculates the theoretical output of each unit. It considers the no-load cost of the units and the cost coefficients related to power generation, ensuring that unit output scheduling minimizes power generation costs while satisfying the power system's power balance, thereby improving the power system's efficiency. The system achieves several key benefits: First, it optimizes the power system's economic efficiency. Second, it adjusts the calculated theoretical output range to ensure it falls between the unit's minimum and maximum power output, preventing efficiency losses or equipment damage due to output exceeding reasonable limits. Third, it calculates and allocates power generation based on the adjusted theoretical output, making the allocation of power generation across units more rational and further optimizing power system operation. Fourth, it constructs an objective function to calculate the total market transaction amount and compares it with a preset cost threshold, ensuring market transactions are conducted under cost control, avoiding economic losses due to excessive transaction costs, and guaranteeing the rationality and sustainability of the power market. Fifth, through precise calculation and rational allocation of unit output, and dynamic adjustments based on market transaction conditions, it fully utilizes the power generation capacity of each unit, avoiding resource waste. Finally, accurate understanding of market demand data helps in the rational allocation of ancillary service resources, improving the overall resource utilization efficiency of the power system.

[0021] The process involves retrieving net load data for a specified time period, calculating the rate of change and standard deviation between adjacent time periods, and integrating this data into a demand forecast sequence in chronological order. This approach considers the temporal characteristics and fluctuations of net load, providing comprehensive and representative input data for the demand forecasting model and improving forecast accuracy. By calculating the average net load within the specified time period and combining it with the predicted rate of change of net load, the predicted load demand is calculated. This comprehensive approach considers both the overall level and trend of net load, making the predicted load demand more consistent with reality. The predicted load demand is matched with a demand market database to obtain corresponding predicted demand data, with data types consistent with market demand data. This allows the forecast results to be directly applied to market demand analysis, providing an accurate basis for subsequent decision-making. The difference between historical predicted demand data and actual demand data is used as the forecast error. Market volatility factors are also considered, and these factors are labeled and used to construct a risk assessment function. This comprehensive risk assessment method considers multiple factors that may affect market risk, making the assessment results more accurate and reliable. Adjustments to the power system operation based on the risk level can reduce the impact of risk on the system.

[0022] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0023] Figure 1 A system architecture diagram of a circuit-assisted market operation strategy optimization system based on model analysis provided in this application embodiment; Figure 2 A flowchart illustrating the circuit-assisted market operation strategy optimization method based on model analysis provided in this application embodiment; Figure 3 A flowchart illustrating the circuit-assisted market operation strategy optimization method based on model analysis provided in this application embodiment; Figure 4 A flowchart illustrating the circuit-assisted market operation strategy optimization method based on model analysis provided in this application embodiment; Figure 5 A schematic diagram of the circuit-assisted market operation strategy optimization device based on model analysis provided in this application embodiment; Figure 6 A schematic diagram of the hardware structure of the circuit-assisted market operation strategy optimization device based on model analysis provided in this application embodiment; Detailed Implementation

[0024] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0025] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0026] The circuit-assisted market operation strategy optimization method based on model analysis provided in this application can be applied to, for example... Figure 1 In the server system 100 shown, such as Figure 1 As shown, the communication system includes: acquisition device 101 and processing device 102.

[0027] Among them, the data acquisition device 101 is used to acquire power system operating parameter data and power market transaction data.

[0028] Processing equipment 102 is used to analyze the operating parameter data of the power system to obtain market demand data; adjust the trading strategy based on the market demand data and market transactions; predict the market demand data based on the operating parameter data of the power system to obtain predicted demand data; assess the market risk based on the predicted demand data to obtain the risk level; and implement risk control measures based on the risk level.

[0029] To address the technical problem that existing market optimization techniques may result in overcapacity due to unstable power supply from renewable resources, hindering power system development, this application provides a circuit-assisted market operation strategy optimization method based on model analysis. This method includes: To acquire power system operating parameter data and electricity market transaction data; By analyzing the operating parameter data of the power system, market demand data can be obtained; Adjust trading strategies based on market demand data and market transactions; Market demand data is predicted based on the operating parameters of the power system to obtain the predicted demand data; Market risks are assessed based on forecasted demand data to determine risk levels; Implementing risk management measures based on risk levels allows for timely and accurate understanding of the current actual demand in the electricity market by acquiring and analyzing power system operating parameter data, providing a solid basis for subsequent decision-making. Adjusting trading strategies based on market demand data and market transaction conditions makes these strategies more aligned with market realities, helping to improve trading efficiency, increase revenue or reduce costs, and enhance competitiveness in the electricity market. Predicting market demand based on power system operating parameter data provides advance knowledge of future market demand trends, enabling power companies and relevant market participants to plan and prepare for production, procurement, and reserves in advance. Assessing market risks and determining risk levels based on predicted demand data allows market participants to clearly understand the degree of risk they face, providing a clear direction for risk management. Implementing risk management measures based on risk levels enables timely responses to potential risks, reducing their impact on the power system and electricity market, ensuring the stable and reliable operation of the electricity market, and maintaining market order and the interests of all parties.

[0030] like Figure 2 As shown in the embodiments of this application, the circuit-assisted market operation strategy optimization method based on model analysis includes: S201. Obtain operating parameter data of the power system and market transaction data of the power market; analyze the operating parameter data of the power system to obtain market demand data.

[0031] S202. Adjust trading strategies based on market demand data and market transactions.

[0032] S203. Based on the operating parameter data of the power system, predict the market demand data to obtain the predicted demand data.

[0033] S204. Assess market risks based on forecasted demand data to obtain risk levels; implement risk control measures based on risk levels.

[0034] Based on the above technical solutions, the circuit-assisted market operation strategy optimization method based on model analysis provided in this application obtains power system operation parameter data and analyzes it to obtain market demand data. This allows for timely and accurate understanding of the current actual demand situation in the power market, providing a solid basis for subsequent decision-making. Adjusting trading strategies based on market demand data and market transaction conditions makes the trading strategies more aligned with market realities, helping to improve trading efficiency, increase revenue or reduce costs, and enhance competitiveness in the power market. Predicting market demand based on power system operation parameter data provides advance knowledge of future market demand trends, enabling power companies and relevant market participants to plan and prepare for production, procurement, and reserves in advance. Assessing market risks and determining risk levels based on predicted demand data allows market participants to clearly understand the degree of risk they face, providing a clear direction for risk management. Implementing risk control measures based on risk levels can promptly address potential risks, reduce the impact of risks on the power system and power market, ensure the stable and reliable operation of the power market, and maintain market order and the interests of all parties.

[0035] In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 3 As shown, the above S201-S202 can be specifically implemented through the following S301-S304, which are explained in detail below: S301. Retrieve the operating parameter data of the power system within the set time period; obtain the net load of the power system by calculating the difference between load demand and the proportion of new energy; match the net load with the corresponding demand market database to obtain the corresponding market demand data.

[0036] The operating parameter data includes: the proportion of generating units, load demand, and the proportion of renewable energy; the market demand data includes: frequency regulation capacity demand and reserve capacity demand.

[0037] Example: Retrieve operating parameter data; load demand is 1000; renewable energy accounts for 20%; generating unit accounts for 80%; then the net load is calculated to be 800; match the net load with the corresponding demand market database to obtain the corresponding frequency regulation capacity demand of 50; reserve capacity demand of 100.

[0038] S302. Construct the cost function for each unit: Solve the first derivative of the cost function for each unit to obtain the incremental rate cost function; establish constraints and construct the power balance equation; solve the incremental rate of the unit based on the power balance equation; calculate the theoretical output of each unit based on the incremental rate of the unit.

[0039] in, Let i be the total cost of the i-th unit; Let i be the power generation capacity of the i-th unit; The cost of the i-th unit under no-load conditions; , This is the cost coefficient; >0; Incremental cost function; Expression: Establish constraints: ; The total power consumption of the power system; The total power generation capacity of renewable energy sources; This is the minimum generating capacity of the unit; The maximum generating capacity of the unit; Equilibrium equation: Unit incremental rate: Theoretical output of each unit: .

[0040] Example: Data from the two generator sets are obtained separately as follows: Unit 1: a1=100, b1=20, c1=0.01; P1_min=100; P1_max=500; Unit 2: a2=150, b2=15, c2=0.02; P1_min=50; P1_max=400; The theoretical output of Unit 1 is calculated to be 450, and the theoretical output of Unit 2 is 350, based on the formula.

[0041] S303. When the calculated theoretical output is less than the minimum generating power, the theoretical output is adjusted to the minimum generating power; when the calculated theoretical output is greater than the maximum generating power, the theoretical output is adjusted to the maximum generating power; this is done using the formula... The power generation ratio of each unit is calculated, and the power generation of each unit is allocated according to the power generation ratio.

[0042] For example: Analyzing the theoretical output of Unit 1 and Unit 2, since the theoretical output of both Unit 1 and Unit 2 is within the corresponding range, the allocation ratio of Unit 1 is calculated to be 56.25% and the allocation ratio of Unit 2 is 43.75% through the formula.

[0043] S304. Construct an objective function and calculate the total market transaction amount based on the constructed objective function; when the total market transaction amount is less than the preset cost threshold, generate electricity according to the allocation result; otherwise, reallocate the power generation ratio of the generating units.

[0044] The objective function is: ;in, This represents the power generation of the i-th generating unit; This represents the frequency regulation capacity requirement of the i-th generating unit; TX represents the reserve capacity requirement of the i-th unit; TX is the error adjustment coefficient.

[0045] Example: The frequency regulation price is 10, the reserve price is 5, the power generation price is 30, and the error adjustment coefficient is set to 0. Then, the total market transaction amount calculated by the objective function is 25000, which is less than the cost threshold of 30000.

[0046] Based on the above technical solution, power system operating parameter data within a set time period is retrieved. The net load is obtained by calculating the difference between load demand and the proportion of renewable energy. This net load is then matched with a demand market database to obtain market demand data. This accurately reflects the real demand of the power system for various ancillary services at different times, providing a reliable basis for subsequent unit output scheduling and market transactions. A cost function for each unit is constructed, and the first derivative is solved to obtain the incremental rate cost function. The incremental rate of the units is then solved by combining the power balance equation and constraints, thereby calculating the theoretical output of each unit. This considers the no-load cost of the units and the cost coefficients related to power generation, ensuring that unit output scheduling minimizes power generation costs while satisfying the power system's power balance, thus improving the economic efficiency of the power system. The calculated... Theoretical output is adjusted to ensure it falls between the unit's minimum and maximum generating capacity, preventing efficiency reduction or equipment damage caused by exceeding reasonable output limits. Simultaneously, the adjusted theoretical output is used to calculate and allocate power generation as a percentage, resulting in a more rational distribution of power generation across units and further optimizing power system operation. An objective function is constructed to calculate the total market transaction amount and compare it with a preset cost threshold. This ensures market transactions are conducted within cost-control limits, avoiding economic losses due to excessive transaction costs and guaranteeing the rationality and sustainability of the electricity market. Through precise calculation and rational allocation of unit output, and dynamic adjustments based on market transaction conditions, the generating capacity of each unit can be fully utilized, avoiding resource waste. Furthermore, accurate understanding of market demand data helps in the rational allocation of ancillary service resources, improving the overall resource utilization efficiency of the power system.

[0047] In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 4 As shown, the above S203-S204 can be specifically implemented through the following S401-S405, which will be explained in detail below. S401. Retrieve the net load within the set time period; calculate the rate of change of net load in adjacent time periods; calculate the standard deviation of the net load rate based on the rate of change; integrate the rate of change and standard deviation of net load into a demand forecast sequence in chronological order; call the demand forecast model, input the demand forecast sequence into the demand forecast model, and obtain the corresponding predicted rate of change of net load.

[0048] The demand forecasting model is built on an artificial intelligence model.

[0049] In some implementations, the demand forecasting model is built upon an artificial intelligence model, including: Select appropriate models and deep learning frameworks from the artificial intelligence model library; use the deep learning framework to build the model to obtain the constructed model; Obtain the standard dataset; the standard dataset includes standard input data consistent with the content attributes of the demand forecast sequence; and standard output data consistent with the content attributes of the predicted rate of change. The standard dataset is divided into training, validation, and test sets according to a set ratio; the model is trained using the training set; the internal parameters of the model are adjusted using the validation set; and the trained model is tested using the test set to obtain test metrics. Obtain the indicator threshold and compare the test indicator with the indicator threshold; if all test indicators are greater than the indicator threshold, mark the trained model as the demand prediction model; otherwise, retrain the demand prediction model.

[0050] It should be noted that the test metrics include: accuracy, stability, recall, F1 score, etc.; the thresholds for these metrics are set by technical personnel based on experience; the dataset split ratio is generally set to 8:1:1; when the model needs to be rebuilt, the deep learning framework or the dataset split ratio can be adjusted.

[0051] Example: Suppose the net load for one day is as shown in the table below: Table 1. Schematic diagram of historical net load. The rate of change of adjacent net loads was calculated, resulting in the following rate of change sequence: r = [-0.04, -0.0417, -0.0217, 0.0444, 0.0638, 0.1, 0.0909, 0.0833, 0.0769, 0.0286, 0.0139, 0.0137, -0.0135, -0.0137, -0.0278, -0.0286, -0.0441, -0.0462, -0.0323, -0.0333, -0.0333, -0.0345, -0.0370]; the standard deviation was calculated to be 0.05; the rate of change and standard deviation were integrated into an input sequence in chronological order; the input sequence was then input into the demand forecasting model, resulting in a predicted rate of change of 0.02.

[0052] S402. Calculate the average net load over a set time period; calculate the predicted load demand based on the average value and the predicted rate of change of net load; match the predicted load demand with the demand market database to obtain the corresponding predicted demand data.

[0053] The data type of the forecast demand data is consistent with that of the market demand data.

[0054] Example: The average net load over the past 24 hours is calculated to be 620. Based on the average and the projected rate of change, the projected load demand is calculated to be 632.4.

[0055] S403. Obtain historical forecast demand data and corresponding actual demand data; calculate the difference between forecast demand data and actual demand data to obtain the forecast error; obtain market volatility factor data; label the forecast error, seasonal trend data, and price volatility data as YW, JQ, and JB, respectively; construct a risk assessment function; calculate the market risk coefficient based on the risk assessment function.

[0056] Among them, the risk assessment function is: ;in, , and It is a proportionality coefficient greater than 0; DL is the dimensionless coefficient.

[0057] Example: Assume yesterday's predicted load demand was 630 MW, and the actual load demand was 640 MW, therefore the prediction error is 10; obtain seasonal trend data JQ=1.2; price fluctuation data JB=0.05; set the proportional coefficients as follows: proportional coefficient α=0.5, β=0.3, γ=0.2; DL=100; then the risk coefficient calculated by the formula is 0.0527.

[0058] S404. Compare the risk coefficient with the risk threshold to obtain the coefficient range of the risk coefficient; determine the risk level of the market risk based on the coefficient range; when the risk level is low, monitor and adjust the market demand data and market transaction data in real time.

[0059] The risk levels are categorized as low, medium, and high.

[0060] S405. When the risk level is medium, the operation of the power system is adjusted and evaluated based on the prediction error; when the risk level is high, the market demand data is re-predicted, the market fluctuation factor data is matched with the corresponding factor processing library to obtain the adjustment parameters; and the operation of the power system is adjusted based on the adjustment parameters.

[0061] Example: The risk threshold is set as follows: low risk FX < 0.05, medium risk 0.05 ≤ FX ≤ 0.1, high risk FX > 0.1; FX = 0.0527, which belongs to medium risk; the prediction error is 10, so it is necessary to adjust the unit output or increase the reserve capacity to compensate for the error; adjust the economic dispatch plan and increase the reserve allocation by 10.

[0062] Based on the above technical solution, the net load within a set time period is retrieved, the rate of change and standard deviation of adjacent time periods are calculated, and integrated into a demand forecast sequence in chronological order. The temporal variation characteristics and fluctuations of the net load are considered, providing comprehensive and representative input data for the demand forecasting model, which helps improve forecast accuracy. The predicted load demand is calculated by taking into account the average net load within the set time period and the predicted rate of change of the net load. The overall level and trend of the net load are comprehensively considered, making the predicted load demand more consistent with reality. The predicted load demand is matched with a demand market database to obtain corresponding predicted demand data, and the data type is consistent with market demand data. This allows the forecast results to be directly applied to market demand analysis, providing an accurate basis for subsequent decision-making. The difference between historical predicted demand data and actual demand data is obtained as the prediction error. Market volatility factors are also considered, and these factors are labeled and a risk assessment function is constructed. This comprehensive risk assessment method considers multiple factors that may affect market risk, making the assessment results more accurate and reliable. Adjustments to the operation of the power system based on the risk level can reduce the impact of risk on the system.

[0063] The above primarily describes the solutions of the embodiments of this application from the perspective of device implementation. It is understood that each device, such as the circuit-assisted market operation strategy optimization device based on model analysis, includes at least one of the hardware structures and software modules corresponding to each function in order to achieve the above-mentioned functions. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software-driven hardware manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0064] This application embodiment can divide the circuit-assisted market operation strategy optimization device based on model analysis into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0065] When using integrated units, Figure 5 A possible structural schematic diagram of the circuit-assisted market operation strategy optimization device (denoted as 50) based on model analysis involved in the above embodiments is shown. The circuit-assisted market operation strategy optimization device 50 based on model analysis includes a processing unit 501 and a communication unit 502, and may also include a storage unit 503. Figure 5 The schematic diagram shown can be used to illustrate the structure of the circuit-assisted market operation strategy optimization device based on model analysis involved in the above embodiments.

[0066] when Figure 5 The schematic diagram shown illustrates the structure of the circuit-assisted market operation strategy optimization device based on model analysis involved in the above embodiments. The processing unit 501 is used to control and manage the operation of the circuit-assisted market operation strategy optimization device based on model analysis, the communication unit 502 is used for the circuit-assisted market operation strategy optimization device based on model analysis to communicate with other devices, and the storage unit 503 is used to store the program code and data of the circuit-assisted market operation strategy optimization device based on model analysis.

[0067] For example, communication unit 502 is used to acquire power system operating parameter data and power market transaction data; Processing unit 501 is used to analyze the operating parameter data of the power system to obtain market demand data; adjust the trading strategy based on the market demand data and market transactions; predict the market demand data based on the operating parameter data of the power system to obtain predicted demand data; assess the market risk based on the predicted demand data to obtain the risk level; and implement risk control measures based on the risk level.

[0068] The processing unit 501 can be a processor or a controller, and the communication unit 502 can be a communication interface, transceiver, transceiver circuit, transceiver device, etc. The term "communication interface" is a general term and may include one or more interfaces. The storage unit 503 can be a memory. When the circuit-assisted market operation strategy optimization device 50 based on model analysis is a chip, the processing unit 501 can be a processor or a controller, and the communication unit 502 can be an input interface and / or an output interface, pins, or circuits, etc. The storage unit 503 can be a storage unit within the chip (e.g., a register, cache, etc.) or a storage unit located outside the chip (e.g., read-only memory (ROM), random access memory (RAM, etc.).

[0069] The communication unit can also be called a transceiver unit. The antenna and control circuit with transceiver functions in the circuit-assisted market operation strategy optimization device 50 based on model analysis can be considered as the communication unit 502 of the circuit-assisted market operation strategy optimization device 50 based on model analysis, and the processor with processing functions can be considered as the processing unit 501 of the circuit-assisted market operation strategy optimization device 50 based on model analysis. Optionally, the device in the communication unit 502 used to implement the receiving function can be considered as a communication unit, which is used to execute the receiving steps in the embodiments of this application. The communication unit can be a receiver, a receiver circuit, etc. The device in the communication unit 502 used to implement the transmitting function can be considered as a transmitting unit, which is used to execute the transmitting steps in the embodiments of this application. The transmitting unit can be a transmitter, a transmitter, a transmitting circuit, etc.

[0070] Figure 5If the integrated units in the process are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. Storage media for storing computer software products include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0071] Figure 5 The units in the process can also be called modules; for example, a processing unit can be called a processing module.

[0072] This application also provides a hardware structure diagram of a circuit-assisted market operation strategy optimization device (denoted as 60) based on model analysis, see [link to diagram]. Figure 6 The circuit-assisted market operation strategy optimization device 60 based on model analysis includes a processor 601, and optionally, a memory 602 connected to the processor 601.

[0073] In the first possible implementation, see Figure 6 The circuit-assisted market operation strategy optimization device 60 based on model analysis also includes a transceiver 603. The processor 601, memory 602, and transceiver 603 are connected via a bus. The transceiver 603 is used to communicate with other devices or communication networks. Optionally, the transceiver 603 may include a transmitter and a receiver. The device in the transceiver 603 that implements the receiving function can be considered as a receiver, which is used to perform the receiving steps in the embodiments of this application. The device in the transceiver 603 that implements the transmitting function can be considered as a transmitter, which is used to perform the transmitting steps in the embodiments of this application.

[0074] Based on the first possible implementation method Figure 6 The schematic diagram shown can be used to illustrate the structure of the circuit-assisted market operation strategy optimization device based on model analysis involved in the above embodiments.

[0075] in, Figure 6 The diagram can also illustrate the system chip in a circuit-assisted market operation strategy optimization device based on model analysis. In this case, the actions performed by the aforementioned circuit-assisted market operation strategy optimization device based on model analysis can be implemented by this system chip. The specific actions performed are described above and will not be repeated here.

[0076] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor or by instructions in software form. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0077] The processor in this application may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, etc., which are various computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform calculations or processing. The processor may be a separate semiconductor chip or integrated with other circuits into a single semiconductor chip. For example, it may be integrated with other circuits (such as encoding / decoding circuits, hardware acceleration circuits, or various bus and interface circuits) to form a SoC (System-on-a-Chip), or it may be integrated as a built-in processor within an ASIC. The ASIC with the integrated processor may be packaged separately or together with other circuits. In addition to the cores for executing software instructions to perform calculations or processing, the processor may further include necessary hardware accelerators, such as field-programmable gate arrays (FPGAs), PLDs (programmable logic devices), or logic circuits that implement dedicated logic operations.

[0078] The memory in the embodiments of this application may include at least one of the following types: read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions; random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions; or electrically erasable programmable-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto.

[0079] This application also provides a computer-readable storage medium including instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0080] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0081] This application also provides a chip including a processor and an interface circuit. The interface circuit is coupled to the processor. The processor is used to run computer programs or instructions to implement the above-described method. The interface circuit is used to communicate with other modules outside the chip.

[0082] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0083] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0084] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A circuit-assisted market operation strategy optimization method based on model analysis, characterized in that, include: To acquire power system operating parameter data and electricity market transaction data; By analyzing the operating parameter data of the power system, market demand data can be obtained; Adjust trading strategies based on market demand data and market transactions; Market demand data is predicted based on the operating parameters of the power system to obtain the predicted demand data; Market risks are assessed based on forecasted demand data to determine risk levels; Implement risk control measures based on the risk level.

2. The circuit-assisted market operation strategy optimization method based on model analysis according to claim 1, characterized in that, The analysis of power system operating parameter data to obtain market demand data includes: Retrieve power system operating parameter data for a specified time period; the operating parameter data includes: unit ratio, load demand, and renewable energy ratio; The net load of the power system is obtained by calculating the difference between load demand and the proportion of renewable energy. The net load is then matched with the corresponding demand market database to obtain the corresponding market demand data. The market demand data includes frequency regulation capacity demand and reserve capacity demand.

3. The circuit-assisted market operation strategy optimization method based on model analysis according to claim 1, characterized in that, The adjustment of trading strategies based on market demand data and market transactions includes: Retrieve market transaction data, including frequency regulation quotations, reserve quotations, and electricity prices; allocate the power generation of several generating units according to the net load; and determine the power generation of several generating units based on the allocation results. Construct the objective function: ;in, This represents the power generation of the i-th generating unit; This represents the frequency regulation capacity requirement of the i-th generating unit; This represents the reserve capacity requirement of the i-th unit; TX is the error adjustment coefficient. The total market transaction amount is calculated based on the objective function. If the total market transaction amount is less than the preset cost threshold, power generation is carried out according to the allocation results. Otherwise, the power generation ratio of the generating units is reallocated.

4. The circuit-assisted market operation strategy optimization method based on model analysis according to claim 3, characterized in that, The allocation of power generation from several generating units based on net load includes: Construct the cost function for each unit: ;in, Let i be the total cost of the i-th unit; Let i be the power generation capacity of the i-th unit; The cost of the i-th unit under no-load conditions; , Cost coefficient; >0; Solving for the first derivative of the cost function for each unit yields the incremental rate cost function; its expression is: ; Establish constraints: ;in, The total power consumption of the power system; The total power generation capacity of renewable energy sources; This is the minimum generating capacity of the unit; This is the maximum generating capacity of the unit; Construct the power balance equation: The incremental rate of the unit is obtained by solving the power balance equation: The theoretical output of each unit is calculated based on the incremental rate of change of the unit: When the calculated theoretical output is less than the minimum power generation, the theoretical output is adjusted to the minimum power generation; when the calculated theoretical output is greater than the maximum power generation, the theoretical output is adjusted to the maximum power generation. Through formula The power generation ratio of each unit is calculated, and the power generation of each unit is allocated according to the power generation ratio.

5. The circuit-assisted market operation strategy optimization method based on model analysis according to claim 1, characterized in that, The process of forecasting market demand data based on power system operating parameter data to obtain forecasted demand data includes: Retrieve the net load within the set time period; calculate the rate of change of net load in adjacent time periods; calculate the standard deviation of the net load rate based on the rate of change; integrate the rate of change and standard deviation of net load into a demand forecast sequence in chronological order; The demand forecasting model is invoked by inputting the demand forecasting sequence into the demand forecasting model to obtain the corresponding predicted rate of change of net load; the demand forecasting model is constructed based on an artificial intelligence model. The average net load over a set time period is calculated; the predicted load demand is calculated based on the average value and the predicted rate of change of net load; the predicted load demand is matched with the demand market database to obtain the corresponding predicted demand data; the data type of the predicted demand data is consistent with the market demand data.

6. The circuit-assisted market operation strategy optimization method based on model analysis according to claim 5, characterized in that, The demand forecasting model is built based on an artificial intelligence model and includes: Select appropriate models and deep learning frameworks from the artificial intelligence model library; use the deep learning framework to build the model to obtain the constructed model; Obtain the standard dataset; the standard dataset includes standard input data consistent with the content attributes of the demand forecast sequence; and standard output data consistent with the content attributes of the predicted rate of change. The standard dataset is divided into training, validation, and test sets according to a set ratio; the model is trained using the training set; the internal parameters of the model are adjusted using the validation set; and the trained model is tested using the test set to obtain test metrics. Obtain the indicator threshold and compare the test indicator with the indicator threshold; if all test indicators are greater than the indicator threshold, mark the trained model as the demand prediction model; otherwise, retrain the demand prediction model.

7. The circuit-assisted market operation strategy optimization method based on model analysis according to claim 1, characterized in that, The assessment of market risk based on forecasted demand data to obtain a risk level includes: Obtain historical forecast demand data and corresponding actual demand data; calculate the difference between forecast demand data and actual demand data to obtain the forecast error; obtain market volatility factor data; among which, volatility factor data includes: seasonal trend data and price fluctuation data; A comprehensive analysis of market risk is conducted based on prediction errors and volatility factors to obtain a risk coefficient. The risk coefficient is then compared with a risk threshold to obtain a coefficient range. The risk level of the market risk is determined based on the coefficient range. The risk levels include: low, medium, and high.

8. The circuit-assisted market operation strategy optimization method based on model analysis according to claim 7, characterized in that, The comprehensive analysis of market risk based on prediction errors and volatility factor data includes: Retrieve data on forecast errors and market volatility factors; label forecast errors, seasonal trend data, and price volatility data as YW, JQ, and JB, respectively. Construct a risk assessment function: ;in, , and It is a proportionality coefficient greater than 0; DL is the dimensionless coefficient. The market risk coefficient is calculated based on the risk assessment function.

9. The circuit-assisted market operation strategy optimization method based on model analysis according to claim 1, characterized in that, The risk control measures implemented according to the risk level include: When the risk level is low, market demand data and market transaction data are monitored and adjusted in real time; when the risk level is medium, the operation of the power system is adjusted and evaluated based on the forecast error. When the risk level is high, the market demand data is re-forecasted, the market fluctuation factor data is matched with the corresponding factor processing library to obtain adjustment parameters, and the operation of the power system is adjusted according to the adjustment parameters.

10. A circuit-assisted market operation strategy optimization device based on model analysis, applied to the circuit-assisted market operation strategy optimization method based on model analysis as described in any one of claims 1-9, characterized in that, include: Communication unit and processing unit; The communication unit is used to acquire power system operating parameter data and power market transaction data; The processing unit is used to analyze the operating parameter data of the power system to obtain market demand data; adjust the trading strategy based on the market demand data and market transactions; predict the market demand data based on the operating parameter data of the power system to obtain predicted demand data; and assess the market risk based on the predicted demand data to obtain the risk level. Implement risk control measures based on the risk level.

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