Electric energy frequency modulation combined clearing optimization method and system considering thermal power deep modulation state

Abstract

The invention belongs to the technical field of electric energy frequency modulation, and provides an electric energy frequency modulation joint clearing optimization method and system considering a thermal power deep modulation state, and the method comprises the steps: collecting the operation data of a thermal power generating unit in real time, and carrying out the preprocessing of the operation data, and obtaining a standardized data set; based on the standardized data set, performing multi-dimensional quantification on the deep peak regulation state of the thermal power generating unit to obtain a thermal power generating unit state sensing vector including state grade, dynamic frequency modulation capability and marginal cost information; calculating a dynamic frequency modulation capability parameter of the thermal power generating unit by combining the unit state sensing vector and the unit real-time operation data; an electric energy frequency modulation combined clearing calculation model considering the thermal power deep modulation state is constructed by introducing state perception, a thermal power generating unit state perception vector and a dynamic frequency modulation capability parameter are used as input, optimization calculation is carried out by taking total cost minimization as a target, and a combined clearing result of electric energy and frequency modulation capacity is output. According to the invention, a more accurate price signal is formed, optimal configuration of resources is guided, and the efficiency of an electricity market is improved.

Description

Technical Field

[0001] This invention belongs to the field of power frequency regulation technology, and specifically relates to a power frequency regulation joint clearing optimization method and system that takes into account the deep regulation state of thermal power plants. Background Technology

[0002] The power system faces severe challenges in absorbing clean energy and ensuring its safe and stable operation. Issues such as the volatility and randomness of new energy sources, the inverse distribution of resources and loads, and the insufficient resources for flexible system regulation place higher demands on the existing power system's dispatch and market mechanisms.

[0003] The core problem facing current technology is that traditional dispatching and market clearing models fail to fully consider the dynamic changes in frequency regulation capabilities and energy consumption costs of thermal power units under severe operating conditions such as deep peak shaving, resulting in deficiencies in the clearing results in terms of technical feasibility, system safety, and economic fairness.

[0004] To address the optimization of frequency regulation resources, existing technologies propose a joint clearing model for electricity and frequency regulation ancillary services, aiming to unify optimization and reduce overall procurement costs. At a more specific technical level, to improve the flexibility of thermal power units in response to fluctuations in renewable energy sources, existing solutions mainly include combustion optimization, oil-fired combustion assistance, and coupled energy storage. However, these technologies either have limited peak-shaving depth, poor economic efficiency, or high investment costs, and the control strategies between different subsystems may conflict.

[0005] The closest existing joint clearing model is a static, rated parameter-based model. Its core problem is that it treats key parameters such as the frequency regulation capacity and ramp rate provided by the generating units as fixed constants, failing to establish a dynamic relationship between these parameters and the real-time active power output of the units (especially during deep peak shaving). Therefore, this model cannot perceive or quantify the actual frequency regulation capacity degradation and additional cost increases caused by efficiency decline and equipment wear during deep peak shaving. Its clearing result may assign frequency regulation services that the units cannot safely provide, and the clearing price cannot reflect the marginal cost over the entire lifecycle, making it difficult to provide accurate price signals to guide optimal resource allocation. Summary of the Invention

[0006] The purpose of this invention is to provide a power frequency regulation joint clearing optimization method and system that takes into account the deep regulation state of thermal power plants, so as to solve the problems that the clearing results caused by the inaccuracy of the existing technology are technically infeasible, have hidden dangers in safety, and are economically unfair.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a power frequency regulation joint clearing optimization method taking into account the deep regulation state of thermal power plants, including: Real-time acquisition of operating data from thermal power units; preprocessing of the operating data to obtain a standardized dataset. Based on a standardized dataset, the deep peak-shaving status of thermal power units is quantified in multiple dimensions to obtain a thermal power unit status perception vector that includes status level, dynamic frequency regulation capability and marginal cost information. By combining the unit status perception vector with the unit's real-time operating data, the dynamic frequency regulation capability parameters of the thermal power unit are calculated. By introducing a state-aware construction model and a joint clearing calculation model for power frequency regulation based on the deep regulation state of thermal power plants, the state-aware vector of thermal power units and dynamic frequency regulation capability parameters are used as inputs. The optimization calculation is performed with the goal of minimizing the total cost, and the joint clearing result of power and frequency regulation capacity is output.

[0008] Furthermore, the real-time acquisition of operating data from thermal power units includes: Basic data of the power grid, including overall load forecast, line power flow data, network topology and information on key metering points; Data for thermal power units include real-time active power output, reactive power output, terminal voltage, main steam pressure / temperature, coal feed rate, and boiler combustion stability indicators. The joint clearing calculation data includes market participants' electricity bids and frequency regulation capacity bids.

[0009] Furthermore, the preprocessing of the runtime data to obtain a standardized dataset includes: Data cleaning: Check if the data is within a reasonable physical range; check if the rate of change between adjacent sampling points exceeds physical limits; check if the data values ​​remain unchanged for a long time; mark invalid data points as invalid and remove them; use linear interpolation to repair noisy or transiently missing data; trigger an alarm for persistently missing data. Data alignment: unify the timestamps of all data to the standard clock source of the scheduling center; unify the interpolation or aggregation of all time-series data to a common time network; Data fusion and association: Associating data from different sources but belonging to the same object to form a complete record.

[0010] Furthermore, based on a standardized dataset, the deep peak-shaving state of thermal power units is quantified in multiple dimensions to obtain a thermal power unit state-aware vector containing state level, dynamic frequency regulation capability, and marginal cost information, including: Quantification of operational status: Quantified by percentage of active power output, defined as:

[0011] in Percentage of effort contributed For the current active power output of the unit, This refers to the rated capacity of the unit; Quantification of frequency modulation capability: 1) Define the unit's stability score S: S≈1.0, the unit is very stable; S≈0.5, the unit's stability decreases and it is in a state of alert; S<0.3, the unit is unstable and it is in a state of deep peak shaving. 2) Establish the functional relationship of actual frequency regulation capability through historical data analysis or unit performance tests:

[0012]

[0013] in For actual adjustable capacity, This refers to the actual adjustable capacity. The actual output of unit i at time t, Assess the stability of the generating unit; Quantification of operating costs: Calculate the additional cost of power generation using the heat consumption characteristic curve of the unit; By statistically analyzing load cycles using the rainflow counting method and combining them with the material's fatigue life curve, unstable operating conditions are converted into equivalent operating hours, thus quantifying their lifespan loss cost.

[0014] Furthermore, the magnitude differentiation in the quantization of the operational state dimension includes: Normal peak-shaving range: active power output percentage is between 50% and 100%, boiler combustion is stable, and the unit is in the designed high-efficiency range; Shallow-deep peak shaving: When the percentage of active power output is between 40% and 50%, oil injection is required for combustion, and efficiency begins to decline; Deep peak shaving: When the percentage of active power output is between 30% and 40%, the boiler combustion stability deteriorates and the auxiliary equipment is close to the minimum technical output. Extreme peak shaving: The percentage of active power output is less than 30%, the unit is operating in an extreme state, and the risk is high.

[0015] Furthermore, the calculation of the dynamic frequency regulation capability parameters of the thermal power unit by combining the unit status perception vector with the unit's real-time operating data includes: Dynamic ramp rate refers to the actual safe rate of power change that can be achieved under real-time, continuously changing operating conditions. It is a function of time. Based on real-time data, the instantaneous rate is calculated. The actual power generation signal of the unit is acquired at high speed, and differential calculation is performed using a sliding time window, as shown below:

[0016] in Let be the instantaneous ramp rate at time t. For the current moment, For the current moment The real-time active power output of the generator unit. For the current moment A short period of time before The active power generated by the generator unit, The time interval used for calculation; Response delay time refers to the time interval between the occurrence of the regulation command and the moment when the unit begins to generate a noticeable and measurable power response. It is measured by analyzing the power response curve of a frequency step disturbance: the unit is operated under a certain stable load, a standard frequency step signal is applied, and the active power change process of the unit is recorded at high speed. Dynamic adjustment accuracy refers to the degree of consistency between the actual output power of the unit and the target commanded power during the dynamic adjustment process. The calculation formula is as follows:

[0017] Where N is the total number of sampling points. Let i be the actual active power generated by the unit at the i-th sampling time. The target active power command issued by the dispatch center at the i-th sampling time.

[0018] Furthermore, the method of introducing a state-aware construction model and a joint clearing calculation model for power frequency regulation based on the deep regulation state of thermal power plants, using the state-aware vector of the thermal power unit and the dynamic frequency regulation capability parameters as inputs, performs optimization calculations with the goal of minimizing total cost, and outputs the joint clearing result of power and frequency regulation capacity, including: The goal of the joint clearing model is to minimize the total cost of the entire system, including the cost of electricity production and the cost of frequency regulation service procurement, while satisfying all security constraints, as follows:

[0019] in: T: Total number of time periods in the scheduling cycle; N: Total number of generating units participating in the market; The planned power output of unit i during time period t; , : The up-frequency regulation capacity and down-frequency regulation capacity of unit i that are cleared in time period t; Unit i at output is The marginal cost of electricity at that time; : The marginal cost of frequency regulation service for unit i at time t; Model constraints: (1) Power balance constraint

[0020] in The power output plan for unit i during time period t. Forecast the total system load; (2) Frequency modulation capacity requirement constraints; ,

[0021] in , For unit i, the up-frequency regulation capacity and down-frequency regulation capacity are cleared during time period t; , The total up-modulation and down-modulation capacities required by the system; (3) Dynamic frequency modulation capability constraints

[0022]

[0023] in , For unit i, the up-frequency regulation capacity and down-frequency regulation capacity are cleared during time period t; , The actual frequency regulation capacity and down-frequency regulation capacity of the unit are generated by quantifying the deep peak shaving state of the thermal power unit. (4) Gradient constraint

[0024] in and These are the unit's maximum uphill and downhill ramp rates; and The unit outputs power at time t and time t-1; After the solution is completed, the model outputs the cleared electricity volume, nodal marginal price, frequency regulation capacity and frequency regulation capacity price for each unit in each time period, and publishes the clearing results.

[0025] Secondly, the present invention provides a power frequency regulation joint clearing optimization system that takes into account the deep regulation state of thermal power plants, comprising: The data acquisition module is used to collect real-time operating data of thermal power units and preprocess the operating data to obtain a standardized dataset. The quantization module is used to perform multi-dimensional quantization of the deep peak-shaving status of thermal power units based on a standardized dataset, and obtain a thermal power unit status perception vector that includes status level, dynamic frequency regulation capability and marginal cost information. The calculation module is used to combine the unit's state perception vector with the unit's real-time operating data to calculate the dynamic frequency regulation capability parameters of the thermal power unit. The joint output module is used to construct a joint clearing calculation model for power frequency regulation based on state perception and the deep regulation state of thermal power plants. It takes the state perception vector of thermal power units and dynamic frequency regulation capability parameters as input, performs optimization calculation with the goal of minimizing total cost, and outputs the joint clearing result of power and frequency regulation capacity.

[0026] Furthermore, the real-time acquisition of operating data from thermal power units includes: Basic data of the power grid, including overall load forecast, line power flow data, network topology and information on key metering points; Data for thermal power units include real-time active power output, reactive power output, terminal voltage, main steam pressure / temperature, coal feed rate, and boiler combustion stability indicators. The joint clearing calculation data includes market participants' electricity bids and frequency regulation capacity bids.

[0027] Furthermore, the preprocessing of the runtime data to obtain a standardized dataset includes: Data cleaning: Check if the data is within a reasonable physical range; check if the rate of change between adjacent sampling points exceeds physical limits; check if the data values ​​remain unchanged for a long time; mark invalid data points as invalid and remove them; use linear interpolation to repair noisy or transiently missing data; trigger an alarm for persistently missing data. Data alignment: unify the timestamps of all data to the standard clock source of the scheduling center; unify the interpolation or aggregation of all time-series data to a common time network; Data fusion and association: Associating data from different sources but belonging to the same object to form a complete record.

[0028] Furthermore, based on a standardized dataset, the deep peak-shaving state of thermal power units is quantified in multiple dimensions to obtain a thermal power unit state-aware vector containing state level, dynamic frequency regulation capability, and marginal cost information, including: Quantification of operational status: Quantified by percentage of active power output, defined as:

[0029] in Percentage of effort contributed For the current active power output of the unit, This refers to the rated capacity of the unit; Quantification of frequency modulation capability: 1) Define the unit's stability score S: S≈1.0, the unit is very stable; S≈0.5, the unit's stability decreases and it is in a state of alert; S<0.3, the unit is unstable and it is in a state of deep peak shaving. 2) Establish the functional relationship of actual frequency regulation capability through historical data analysis or unit performance tests:

[0030]

[0031] in For actual adjustable capacity, This refers to the actual adjustable capacity. The actual output of unit i at time t, Assess the stability of the generating unit; Quantification of operating costs: Calculate the additional cost of power generation using the heat consumption characteristic curve of the unit; By statistically analyzing load cycles using the rainflow counting method and combining them with the material's fatigue life curve, unstable operating conditions are converted into equivalent operating hours, thus quantifying their lifespan loss cost.

[0032] Furthermore, the magnitude differentiation in the quantization of the operational state dimension includes: Normal peak-shaving range: active power output percentage is between 50% and 100%, boiler combustion is stable, and the unit is in the designed high-efficiency range; Shallow-deep peak shaving: When the percentage of active power output is between 40% and 50%, oil injection is required for combustion, and efficiency begins to decline; Deep peak shaving: When the percentage of active power output is between 30% and 40%, the boiler combustion stability deteriorates and the auxiliary equipment is close to the minimum technical output. Extreme peak shaving: The percentage of active power output is less than 30%, the unit is operating in an extreme state, and the risk is high.

[0033] Furthermore, the calculation of the dynamic frequency regulation capability parameters of the thermal power unit by combining the unit status perception vector with the unit's real-time operating data includes: Dynamic ramp rate refers to the actual safe rate of power change that can be achieved under real-time, continuously changing operating conditions. It is a function of time. Based on real-time data, the instantaneous rate is calculated. The actual power generation signal of the unit is acquired at high speed, and differential calculation is performed using a sliding time window, as shown below:

[0034] in Let be the instantaneous ramp rate at time t. For the current moment, For the current moment The real-time active power output of the generator unit. For the current moment A short period of time before The active power generated by the generator unit, The time interval used for calculation; Response delay time refers to the time interval between the occurrence of the regulation command and the moment when the unit begins to generate a noticeable and measurable power response. It is measured by analyzing the power response curve of a frequency step disturbance: the unit is operated under a certain stable load, a standard frequency step signal is applied, and the active power change process of the unit is recorded at high speed. Dynamic adjustment accuracy refers to the degree of consistency between the actual output power of the unit and the target commanded power during the dynamic adjustment process. The calculation formula is as follows:

[0035] Where N is the total number of sampling points. Let i be the actual active power generated by the unit at the i-th sampling time. The target active power command issued by the dispatch center at the i-th sampling time.

[0036] Furthermore, the method of introducing a state-aware construction model and a joint clearing calculation model for power frequency regulation based on the deep regulation state of thermal power plants, using the state-aware vector of the thermal power unit and the dynamic frequency regulation capability parameters as inputs, performs optimization calculations with the goal of minimizing total cost, and outputs the joint clearing result of power and frequency regulation capacity, including: The goal of the joint clearing model is to minimize the total cost of the entire system, including the cost of electricity production and the cost of frequency regulation service procurement, while satisfying all security constraints, as follows:

[0037] in: T: Total number of time periods in the scheduling cycle; N: Total number of generating units participating in the market; The planned power output of unit i during time period t; , : The up-frequency regulation capacity and down-frequency regulation capacity of unit i that are cleared in time period t; Unit i at output is The marginal cost of electricity at that time; : The marginal cost of frequency regulation service for unit i at time t; Model constraints: (1) Power balance constraint

[0038] in The power output plan for unit i during time period t. Forecast the total system load; (2) Frequency modulation capacity requirement constraints; ,

[0039] in , For unit i, the up-frequency regulation capacity and down-frequency regulation capacity are cleared during time period t; , The total up-modulation and down-modulation capacities required by the system; (3) Dynamic frequency modulation capability constraints

[0040]

[0041] in , For unit i, the up-frequency regulation capacity and down-frequency regulation capacity are cleared during time period t; , The actual frequency regulation capacity and down-frequency regulation capacity of the unit are generated by quantifying the deep peak shaving state of the thermal power unit. (4) Gradient constraint

[0042] in and These are the unit's maximum uphill and downhill ramp rates; and The unit outputs power at time t and time t-1; After the solution is completed, the model outputs the cleared electricity volume, nodal marginal price, frequency regulation capacity and frequency regulation capacity price for each unit in each time period, and publishes the clearing results.

[0043] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the power frequency regulation joint clearing optimization method taking into account the deep regulation state of thermal power plants.

[0044] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the power frequency regulation joint clearing optimization method taking into account the deep regulation state of thermal power plants.

[0045] Compared with the prior art, the present invention has the following technical effects: This invention quantifies the deep peak-shaving status of thermal power units and calculates their dynamic frequency regulation capabilities, inputting the results into the core market clearing algorithm. The frequency regulation capabilities provided by the model are real-time, reliable, and physically executable. Through stability scores, units on the safety margin can be identified in advance, and the clearing model automatically avoids assigning them high-risk frequency regulation tasks. For the power grid, it gains more reliable and realistic frequency regulation resources, improving the safety and control accuracy of grid operation. For thermal power units, the negative value and high risk of deep peak shaving are accurately identified by the market and reasonably compensated, stimulating their enthusiasm for participating in deep peak shaving and providing frequency regulation services. For the market, it generates more accurate price signals, guiding optimal resource allocation and improving the efficiency and fairness of the electricity market.

[0046] This invention deeply couples the internal physical operating status of the generating unit with the clearing mechanism of the external market, solving the problems of "unknown status, mismatched capacity, and unreasonable compensation" in traditional methods. Status quantification transforms operational risks into calculable parameters, cost modeling embeds risk costs into market bids, and joint clearing constructs a status-aware market decision-making engine. Ultimately, this creates a new market environment that accurately assesses risks, precisely quantifies capabilities, and provides reasonable compensation, incentivizing power plants to proactively optimize their deep peak-shaving performance while providing the grid with reliable and flexible resources, supporting the reliable consumption of a high proportion of renewable energy. Attached Figure Description

[0047] Figure 1 This is a flowchart of the present invention.

[0048] Figure 2 This is a system structure diagram of the present invention. Detailed Implementation

[0049] The present invention will be further described below with reference to the accompanying drawings: Explanation of related terms Deep peak shaving: refers to the operation mode of thermal power units operating at load levels below their minimum technical output in order to cope with fluctuations in the net load of the power grid.

[0050] Minimum technical output: refers to the lowest continuous output that the unit can maintain safe and stable combustion and meet environmental standards without additional measures, which is usually about 50% of the rated capacity.

[0051] Net load of the power grid: Total electricity load minus the output of uncontrollable renewable energy sources such as wind power and solar power.

[0052] Clean energy consumption refers to the entire process of effectively connecting, transmitting, and ultimately using electricity generated from clean energy sources such as wind and solar power to the grid. Its core objective is to minimize the waste of electricity caused by "curtailment" of wind and solar power.

[0053] Example 1, please refer to Figure 1 This invention provides a power frequency regulation joint clearing optimization method considering the deep regulation state of thermal power plants, including: Real-time acquisition of operating data from thermal power units; preprocessing of the operating data to obtain a standardized dataset. Based on a standardized dataset, the deep peak-shaving status of thermal power units is quantified in multiple dimensions to obtain a thermal power unit status perception vector that includes status level, dynamic frequency regulation capability and marginal cost information. By combining the unit status perception vector with the unit's real-time operating data, the dynamic frequency regulation capability parameters of the thermal power unit are calculated. By introducing a state-aware construction model and a joint clearing calculation model for power frequency regulation based on the deep regulation state of thermal power plants, the state-aware vector of thermal power units and dynamic frequency regulation capability parameters are used as inputs. The optimization calculation is performed with the goal of minimizing the total cost, and the joint clearing result of power and frequency regulation capacity is output.

[0054] This invention dynamically identifies and quantifies these hidden safety boundaries, ensuring that every frequency regulation command issued during the clearing process falls within the physically feasible domain of the unit's current state. This eliminates major safety risks such as unit operational oscillations, equipment damage, and even unplanned shutdowns caused by "over-scheduling." By establishing a precise "deep-schedule state-frequency regulation capability" coupling model, this invention strongly correlates the internal physical state of the unit (such as coal quantity, pressure, and temperature) with external market clearing commands. This ensures that the optimization calculation process is always aware of the real-time health status of the thermal power unit, achieving a safe closed-loop control for "source-grid" coordination. By more safely, accurately, and economically mobilizing thermal power—currently the most important flexible resource—this invention maximizes the release of the system's regulation capacity, providing solid support for the large-scale consumption of intermittent new energy sources such as wind and solar power.

[0055] Example 2: This invention provides a power frequency regulation joint clearing optimization method considering the deep regulation state of thermal power plants, including: (1) Quantification of deep peak shaving status of thermal power units. Real-time collection of operating data of thermal power units, including real-time active power, main steam pressure, coal quantity, air volume, boiler combustion stability index, and environmental protection facility operating status. Definition of deep peak shaving status index to quantify the degree of deep peak shaving of the unit. Finally, output the real-time deep peak shaving status identifier of the thermal power unit.

[0056] (2) Calculation of dynamic frequency regulation capability of thermal power units. Establish a mapping function of "deep peak shaving state - frequency regulation capability", receive the state identifier output by the state quantization module, and the frequency regulation capability parameters of the dynamic computer unit in the current state. Output the set of dynamic frequency regulation capability parameters of each thermal power unit participating in the clearing at the current moment.

[0057] (3) Generate a joint clearing optimization model that takes into account the deep regulation state. Based on the traditional objective function of minimizing "electricity generation cost + frequency regulation ancillary service cost", the additional cost of the deep regulation state is added to obtain a new objective function. In addition to the traditional joint clearing model, dynamic constraints generated by the aforementioned modules are introduced, such as dynamic constraints on unit frequency regulation capability, dynamic constraints on unit operating feasible domain, and system frequency regulation demand constraints, to obtain a joint clearing calculation model for electricity frequency regulation that takes into account the deep regulation state of thermal power.

[0058] This solution, through an innovative design encompassing state perception, capability assessment, and optimized clearing, transforms previously overlooked physical operating states into key decision variables for market clearing. It systematically addresses the safety, accuracy, and economic issues in the combined power-frequency regulation clearing technology, providing a practical and feasible technical path for achieving safe, efficient, and economical operation of a high-proportion renewable energy power grid.

[0059] Example 3: This invention provides a power frequency regulation joint clearing optimization method considering the deep regulation state of thermal power plants, including: Raw data input and data preprocessing Raw data input and data preprocessing are the cornerstones of the reliable operation of the entire system, aiming to provide accurate, complete, consistent, and timely data for other modules. The following are detailed methods and steps for raw data processing: (1) Data collection Collect multi-source data related to thermal power units and power load, mainly including: Basic data of the power grid, including overall load forecast, line power flow data, network topology, and metering point information, are mainly obtained from the power grid dispatch center. Data on thermal power units, including real-time active power output, reactive power output, terminal voltage, main steam pressure / temperature, coal feed rate, and boiler combustion stability indicators, are mainly obtained from the plant monitoring system. The data required for joint clearing calculations, including market participants' electricity bids and frequency regulation capacity bids, are mainly obtained from the power trading center.

[0060] (2) Data cleaning The main focus is on processing outliers in the original data to remove errors and missing values, thereby ensuring data quality. The main steps are as follows: 1) Range check. Check whether the data is within a reasonable physical range, such as power not exceeding 120% of rated capacity and pressure not being negative.

[0061] 2) Sudden change check. Check whether the rate of change between adjacent sampling points exceeds the physical possibility, such as a 100MW unit power changing by 50MW within 1 second.

[0062] 3) Stagnation check. Check if the data value remains unchanged for a long time. If the data stagnates, it may be due to communication interruption or sensor failure.

[0063] 4) Abnormal data removal. For obviously invalid data points, such as those that are out of range, they are directly marked as invalid and removed.

[0064] 5) Missing value handling. For minor noise or transient missing values, linear interpolation is used for repair. For persistent missing data, an alarm is triggered.

[0065] (3) Data alignment Since data from different sources arrive at different times and have different sampling frequencies (e.g., SCADA data is sampled every 2 seconds, SIS data every 5 seconds, and market data every 15 minutes), it is necessary to align the different data.

[0066] 1) Unify the timestamp. Unify the timestamps of all data to the standard clock source of the scheduling center.

[0067] 2) Resampling. Interpolate or aggregate all time-series data onto a common time network.

[0068] (4) Data fusion and association Data from different sources but belonging to the same object can be associated. For example, the unit output from the remote control unit and the coal consumption from the SIS can be associated to form a complete record through the unit ID and a unified timestamp.

[0069] Quantification of Deep Peak Shaving Status of Thermal Power Units After completing the input and preprocessing of the raw data, the deep peak-shaving status of thermal power units needs to be quantified. The specific steps are as follows: (1) Quantification of operating status. This dimension mainly quantifies whether the unit is in a deep peak-shaving state, and if so, how deep it is. It is primarily quantified through the percentage of active power output. Its definition is:

[0070] in Percentage of effort contributed For the current active power output of the unit, This refers to the rated capacity of the unit.

[0071] The main differences in scale include: Normal peak-shaving range: active power output percentage is between 50% and 100%, boiler combustion is stable, and the unit is in the designed high-efficiency range.

[0072] Shallow-deep peak shaving: When the percentage of active power output is between 40% and 50%, oil injection is required for combustion, and efficiency begins to decline significantly.

[0073] Deep peak shaving: When the percentage of active power output is between 30% and 40%, the boiler combustion stability deteriorates, and the main auxiliary equipment may be close to the minimum technical output.

[0074] Extreme peak shaving: When the percentage of active power output is less than 30%, the unit is operating in an extreme state, which is highly risky and requires special measures to ensure the safety of the unit.

[0075] (2) Quantification of frequency regulation capability. This dimension mainly quantifies how much frequency regulation service the unit can actually provide under deep peak shaving conditions. The frequency regulation capability of the unit is a function of its operating state, rather than a fixed value.

[0076] 1) Define the unit's stability score S: S≈1.0, the unit is very stable; S≈0.5, the unit's stability decreases and it is in a state of alert; S<0.3, the unit is unstable and it is in a state of deep peak shaving.

[0077] 2) Establish the functional relationship of actual frequency regulation capability through historical data analysis or unit performance tests:

[0078]

[0079] in For actual adjustable capacity, This refers to the actual adjustable capacity. The actual output of unit i at time t, The unit's stability is scored. After generating the above function, the actual adjustable capacity of the unit can be obtained from the unit's current actual output and the unit's stability score.

[0080] (3) Quantification of operating costs. This dimension mainly quantifies the actual cost of generating electricity and providing frequency regulation services under deep peak-shaving conditions. It mainly includes: 1) Additional generation costs. Increased generation costs are due to decreased efficiency, increased fuel costs, and a higher proportion of electricity used by the plant. These additional costs are calculated using the unit's heat consumption characteristic curve. In the deep adjustment range, even a slight increase in heat consumption rate will lead to a sharp increase, meaning a surge in marginal costs.

[0081] 2) Additional Costs of Frequency Regulation Services. Under deep frequency regulation conditions, major equipment such as boilers and turbines experience greater thermal stress cycles, accelerating component lifespan decline. Load cycles can be statistically analyzed using the "rainflow counting method" and combined with material fatigue life curves to convert unstable operating conditions into equivalent operating hours, thereby quantifying the lifespan decline cost. Providing frequency regulation services under deep frequency regulation conditions carries a significantly higher risk of tripping and equipment failure than under normal conditions. This risk needs to be quantified in the form of "insurance premiums." Due to the actual reduction in frequency regulation capacity and performance, the unit needs to provide more output to achieve the same frequency regulation effect (e.g., respond to the same commands), which inherently leads to additional wear and tear and costs.

[0082] Calculation of dynamic frequency regulation capability of thermal power units Calculating the dynamic frequency regulation capability of thermal power units is a more complex and realistic process than static evaluation. It focuses on the dynamic, continuous, and variable characteristics of the unit's frequency regulation capability under constantly changing real-time operating conditions. This mainly includes dynamic ramp rate, response delay time, and dynamic adjustment accuracy.

[0083] (1) Dynamic ramp rate. This refers to the actual, safe rate of power change that can be achieved under real-time, continuously changing operating conditions. It is a function that varies with time, not a constant. The instantaneous rate is calculated based on real-time data. The actual power signal generated by the unit is obtained at high speed, and differential calculation is performed using a sliding time window. The relevant formulas are as follows:

[0084] in Let be the instantaneous ramp rate at time t. For the current moment, For the current moment The real-time active power output of the generator unit. For the current moment A very short time ago The active power generated by the generator unit, The time intervals used in the calculations need to be handled flexibly.

[0085] (2) Response delay time. This refers to the time interval between the occurrence of the regulation command and the moment when the unit begins to generate a noticeable and measurable power response. It is measured by analyzing the power response curve of a frequency step disturbance. Specifically, the unit is operated under a stable load, and a standard frequency step signal is suddenly applied, while the active power change process of the unit is recorded at high speed. The response delay time is used to evaluate the unit's ability to meet the grid frequency regulation requirements.

[0086] (3) Dynamic adjustment accuracy. Dynamic adjustment accuracy measures the degree of consistency between the actual output power of the unit and the target commanded power during dynamic adjustment. It focuses not on the static error at a single point in time, but on the ability of the power curve to track the command curve throughout the entire process of change. The specific calculation formula is as follows:

[0087] Where N is the total number of sampling points. Let i be the actual active power generated by the unit at the i-th sampling time. The target active power command issued by the dispatch center at the i-th sampling time.

[0088] This dynamic adjustment accuracy can be compared horizontally or vertically with other units or other operating conditions, thereby quantitatively evaluating the quality of the adjustment accuracy. Enhanced joint clearing optimization calculation Compared with the traditional joint clearing model, the core innovation of this model lies in the introduction of "state awareness" and "dynamic binding" mechanisms: State awareness: The model is no longer based on fixed, rated parameters of the unit for clearing, but instead accesses the deep peak shaving state vector quantified in the previous stage for each thermal power unit in real time.

[0089] Dynamic binding: The upper and lower limits of the unit's frequency regulation capability and the marginal cost of providing frequency regulation services are no longer constants, but variables that are dynamically bound to its real-time operating status.

[0090] The objective function of the model: The goal of the joint clearing model is to minimize the total cost of the entire system, including electricity production costs and frequency regulation service procurement costs, while satisfying all security constraints. Specifically:

[0091] in: T: Total number of time periods in the scheduling cycle.

[0092] N: The total number of generating units participating in the market.

[0093] : The planned power output of unit i during time period t.

[0094] , : The up-frequency regulation capacity and down-frequency regulation capacity of unit i that are cleared during time period t.

[0095] Unit i at output is Marginal cost of electricity at that time.

[0096] : The marginal cost of frequency regulation service for unit i at time t. It is a dynamic function whose input is the unit's current planned power output. and stability score from the quantization module This function encapsulates all cost information in deep peaking state quantization.

[0097] Model constraints: (1) Energy balance constraints.

[0098]

[0099] in The power output plan for unit i during time period t. Forecasting the total system load.

[0100] (2) Frequency modulation capacity requirement constraints.

[0101] ,

[0102] in , For unit i, the up-frequency regulation capacity and down-frequency regulation capacity are cleared during time period t; , This represents the total up-frequency modulation capacity and down-frequency modulation capacity required by the system.

[0103] (3) Dynamic frequency modulation capability constraints

[0104]

[0105] in , For unit i, the up-frequency regulation capacity and down-frequency regulation capacity are cleared during time period t; , The actual frequency regulation capacity and down-frequency regulation capacity of the unit are generated by quantifying the deep peak shaving state of the thermal power unit.

[0106] (4) Gradient constraint

[0107] in and It is the unit's maximum uphill and downhill ramp rate. and It provides power to the unit at time t and time t-1.

[0108] After the solution is completed, the model outputs the cleared electricity volume, nodal marginal price, frequency regulation capacity and frequency regulation capacity price for each unit in each time period, and publishes the clearing results and issues instructions.

[0109] Please see Figure 2 In another embodiment of the present invention, a power frequency regulation joint clearing optimization system considering the deep regulation state of thermal power plants is provided. This system can be used to implement the above-mentioned power frequency regulation joint clearing optimization method considering the deep regulation state of thermal power plants. Specifically, the system includes: The data acquisition module is used to collect real-time operating data of thermal power units and preprocess the operating data to obtain a standardized dataset. The quantization module is used to perform multi-dimensional quantization of the deep peak-shaving status of thermal power units based on a standardized dataset, and obtain a thermal power unit status perception vector that includes status level, dynamic frequency regulation capability and marginal cost information. The calculation module is used to combine the unit's state perception vector with the unit's real-time operating data to calculate the dynamic frequency regulation capability parameters of the thermal power unit. The joint output module is used to construct a joint clearing calculation model for power frequency regulation based on state perception and the deep regulation state of thermal power plants. It takes the state perception vector of thermal power units and dynamic frequency regulation capability parameters as input, performs optimization calculation with the goal of minimizing total cost, and outputs the joint clearing result of power and frequency regulation capacity.

[0110] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0111] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of a power frequency regulation joint clearing optimization method considering the deep regulation state of thermal power plants.

[0112] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the power frequency regulation joint clearing optimization method considering the deep regulation state of thermal power plants in the above embodiments.

[0113] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0114] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0115] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0116] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A power frequency regulation joint clearing optimization method considering the deep regulation state of thermal power plants, characterized in that, include: Real-time acquisition of operating data from thermal power units; preprocessing of the operating data to obtain a standardized dataset. Based on a standardized dataset, the deep peak-shaving status of thermal power units is quantified in multiple dimensions to obtain a thermal power unit status perception vector that includes status level, dynamic frequency regulation capability and marginal cost information. By combining the unit status perception vector with the unit's real-time operating data, the dynamic frequency regulation capability parameters of the thermal power unit are calculated. By introducing a state-aware construction model and a joint clearing calculation model for power frequency regulation based on the deep regulation state of thermal power plants, the state-aware vector of thermal power units and dynamic frequency regulation capability parameters are used as inputs. The optimization calculation is performed with the goal of minimizing the total cost, and the joint clearing result of power and frequency regulation capacity is output.

2. The power frequency regulation joint clearing optimization method considering the deep regulation state of thermal power plants according to claim 1, characterized in that, The real-time acquisition of operating data from thermal power units includes: Basic data of the power grid, including overall load forecast, line power flow data, network topology and information on key metering points; Data for thermal power units include real-time active power output, reactive power output, terminal voltage, main steam pressure / temperature, coal feed rate, and boiler combustion stability indicators. The joint clearing calculation data includes market participants' electricity bids and frequency regulation capacity bids.

3. The power frequency regulation joint clearing optimization method considering the deep regulation state of thermal power plants according to claim 1, characterized in that, The preprocessing of the runtime data to obtain a standardized dataset includes: Data cleaning: Check if the data is within a reasonable physical range; check if the rate of change between adjacent sampling points exceeds physical limits; check if the data values ​​remain unchanged for a long time; mark invalid data points as invalid and remove them; use linear interpolation to repair noisy or transiently missing data; trigger an alarm for persistently missing data. Data alignment: unify the timestamps of all data to the standard clock source of the scheduling center; unify the interpolation or aggregation of all time-series data to a common time network; Data fusion and association: Associating data from different sources but belonging to the same object to form a complete record.

4. The power frequency regulation joint clearing optimization method considering the deep regulation state of thermal power plants according to claim 1, characterized in that, The method, based on a standardized dataset, quantifies the deep peak-shaving status of thermal power units in multiple dimensions, obtaining a thermal power unit status perception vector that includes status level, dynamic frequency regulation capability, and marginal cost information, including: Quantification of operational status: Quantified by percentage of active power output, defined as: in Percentage of effort contributed For the current active power output of the unit, This refers to the rated capacity of the unit; Quantification of frequency modulation capability: 1) Define the unit's stability score S: S≈1.0, the unit is very stable; S≈0.5, the unit's stability decreases and it is in a state of alert; S<0.3, the unit is unstable and it is in a state of deep peak shaving. 2) Establish the functional relationship of actual frequency regulation capability through historical data analysis or unit performance tests: in For actual adjustable capacity, This refers to the actual adjustable capacity. The actual output of unit i at time t. Assess the stability of the generating unit; Quantification of operating costs: Calculate the additional cost of power generation using the heat consumption characteristic curve of the unit; By statistically analyzing load cycles using the rainflow counting method and combining them with the material's fatigue life curve, unstable operating conditions are converted into equivalent operating hours, thus quantifying their lifespan loss cost.

5. The power frequency regulation joint clearing optimization method considering the deep regulation state of thermal power plants according to claim 4, characterized in that, The magnitude differentiation in the quantization of the operational state dimension includes: Normal peak-shaving range: active power output percentage is between 50% and 100%, boiler combustion is stable, and the unit is in the designed high-efficiency range; Shallow-deep peak shaving: When the percentage of active power output is between 40% and 50%, oil injection is required for combustion, and efficiency begins to decline; Deep peak shaving: When the percentage of active power output is between 30% and 40%, the boiler combustion stability deteriorates and the auxiliary equipment is close to the minimum technical output. Extreme peak shaving: The percentage of active power output is less than 30%, the unit is operating in an extreme state, and the risk is high.

6. The power frequency regulation joint clearing optimization method considering the deep regulation state of thermal power plants according to claim 1, characterized in that, The calculation of the dynamic frequency regulation capability parameters of the thermal power unit by combining the unit status perception vector and the unit's real-time operating data includes: Dynamic ramp rate refers to the actual safe rate of power change that can be achieved under real-time, continuously changing operating conditions. It is a function of time. Based on real-time data, the instantaneous rate is calculated. The actual power generation signal of the unit is acquired at high speed, and differential calculation is performed using a sliding time window, as shown below: in Let be the instantaneous ramp rate at time t. For the current moment, For the current moment The real-time active power output of the generator unit. For the current moment A short period of time before The active power generated by the generator unit, The time interval used for calculation; Response delay time refers to the time interval between the occurrence of the regulation command and the moment when the unit begins to generate a noticeable and measurable power response. It is measured by analyzing the power response curve of a frequency step disturbance: the unit is operated under a certain stable load, a standard frequency step signal is applied, and the change process of the unit's active power is recorded at high speed. Dynamic adjustment accuracy refers to the degree of consistency between the actual output power of the unit and the target commanded power during the dynamic adjustment process. The calculation formula is as follows: Where N is the total number of sampling points. Let i be the actual active power generated by the unit at the i-th sampling time. The target active power command issued by the dispatch center at the i-th sampling time.

7. The power frequency regulation joint clearing optimization method considering the deep regulation state of thermal power plants according to claim 1, characterized in that, The aforementioned joint clearing calculation model for power frequency regulation, which introduces state-aware construction and thermal power deep-state regulation, takes the thermal power unit state-aware vector and dynamic frequency regulation capability parameters as inputs, performs optimization calculations with the goal of minimizing total cost, and outputs the joint clearing result of power and frequency regulation capacity, including: The goal of the joint clearing model is to minimize the total cost of the entire system, including the cost of electricity production and the cost of frequency regulation service procurement, while satisfying all security constraints, as follows: in: T: Total number of time periods in the scheduling cycle; N: Total number of generating units participating in the market; : The planned power output of unit i during time period t; , : The up-frequency regulation capacity and down-frequency regulation capacity of unit i that are cleared in time period t; Unit i at output is The marginal cost of electricity at that time; : The marginal cost of frequency regulation service for unit i at time t; Model constraints: (1) Power balance constraint in The power output plan for unit i during time period t. Forecast the total system load; (2) Frequency modulation capacity requirement constraints; ， in , For unit i, the up-frequency regulation capacity and down-frequency regulation capacity are cleared during time period t; , The total up-modulation and down-modulation capacities required by the system; (3) Dynamic frequency modulation capability constraints in , For unit i, the up-frequency regulation capacity and down-frequency regulation capacity are cleared during time period t; , The actual frequency regulation capacity and down-frequency regulation capacity of the unit are generated by quantifying the deep peak shaving state of the thermal power unit. (4) Gradient constraint in and These are the unit's maximum uphill and downhill ramp rates; and The unit outputs power at time t and time t-1; After the solution is completed, the model outputs the cleared electricity volume, nodal marginal price, frequency regulation capacity and frequency regulation capacity price for each unit in each time period, and publishes the clearing results.

8. A power frequency regulation joint clearing and optimization system taking into account the deep regulation state of thermal power plants, characterized in that, include: The data acquisition module is used to collect real-time operating data of thermal power units and preprocess the operating data to obtain a standardized dataset. The quantization module is used to perform multi-dimensional quantization of the deep peak-shaving status of thermal power units based on a standardized dataset, and obtain a thermal power unit status perception vector that includes status level, dynamic frequency regulation capability and marginal cost information. The calculation module is used to combine the unit's state perception vector with the unit's real-time operating data to calculate the dynamic frequency regulation capability parameters of the thermal power unit. The joint output module is used to construct a joint clearing calculation model for power frequency regulation based on state perception and the deep regulation state of thermal power plants. It takes the state perception vector of thermal power units and dynamic frequency regulation capability parameters as input, performs optimization calculation with the goal of minimizing total cost, and outputs the joint clearing result of power and frequency regulation capacity.

9. The power frequency regulation joint clearing and optimization system considering the deep regulation state of thermal power plants according to claim 8, characterized in that, The real-time acquisition of operating data from thermal power units includes: Basic data of the power grid, including overall load forecast, line power flow data, network topology and information on key metering points; Data for thermal power units include real-time active power output, reactive power output, terminal voltage, main steam pressure / temperature, coal feed rate, and boiler combustion stability indicators. The joint clearing calculation data includes market participants' electricity bids and frequency regulation capacity bids.

10. The power frequency regulation joint clearing and optimization system considering the deep regulation state of thermal power plants according to claim 8, characterized in that, The preprocessing of the runtime data to obtain a standardized dataset includes: Data cleaning: Check if the data is within a reasonable physical range; check if the rate of change between adjacent sampling points exceeds physical limits; check if the data values ​​remain unchanged for a long time; mark invalid data points as invalid and remove them; use linear interpolation to repair noisy or transiently missing data; trigger an alarm for persistently missing data. Data alignment: unify the timestamps of all data to the standard clock source of the scheduling center; unify the interpolation or aggregation of all time-series data to a common time network; Data fusion and association: Associating data from different sources but belonging to the same object to form a complete record.

11. The power frequency regulation joint clearing and optimization system considering the deep regulation state of thermal power plants according to claim 8, characterized in that, The method, based on a standardized dataset, quantifies the deep peak-shaving status of thermal power units in multiple dimensions, obtaining a thermal power unit status perception vector that includes status level, dynamic frequency regulation capability, and marginal cost information, including: Quantification of operational status: Quantified by percentage of active power output, defined as: in Percentage of effort contributed For the current active power output of the unit, This refers to the rated capacity of the unit; Quantification of frequency modulation capability: 1) Define the unit's stability score S: S≈1.0, the unit is very stable; S≈0.5, the unit's stability decreases and it is in a state of alert; S<0.3, the unit is unstable and it is in a state of deep peak shaving. 2) Establish the functional relationship of actual frequency regulation capability through historical data analysis or unit performance tests: in For actual adjustable capacity, This refers to the actual adjustable capacity. The actual output of unit i at time t. Assess the stability of the generating unit; Quantification of operating costs: Calculate the additional cost of power generation using the heat consumption characteristic curve of the unit; By statistically analyzing load cycles using the rainflow counting method and combining them with the material's fatigue life curve, unstable operating conditions are converted into equivalent operating hours, thus quantifying their lifespan loss cost.

12. The power frequency regulation joint clearing and optimization system considering the deep regulation state of thermal power plants according to claim 11, characterized in that, The magnitude differentiation in the quantization of the operational state dimension includes: Normal peak-shaving range: active power output percentage is between 50% and 100%, boiler combustion is stable, and the unit is in the designed high-efficiency range; Shallow-deep peak shaving: When the percentage of active power output is between 40% and 50%, oil injection is required for combustion, and efficiency begins to decline; Deep peak shaving: When the percentage of active power output is between 30% and 40%, the boiler combustion stability deteriorates and the auxiliary equipment is close to the minimum technical output. Extreme peak shaving: The percentage of active power output is less than 30%, the unit is operating in an extreme state, and the risk is high.

13. The power frequency regulation joint clearing and optimization system considering the deep regulation state of thermal power plants according to claim 8, characterized in that, The calculation of the dynamic frequency regulation capability parameters of the thermal power unit by combining the unit status perception vector and the unit's real-time operating data includes: Dynamic ramp rate refers to the actual safe rate of power change that can be achieved under real-time, continuously changing operating conditions. It is a function of time. Based on real-time data, the instantaneous rate is calculated. The actual power generation signal of the unit is acquired at high speed, and differential calculation is performed using a sliding time window, as shown below: in Let be the instantaneous ramp rate at time t. For the current moment, For the current moment The real-time active power output of the generator unit. For the current moment A short period of time before The active power generated by the generator unit, The time interval used for calculation; Response delay time refers to the time interval between the occurrence of the regulation command and the moment when the unit begins to generate a noticeable and measurable power response. It is measured by analyzing the power response curve of a frequency step disturbance: the unit is operated under a certain stable load, a standard frequency step signal is applied, and the change process of the unit's active power is recorded at high speed. Dynamic adjustment accuracy refers to the degree of consistency between the actual output power of the unit and the target commanded power during the dynamic adjustment process. The calculation formula is as follows: Where N is the total number of sampling points. Let i be the actual active power generated by the unit at the i-th sampling time. The target active power command issued by the dispatch center at the i-th sampling time.

14. The power frequency regulation joint clearing and optimization system considering the deep regulation state of thermal power plants according to claim 8, characterized in that, The aforementioned joint clearing calculation model for power frequency regulation, which introduces state-aware construction and thermal power deep-state regulation, takes the thermal power unit state-aware vector and dynamic frequency regulation capability parameters as inputs, performs optimization calculations with the goal of minimizing total cost, and outputs the joint clearing result of power and frequency regulation capacity, including: The goal of the joint clearing model is to minimize the total cost of the entire system, including the cost of electricity production and the cost of frequency regulation service procurement, while satisfying all security constraints, as follows: in: T: Total number of time periods in the scheduling cycle; N: Total number of generating units participating in the market; : The planned power output of unit i during time period t; , : The up-frequency regulation capacity and down-frequency regulation capacity of unit i that are cleared in time period t; Unit i at output is The marginal cost of electricity at that time; : The marginal cost of frequency regulation service for unit i at time t; Model constraints: (1) Power balance constraint in The power output plan for unit i during time period t. Forecast the total system load; (2) Frequency modulation capacity requirement constraints; ， in , For unit i, the up-frequency regulation capacity and down-frequency regulation capacity are cleared during time period t; , The total up-modulation and down-modulation capacities required by the system; (3) Dynamic frequency modulation capability constraints in , For unit i, the up-frequency regulation capacity and down-frequency regulation capacity are cleared during time period t; , The actual frequency regulation capacity and down-frequency regulation capacity of the unit are generated by quantifying the deep peak shaving state of the thermal power unit. (4) Gradient constraint in and These are the unit's maximum uphill and downhill ramp rates; and The unit outputs power at time t and time t-1; After the solution is completed, the model outputs the cleared electricity volume, nodal marginal price, frequency regulation capacity and frequency regulation capacity price for each unit in each time period, and publishes the clearing results.

15. A computer 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, it implements the steps of the power frequency regulation joint clearing optimization method as described in any one of claims 1 to 7, which takes into account the deep regulation state of thermal power plants.

16. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the power frequency regulation joint clearing optimization method as described in any one of claims 1 to 7, which takes into account the deep regulation state of thermal power plants.

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