A peak regulation optimization control system for a thermal power unit

Abstract

This invention relates to the field of automated control technology for thermal power generation, and discloses a peak-shaving optimization control system for thermal power units. The system includes a data acquisition and processing module, a multi-objective optimization module, and a peak-shaving execution module. This invention connects to the power plant's distributed control system and plant-level monitoring information system to collect real-time unit operation data and user demand data. The collected data is preprocessed, and the peak electricity demand for the day is predicted based on the user demand data. The theoretical power generation is calculated using an objective function model based on the predicted peak demand and unit operation data. Peak-shaving is then performed based on the theoretical power generation; if the theoretical power generation exceeds the current actual power generation, peak-shaving is executed. The objective function model comprehensively analyzes multiple aspects, including economic cost, power generation revenue, and user demand, achieving coordinated optimization of multiple objectives during peak-shaving and improving the intelligence and economy of peak-shaving control.

Description

Technical Field

[0001] This invention relates to the field of automated control technology for thermal power generation, specifically to a peak-shaving optimization control system for thermal power units. Background Technology

[0002] With the increasing proportion of renewable energy generation, the power fluctuation of the power grid has significantly increased, placing higher demands on the peak-shaving capabilities of traditional thermal power units. Thermal power units need to adjust more frequently and quickly across a wide load range to balance the supply and demand of the power grid.

[0003] Traditional peak-shaving control methods for thermal power units are mostly based on operator experience or simple load command tracking strategies. Their limitations are mainly as follows: First, the control objective is singular, usually focusing only on rapid response to grid commands or local optimization of operational economy, lacking coordinated optimization of multiple objectives such as economic cost, power generation revenue, and strict satisfaction of user needs throughout the peak-shaving cycle; Second, the peak-shaving optimization model is not closely integrated with engineering practice. Existing methods often do not fully consider the physical boundaries and operational safety constraints of the unit when modeling, such as the temperature limit of the boiler and the power generation limit of the unit, which sometimes causes the optimization results to deviate from the actual safe execution range, or sacrifice equipment life and grid reliability in pursuit of economy; Third, the safety risks are high. Frequent and drastic load changes exacerbate the thermal stress fatigue of main equipment such as boilers and turbines, affecting equipment life and may even cause parameter over-limits, endangering operational safety. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a peak-shaving optimization control system for thermal power units. This system has the advantages of real-time acquisition and preprocessing of unit operation data and user demand data to predict the peak electricity demand of users during the day, calculating the theoretical power generation through an objective function model, and then determining whether to perform peak shaving. This system achieves coordinated optimization of economic costs, power generation revenue, and meeting user demand during the peak shaving process, thus solving the aforementioned technical problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a peak-shaving optimization control system for thermal power units, comprising a data acquisition and processing module, a multi-objective optimization module, and a peak-shaving execution module;

[0006] The data acquisition and processing module is connected to the power plant's distributed control system and plant-level monitoring information system, and collects unit operation data and user demand data in real time. It also performs preprocessing on the collected data, including filtering, verification and normalization.

[0007] The multi-objective optimization module includes a prediction unit and an optimization unit. The prediction unit predicts the electricity demand of users during peak hours of the day based on user demand data. The optimization unit calculates the theoretical power generation based on the predicted electricity demand of users during peak hours of the day and the unit operation data.

[0008] The peak shaving execution module determines whether to perform peak shaving based on the theoretical power generation.

[0009] As a preferred embodiment of the present invention, the expression for the unit operating data is: ,in, Indicates the thermal power unit number Daily operational data Indicates the thermal power unit number The daily operating data for thermal power units includes the power generation for each time period of the day, the real-time power output of the thermal power unit, and the real-time temperature of the boiler inner wall.

[0010] As a preferred embodiment of the present invention, the expression for the user demand data is: , Indicates user number Daily electricity demand Indicates user number Daily electricity demand includes the electricity demand for each time period of the day.

[0011] As a preferred embodiment of the present invention, the prediction unit predicts the electricity demand of users during peak hours on a given day based on user demand data, including the following steps:

[0012] Step A1: Use the peak electricity demand period of the previous day as the peak period for the current day. ;

[0013] Step A2: Predict the electricity demand during the peak hours of the day, using the following expression: in, This indicates the predicted electricity demand from users during peak hours of the day. Indicates that the user on that day was Electricity demand for each time period; Indicates that the user on that day was The period before Electricity demand during a given time period; This indicates the time period from the peak period. ,in, Indicates peak hours. Indicates the current time period.

[0014] As a preferred embodiment of the present invention, the optimization unit calculates the theoretical power generation capacity based on the predicted electricity demand during the peak hours of the day and the unit operation data, including the following steps:

[0015] Step B1: Input the predicted electricity demand during peak hours of the day and the unit operation data into the objective function model. The overall objective function of the objective function model is as follows: in, Represent the overall objective function; This indicates taking the maximum value; Indicates the first Electricity revenue for each time period; Indicates the first Coal consumption cost for each time period; This indicates the predicted electricity demand from users during peak hours of the day. Indicates the first Planned power generation for each time period; The power generation revenue weighting coefficient, This is the coal consumption cost weighting coefficient. For demand tracking weighting coefficients, ;

[0016] Step B2: Solve using a genetic algorithm to obtain the theoretical power generation. .

[0017] As a preferred embodiment of the present invention, the objective condition of the objective function model is as follows: in, Maximize the first Electricity revenue for each time period; Minimize the first Coal consumption cost for each time period; Indicates the first Planned power generation for each time period; This indicates the predicted electricity demand from users during peak hours of the day. This indicates taking the minimum value;

[0018] The constraints of the objective function model are as follows: in, This indicates the real-time power generation capacity of the thermal power unit; This indicates the minimum generating capacity of a thermal power unit; This indicates the maximum generating capacity of the thermal power unit; This indicates the real-time temperature of the boiler's inner wall; This indicates the maximum boiler inner wall temperature; Indicates the first Planned power generation for each time period; This indicates the predicted electricity demand from users during peak hours on that day.

[0019] As a preferred technical solution of the present invention, the first Electricity revenue per period The expression is as follows: in, Indicates the first Planned power generation for each time period; Indicates the first Electricity price per unit time period.

[0020] As a preferred technical solution of the present invention, the first Planned power generation for each time period The expression is as follows: in, This indicates the real-time power generation capacity of the thermal power unit; Indicates the length of the time period.

[0021] As a preferred technical solution of the present invention, the first Coal consumption cost per period The expression is as follows: in, Indicates the first Coal consumption in each time period; This indicates the unit price of coal.

[0022] As a preferred embodiment of the present invention, the peak shaving execution module determines whether to perform peak shaving based on theoretical power generation as follows: when theoretical power generation... >Actual power generation Theoretical power generation Peak shaving is not implemented when the actual power generation is less than or equal to the actual power generation.

[0023] Compared with the prior art, the present invention provides a peak-shaving optimization control system for thermal power units, which has the following beneficial effects:

[0024] This invention connects with the power plant's distributed control system and plant-level monitoring information system to collect real-time unit operation data and user demand data. The collected data is preprocessed, and the peak electricity demand of users during the day is predicted based on the user demand data. The theoretical power generation is calculated through an objective function model based on the predicted peak electricity demand and unit operation data. The determination of whether to perform peak shaving is based on the theoretical power generation. If the theoretical power generation exceeds the current actual power generation, peak shaving is performed. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the system framework of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Please see Figure 1 A peak-shaving optimization control system for thermal power units includes a data acquisition and processing module, a multi-objective optimization module, and a peak-shaving execution module.

[0028] The data acquisition and processing module is connected to the power plant's distributed control system and plant-level monitoring information system to collect unit operation data and user demand data in real time, and to preprocess the collected data, including filtering, verification and normalization.

[0029] The expression for the unit's operating data is: ,in, Indicates the thermal power unit number Daily operational data Indicates the thermal power unit number The daily operating data of the thermal power unit includes the power generation of each time period of the day, the real-time power generation of the thermal power unit, and the real-time temperature of the boiler inner wall.

[0030] The expression for user demand data is: , Indicates user number Daily electricity demand Indicates user number Daily electricity demand: The user's daily electricity demand includes the electricity demand for each time period of the day;

[0031] The data acquisition and processing module preprocesses the acquired raw data to ensure its reliability, consistency, and usability. This preprocessing mainly includes the following three steps: First, filtering removes high-frequency noise and abnormal fluctuations from the signal using digital filters, retaining only the effective trend components reflecting the unit's true operating status. Second, verification performs threshold verification, logical consistency verification, and timeliness verification on the data, identifying and eliminating invalid data and outliers that exceed reasonable physical ranges, violate process logic, or are severely lagging. Finally, normalization maps raw data from different sources, with different dimensions and magnitudes, to a standard numerical range, making it suitable for the input requirements of subsequent modules. Through this preprocessing process, high-quality, interpretable, and uniformly formatted input data can be provided to the downstream multi-objective optimization module, thereby ensuring the accuracy and stability of the optimization control.

[0032] The multi-objective optimization module includes a prediction unit and an optimization unit. The prediction unit predicts the electricity demand of users during peak hours of the day based on user demand data, and the optimization unit calculates the theoretical power generation based on the predicted electricity demand of users during peak hours of the day and the unit operation data.

[0033] The forecasting unit predicts the peak electricity demand of users during the day based on user demand data, including the following steps:

[0034] Step A1: Use the peak electricity demand period of the previous day as the peak period for the current day. ;

[0035] Step A2: Predict the electricity demand during the peak hours of the day, using the following expression: in, This indicates the predicted electricity demand from users during peak hours of the day. Indicates that the user on that day was Electricity demand for each time period; Indicates that the user on that day was The period before Electricity demand during a given time period; This indicates the time period from the peak period. ,in, Indicates peak hours. Indicates the current time period;

[0036] The optimization unit calculates the theoretical power generation based on the predicted electricity demand during peak user periods and unit operation data for the day, including the following steps:

[0037] Step B1: Input the predicted electricity demand during peak hours of the day and the unit operation data into the objective function model. The overall objective function of the objective function model is as follows: in, Represent the overall objective function; This indicates taking the maximum value; Indicates the first Electricity revenue for each time period; Indicates the first Coal consumption cost for each time period; This indicates the predicted electricity demand from users during peak hours of the day. Indicates the first Planned power generation for each time period; The power generation revenue weighting coefficient, This is the coal consumption cost weighting coefficient. For demand tracking weighting coefficients, ;

[0038] Step B2: Solve using a genetic algorithm to obtain the theoretical power generation. ;

[0039] The objective conditions for the objective function model are as follows: in, Maximize the first Electricity revenue for each time period; Minimize the first Coal consumption cost for each time period; Indicates the first Planned power generation for each time period; This indicates the predicted electricity demand from users during peak hours of the day. This objective function model aims to achieve the synergistic optimization of the economy, profitability, and grid demand tracking capability of thermal power units during peak shaving through mathematical optimization methods. The model minimizes the total cost of peak shaving and comprises three core components: first, a coal consumption cost term quantifies the fuel consumption cost during power generation based on current fuel prices; second, a power generation revenue term combines time-of-use pricing to calculate the economic benefits generated by the unit's power generation; and finally, a user demand tracking penalty term, in a quadratic form, constrains the deviation between the unit's power generation and user electricity demand, ensuring the accuracy of peak shaving command responses. By adjusting the weighting coefficients α (coal consumption cost), β (power generation revenue), and γ (demand tracking), this objective function model can flexibly balance multiple objectives such as economy, profitability, and grid adaptability according to actual operating scenarios, thereby outputting the globally optimal power generation and providing a scientific, accurate, and economical decision-making basis for thermal power units to participate in grid peak shaving.

[0040] The constraints of the objective function model are as follows: in, This indicates the real-time power generation capacity of the thermal power unit; This indicates the minimum generating capacity of a thermal power unit; This indicates the maximum generating capacity of the thermal power unit; This indicates the real-time temperature of the boiler's inner wall; This indicates the maximum boiler inner wall temperature; Indicates the first Planned power generation for each time period; This indicates the predicted electricity demand from users during peak hours of the day.

[0041] To ensure the engineering feasibility and operational safety of the output results of the objective function model, the objective function model must satisfy the following key constraints during the solution process: real-time power generation of the unit. It must be strictly within the minimum power generation capacity allowed by its technology. With maximum power generation Within the defined range, to ensure the unit operates within a safe and stable range; real-time temperature of the boiler inner wall. It must not exceed the maximum limit that its materials and structure can withstand. This is the core constraint for preventing equipment overheating damage and ensuring long-term operational safety; furthermore, in the... The planned power generation of the unit within a certain period It should not be lower than the predicted maximum electricity demand of users during the peak hours of the day. This constraint aims to ensure that the optimized power generation plan can fully meet the load demand of the power grid during peak hours, thereby ensuring the power supply reliability and stability of the power grid and ensuring that the generated power generation plan is economical, safe and reliable.

[0042] No. Electricity revenue per period The expression is as follows: in, Indicates the first Planned power generation for each time period; Indicates the first Electricity price per unit time period;

[0043] No. Planned power generation for each time period The expression is as follows: in, This indicates the real-time power generation capacity of the thermal power unit; Indicates the length of the time period;

[0044] No. Coal consumption cost per period The expression is as follows: in, Indicates the first Coal consumption in each time period; This indicates the unit price of coal;

[0045] The peak shaving execution module determines whether to perform peak shaving based on theoretical power generation. Specifically, when the theoretical power generation... >Actual power generation Theoretical power generation Peak shaving is not implemented when the actual power generation is less than or equal to the actual power generation.

[0046] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A peak-shaving optimization control system for thermal power units, characterized in that: It includes a data acquisition and processing module, a multi-objective optimization module, and a peak-shaving execution module; The data acquisition and processing module is connected to the power plant's distributed control system and plant-level monitoring information system, and collects unit operation data and user demand data in real time. It also performs preprocessing on the collected data, including filtering, verification and normalization. The multi-objective optimization module includes a prediction unit and an optimization unit. The prediction unit predicts the electricity demand of users during peak hours of the day based on user demand data. The optimization unit calculates the theoretical power generation based on the predicted electricity demand of users during peak hours of the day and the unit operation data. The peak shaving execution module determines whether to perform peak shaving based on the theoretical power generation.

2. The peak-shaving optimization control system for thermal power units according to claim 1, characterized in that: The expression for the unit operating data is: ,in, Indicates the thermal power unit number Daily operational data Indicates the thermal power unit number The daily operating data for thermal power units includes the power generation for each time period of the day, the real-time power output of the thermal power unit, and the real-time temperature of the boiler inner wall.

3. The peak-shaving optimization control system for thermal power units according to claim 1, characterized in that: The expression for the user demand data is: , Indicates user number Daily electricity demand Indicates user number Daily electricity demand includes the electricity demand for each time period of the day.

4. The peak-shaving optimization control system for thermal power units according to claim 1, characterized in that: The prediction unit predicts the peak electricity demand of users during the day based on user demand data, including the following steps: Step A1: Use the peak electricity demand period of the previous day as the peak period for the current day. ; Step A2: Predict the electricity demand during the peak hours of the day, using the following expression: in, This indicates the predicted electricity demand from users during peak hours of the day. Indicates that the user on that day was Electricity demand for each time period; Indicates that the user on that day was The period before Electricity demand during a given time period; This indicates the time period from the peak period. ,in, Indicates peak hours. Indicates the current time period.

5. The peak-shaving optimization control system for thermal power units according to claim 1, characterized in that: The optimization unit calculates the theoretical power generation based on the predicted electricity demand during peak user periods and the unit's operating data, including the following steps: Step B1: Input the predicted electricity demand during peak hours of the day and the unit operation data into the objective function model. The overall objective function of the objective function model is as follows: in, Represent the overall objective function; This indicates taking the maximum value; Indicates the first Electricity revenue for each time period; Indicates the first Coal consumption cost for each time period; This indicates the predicted electricity demand from users during peak hours of the day. Indicates the first Planned power generation for each time period; The power generation revenue weighting coefficient, This is the coal consumption cost weighting coefficient. For demand tracking weighting coefficients, ; Step B2: Solve using a genetic algorithm to obtain the theoretical power generation. .

6. The peak-shaving optimization control system for thermal power units according to claim 5, characterized in that: The objective conditions of the objective function model are as follows: in, Maximize the first Electricity revenue for each time period; Minimize the first Coal consumption cost for each time period; Indicates the first Planned power generation for each time period; This indicates the predicted electricity demand from users during peak hours of the day. This indicates taking the minimum value; The constraints of the objective function model are as follows: in, This indicates the real-time power generation capacity of the thermal power unit; This indicates the minimum generating capacity of a thermal power unit; This indicates the maximum generating capacity of the thermal power unit; This indicates the real-time temperature of the boiler's inner wall; This indicates the maximum boiler inner wall temperature; Indicates the first Planned power generation for each time period; This indicates the predicted electricity demand from users during peak hours on that day.

7. The peak-shaving optimization control system for thermal power units according to claim 5, characterized in that: The first Electricity revenue per period The expression is as follows: in, Indicates the first Planned power generation for each time period; Indicates the first Electricity price per unit time period.

8. The peak-shaving optimization control system for thermal power units according to claim 5, characterized in that: The first Planned power generation for each time period The expression is as follows: in, This indicates the real-time power generation capacity of the thermal power unit; Indicates the length of the time period.

9. A peak-shaving optimization control system for thermal power units according to claim 5, characterized in that: The first Coal consumption cost for each time period The expression is as follows: in, Indicates the first Coal consumption in each time period; This indicates the unit price of coal.

10. A peak-shaving optimization control system for thermal power units according to claim 1, characterized in that: The peak shaving execution module determines whether to perform peak shaving based on theoretical power generation as follows: when the theoretical power generation... >Actual power generation Theoretical power generation Peak shaving is not implemented when the actual power generation is less than or equal to the actual power generation.

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