A deep peak shaving method for thermal power generating units

Abstract

The present application relates to the technical field of thermal power generation, and discloses a deep peak regulation method for thermal power generating units. The present application collects the operation state data of each generating unit in a thermal power plant in real time, determines the peak regulation depth and average load change rate to be allocated in combination with the total load instruction issued by power grid dispatching, predicts the life consumption of each generating unit under different load allocation schemes based on the collected data, constructs a multi-objective optimization model considering the fuel cost of each generating unit and the equipment life consumption cost caused by deep peak regulation, and then solves to obtain the optimal load allocation scheme of each unit. The hidden cost caused by the equipment life consumption is taken into account as the overall operation cost of the thermal power plant, which improves the accuracy and economy of subsequent load allocation.

Description

Technical Field

[0001] This invention relates to the field of thermal power generation technology, specifically a deep peak shaving method for thermal power generating units. Background Technology

[0002] As the proportion of fluctuating renewable energy sources such as wind power and solar power in the power system continues to increase rapidly, the grid's demand for flexible power supply regulation is becoming increasingly urgent. Against this backdrop, the functional positioning of traditional coal-fired power generating units is undergoing a fundamental transformation, shifting from providing stable base load power to undertaking the important tasks of ancillary services such as peak shaving and frequency regulation, especially requiring frequent deep peak shaving.

[0003] However, existing technologies face significant technical bottlenecks and systemic defects when dealing with deep peak shaving. First, at the operational optimization level, current power plant-level load allocation methods have a singular optimization objective: minimizing total coal consumption at a specific load point. This approach completely ignores the cumulative lifespan losses caused to critical equipment by drastic load changes during deep peak shaving. Second, at the lifespan loss management level, existing technologies lack precise, online, and quantifiable assessment methods. Equipment lifespan loss is an implicit, long-term, and non-linear process. Traditional methods mainly rely on periodic maintenance and conservative operating procedures to mitigate risks, failing to quantify specific damage in real time for each peak shaving decision. This results in operators lacking a scientific basis for decision-making when balancing peak shaving costs and equipment safety. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a deep peak-shaving method for thermal power generating units. This method can predict the lifespan loss of each generating unit under different load allocation schemes by collecting real-time operating status data of each generating unit in a thermal power plant. It also considers both equipment lifespan loss costs and fuel costs to obtain the optimal load allocation scheme. This method quantifies the implicit impact of equipment lifespan loss on the generating units and improves the accuracy and economy of load allocation schemes, thus solving the aforementioned technical problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for deep peak shaving of thermal power generating units, comprising the following steps:

[0006] S1: Real-time acquisition of operating status data of each generator unit in the thermal power plant;

[0007] S2: Receive the total load instruction for the entire plant issued by the power grid dispatching authority, and determine the peak shaving depth and average load change rate to be allocated;

[0008] S3: Based on the data collected by S1, predict the life loss of each generator set under different load distribution schemes;

[0009] S4: Construct a multi-objective optimization model that considers lifetime loss;

[0010] S5: Solve the multi-objective optimization model to obtain the optimal load allocation scheme for each unit.

[0011] As a preferred embodiment of the present invention, the expression for the operating status data of each generator unit in the S1 thermal power plant is as follows: ,in, , and These represent the number of thermal power plants. The generator set, the first The generator set and the first The operating status data of each generator set includes the real-time temperature of the boiler inner wall and the real-time speed of the turbine rotor.

[0012] As a preferred embodiment of the present invention, the expression for the peak-shaving depth of S2 is as follows: in, This indicates the total peak-shaving depth of a thermal power plant; This indicates the current total load of the thermal power plant; Indicates the target load required for scheduling;

[0013] The expression for the average load change rate of S2 is as follows: in, Indicates the average rate of change of load; Indicates the time of change in scheduling requirements; It represents the absolute value.

[0014] As a preferred embodiment of the present invention, the expression for the S3 lifetime loss is as follows: in, Indicates the first The life damage coefficient of a generator set during a single peak shaving operation; Indicates the first Boiler damage coefficient for each generator set; Indicates the first Turbine damage coefficient of each generator set; Indicates the weighting coefficient; Indicates the first Distributed load of each generator set; Indicates the first Average power generation of each generator set; Indicates the time of change in scheduling requirements; It represents the absolute value.

[0015] As a preferred technical solution of the present invention, the boiler damage coefficient and turbine damage coefficient The relevant expressions are as follows: in, Indicates the first Boiler damage coefficient for each generator set; Indicates the boiler's first The number of cycles required for deep peak shaving of each key component; The boiler's number The number of safe cycle times for each key component; The boiler's number The number of cycles completed for each key component; Indicates the boiler's first The weighting coefficients corresponding to each key component ; Indicates the boiler Summation calculations are performed on each key component; Indicates the first Turbine damage coefficient of each generator set; Indicates the first of the steam turbine The number of cycles required for deep peak shaving of each key component; Indicates the first of the steam turbine The number of safe cycle times for each key component; Indicates the first of the steam turbine The number of cycles completed for each key component; Indicates the first of the steam turbine The weighting coefficients corresponding to each key component ; Indicates the steam turbine The summation operation is performed on each key component.

[0016] As a preferred embodiment of the present invention, the overall objective function of the S4 multi-objective optimization model is as follows: in, Indicates the total cost; This indicates taking the minimum value; Indicates the first Fuel cost of a generator set; Indicates the first The cost of equipment wear and tear on a generator set due to equipment lifespan. This represents the summation operation; This indicates the total number of generating units in a thermal power plant; and These represent the weighting coefficients, .

[0017] As a preferred embodiment of the present invention, the objective conditions of the multi-objective optimization model are as follows: in, This represents minimizing the fuel cost for each generator set; This represents minimizing the cost of each generator set due to equipment lifespan depletion.

[0018] The constraints of the multi-objective optimization model are as follows: in, Indicates the first Distributed load of each generator set; Indicates the first Minimum safe load for each generator set; Indicates the first The maximum safe load of each generator set; This indicates the total peak-shaving depth of a thermal power plant; Indicating the opinion of thermal power plants Perform summation on each generator set; Indicates the time of change in scheduling requirements; Indicates the average rate of change of load; Indicates the first The maximum safe load change rate of each generator set; Indicates the first Real-time temperature of the boiler inner wall of each generator set; Indicates the first The highest safe temperature of the boiler inner wall of each generator set; Indicates the first Real-time rotational speed of the turbine rotor of each generator set; Indicates the first The maximum safe speed of the turbine rotor of each generator set.

[0019] As a preferred technical solution of the present invention, the first Fuel cost of a generator set The expression is as follows: in, Indicates the first Distributed load of each generator set; This indicates the fuel consumption per unit load. This indicates the unit price of fuel consumption.

[0020] As a preferred technical solution of the present invention, the first Costs incurred by individual generator sets due to equipment lifespan deterioration The expression is as follows: in, Indicates the first The life damage coefficient of a generator set during a single peak shaving operation; Indicates the first The original value of the assets of each generator set.

[0021] As a preferred embodiment of the present invention, the expression for the S5 optimal load allocation scheme is as follows: ,in, , and These represent the number of thermal power plants. The generator set, the first The generator set and the first Distribute the charge to each generator set.

[0022] Compared with the prior art, the present invention provides a deep peak shaving method for thermal power generating units, which has the following beneficial effects:

[0023] This invention collects real-time operating status data of each generator unit in a thermal power plant, combines this data with the total load command issued by the power grid dispatch center to determine the required peak shaving depth and average load change rate, predicts the lifespan loss of each generator unit under different load allocation schemes based on the collected data, and constructs a multi-objective optimization model that considers the fuel cost of each generator unit and the equipment lifespan loss cost caused by deep peak shaving. The optimal load allocation scheme for each unit is then obtained by solving the model. The implicit cost of equipment lifespan loss is taken into account as the overall operating cost of the thermal power plant, thus improving the accuracy and economy of subsequent load allocation. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0025] 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.

[0026] Please see Figure 1A method for deep peak shaving of thermal power generating units includes the following steps:

[0027] S1: Real-time acquisition of operating status data of each generator unit in the thermal power plant, its expression is: ,in, , and These represent the number of thermal power plants. The generator set, the first The generator set and the first The operating status data of each generator set includes the real-time temperature of the boiler inner wall and the real-time speed of the turbine rotor.

[0028] S2: Receives the total plant load instruction from the power grid dispatch center, determines the required peak-shaving depth and average load change rate, and the expression for the peak-shaving depth is as follows: in, This indicates the total peak-shaving depth of a thermal power plant; This indicates the current total load of the thermal power plant; Indicates the target load required for scheduling;

[0029] The expression for the average load change rate is as follows: in, Indicates the average rate of change of load; Indicates the time of change in scheduling requirements; Represents absolute value;

[0030] S3: Based on the data collected in S1, predict the lifespan loss of each generator unit under different load distribution schemes. The expression is as follows: in, Indicates the first The life damage coefficient of a generator set during a single peak shaving operation; Indicates the first Boiler damage coefficient for each generator set; Indicates the first Turbine damage coefficient of each generator set; Indicates the weighting coefficient; Indicates the first Distributed load of each generator set; Indicates the first Average power generation of each generator set; Indicates the time of change in scheduling requirements; Represents absolute value; the proportional term in the formula This quantifies the degree of load regulation by the generating units during this peak-shaving task. The larger the value, the greater the degree of load regulation by the generating unit. This reflects the allocated peak load and the average power output of the generating units during the peak load period. The absolute value of the deviation between the electrical quantities generated during operation; this formula indicates the lifespan damage coefficient. Due to the inherent damage characteristics of the equipment and This, along with the severity of the load change during this peak shaving, provides a crucial quantitative basis for converting lifetime loss into loss costs and incorporating them into multi-objective optimization.

[0031] Boiler damage coefficient and turbine damage coefficient The relevant expressions are as follows: in, Indicates the first Boiler damage coefficient for each generator set; Indicates the boiler's first The number of cycles required for deep peak shaving of each key component; Indicates the boiler's first The number of safe cycle times for each key component; Indicates the boiler's first The number of cycles completed for each key component; Indicates the boiler's first The weighting coefficients for each key component ; Indicates the boiler Summation calculations are performed on each key component; Indicates the first Turbine damage coefficient of each generator set; Indicates the first of the steam turbine The number of cycles required for deep peak shaving of each key component; Indicates the first of the steam turbine The number of safe cycle times for each key component; Indicates the first of the steam turbine The number of cycles completed for each key component; Indicates the first of the steam turbine The weighting coefficients corresponding to each key component ; Indicates the steam turbine Summation calculations are performed on each key component;

[0032] The cycle count is used to assess the number of peak shaving cycles for critical components. One peak shaving cycle is equivalent to one cycle. Critical components of a boiler include the boiler drum and boiler water-cooled walls, while critical components of a steam turbine include the turbine rotor and turbine cylinder. If a thermal power plant receives a dispatch order to reduce its capacity from 600MW to 240MW within 2 hours, maintain that capacity for 2 hours, and then increase it to 480MW within 1 hour, the turbine rotor will experience two temperature changes, which is also equivalent to two peak shaving cycles. For both the boiler and the turbine, each peak shaving cycle causes irreversible damage to the unit components. Furthermore, each large load change will subject these components to strong thermal stress shocks. Over time, micro-cracks will appear on the turbine rotor, and the pipes in the boiler will also rupture due to the strong temperature difference. Moreover, the boiler and the turbine are both critical equipment of the unit, and their lifespan directly affects the safety and economy of the thermal power plant.

[0033] S4: Construct a multi-objective optimization model that considers lifetime loss. Its overall objective function is as follows: in, Indicates the total cost; This indicates taking the minimum value; Indicates the first Fuel cost of a generator set; Indicates the first The cost of equipment wear and tear on a generator set due to equipment lifespan. This represents the summation operation; This indicates the total number of generating units in a thermal power plant; and These represent the weighting coefficients, ;

[0034] The objective conditions for the multi-objective optimization model are as follows: in, This represents minimizing the fuel cost for each generator set; This means minimizing the cost of each generator set due to equipment lifespan loss. The multi-objective optimization model aims to minimize two key costs simultaneously: the fuel cost of each generator set and the cost of equipment lifespan loss due to deep peak shaving. These two objective functions together form the basis of optimization decisions, reflecting that while seeking operational economy, the long-term asset value of equipment lifespan must be included in the core consideration. This changes the limitation of only taking the minimum coal consumption as the single objective, marking a fundamental shift from short-term operational optimization to the optimization of comprehensive cost throughout the entire life cycle.

[0035] The constraints of the multi-objective optimization model are as follows: in, Indicates the first Distributed load of each generator set; Indicates the first Minimum safe load for each generator set; Indicates the first The maximum safe load of each generator set; This indicates the total peak-shaving depth of thermal power plants, which is the total load that needs to be reduced; Indicating the opinion of thermal power plants Perform summation on each generator set; Indicates the time of change in scheduling requirements; Indicates the average rate of change of load; Indicates the first The maximum safe load change rate of each generator set; Indicates the first Real-time temperature of the boiler inner wall of each generator set; Indicates the first The highest safe temperature of the boiler inner wall of each generator set; Indicates the first Real-time rotational speed of the turbine rotor of each generator set; Indicates the first The maximum safe speed of the turbine rotor of each generator set;

[0036] The multi-objective optimization model defines the following four sets of core constraints to ensure the technical feasibility and operational safety of the load allocation scheme: First, the load allocation for each unit. It must be at its minimum safe load. With maximum safe load Within the determined output range; secondly, the sum of the loads allocated to all units must be precisely equal to the total load reduction required by the power grid dispatch for the entire plant. To achieve power balance; third, the load change rate of each unit must be limited to the minimum rate required by dispatch. With respect to the unit's own maximum safe rate of change Fourth, it introduces safe operating boundaries for critical equipment, requiring real-time temperature control of the boiler inner wall to ensure a smooth response; and the real-time speed of the steam turbine rotor Each must not exceed its set maximum safety limit. and These constraints together constitute the search space for the optimization scheme, ensuring that the optimal load allocation scheme can satisfy external dispatch instructions while strictly guaranteeing the operational safety of the main equipment inside the power plant.

[0037] No. Fuel cost of a generator set The expression is as follows: in, Indicates the first Distributed load of each generator set; This indicates the fuel consumption per unit load. This indicates the unit price of fuel consumption;

[0038] No. Costs incurred by individual generator sets due to equipment lifespan deterioration The expression is as follows: in, Indicates the first The life damage coefficient of a generator set during a single peak shaving operation; Indicates the first Original asset value of each generator set;

[0039] S5: Based on the genetic algorithm, the multi-objective optimization model is solved to obtain the optimal load allocation scheme for each unit, the expression of which is: ,in, , and These represent the number of thermal power plants. The generator set, the first The generator set and the first Distribute the charge to each generator set.

[0040] 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 method for deep peak shaving of thermal power generating units, characterized in that: Includes the following steps: S1: Real-time acquisition of operating status data of each generator unit in thermal power plants; S2: Receive the total load instruction for the entire plant issued by the power grid dispatching authority, and determine the peak shaving depth and average load change rate to be allocated; S3: Based on the data collected by S1, predict the life loss of each generator set under different load distribution schemes; S4: Construct a multi-objective optimization model that considers lifetime loss; S5: Solve the multi-objective optimization model to obtain the optimal load allocation scheme for each unit.

2. The method for deep peak shaving of thermal power generating units according to claim 1, characterized in that: The expression for the operating status data of each generator unit in the S1 thermal power plant is as follows: ,in, , and These represent the number of thermal power plants. The generator set, the first The generator set and the first The operating status data of each generator set includes the real-time temperature of the boiler inner wall and the real-time speed of the turbine rotor.

3. The deep peak-shaving method for thermal power generating units according to claim 1, characterized in that: The expression for the peak-shaving depth of S2 is as follows: in, This indicates the total peak-shaving depth of a thermal power plant; This indicates the current total load of the thermal power plant; Indicates the target load required for scheduling; The expression for the average load change rate of S2 is as follows: in, Indicates the average rate of change of load; Indicates the time of change in scheduling requirements; It represents the absolute value.

4. The method for deep peak shaving of thermal power generating units according to claim 1, characterized in that: The expression for the S3 lifetime loss is as follows: in, Indicates the first The life damage coefficient of a generator set during a single peak shaving operation; Indicates the first Boiler damage coefficient for each generator set; Indicates the first Turbine damage coefficient of each generator set; Indicates the weighting coefficient; Indicates the first Distributed load of each generator set; Indicates the first Average power generation of each generator set; Indicates the time of change in scheduling requirements; It represents the absolute value.

5. The deep peak-shaving method for thermal power generating units according to claim 4, characterized in that: Boiler damage coefficient and turbine damage coefficient The relevant expressions are as follows: in, Indicates the first Boiler damage coefficient for each generator set; Indicates the boiler's first The number of cycles required for deep peak shaving of each key component; Indicates the boiler's first The number of safe cycle times for each key component; Indicates the boiler's first The number of cycles completed for each key component; Indicates the boiler's first The weighting coefficients for each key component ; Indicates the boiler Summation calculations are performed on each key component; Indicates the first Turbine damage coefficient of each generator set; Indicates the first of the steam turbine The number of cycles required for deep peak shaving of each key component; Indicates the first of the steam turbine The number of safe cycle times for each key component; Indicates the first of the steam turbine The number of cycles completed for each key component; Indicates the first of the steam turbine The weighting coefficients for each key component ; Indicates the steam turbine The summation operation is performed on each key component.

6. The method for deep peak shaving of thermal power generating units according to claim 1, characterized in that: The overall objective function of the S4 multi-objective optimization model is as follows: in, Indicates the total cost; This indicates taking the minimum value; Indicates the first Fuel cost of a generator set; Indicates the first The cost of equipment wear and tear on a generator set due to equipment lifespan. This represents the summation operation; This indicates the total number of generating units in a thermal power plant; and These represent the weighting coefficients, .

7. The method for deep peak shaving of thermal power generating units according to claim 6, characterized in that: The objective conditions of the multi-objective optimization model are as follows: in, This represents minimizing the fuel cost for each generator set; This represents minimizing the cost of each generator set due to equipment lifespan depletion. The constraints of the multi-objective optimization model are as follows: in, Indicates the first Distributed load of each generator set; Indicates the first Minimum safe load for each generator set; Indicates the first The maximum safe load of each generator set; This indicates the total peak-shaving depth of a thermal power plant; Indicating the opinion of thermal power plants Perform summation on each generator set; Indicates the time of change in scheduling requirements; Indicates the average rate of change of load; Indicates the first The maximum safe load change rate of each generator set; Indicates the first Real-time temperature of the boiler inner wall of each generator set; Indicates the first The highest safe temperature of the boiler inner wall of each generator set; Indicates the first Real-time rotational speed of the turbine rotor of each generator set; Indicates the first The maximum safe speed of the turbine rotor of each generator set.

8. The method for deep peak shaving of a thermal power generating unit according to claim 6, characterized in that: The first Fuel cost of a generator set The expression is as follows: in, Indicates the first Distributed load of each generator set; This indicates the fuel consumption per unit load. This indicates the unit price of fuel consumption.

9. A deep peak-shaving method for thermal power generating units according to claim 6, characterized in that: The first Costs incurred by individual generator sets due to equipment lifespan deterioration The expression is as follows: in, Indicates the first The life damage coefficient of a generator set during a single peak shaving operation; Indicates the first The original value of the assets of each generator set.

10. The method for deep peak shaving of a thermal power generating unit according to claim 1, characterized in that: The expression for the S5 optimal load allocation scheme is: ,in, , and These represent the number of thermal power plants. The generator set, the first The generator set and the first Distribute the charge to each generator set.

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