Source-network collaborative thermoelectric load optimization and unit start-stop auxiliary control method and system

Abstract

The invention discloses a source-network collaborative thermoelectric load optimization and unit start-stop auxiliary control method and system. The method comprises the following steps: establishing a personalized thermal performance calculation model based on unit performance test data; an equipment aging dynamic correction coefficient is introduced for dynamic correction; establishing a production profit model with profit maximization as a target, wherein the total cost of the production profit model comprises fuel cost, water production cost, start-stop cost and maintenance apportioned cost; and solving an optimal unit start-stop and load distribution control scheme by using an optimization algorithm on the basis of the corrected model by taking predicted thermoelectric load and safety parameters as constraints. Compared with the prior art, the method has the advantages that the personalized thermodynamic calculation model is established by introducing the equipment aging dynamic correction coefficient, the peak regulation recessive cost is comprehensively calculated, unit start and stop and load distribution are collaboratively optimized, and global economic benefit maximization of the cogeneration unit in a deep peak regulation scene is realized.

Description

Technical Field

[0001] This invention relates to the field of thermal power plant operation optimization and automation control technology, and in particular to a source-grid coordinated thermal power load optimization and unit start-up and shutdown auxiliary control method and system. Background Technology

[0002] Against the backdrop of the continued deepening of power market reform and the gradual implementation of the deep peak-shaving market mechanism, the profit model and profitability of combined heat and power (CHP) units are facing structural reshaping.

[0003] Firstly, after the deep peak shaving market mechanism is in operation, the units can participate in deep peak shaving ancillary services by reducing the load to obtain compensation fees. However, the reduction of the load will also directly cause the unit efficiency to decline, and the heating capacity will also decrease. The usual approach is to use a general thermal calculation model to calculate the thermal characteristics of the unit. However, if the unit's performance under different loads is directly calculated using a general model, it will often ignore the characteristics of the equipment itself and the differences caused by the aging of the equipment after a period of operation.

[0004] In addition, the deep peak shaving mechanism of the power grid can bring new revenue space to the generating units: the generating units can obtain compensation by actively reducing load to provide peak shaving services or reduce peak shaving sharing costs; however, load reduction will also lead to a decrease in unit efficiency and a weakening of heating capacity. If the peak shaving demand is frequent or lasts for a long time, it may also trigger the start-up and shutdown of the units. The start-up and shutdown process not only generates direct costs, such as additional fuel consumption and equipment preheating losses during the start-up phase, but also indirect costs, such as power generation loss and equipment lifespan reduction during the start-up and shutdown period; at the same time, peak shaving will also lead to a further increase in the unit maintenance costs, and these implicit costs are often not considered in commonly used optimization models.

[0005] Finally, the frequent fluctuations in primary energy prices and grid-connected electricity prices have disrupted traditional operating logic. The past load allocation method for combined heat and power (CHP) units, which focused solely on minimizing coal / gas consumption, is inadequate to cover start-up and shutdown costs and fails to meet profit targets in a market-driven environment. Therefore, units need to shift towards maximizing production profits, comprehensively considering fluctuations in primary energy prices and grid-connected electricity prices, as well as the dynamic balance between start-up and shutdown costs and peak-shaving revenue. This requires precise selection of operating schemes, such as whether to start up or shut down, the timing of start-up and shutdown, and the magnitude of load adjustments, to achieve optimal economic efficiency across all operating conditions. Therefore, after establishing a unit production profit model as the foundation and maximizing profit as the objective function, further research into optimization algorithms is needed to find the economically optimal operating conditions that satisfy the constraints. Unit start-up and shutdown optimization aims to establish the most economically efficient start-up and shutdown schemes to meet the demands of the heating network in the near future. Thermal power plant operation follows unique patterns. The start-up and shutdown of units require consideration of numerous factors, such as the unit's initial state, the number of start-ups and shutdowns, the frequency of start-ups and shutdowns, and the future demand from the heating network. If a model is built using the unit's start-up and shutdown status at each time point as the optimization variable, the solution process becomes extremely complex due to the large number of variables. For example, optimizing unit start-up and shutdown for the next 48 hours involves 240 optimization variables. To obtain a solution for start-up and shutdown optimization, it is necessary to study optimization strategies for unit operation, taking into account the characteristics of unit production, and ultimately find the start-up and shutdown methods that meet heating network demand, align with load unit operating patterns, and maximize profitability, thus providing guidance for power plants.

[0006] A search revealed Chinese Patent Publication No. CN114818250A, which discloses a method for constructing a full-condition thermal power load optimization model for multi-source, multi-pressure industrial heating networks. This method uses unit production profit as the objective function and considers the impact of peak-shaving ancillary services, primary energy prices, and grid-connected electricity price changes. Based on this, it rationally selects the unit's operating scheme to achieve optimal economic efficiency. However, the thermal calculation model of this method is only constructed based on full-condition performance test data and is a static model, failing to consider performance degradation caused by equipment aging during unit operation. Furthermore, the profit calculation of this method only covers explicit costs and expenses, neglecting implicit costs caused by peak shaving. In addition, it only optimizes the thermal power load allocation under the current operating conditions and does not involve unit start-up and shutdown optimization.

[0007] Therefore, how to achieve the optimal overall economic benefits of combined heat and power units in a deep peak-shaving scenario within a market-oriented electricity environment is a core technical problem that needs to be solved. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a source-grid coordinated thermal power load optimization and unit start-up and shutdown auxiliary control method and system.

[0009] The objective of this invention can be achieved through the following technical solutions: According to a first aspect of the present invention, a method for optimizing thermal and power loads and auxiliary control of unit start-up and shutdown in a grid-source coordinated manner is provided, the method comprising: A personalized thermodynamic performance calculation model for the relationship between key thermodynamic parameters and operating variables of the units is established based on the historical performance test data of the units in the thermal power plant. A dynamic correction factor for equipment aging is introduced to correct the output of the personalized thermal performance calculation, wherein the dynamic correction factor for equipment aging is calculated based on the cumulative operating time and historical peak shaving times of the unit. A production profit model with the goal of maximizing profits is established. The total cost of the production profit model includes fuel costs, water production costs, start-up and shutdown costs, and maintenance and amortization costs related to the intensity and frequency of peak-shaving operations. Using the predicted thermal and power load for a future preset period and the safety parameters of unit operation as constraints, and based on the dynamically corrected personalized thermal performance calculation and the production profit model, an optimization algorithm is used to solve and generate a unit start-up and load distribution control scheme that maximizes production profit. The unit start-up and load distribution control is carried out based on the generated unit start-up and shutdown and load distribution control scheme.

[0010] As a preferred technical solution, the key thermodynamic parameters include power generation, turbine exhaust temperature and steam consumption rate, and the operating variables include steam intake and turbine power; the personalized thermodynamic performance calculation is used to characterize the functional relationship between the key thermodynamic parameters and the operating variables.

[0011] As a preferred technical solution, the calculation expression for the dynamic correction coefficient y for equipment aging is as follows: , Where t is the cumulative running time, m is the design life, c is the number of peak shaving operations, d is the design number of peak shaving operations, and a and b are weighting coefficients, the values ​​of which are determined by analyzing the historical operating data of the unit and updated through periodic tests.

[0012] As a preferred technical solution, the maintenance cost allocation is used to quantify the damage to the lifespan of the unit equipment caused by peak shaving operations, including maintenance costs allocated per unit cost based on the number of peak shaving start-ups and shutdowns and maintenance costs allocated per unit cost based on the fluctuation range of a single peak shaving load.

[0013] As a preferred technical solution, the start-up and shutdown costs include start-up fuel costs, power generation loss costs during the start-up and shutdown process, and plant power consumption costs during start-up and shutdown.

[0014] As a preferred technical solution, the constraints specifically include: the requirement to meet the heating network demand equation constraint for predicted heat load, the upper and lower limits of steam turbine inlet capacity constraint, the upper and lower limits of boiler evaporation capacity constraint, and the constraint of the temperature difference of exhaust steam from parallel-operating steam turbines.

[0015] As a preferred technical solution, the optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm.

[0016] As a preferred technical solution, before using the optimization algorithm to solve the problem, the method further includes the step of: collecting the operating parameters of the source-side units and the grid-side power grid and heating network data to predict the heat and power load for the future preset period.

[0017] According to a second aspect of the present invention, a source-grid coordinated thermal power load optimization and unit start-up and shutdown auxiliary control system is provided, the system comprising: The performance modeling and correction module is used to store and execute the personalized thermodynamic performance calculation model, and to calculate the dynamic correction coefficient for equipment aging. The data acquisition and prediction module is used to collect source network data and predict the thermal power load for a future preset period. Profit model building module: used to build the production profit model; The optimization and solution module is used to call optimization algorithms to solve the problem under the constraints. The decision output module is used to output the final unit start-up and shutdown and load distribution control scheme.

[0018] As a preferred technical solution, the decision output module displays the unit start-up and shutdown status within a future preset period in the form of a visual timeline.

[0019] Compared with the prior art, the present invention has the following advantages: 1. This invention establishes a personalized thermal performance calculation model for each unit and introduces a dynamic correction coefficient for equipment aging, thereby constructing a production profit model that includes start-up and shutdown costs and peak-shaving maintenance allocation costs. This solves the problems of insufficient accuracy of general models and lack of implicit cost accounting, and realizes precise optimization of the overall economic benefits of cogeneration units in deep peak-shaving scenarios.

[0020] 2. The quadratic polynomial model and dynamic correction coefficient for equipment aging proposed in this invention enable the thermal performance calculation to dynamically match the actual operating state and performance degradation of the unit, effectively improving the accuracy of optimized control under complex operating conditions such as low load and high aging.

[0021] 3. This invention incorporates implicit costs such as start-up costs and peak-shaving maintenance allocation costs into the profit model, making profit calculation more comprehensive and accurate. It avoids frequent start-ups and shutdowns of generating units in pursuit of short-term peak-shaving benefits, which can lead to long-term maintenance costs far exceeding peak-shaving benefits. This invention guides power plants to find the optimal balance between increasing revenue at high loads and reducing costs at low loads, as well as between peak-shaving benefits and maintenance costs, ultimately maximizing long-term profits rather than artificially inflating short-term gains.

[0022] 4. While studying the thermodynamic performance under different loads, this invention simultaneously establishes safety constraints for optimization calculations, ensuring that all optimization schemes are executed within the safety boundary. This effectively avoids the risk of sacrificing unit safety for optimization, reduces equipment failures and unplanned shutdowns caused by improper load allocation, ensures long-term stable operation of the unit, and reduces economic losses caused by shutdowns.

[0023] 5. This invention can calculate the optimal load allocation scheme in a short time through optimization algorithms, effectively solving the problem of complex solution caused by too many dependent variables in the start-stop optimization in the prior art. It can output hourly feasible operation schemes and adapt to the dynamic response requirements of the power grid such as peak shaving and heat and power supply and demand fluctuations. Attached Figure Description

[0024] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart of the particle swarm optimization algorithm of the present invention; Figure 3 This is the unit start-up and shutdown optimization calculation interface 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] Example 1: This invention provides a method for optimizing thermal and power loads through source-grid coordination and for auxiliary control of unit start-up and shutdown, such as... Figure 1 As shown, the method specifically includes: Step S1: Conduct a comprehensive on-site performance test on the target unit. The test should cover the main operating modes and full load range of the unit. The main operating modes include heating with and without intermediate extraction. In each mode, select representative stable load conditions at medium and low pressure for testing. At each steady-state operating point, key parameters such as steam intake, power generation, and turbine exhaust temperature are simultaneously and accurately measured and recorded. Each operating point must maintain stable operation for a sufficient period of time. Based on the collected test data, the least squares method was used for regression analysis, with steam flow or power generation as input variables, to fit and establish a quadratic polynomial empirical model describing the unit performance.

[0027] Step S2: Construct a dynamic correction coefficient for equipment aging that is correlated with the unit's cumulative operating time and historical peak shaving frequency. This coefficient is used to correct the output of the personalized thermal performance calculation in real time, accurately representing the current state of the equipment performance. The expression for calculating the dynamic correction coefficient y is as follows: , Where t is the cumulative running time, m is the design life, c is the number of peak shaving operations, d is the design number of peak shaving operations, and a and b are weighting coefficients, which are determined by regression analysis based on the historical performance degradation data and peak shaving operation historical data of the unit. The dynamic correction coefficient for equipment aging is embedded into the calculation model of the thermodynamic model, and the calculation formula is as follows: Equipment aging dynamic correction factor × z Where z represents parameters such as power generation, exhaust steam temperature, and gas consumption rate in step S1; Regularly update the correction coefficients through supplementary experiments to avoid model drift.

[0028] Step S3: Collect source-side unit parameters and grid-side power grid / heating network data in real time. After noise reduction and completion, generate a standardized dataset. If data anomalies are detected, trigger an alarm and pause the optimization process until the data returns to normal. Based on preprocessed historical data, meteorological parameters, and grid-side peak-shaving demand, a basic prediction curve is generated using an LSTM model. Combined with corrections for transmission losses in the thermal pipeline network, hourly predictions of thermal and power loads for future preset times are obtained.

[0029] Step S4: Construct a production profit model with the goal of maximizing profits. The unit's production profit includes the unit's production cost and production revenue. The unit's production revenue includes electricity sales revenue, heat sales revenue, and peak shaving revenue. Among them, electricity sales revenue is the electricity sales volume multiplied by the grid-connected electricity price; heat sales revenue is the sum of the heat sales revenue of each user, and the heat sales revenue of each user is the heat sales volume multiplied by the heat sales price, or it can be calculated by deducting the heat network loss based on the heat supply of the unit and then multiplying by the heat sales price; the total peak shaving revenue is the sum of the peak shaving ancillary service compensation revenue and the change in electricity revenue. The mathematical model for calculating production revenue is as follows: , , , Where I represents total revenue, For electricity sales revenue, For car sales revenue, To generate peak revenue, For grid connection electricity price, For steam turbine power generation, For plant power consumption rate, This is the formula relating turbine power to steam flow rate. This refers to the steam intake of the steam turbine. This refers to the unit price of the car. To meet the low-voltage requirements of the heating network, To meet the medium-pressure requirements of the heating network, For the loss of heating network; When the dual-reduction heating system is not activated, the heating input can also be expressed as: , , , , , in, This refers to the boiler's evaporation capacity. This refers to the proportion of steam used by the generating unit itself. To compensate for peak shaving income, To compensate for revenue from peak-shaving electricity consumption. To compensate for revenue from peak-shaving capacity, For peak shaving capacity, To compensate for the unit price, The number of days for peak shaving; Production costs are divided into fixed costs and variable costs. Fixed costs are not considered when conducting operation control. Variable costs mainly include fuel costs, start-up and shutdown costs, water production costs, and peak shaving costs. Among them, fuel costs are generated by the coal consumed by the unit and depend on the unit's thermal performance; water production costs are the water production volume multiplied by the water production unit price, and the water production volume is related to the unit's heat supply and water consumption; peak shaving costs mainly include peak shaving start-up and shutdown costs and maintenance amortization costs caused by peak shaving. The mathematical model for calculating production costs is as follows: , , , , , , , , , , Where C represents the total cost. For fuel costs, For water production costs, For start-stop costs, To share the maintenance costs, For fuel prices, This refers to the evaporation capacity of boiler number j. The enthalpy value of the main vapor. For the enthalpy value of the feedwater, For boiler number j, The calorific value of the fuel. The unit price of water production. To start with fuel costs, To cover the costs of power generation losses due to starting and stopping power generation, The cost of electricity consumption for starting and stopping the plant. This refers to the fuel consumption for unit startup. For fuel unit price, To account for the power loss during the start-stop process, Electricity consumption for plant start-up and shutdown. For the cost of electricity used by the plant, To allocate costs based on the number of starts and stops, To allocate costs according to load fluctuations, Unit start-up, shutdown, and maintenance costs This refers to the number of times peak-shaving operations are started and stopped. Maintenance cost per unit load fluctuation This refers to the fluctuation range of a single peak load adjustment. The final production profit model is as follows: .

[0030] Step S5: Using the predicted thermal and power load for the future preset period as the demand boundary that must be met, and the safety parameters of unit operation as the operating boundary, together they constitute the constraints for the optimization solution, specifically including: Heating network demand constraints: Heating network demand constraints are equality constraints. When high-low heating is not activated, heating network demand constraints can be expressed as: , , in, To meet the needs of low-pressure heating networks, To meet the needs of medium-pressure heating networks, This is obtained by multiplying the total heating demand by the corresponding coefficient. Steam turbine inlet steam flow upper and lower limit constraints: The upper and lower limits of the steam turbine inlet steam flow are related to whether the unit has an intermediate extraction valve. This constraint is an inequality constraint, expressed as follows: , in, and These are the upper and lower limits of the steam inlet flow rate for the j-th steam turbine, respectively. Boiler evaporation capacity upper and lower limit constraints: Each boiler has a maximum and minimum evaporation capacity. This constraint is an inequality constraint and can be expressed as: , in, and These are the upper and lower limits of the evaporation capacity of the j-th boiler, respectively. Steam turbine exhaust temperature difference constraint: Since the exhaust steam from j steam turbines enters the same steam distribution cylinder, to ensure safe unit operation, the exhaust temperature difference between the j steam turbines should not be too large. The exhaust temperature of the steam turbine is related to the steam inlet flow rate of the unit and can be determined according to the relationship between exhaust temperature and steam inlet flow rate. The steam turbine exhaust temperature difference constraint can be expressed as: or , in, This represents the exhaust temperature of the j-th steam turbine. This indicates the maximum permissible difference threshold for exhaust steam temperature. , These are the established relationships between turbine exhaust temperature and steam inlet flow rate.

[0031] Step S6: Based on the established production profit model, with profit maximization as the objective value, optimize the unit's operating mode under the predicted thermal and power load at the preset future time. Figure 2 As shown, the optimization steps of the particle swarm optimization algorithm include: (1) Determine the number of particles participating in the search action and initialize the attributes of each particle: initialize the random position value of each particle in the d-th dimension solution space; the experience and learning ability of each particle are equal to the initial position; the initial search speed of each particle is 0; initialize the fitness value of each particle, and obtain it by substituting its position into the set fitness function. (2) Initialize the individual optimal solution and the global optimal solution, define the current quality of each particle, substitute the position of each initialized particle into the fitness function to obtain the corresponding individual optimal solution, and then take the current extreme value as the global optimal solution. (3) Update the search speed and position of each particle. Each particle will use its own learning ability and population experience to adjust its search direction and speed. That is, it will perform an iteration according to the speed update formula and the position update formula to update its speed and position in order to move closer to the global optimal solution. (4) Determine whether the algorithm has ended the loop. The determination principle is whether the set number of iterations or the expected accuracy has been reached to meet the minimum limit. If the determination condition is met, the velocity and position of the particle of the global optimal solution are obtained according to the current minimum fitness value, and the global optimal solution is output. If the condition is not met, the loop continues to return to step (3).

[0032] (5) The algorithm ends and outputs the optimal solution of the objective function for this population.

[0033] Step S7: As Figure 3 As shown, the start-up and shutdown calculation results for meeting the heating network demand and the economical operation of the heat source units within the future preset period are displayed hourly in the form of a visual timeline. The red bottom box indicates that the unit is running at the current time, and the green bottom box indicates that the unit is shut down at the current time. The interface also shows the differences in equipment combinations under different operating modes, which can intuitively compare the feasibility of multiple alternative schemes and finally select the optimal unit start-up and shutdown and heat and power load allocation strategy.

[0034] This invention provides a source-grid coordinated thermal power load optimization and unit start-up and shutdown auxiliary control method. By establishing a personalized thermal calculation model for each unit based on performance tests and introducing dynamic correction for equipment aging, a production profit model considering peak-shaving revenue, start-up and shutdown costs, and maintenance allocation costs is constructed. Using optimization algorithms, under the conditions of meeting thermal power demand and safety constraints, the unit start-up and shutdown and load allocation scheme that maximizes profits is obtained, which is accurately adapted to the deep peak-shaving market environment.

[0035] Example 2: This invention provides a source-grid coordinated thermal power load optimization and unit start-up / shutdown auxiliary control system for implementing the aforementioned method. The system includes the following modules: Data acquisition and prediction module: used to collect real-time operating parameters of source-side units and grid-side power grid and heating network data, and predict the heat and power load of future preset cycles based on historical data and LSTM model.

[0036] Performance modeling and correction module: used to store and perform personalized thermal performance calculations, and to calculate and apply the dynamic correction coefficient for equipment aging.

[0037] Profit Model Building Module: Used to build the production profit model that includes start-up and shutdown costs and maintenance allocation costs.

[0038] Optimization and solution module: used to call optimization algorithms to solve the problem under the constraints.

[0039] Decision output module: Used to output the final unit start-up and load distribution control scheme in the form of a visual timeline, etc.

[0040] This invention provides a source-grid coordinated thermal power load optimization and unit start-up and shutdown auxiliary control system. The system integrates five major modules in sequence: data acquisition and prediction, performance modeling and correction, profit model construction, optimization solution and decision output. Through the coordinated operation of the modules, it can automatically complete the entire process from data perception and model calculation to the generation of the optimal unit start-up and shutdown and load distribution control scheme, and output the unit start-up and shutdown plan in a visualized manner, providing precise automated control support for power plant operation.

[0041] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing thermal power load and auxiliary control of unit start-up and shutdown in a source-grid coordinated manner, characterized in that, The method includes: A personalized thermodynamic performance calculation model for the relationship between key thermodynamic parameters and operating variables of the units is established based on the historical performance test data of the units in the thermal power plant. A dynamic correction coefficient for equipment aging is introduced to correct the output of the personalized thermal performance calculation model. The dynamic correction coefficient for equipment aging is calculated based on the cumulative operating time and historical peak shaving times of the unit. A production profit model with the goal of maximizing profits is established. The total cost of the production profit model includes fuel costs, water production costs, start-up and shutdown costs, and maintenance and amortization costs related to the intensity and frequency of peak-shaving operations. Using the predicted thermal and power load for a future preset period and the safety parameters of unit operation as constraints, and based on the dynamically corrected personalized thermal performance calculation and the production profit model, an optimization algorithm is used to solve and generate a unit start-up and load distribution control scheme that maximizes production profit. The unit start-up and load distribution control is carried out based on the generated unit start-up and shutdown and load distribution control scheme.

2. The method for source-grid coordinated thermal power load optimization and unit start-up / shutdown auxiliary control according to claim 1, characterized in that, The key thermodynamic parameters include power generation, turbine exhaust temperature, and steam consumption rate; the operating variables include steam intake and turbine power; the personalized thermodynamic performance calculation is used to characterize the functional relationship between the key thermodynamic parameters and the operating variables.

3. The method for source-grid coordinated thermal power load optimization and unit start-up / shutdown auxiliary control according to claim 1, characterized in that, The calculation expression for the dynamic correction coefficient y for equipment aging is as follows: ， Where t is the cumulative running time, m is the design life, c is the number of peak shaving operations, d is the design number of peak shaving operations, and a and b are weighting coefficients, the values ​​of which are determined by analyzing the historical operating data of the unit and updated through periodic tests.

4. The method for source-grid coordinated thermal power load optimization and unit start-up / shutdown auxiliary control according to claim 1, characterized in that, The maintenance cost allocation is used to quantify the damage to the lifespan of the unit equipment caused by peak shaving operations. It includes maintenance costs allocated per unit cost based on the number of peak shaving start-ups and shutdowns, and maintenance costs allocated per unit cost based on the fluctuation range of a single peak shaving load.

5. The method for source-grid coordinated thermal power load optimization and unit start-up / shutdown auxiliary control according to claim 1, characterized in that, The start-up and shutdown costs include start-up fuel costs, power generation loss costs during start-up and shutdown, and plant power consumption costs during start-up and shutdown.

6. The method for source-grid coordinated thermal power load optimization and unit start-up and shutdown auxiliary control according to claim 1, characterized in that, The constraints specifically include: the requirement to meet the heating network demand equation for the predicted heat load, the upper and lower limits of the steam turbine inlet capacity, the upper and lower limits of the boiler evaporation capacity, and the constraint of the temperature difference between the exhaust steam of parallel-operating steam turbines.

7. The method for source-grid coordinated thermal power load optimization and unit start-up and shutdown auxiliary control according to claim 1, characterized in that, The optimization algorithm is either a genetic algorithm or a particle swarm optimization algorithm.

8. The method for source-grid coordinated thermal power load optimization and unit start-up and shutdown auxiliary control according to claim 1, characterized in that, Before using the optimization algorithm to solve the problem, the method further includes the following steps: collecting source-side unit operating parameters and grid-side power grid and heating network data to predict the heat and power load for the future preset period.

9. A source-grid coordinated thermal power load optimization and unit start-up / shutdown auxiliary control system for implementing the method of any one of claims 1-8, characterized in that, The system includes: The performance modeling and correction module is used to store and execute the personalized thermodynamic performance calculation model, and to calculate the dynamic correction coefficient for equipment aging. The data acquisition and prediction module is used to collect source network data and predict the thermal power load for a future preset period. Profit Model Building Module: Used to build the production profit model; The optimization and solution module is used to call optimization algorithms to solve the problem under the constraints. The decision output module is used to output the final unit start-up and shutdown and load distribution control scheme.

10. The source-grid coordinated thermal power load optimization and unit start-up / shutdown auxiliary control system according to claim 9, characterized in that, The decision output module displays the unit start-up and shutdown status within a future preset period in the form of a visual timeline.

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