Method for improving power supply capacity of cluster wind power and thermodynamic system in cold-wave weather

Abstract

The invention relates to a method for improving the power supply capacity of a cluster wind power and thermodynamic system in cold-wave weather, which belongs to the technical field of power system operation scheduling, and comprises the following steps: constructing an electrothermal coupling system model, considering the operation cost of each device of the system, and forming a comprehensive cost minimization objective function; considering the operation characteristics of the wind power plant under cold waves, introducing external reserve, and establishing a system safe operation constraint system; a Wasserstein distance is adopted to construct a fuzzy set, a probability distribution spherical set driven by sample data is utilized to describe the uncertainty of wind power prediction errors, a model is reconstructed, and a distribution robust optimization model is established; converting an inner-layer extreme value problem into a single-layer optimization problem by applying a duality theory, performing convex approximation processing on opportunity constraints in combination with a conditional value-at-risk theory, and constructing a solvable linear programming model; and designing a dynamic confidence level, and balancing system operation cost and power supply reliability in cold-wave weather by optimizing and coordinating economy and system risks.

Description

Technical Field

[0001] The present invention relates to the field of power system operation and dispatching under extreme weather conditions, and in particular to a method for improving the power supply capacity of clustered wind power and thermal power systems under cold wave weather conditions. Background Art

[0002] In winter cold waves, extreme meteorological conditions such as low temperature and freezing will significantly change the output characteristics of wind power installations, and the meteorological forecast error will expand to more than three times that of normal weather. During the cold wave, the demand for electricity and heat loads shows strong spatiotemporal coupling characteristics. The sharp increase in heating load tightens the "heat-to-electricity" operation constraints of thermal power units, and the regulation capacity of flexible loads such as electric boilers and heat pumps is limited, resulting in a decrease in the system regulation margin. Existing studies mostly use stochastic programming or robust optimization methods to deal with uncertainty, but the former relies on accurate probability distribution assumptions, and the latter is overly conservative and leads to economic losses. Although the stochastic programming method can consider multiple possibilities, its core defect is that it relies on a preset probability distribution model. When encountering extreme working conditions, this a priori assumption will lead to inaccurate predictions. The robust optimization method ensures system safety by presetting the worst scenario, which inevitably generates excessive spare capacity and causes deterioration of economic indicators. Therefore, it is urgent to establish a method to improve the system power supply capacity considering cold wave weather to improve the operational resilience and economy of the integrated energy system. Summary of the invention

[0003] In view of the shortcomings in the prior art, the purpose of the present invention is to provide a method for improving the power supply capacity of cluster wind power and thermal systems under cold wave weather, and to optimize and fine-tune the scheduling of the electric and thermal integrated energy system based on the operating characteristics of wind power in cold wave weather, so as to achieve better stability and economy.

[0004] In order to solve the above technical problems, the technical solution provided by the present invention is: A method for improving the power supply capacity of cluster wind power and thermal power systems under cold wave weather, comprising the following steps: S1. Construct an electric-thermal coupling system model, establish a mathematical expression that includes the operating costs of thermal power units and cogeneration units, comprehensively consider the cost factors of electric boilers, energy storage equipment, heat storage tanks, wind power adjustment and external backup, and form an objective function with the goal of minimizing the comprehensive operating cost; S2. Based on the objective function in S1, the constraints of power and heat dual balance, operation constraints of thermal power units, rotational reserve constraints of thermal power units, operation constraints of cogeneration units, energy storage equipment constraints, heat storage tank constraints and DC power flow safety constraints are introduced to establish a system safety operation constraint system to ensure that the system operation meets the physical feasibility requirements; S3. Empirical distribution based on historical wind power forecast error dataset , the Wasserstein distance is used to construct fuzzy sets, and the probability distribution spherical sets driven by sample data are used to describe the uncertainty of wind power forecast errors, so as to incorporate wind power fluctuations into the optimization model. Then, the objective function of S1 and the opportunity constraints in the constraint system of S2 are reconstructed according to the fuzzy sets, and a distributed robust optimization model that takes into account both economy and uncertainty is established; S4. Use the duality theory to transform the inner extreme value problem of the reconstruction objective function in S3 into a single-layer optimization problem. Combine the conditional value at risk theory to perform convex approximation on the reconstruction opportunity constraint in S3. Finally, combine the deterministic constraints, the reconstruction opportunity constraint and the single-layer objective function to construct a solvable linear programming model and form a reconstruction distribution robust optimization model. S5. A dynamic chance constraint modeling method is used to construct an adaptive confidence level based on the real-time thermal power load status, so as to coordinate the relationship between system operating cost and power supply reliability in the S4 reconstructed distributed robust optimization model, and achieve a balance between the economy and risk controllability of the electric-thermal integrated energy system under cold wave weather.

[0005] Preferably, In S1, the comprehensive cost minimization objective function is established as shown in formula (1): ; in, For thermal power units The coal consumption coefficient, It is a thermal power unit exist The effort of the moment; It is a thermal power unit exist The upper and lower spinning reserve capacity at the moment; For thermal power units Cost factors for upper and lower spinning reserves; For combined heat and power units The coal consumption characteristic fitting coefficient of the thermal power unit is calculated by the coal consumption coefficient and thermal power ratio Calculated; It is a combined heat and power unit exist The effort of the moment, It is a combined heat and power unit Thermal power; It is an electric boiler Power consumed, Wind Farm exist Always plan and work hard, and It is an energy storage device exist The charging and discharging power at each moment; Heat storage tank The heat release and charging power, is External spare capacity introduced at all times, The actual output of wind farms; Electric boiler Cost coefficient of energy storage equipment Cost coefficient of heat storage tank Cost coefficient of external reserve, cost coefficient of wind power adjustment; It is the time unit of scheduling.

[0006] Preferably, In S2, the expression of the established constraint system is as follows: Electrical / thermal power balance: ; in It is a thermal power unit exist The effort of the moment, It is a combined heat and power unit exist The effort of the moment, It is an electric boiler Power consumed, Wind Farm exist Always plan and work hard, and It is an energy storage device exist The charging and discharging power at the moment, Is a node exist The load of the moment; It is a combined heat and power unit The thermal power, It is an electric boiler The thermal power, Heat storage tank The heat release and charging power; It is a system Heat load at any time; Constraints on thermal power units: ; in, It is a thermal power unit The upper and lower limits of output, It is a thermal power unit The uphill speed, is the downhill rate; It is a thermal power unit exist The upper and lower spinning reserve capacity at the moment, is the spinning reserve factor, is the maximum load of the system; is the forecast error random variable; Wind farm constraints: ; in, For wind farms The planned output of the wind farm and the predicted output of the wind farm; The actual output of wind farms; Thermal power unit constraints: ; in are the upper and lower limits of the thermal power unit power, The upper and lower limits of thermal power of thermal power units; is the ramp rate of the thermal power unit, The slope of the minimum condensation curve; is a constant; Electric boiler constraints: ; in For electric boiler Electric-to-heat conversion efficiency; Thermal storage tank constraints: ; in For heat storage tank Maximum charging and discharging heat power, and is the state variable of heat charging and discharging, For heat storage tank The heat of the moment, is the maximum and minimum heat storage capacity, and is the heat at the initial and final moments; For heat storage tank Efficiency coefficient of heat charging and discharging; Energy storage constraints ; in It is an energy storage device The maximum charge and discharge power, For energy storage devices exist The amount of electricity at the moment, For energy storage devices Rated capacity, is the minimum and maximum charge factor; is the power of the energy storage device at the initial and final moments, is the charge and discharge efficiency coefficient; Flow safety constraints: ; in For line The active power flowing through are the upper and lower limits of the active power on the line, is a collection of lines; and Section The upper and lower limits of active power allowed to pass, For cross section The set of included branches.

[0007] Preferably, In S3, the fuzzy set constructed using the Wasserstein distance is as follows: ; ; in Represents the random variable space All probability distributions above, Indicates the confidence level of the unknown probability distribution; represents the number of samples, represents the confidence level of the radius, and is an auxiliary variable, is the sample mean, For the Group samples, is the 1-norm; For distribution and distribution Wassertein distance between them; According to the established fuzzy sets, the objective function and chance constraints of the original model are reconstructed and transformed: ; ; in is the decision variable, is the wind power forecast error random variable vector, is the risk factor for the chance constraint; It is a constraint expression simplified to facilitate subsequent derivation; Preferably, The process of transforming the reconstruction objective function of S3 in S4 is as follows: ; in To simplify the objective function, is the decision variable, is a random variable, is the introduced dual factor, and are the upper and lower bounds of the prediction error sample set, respectively. is an auxiliary variable; The opportunity constraint transformation process is as follows: ; in are auxiliary variables, is the dual norm, is the dual operator.

[0008] Preferably, In said S5, a dynamic confidence level is designed for the reconstruction opportunity constraint, Risk factor for time-based spinning reserve opportunity constraints It is obtained from the following formula: ; in is the maximum and minimum risk coefficient, are weight parameters for the electrical load level and the thermal load level; and yes The electrical load and thermal load ratio at the moment; They are the threshold levels of the electrical load and thermal load ratios, respectively. When these values ​​are exceeded, the confidence level will increase to ensure the safe operation of the system.

[0009] The beneficial effects of the present invention are: 1. Aiming at the dual impact of cold wave weather on wind power output and electric heat load, the present invention establishes a refined model of wind power operation characteristics taking into account extreme meteorological parameters, breaking through the limitation of traditional scheduling methods that ignore the correlation between wind turbine output characteristics and grid load under extreme weather conditions.

[0010] 2. A data-driven robust optimization method is adopted, which integrates the probability distribution characteristics of wind power forecast errors. Through the optimization model, a dispatching strategy that takes into account both economy and robustness is generated. Compared with traditional robust optimization methods, it can effectively reduce the cost of rotating reserve capacity configuration, improve the economy of system operation, and ensure the reliability of power supply and heating in cold weather. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1A text flow chart of a method for improving the power supply capacity of cluster wind power and thermal power systems under cold wave weather; Figure 2 This is an overall framework diagram of a method for improving the power supply capacity of cluster wind power and thermal systems under cold wave weather. DETAILED DESCRIPTION

[0012] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the following embodiments are provided only for the purpose of illustration and are not intended to limit the scope of the present invention. Those skilled in the art may make various modifications and substitutions to the present invention without departing from the purpose and spirit of the present invention.

[0013] like Figure 1 As shown, the present invention provides a method for improving the power supply capacity of cluster wind power and thermal power systems under cold wave weather, comprising the following steps: S1. Establish a mathematical expression that includes the operating costs of thermal power units and cogeneration units, comprehensively consider the cost factors of electric boilers, energy storage equipment, heat storage tanks, wind power adjustment and external backup, and form an objective function with the goal of minimizing the comprehensive operating cost, laying the foundation for subsequent optimization and scheduling; S2. Based on the objective function in S1, the constraints of power and heat dual balance, operation constraints of thermal power units, rotation reserve constraints of thermal power units, operation constraints of cogeneration units, energy storage equipment constraints, heat storage tank constraints and DC power flow safety constraints are introduced to establish a system safety operation constraint system to ensure that the system operation meets the physical feasibility requirements and provide constraint guarantees for optimal scheduling; S3. Empirical distribution based on historical wind power forecast error dataset , the Wasserstein distance is used to construct fuzzy sets, and the probability distribution spherical set driven by historical sample data is used to describe the uncertainty of wind power forecast error, so as to incorporate wind power fluctuation into the optimization model. Then, the objective function of S1 and the opportunity constraint in the constraint system of S2 are reconstructed according to the fuzzy set, and a distributed robust optimization model that takes into account both economy and uncertainty is established; S4. Use the duality theory to transform the inner extreme value problem of the reconstruction objective function in S3 into a single-layer optimization problem, and combine the conditional value at risk theory (CVaR) to perform convex approximation on the reconstruction opportunity constraint in S3; finally, combine the deterministic constraints, the reconstruction opportunity constraint and the single-layer objective function to construct a solvable linear programming model to form a reconstruction distribution robust optimization model; S5. A dynamic chance constraint modeling method is used to construct an adaptive confidence level based on the real-time thermal power load status, so as to coordinate the relationship between system operating cost and power supply reliability in the S4 reconstructed distributed robust optimization model, and achieve a balance between the economy and risk controllability of the electric-thermal integrated energy system under cold wave weather.

[0014] In S1, the expressions of the operating costs of thermal power units and cogeneration units are established, and the transaction costs of electric boilers, energy storage equipment and external energy are considered, forming the objective function as follows: ; in, For thermal power units The coal consumption coefficient, It is a thermal power unit exist The effort of the moment; It is a thermal power unit exist The upper and lower spinning reserve capacity at the moment, For thermal power units Cost factors for upper and lower spinning reserves; For combined heat and power units The coal consumption characteristic fitting coefficient of the thermal power unit is calculated by the coal consumption coefficient and thermal power ratio Calculated; It is a combined heat and power unit exist The effort of the moment, It is a combined heat and power unit Thermal power; It is an electric boiler Power consumed, Wind Farm exist Always plan and work hard, and It is an energy storage device exist The charging and discharging power at each moment; Heat storage tank The heat release and charging power, is External spare capacity introduced at all times, The actual output of wind farms; Electric boiler Cost coefficient of energy storage equipment Cost coefficient of heat storage tank Cost coefficient of external reserve, cost coefficient of wind power adjustment; It is the time unit of scheduling.

[0015] ; in, For thermal power units Coal consumption coefficient.

[0016] In said S2, based on the objective function in step S1, a system safety operation constraint system is established, including dual balance constraints of electricity and heat, climbing rate limits of thermal power units, condensing curve constraints of cogeneration units, energy conservation constraints of heat storage devices and DC power flow safety constraints, to ensure that the system operates in the physically feasible domain.

[0017] Electrical / thermal power balance constraints: ; in It is a thermal power unit exist The effort of the moment, It is a combined heat and power unit exist The effort of the moment, It is an electric boiler Power consumed, Wind Farm exist Always plan and work hard, and It is an energy storage device exist The charging and discharging power at the moment, Is a node exist Load of time. It is a combined heat and power unit The thermal power, It is an electric boiler The thermal power, Not a heat storage tank The heat release and charging power. It is a system Heat load at all times.

[0018] Constraints on thermal power units: ; in, It is a thermal power unit The upper and lower limits of output, It is a thermal power unit The uphill speed, is the downhill rate. It is a thermal power unit exist The upper and lower spinning reserve capacity at the moment, is the spinning reserve factor, is the maximum load of the system; is the forecast error random variable; Wind farm constraints: ; in, The planned output of the wind farm and the predicted output of the wind farm; The actual output of wind farms; Thermal power unit constraints: ; in are the upper and lower limits of the thermal power unit power, It is the upper and lower limits of thermal power of thermal power unit. is the ramp rate of the thermal power unit, The slope of the minimum condensation curve. Is a constant.

[0019] Electric boiler constraints: ; in For electric boiler Electric heat conversion efficiency.

[0020] Thermal storage tank constraints: ; in For heat storage tank Maximum charging and discharging heat power, and is the state variable of heat charging and discharging, For heat storage tank The heat of the moment, is the maximum and minimum heat storage capacity, and is the heat at the initial and final moments. For heat storage tank Efficiency coefficient of heat charging and discharging.

[0021] Energy storage constraints: ; in It is an energy storage device The maximum charge and discharge power, For energy storage devices exist The amount of electricity at the moment, For energy storage devices Rated capacity, is the minimum and maximum charge factor. is the power of the energy storage device at the initial and final moments, is the charge and discharge efficiency coefficient.

[0022] Flow safety constraints: ; in For line The active power flowing through are the upper and lower limits of the active power on the line, A collection of lines. and Section The upper and lower limits of active power allowed to pass, For cross section The set of included branches.

[0023] In S3, Wasserstein distance is used to construct a fuzzy set to describe the distribution of wind power forecast errors, and the uncertainty is represented by a probability distribution spherical set driven by sample data. and the true distribution , the constructed fuzzy set is as follows: ; ; in Represents the random variable space All probability distributions above, Represents the confidence level of an unknown probability distribution. represents the number of samples, represents the confidence level of the radius, and is an auxiliary variable, is the sample mean, For the Group samples, is the 1-norm. For distribution and distribution Wassertein distance between them; According to the established fuzzy sets, the objective function and chance constraints of the original model can be reconstructed and transformed: ; ; in is the decision variable, is the wind power forecast error random variable vector. is the risk factor for the chance constraint; It is a constraint expression simplified to facilitate subsequent derivation.

[0024] In S4, the inner layer extreme value problem is transformed into a single-layer optimization problem by using the duality theory, and the opportunity constraint is convexly approximated by the conditional risk value theory to construct a solvable linear model. The objective function transformation process is as follows: ; in To simplify the objective function, is the decision variable, random variable, The dual factor introduced, and are the upper and lower bounds of the prediction error sample set, respectively. Auxiliary variables.

[0025] The opportunity constraint transformation process is as follows: ; in are auxiliary variables, is the dual norm, is the dual operator.

[0026] In S5, in order to improve the elastic regulation capability of the integrated energy system, a dynamic opportunity constraint modeling method is adopted to construct an adaptive confidence level mechanism based on the real-time thermal power load status. Risk factor for time-based spinning reserve opportunity constraints It can be obtained by the following formula: ; in is the maximum and minimum risk coefficient, is the weight parameter of the electrical load level and the thermal load level. and yes The electrical load and thermal load ratio at each moment. They are the threshold levels of the electrical load and thermal load ratios, respectively. When these values ​​are exceeded, the confidence level will increase to ensure the safe operation of the system.

[0027] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0028] Finally, it should be noted that the above description is only a preferred example of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for improving the power supply capacity of cluster wind power and thermal power systems under cold wave weather, characterized in that: The following steps are involved: S1. Construct an electric-thermal coupling system model, establish a mathematical expression that includes the operating costs of thermal power units and cogeneration units, comprehensively consider the cost factors of electric boilers, energy storage equipment, heat storage tanks, wind power adjustment and external backup, and form an objective function with the goal of minimizing the comprehensive operating cost; S2. Based on the objective function in S1, the constraints of power and heat dual balance, operation constraints of thermal power units, rotational reserve constraints of thermal power units, operation constraints of cogeneration units, energy storage equipment constraints, heat storage tank constraints and DC power flow safety constraints are introduced to establish a system safety operation constraint system to ensure that the system operation meets the physical feasibility requirements; S3. Empirical distribution based on historical wind power forecast error dataset , the Wasserstein distance is used to construct fuzzy sets, and the probability distribution spherical sets driven by sample data are used to describe the uncertainty of wind power forecast errors, so as to incorporate wind power fluctuations into the optimization model. Then, the objective function of S1 and the opportunity constraints in the constraint system of S2 are reconstructed according to the fuzzy sets, and a distributed robust optimization model that takes into account both economy and uncertainty is established; S4. Use the duality theory to transform the inner extreme value problem of the reconstruction objective function in S3 into a single-layer optimization problem. Combine the conditional value at risk theory to perform convex approximation on the reconstruction opportunity constraint in S3. Finally, combine the deterministic constraints, the reconstruction opportunity constraint and the single-layer objective function to construct a solvable linear programming model and form a reconstruction distribution robust optimization model. S5. A dynamic chance constraint modeling method is used to construct an adaptive confidence level based on the real-time thermal power load status, so as to coordinate the relationship between system operating cost and power supply reliability in the S4 reconstructed distributed robust optimization model, and achieve a balance between the economy and risk controllability of the electric-thermal integrated energy system under cold wave weather.

2. According to a method for improving the power supply capacity of cluster wind power and thermal power systems in cold wave weather according to claim 1, it is characterized in that: In S1, the comprehensive cost minimization objective function is established as shown in formula (1): ; in, For thermal power units The coal consumption coefficient, It is a thermal power unit exist The effort of the moment; It is a thermal power unit exist The upper and lower spinning reserve capacity at the moment; For thermal power units Cost factors for upper and lower spinning reserves; For combined heat and power units The coal consumption characteristic fitting coefficient of the thermal power unit is calculated by the coal consumption coefficient and thermal power ratio Calculated; It is a combined heat and power unit exist The effort of the moment, It is a combined heat and power unit Thermal power; It is an electric boiler Power consumed, Wind Farm exist Always plan and work hard, and It is an energy storage device exist The charging and discharging power at the moment; Heat storage tank The heat release and charging power, is External spare capacity introduced at all times, The actual output of wind farms; Electric boiler Cost coefficient of energy storage equipment Cost coefficient of heat storage tank Cost coefficient of external reserve, cost coefficient of wind power adjustment; It is the time unit of scheduling.

3. The method for improving the power supply capacity of cluster wind power and thermal power systems in cold wave weather according to claim 2 is characterized in that: In S2, the expression of the established constraint system is as follows: Electrical / thermal power balance: ; in It is a thermal power unit exist The effort of the moment, It is a combined heat and power unit exist The effort of the moment, It is an electric boiler Power consumed, Wind Farm exist Always plan and work hard, and It is an energy storage device exist The charging and discharging power at the moment, Is a node exist The load of the moment; It is a combined heat and power unit The thermal power, It is an electric boiler The thermal power, Heat storage tank The heat release and charging power; It is a system Heat load at any time; Constraints on thermal power units: ; in, It is a thermal power unit The upper and lower limits of output, It is a thermal power unit The uphill speed, is the downhill rate; It is a thermal power unit exist The upper and lower spinning reserve capacity at the moment, is the spinning reserve factor, is the maximum load of the system; is the forecast error random variable; Wind farm constraints: ; in, For wind farms The planned output of the wind farm and the predicted output of the wind farm; The actual output of wind farms; Thermal power unit constraints: ; in are the upper and lower limits of the thermal power unit power, The upper and lower limits of thermal power of thermal power units; is the ramp rate of the thermal power unit, The slope of the minimum condensation curve; is a constant; Electric boiler constraints: ; in For electric boiler Electric-to-heat conversion efficiency; Thermal storage tank constraints: ; in For heat storage tank Maximum charging and discharging heat power, and is the state variable of heat charging and discharging, For heat storage tank The heat of the moment, is the maximum and minimum heat storage capacity, and is the heat at the initial and final moments; For heat storage tank Efficiency coefficient of heat charging and discharging; Energy storage constraints ; in It is an energy storage device The maximum charge and discharge power, For energy storage devices exist The amount of electricity at the moment, For energy storage devices Rated capacity, is the minimum and maximum charge factor; is the power of the energy storage device at the initial and final moments, is the charge and discharge efficiency coefficient; Flow safety constraints: ; in For line The active power flowing through are the upper and lower limits of the active power on the line, is a collection of lines; and Section The upper and lower limits of active power allowed to pass, For cross section The set of included branches.

4. The method for improving the power supply capacity of cluster wind power and thermal power systems in cold wave weather according to claim 3 is characterized in that: In S3, the fuzzy set constructed using the Wasserstein distance is as follows: ; ; in Represents the random variable space All probability distributions above, Indicates the confidence level of the unknown probability distribution; represents the number of samples, represents the confidence level of the radius, and is an auxiliary variable, is the sample mean, For the Group samples, is the 1-norm; For distribution and distribution Wassertein distance between them; According to the established fuzzy sets, the objective function and chance constraints of the original model are reconstructed and transformed: ; ; in is the decision variable, is the wind power forecast error random variable vector, is the risk factor of the chance constraint; It is a constraint expression simplified to facilitate subsequent derivation.

5. A method for improving the power supply capacity of cluster wind power and thermal power systems in cold wave weather according to claim 4, characterized in that: The process of transforming the reconstruction objective function of S3 in S4 is as follows: ; in To simplify the objective function, is the decision variable, is a random variable, is the introduced dual factor, and are the upper and lower bounds of the prediction error sample set, respectively. is an auxiliary variable; The opportunity constraint transformation process is as follows: ; in are auxiliary variables, is the dual norm, is the dual operator.

6. A method for improving the power supply capacity of cluster wind power and thermal power systems in cold wave weather according to claim 5, characterized in that: In said S5, a dynamic confidence level is designed for the reconstruction opportunity constraint, Risk factor for time-constrained spinning reserve opportunities It is obtained from the following formula: ； in is the maximum and minimum risk coefficient, are weight parameters for the electrical load level and the thermal load level; and yes The electrical load and thermal load ratio at the moment; They are the threshold levels of the electrical load and thermal load ratios, respectively. When these values ​​are exceeded, the confidence level will increase to ensure the safe operation of the system.

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