A power system chance-constrained stochastic optimization congestion management method and system considering wind power uncertainty

Abstract

The application relates to the field of electric power, and particularly discloses a power system chance constraint random optimization congestion management method and system considering wind power uncertainty, which comprises the following steps: firstly, obtaining thermal power generator parameters, power grid related parameters, and wind power prediction and load prediction data, establishing node-line power transfer distribution factors according to the power grid parameters, and performing uncertainty probability modeling on wind power output and load prediction; then, based on a chance constraint, a two-stage model of random congestion optimization scheduling considering wind power uncertainty is established, wherein a first-stage model is a pre-scheduling model based on prediction information, and a second-stage model is an optimization correction model based on wind power uncertainty; finally, the chance constraint random optimization model is transformed for uncertainty to obtain a power optimization scheduling strategy.

Description

Technical Field

[0001] This invention relates to a power system transmission congestion management method, and more particularly to a power system chance-constrained stochastic optimization congestion management method and system that considers the uncertainty of wind power. Background Technology

[0002] To address the growing energy crisis and reduce the environmental impact of carbon emissions, power systems are required to prioritize the use of renewable green energy sources, including wind power. As the penetration rate of renewable energy sources like wind power in the power system continues to increase, the uncertainty of intermittent resource output poses a serious challenge to the system's congestion management system. On the one hand, due to the limited accuracy of wind power output prediction, more generation capacity needs to be reserved to ensure the reliability of power generation serving the load, placing significant demands on the power transmission capacity of the power system's transmission lines. On the other hand, the high generation cost and uncertainty of wind power, during real-time dispatching, may lead to severe random congestion of system lines, wind curtailment, and load shedding, causing serious system safety issues and economic losses.

[0003] Current research on congestion management methods largely focuses on responding to demand-side signals and scheduling power generation based on different load-side electricity consumption scenarios and operating modes, assuming relatively fixed power transmission and generation resources. This includes considering transmission rights scheduling based on user-side incentive responses. However, with the widespread integration of renewable energy sources like wind power, insufficient consideration of the uncertainty in wind power output can lead to severe wind curtailment and system congestion risks. Since the uncertainty in wind power output corresponds to a probabilistic event, treating this uncertainty as a chance constraint to modify prediction-based power generation scheduling schemes, thereby reducing the probability of random congestion and wind curtailment, becomes a feasible solution to the congestion management problem. Summary of the Invention

[0004] To address the aforementioned shortcomings of existing technologies, this invention proposes a power system congestion management method and system that considers the uncertainties of wind power. It is mainly applied to the generation optimization scheduling and congestion management of day-ahead power markets with wind power participation. The aim is to effectively reduce the probability of grid line congestion and improve grid stability and market economic benefits by establishing a congestion stochastic optimization model with chance constraints.

[0005] This invention is achieved through the following technical solution: This invention relates to a chance-constrained stochastic optimization congestion management method for power systems that considers the uncertainties of wind power, comprising the following steps: Step (1) Obtain thermal power generator parameters, grid parameters, wind power forecast and load forecast data, establish node-line power transfer distribution factor based on grid parameters, and perform uncertainty probability modeling for wind power output and load forecast; Step (2) Establish a two-stage model of stochastic congestion optimization scheduling that considers the uncertainty of wind power based on chance constraints. The first-stage model is a pre-scheduling model based on prediction information, while the second-stage model is an optimization correction model based on the uncertainty of wind power. Step (3) solves the chance-constrained stochastic optimization model by performing uncertainty transformation, and obtains the power generation optimization scheduling strategy.

[0006] As a further aspect of the present invention, the parameters of the thermal power generator in step (1) include: thermal power generator cost coefficient, upper and lower limits of output, ramp rate and upper limit of standby capacity.

[0007] As a further aspect of the present invention, the power grid parameters in step (1) include: upper and lower limits of transmission line transmission threshold, branch reactance matrix and node admittance matrix.

[0008] As a further aspect of the present invention, after obtaining wind power forecasting and load forecasting data in step (1), line power flow is constructed based on grid parameters. Corresponding node PTDF element of power flow transfer distribution factor : in Represents node n for the line The power flow transfer distribution factor; and For reactance matrix Corresponding elements, Here is the nodal admittance matrix; For the line Reactance.

[0009] As a further aspect of the present invention, it also includes modeling and fitting the day-ahead forecasted wind power output and load forecast using a normal distribution: in To account for the actual wind power output and actual load demand after considering uncertainties; To predict wind power output and load demand; The predicted variance of wind power output and load demand when modeling a normal distribution.

[0010] As a further aspect of the present invention, step (2) specifically includes: A one-stage pre-scheduling model is established. The objective function of the model is to minimize the system operating cost based on predictive information, including the scheduling cost of thermal power generators, the electricity cost of spinning reserve, and the expected cost of the lower-level model's objective function. in These represent the conventional power generation cost and spinning reserve cost of thermal power unit g at time t, respectively. The objective function of the lower-level model; These are the power generation cost coefficients for thermal power unit g, respectively; Let g be the power generation of thermal power unit g at time t; is the cost coefficient for the spinning reserve capacity of thermal power unit g; Let g be the standby capacity of the thermal power unit g at time t, representing its rotational reserve.

[0011] As a further aspect of the present invention, it also includes establishing relevant constraints on the upper-level model: in These represent the reactive power output of thermal power units, the reactive power output of wind power units, and the reactive power demand of nodal loads, respectively. These represent the active power output of wind turbines and the active power demand of node loads during the pre-scheduling of the upper-level model, respectively. in, These are the upper and lower limits of the output of thermal power units; in These represent the upper and lower limits for the ramp-up of thermal power units; simultaneously, based on the output characteristics of wind turbine units, thresholds are set for the pre-scheduled output of wind power: This constraint requires that the pre-dispatch output of wind power cannot exceed the upper limit of the day-ahead forecast of wind power; Calculate the net input of each node to establish the node net power flux variable, and then use PTDF to transfer and distribute the node flux to each transmission line of the system to form a power flow: in For the power flow variables on each transmission line during the pre-scheduling process, The power transfer distribution factor of node r to branch l is given. The transmission line power flow is equal to the product of the power transfer factor of each node and its net power flux. A threshold constraint is then established for the transmission line power flow. in This is the upper limit threshold for power flow corresponding to the transmission line.

[0012] As a further aspect of the present invention, step (2) further includes: establishing a two-stage optimization correction model based on wind power uncertainty, with the objective function being to minimize the wind curtailment cost of wind power and load under uncertain operating conditions, while simultaneously satisfying transmission congestion, wind curtailment, and load curtailment opportunity constraints: in To optimize and correct the amount of wind curtailment considering the uncertainties of wind power, This is the wind curtailment penalty coefficient. This refers to the nodal load shedding caused by the optimization of wind power output uncertainty. As the off-load penalty factor, after considering the uncertainties of wind power, adjustments are made to the power supply and demand balance constraints based on the actual wind power output: in The following adjustments are made to optimize the power flow constraints of transmission lines: First, the actual output of thermal power units, the actual output of wind power units, and the actual load at nodes are considered in the optimized scheduling. in To correct the transmission line power flow variables in the optimized scheduling, the non-negativity of wind curtailment and load loss is shown in the computational optimization model, thus providing a non-negativity constraint: By adjusting the power output of thermal power units through the upper / lower rotational reserve capacity in the lower-level modified optimization model, the power output correction constraints of thermal power units are given: in To adjust the on / off spinning reserve capacity of thermal power units in the two-stage correction and optimization model; to constrain the spinning reserve capacity of the lower-level model, limiting it to the range of the reserve capacity pre-scheduled in the upper-level model: Opportunity Constraints: As decision variables, Let Pr be a random variable, and let Pr{} represent the probability of a random event occurring. This represents the objective function and constraints containing random variables; the optimization objective is to achieve the desired result at a confidence level of [missing information]. Search under the circumstances The minimum value, Given the acceptable probability that a random event is not true, chance constraints are then established for wind power utilization, load utilization, and line congestion probability, respectively: in This indicates the wind power utilization rate, requiring that the amount of wind power curtailed cannot exceed the actual wind power output. times; To account for the confidence level set for the probability of wind curtailment, it is required that after correction and optimization, the probability that the system's wind power absorption threshold is greater than the actual wind power output meets the confidence level. ; The following considers the probability of load loss to construct opportunity constraints for load demand: To account for the confidence level set for the probability of load loss, it is required that the probability that the actual load demand of the system is less than the maximum available power after the optimization meets the confidence level. ; The following considers the probability of line congestion to construct opportunity constraints for line power flow: in To account for the confidence level set for the probability of line congestion, it is required that the probability of the transmission line power flow being within the maximum transmission power threshold after correction and optimization meets the confidence level. .

[0013] As a further aspect of the present invention, in step (3), for any form of Opportunity constraints, if Follow the mean The variance is If the distribution follows a normal pattern, then its deterministic equivalent form is: in The cumulative distribution function of the standard normal distribution is the inverse function, i.e., the quantile function. To determine the chance constraint of the wind curtailment probability (WCP), a comprehensive random variable is first constructed: Based on the independence of random variables, calculate respectively mean and standard deviation Then, the chance constraint is transformed into a deterministic linear inequality using the quantile function of the standard normal distribution: Transform the probability-chance constraint of load failure into For the transformation of line congestion probability constraints, the absolute value upper limit constraint is split into two sets of deterministic constraints: upper and lower limits. in The power flow mean and standard deviation are based on pre-scheduled and predicted values, respectively. The power flow transfer factor PTDF is used for initial calculation. After a deterministic transformation, the two-stage model is solved using an optimization solver to obtain the system congestion management and scheduling scheme that takes into account the uncertainty of wind power.

[0014] Furthermore, the present invention also provides a power system chance-constrained stochastic optimization congestion management system that considers wind power uncertainty, comprising: Parameter acquisition module: Acquires thermal power generator parameters, grid parameters, and wind power forecast and load forecast data; establishes node-line power transfer distribution factor based on grid parameters; and performs uncertainty probability modeling for wind power output and load forecast. Model building module: Based on chance constraints, a two-stage model of stochastic congestion optimization scheduling considering wind power uncertainty is established. The first-stage model is a pre-scheduling model based on prediction information, while the second-stage model is an optimization correction model based on wind power uncertainty. The solution module transforms the chance-constrained stochastic optimization model into an uncertainty-based solution, thereby deriving an optimal power generation scheduling strategy.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention directly incorporates the uncertainty of wind power output into the congestion management optimization model for overall solution. Through chance constraints, it effectively controls the probability of power flow exceeding limits and causing congestion on power lines. While ensuring system operational safety, it effectively reduces the risks of wind curtailment and load shedding caused by wind power uncertainty, reduces unnecessary generation reserve provisions, and improves wind power absorption and the overall economic efficiency of the system. Compared to traditional deterministic congestion scheduling methods based solely on predicted output, this method fully utilizes the probability distribution information of wind power output, balancing the operability of the optimization solution with the ability to cope with uncertainty, making it more suitable for the congestion management needs of power systems with a high proportion of wind power integration. Attached Figure Description

[0016] Figure 1 This is a flowchart of an implementation example; Detailed Implementation This embodiment provides a power system opportunity-constrained stochastic optimization congestion management method that considers the uncertainty of wind power.

[0017] Step (1) First, obtain the parameters of the thermal power generator, including: cost coefficient, upper and lower limits of output, ramp rate, and upper limit of reserve capacity; obtain the grid parameters, including: upper and lower limits of transmission line transmission threshold, branch reactance matrix, and node admittance matrix; obtain wind power forecast data and load forecast data. Then, construct the line power flow based on the grid parameters. Corresponding node Power Transform Distribution Factor (PTDF) elements : in Represents node n for the line The power flow transfer distribution factor; and For reactance matrix Corresponding elements, Here is the nodal admittance matrix; For the line Reactance.

[0018] Since the uncertainties in wind power generation and load are inherently random, this method uses a normal distribution model to fit the day-ahead forecasts of wind power output and load: in To account for the actual wind power output and actual load demand after considering uncertainties; To predict wind power output and load demand; The predicted variance of wind power output and load demand when modeling a normal distribution.

[0019] Step (2) Based on the DC-OPF model, a two-stage model based on chance-constrained stochastic optimization is performed on the day-ahead scheduling considering wind power uncertainty. The first-stage model is a pre-scheduling model based on prediction information, while the second-stage model is an optimization correction model based on wind power uncertainty.

[0020] 1) First, a one-stage pre-scheduling model is established. The objective function of the model is to minimize the system operating cost based on predictive information, including the scheduling cost of thermal power generators, the electricity cost of spinning reserve, and the expected cost of the lower-level model's objective function: in These represent the conventional power generation cost and spinning reserve cost of thermal power unit g at time t, respectively. The objective function of the lower-level model; These are the power generation cost coefficients for thermal power unit g, respectively; Let g be the power generation of thermal power unit g at time t; is the cost coefficient for the spinning reserve capacity of thermal power unit g; Let g be the standby capacity of the thermal power unit g at time t, representing its rotational reserve.

[0021] Next, relevant constraints are established for the upper-level model, firstly ensuring the balance between the total active and reactive power output of the power system: in These represent the reactive power output of thermal power units, the reactive power output of wind power units, and the reactive power demand of nodal loads, respectively. These represent the active power output of wind turbines and the active power demand of node loads during the pre-scheduling phase of the upper-level model. This method, by setting the spinning reserve capacity of conventional thermal power units, addresses the load redistribution caused by the uncertainty of wind power output during scheduling, while simultaneously mitigating wind curtailment to some extent. in, These represent the upper and lower limits of the thermal power unit's output, ensuring that the dispatching operation remains within the threshold range that the generator output can withstand. Next, we add ramp constraints for the thermal power unit: in These are the upper and lower limits for the ramp-up of thermal power units, ensuring that changes in thermal power unit output during dispatch do not exceed the corresponding ramp-up thresholds. Simultaneously, based on the output characteristics of wind turbine units, thresholds are set for the pre-dispatch output of wind power. This constraint requires that the pre-dispatch output of wind power cannot exceed the upper limit of the day-ahead wind power forecast. This method establishes the node net power flux variable by calculating the net input of each node, and then uses PTDF to transfer and distribute the node flux to various transmission lines in the system to form a power flow: in For the power flow variables on each transmission line during the pre-scheduling process, The power transfer distribution factor of node r to branch l corresponds to the power transfer factor of each node. The transmission line power flow is equal to the product of the power transfer factor of each node and its net power flux. Using PTDF to calculate the transmission line power flow fully considers the structural constraints of the power system while ensuring the linearity of the power flow model in the optimization model. This simplifies the model, reduces the optimization scheduling time, and facilitates subsequent scheduling processing. Threshold constraints are then established for the transmission line power flow. in This represents the upper limit threshold for power flow corresponding to the transmission line. With this, the first-stage pre-scheduling model is complete.

[0022] 2) Subsequently, a two-stage optimization correction model based on wind power uncertainty is established. The objective function is to minimize the wind curtailment cost of wind power and load under uncertain operating conditions, while satisfying the constraints of transmission congestion, wind curtailment, and load curtailment opportunity. in To optimize and correct the amount of wind curtailment considering the uncertainties of wind power, This is the wind curtailment penalty coefficient. This refers to the nodal load shedding caused by the optimization of wind power output uncertainty. This represents the off-load penalty factor. After considering the uncertainties of wind power, adjustments need to be made to the power supply and demand balance constraints based on the actual wind power output. in This involves considering the actual output of thermal power units, wind power units, and actual load at nodes in the optimized scheduling process. Simultaneously, it involves correcting and optimizing transmission line power flow constraints. in To correct the transmission line power flow variables in the optimized scheduling, the non-negativity of wind curtailment and load loss needs to be demonstrated in the computational optimization model, thus providing a non-negativity constraint: This method mainly adjusts the output of thermal power units in the lower-level correction and optimization model by adjusting the reserve capacity of the thermal power units during their rotation. Therefore, the output correction constraints of thermal power units are given: in To adjust the rotational reserve capacity of thermal power units in the two-stage correction and optimization model, and to maintain the stability of the unit capacity adjustment, it is necessary to constrain the rotational reserve capacity of the lower-level model, limiting it to the range of the reserve capacity pre-scheduled in the upper-level model. Chance constraints are an important branch of stochastic programming, used to solve uncertain optimization problems with random variables under a given confidence level. The general expression for chance constraints is as follows: As decision variables, Let Pr be a random variable, and let Pr{} represent the probability of a random event occurring. This represents the objective function and constraints containing random variables. The optimization objective is to achieve the desired result at a confidence level of [missing information]. Search under the circumstances The minimum value, Let be the acceptable probability that a random event is not true. Then, chance constraints are established for wind power utilization, load utilization, and line congestion probability, respectively: in To express the wind power utilization rate, the amount of wind power curtailed must not exceed the actual wind power output. times; To account for the confidence level set for the probability of wind curtailment, it is required that after correction and optimization, the probability that the system's wind power absorption threshold is greater than the actual wind power output meets the confidence level. .

[0023] The following considers the probability of load loss to construct opportunity constraints for load demand: To account for the confidence level set for the probability of load loss, it is required that the probability that the actual load demand of the system is less than the maximum available power after the optimization meets the confidence level. .

[0024] The following considers the probability of line congestion to construct opportunity constraints for line power flow: in To account for the confidence level set for the probability of line congestion, it is required that the probability of the transmission line power flow being within the maximum transmission power threshold after correction and optimization meets the confidence level. In the one-stage model, hard constraints are directly applied to the transmission line power flow based on the predicted values. In the two-stage model, chance constraints are used instead of hard constraints, and confidence levels are set. This not only ensures the safe operation of the power system but also enhances the system's economic dispatch capacity.

[0025] Step (3) performs a deterministic transformation of the chance constraints. Because stochastic programming involving chance constraints cannot be solved using conventional solvers, a deterministic transformation of the chance constraints is necessary. For any form of... Opportunity constraints, if Follow the mean The variance is If the distribution follows a normal pattern, then its deterministic equivalent form is: in This is the inverse function of the cumulative distribution function of the standard normal distribution, i.e., the quantile function. For the chance-constrained deterministic transformation of the wind curtailment probability (WCP), we first construct a comprehensive random variable: Based on the independence of random variables, calculate respectively mean and standard deviation Then, using the quantile function of the standard normal distribution, the chance constraint is transformed into a deterministic linear inequality: Similar to the probability and chance constraints of wind curtailment, the probability and chance constraints of load shedding can be transformed into For the transformation of line congestion probability constraints, the absolute value upper limit constraint is split into two sets of deterministic constraints: upper and lower limits. in The power flow mean and standard deviation are based on pre-scheduled and predicted values, respectively, and the PTDF power flow transfer factor is still used for initial calculation. After making a deterministic transformation, the two-stage model is solved using an optimization solver to obtain a system congestion management and scheduling scheme that considers wind power uncertainties.

[0026] As a preferred embodiment of the present invention, the present invention also provides a power system chance-constrained stochastic optimization congestion management system that considers wind power uncertainty, comprising: Parameter acquisition module: Acquires thermal power generator parameters, grid parameters, and wind power forecast and load forecast data; establishes node-line power transfer distribution factor based on grid parameters; and performs uncertainty probability modeling for wind power output and load forecast. Model building module: Based on chance constraints, a two-stage model of stochastic congestion optimization scheduling considering wind power uncertainty is established. The first-stage model is a pre-scheduling model based on prediction information, while the second-stage model is an optimization correction model based on wind power uncertainty. The solution module transforms the chance-constrained stochastic optimization model into an uncertainty-based solution, thereby deriving an optimal power generation scheduling strategy. The above-described specific implementations can be partially adjusted by those skilled in the art in different ways without departing from the principles and purpose of the present invention. The scope of protection of the present invention is defined by the claims and is not limited to the above-described specific implementations. All implementation schemes within the scope of the claims are bound by the present invention.

Claims

1. A chance-constrained stochastic optimization congestion management method for power systems considering wind power uncertainties, characterized in that, Includes the following steps: Step (1) Obtain thermal power generator parameters, grid parameters, wind power forecast and load forecast data, establish node-line power transfer distribution factor based on grid parameters, and perform uncertainty probability modeling for wind power output and load forecast; Step (2) Establish a two-stage model of stochastic congestion optimization scheduling that considers the uncertainty of wind power based on chance constraints. The first-stage model is a pre-scheduling model based on prediction information, while the second-stage model is an optimization correction model based on the uncertainty of wind power. Step (3) solves the chance-constrained stochastic optimization model by performing uncertainty transformation, and obtains the power generation optimization scheduling strategy.

2. The power system chance-constrained stochastic optimization congestion management method considering wind power uncertainty according to claim 1, characterized in that, The parameters of the thermal power generator in step (1) include: thermal power generator cost coefficient, upper and lower limits of output, ramp rate and upper limit of standby capacity.

3. The opportunity-constrained stochastic optimization congestion management method for power systems considering wind power uncertainty according to claim 1, characterized in that, The power grid parameters in step (1) include: upper and lower limits of transmission line transmission threshold, branch reactance matrix and node admittance matrix.

4. The power system chance-constrained stochastic optimization congestion management method considering wind power uncertainty according to claim 1, characterized in that, After obtaining wind power forecast and load forecast data in step (1), line power flow is constructed based on grid parameters. Corresponding node PTDF element of power flow transfer distribution factor : in Represents node n for the line The power flow transfer distribution factor; and For reactance matrix Corresponding elements, Here is the nodal admittance matrix; For the line Reactance.

5. A power system chance-constrained stochastic optimization congestion management method considering wind power uncertainty according to claim 4, characterized in that, This also includes modeling and fitting the day-ahead forecasts of wind power output and load using a normal distribution: in To account for the actual wind power output and actual load demand after considering uncertainties; To predict wind power output and load demand; The predicted variance of wind power output and load demand when modeling a normal distribution.

6. The power system chance-constrained stochastic optimization congestion management method considering wind power uncertainty according to claim 1, characterized in that, Step (2) specifically includes: A one-stage pre-scheduling model is established. The objective function of the model is to minimize the system operating cost based on predictive information, including the scheduling cost of thermal power generators, the electricity cost of spinning reserve, and the expected cost of the lower-level model's objective function. in These represent the conventional power generation cost and spinning reserve cost of thermal power unit g at time t, respectively. The objective function of the lower-level model; These are the power generation cost coefficients for thermal power unit g, respectively; Let g be the power generation of thermal power unit g at time t; is the cost coefficient for the spinning reserve capacity of thermal power unit g; Let g be the standby capacity of the thermal power unit g at time t, representing its rotational reserve.

7. A power system chance-constrained stochastic optimization congestion management method considering wind power uncertainty according to claim 6, characterized in that, This also includes establishing relevant constraints on the upper-level model: in These represent the reactive power output of thermal power units, the reactive power output of wind power units, and the reactive power demand of nodal loads, respectively. These represent the active power output of wind turbines and the active power demand of node loads during the pre-scheduling of the upper-level model, respectively. in, These are the upper and lower limits of the output of thermal power units; in These represent the upper and lower limits for the ramp-up of thermal power units; simultaneously, based on the output characteristics of wind turbine units, thresholds are set for the pre-scheduled output of wind power: This constraint requires that the pre-dispatch output of wind power cannot exceed the upper limit of the day-ahead forecast of wind power; Calculate the net input of each node to establish the node net power flux variable, and then use PTDF to transfer and distribute the node flux to each transmission line of the system to form a power flow: in For the power flow variables on each transmission line during the pre-scheduling process, The power transfer distribution factor of node r to branch l is given. The transmission line power flow is equal to the product of the power transfer factor of each node and its net power flux. A threshold constraint is then established for the transmission line power flow. in This is the upper limit threshold for power flow corresponding to the transmission line.

8. A power system chance-constrained stochastic optimization congestion management method considering wind power uncertainty according to claim 7, characterized in that, Step (2) further includes: establishing a two-stage optimization correction model based on wind power uncertainty, with the objective function being to minimize the wind curtailment cost of wind power and load under uncertain operating conditions, while simultaneously satisfying transmission congestion, wind curtailment, and load curtailment opportunity constraints: in To optimize and correct the amount of wind curtailment considering the uncertainties of wind power, This is the wind curtailment penalty coefficient. This refers to the nodal load shedding caused by the optimization of wind power output uncertainty. As the off-load penalty factor, after considering the uncertainties of wind power, adjustments are made to the power supply and demand balance constraints based on the actual wind power output: in The following adjustments are made to optimize the power flow constraints of transmission lines: First, the actual output of thermal power units, the actual output of wind power units, and the actual load at nodes are considered in the optimized scheduling. in To correct the transmission line power flow variables in the optimized scheduling, the non-negativity of wind curtailment and load loss is shown in the computational optimization model, thus providing a non-negativity constraint: By adjusting the power output of thermal power units through the upper / lower rotational reserve capacity in the lower-level modified optimization model, the power output correction constraints of thermal power units are given: in To adjust the on / off spinning reserve capacity of thermal power units in the two-stage correction and optimization model; to constrain the spinning reserve capacity of the lower-level model, limiting it to the range of the reserve capacity pre-scheduled in the upper-level model: Opportunity Constraints: As decision variables, Let Pr be a random variable, and let Pr{} represent the probability of a random event occurring. This represents the objective function and constraints containing random variables; the optimization objective is to achieve the desired result at a confidence level of [missing information]. Search under the circumstances The minimum value, Given the acceptable probability that a random event is not true, chance constraints are then established for wind power utilization, load utilization, and line congestion probability, respectively: in This indicates the wind power utilization rate, requiring that the amount of wind power curtailed cannot exceed the actual wind power output. times; To account for the confidence level set for the probability of wind curtailment, it is required that after correction and optimization, the probability that the system's wind power absorption threshold is greater than the actual wind power output meets the confidence level. ; The following considers the probability of load loss to construct opportunity constraints for load demand: To account for the confidence level set for the probability of load loss, it is required that the probability that the actual load demand of the system is less than the maximum available power after the optimization meets the confidence level. ; The following considers the probability of line congestion to construct opportunity constraints for line power flow: in To account for the confidence level set for the probability of line congestion, it is required that the probability of the transmission line power flow being within the maximum transmission power threshold after correction and optimization meets the confidence level. .

9. A power system chance-constrained stochastic optimization congestion management method considering wind power uncertainty according to claim 8, characterized in that, In step (3), for any form of Opportunity constraints, if Follow the mean The variance is If the distribution follows a normal pattern, then its deterministic equivalent form is: in The cumulative distribution function of the standard normal distribution is the inverse function, i.e., the quantile function. To determine the chance constraint of the wind curtailment probability (WCP), a comprehensive random variable is first constructed: Based on the independence of random variables, calculate respectively mean and standard deviation Then, the chance constraint is transformed into a deterministic linear inequality using the quantile function of the standard normal distribution: Transform the probability-chance constraint of load failure into For the transformation of line congestion probability constraints, the absolute value upper limit constraint is split into two sets of deterministic constraints: upper and lower limits. in The power flow mean and standard deviation are based on pre-scheduled and predicted values, respectively. The power flow transfer factor PTDF is used for initial calculation. After a deterministic transformation, the two-stage model is solved using an optimization solver to obtain the system congestion management and scheduling scheme that takes into account the uncertainty of wind power.

10. A chance-constrained stochastic optimization congestion management system for power systems considering wind power uncertainties, characterized in that, include: Parameter acquisition module: Acquires thermal power generator parameters, grid parameters, and wind power forecast and load forecast data; establishes node-line power transfer distribution factor based on grid parameters; and performs uncertainty probability modeling for wind power output and load forecast. Model building module: Based on chance constraints, a two-stage model of stochastic congestion optimization scheduling considering wind power uncertainty is established. The first-stage model is a pre-scheduling model based on prediction information, while the second-stage model is an optimization correction model based on wind power uncertainty. The solution module transforms the chance-constrained stochastic optimization model into an uncertainty-based solution, thereby deriving an optimal power generation scheduling strategy.

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