A robust-stochastic decision control method for wind storage system considering internal and external uncertainties

Abstract

The application discloses a kind of wind storage system robust-random decision control methods considering internal and external uncertain factors.Method includes: first, according to the uncertain factors of wind storage system and internal component factors, determine the energy consumption objective function of wind storage system and establish the energy consumption model of wind storage system;Then, according to the factors except wind storage system, determine the clear objective function and establish the clear model;Again, according to the energy consumption model obtained and the clear model obtained, a double-layer model of wind storage system is established;Finally, the embedded Karush-Kuhn-Tucker condition method and the improved constraint and column generation algorithm are used in turn to optimize and solve the double-layer model of wind storage system, and the lowest total energy consumption of wind storage system is obtained.The application considers comprehensively, covers factors sufficiently, so that the parameter evaluation of operation is more in line with the actual, and the lowest energy consumption can bring the maximum storage value.

Description

Technical Field

[0001] This invention relates to a method for minimizing wind and energy storage energy consumption, specifically to a robust-stochastic decision control method for wind and energy storage systems that takes into account internal and external uncertainties. Background Technology

[0002] With the deepening of power system reform, the requirements for energy conservation and carbon reduction are becoming increasingly prominent, and the proportion of renewable energy in total power generation is rising year by year. Wind power generation systems typically require energy storage facilities to form wind-storage systems. This system can not only effectively absorb wind power generation but also utilize energy storage characteristics. However, wind-storage systems face various types of uncertainties and are affected by the fluctuations in wind power output itself. The interplay of multiple uncertainties inevitably affects the energy consumption of wind-storage systems. Therefore, determining a scientifically reasonable energy consumption cost for wind-storage systems has significant research value and practical significance. Summary of the Invention

[0003] To address the problems existing in the background technology, this invention provides a robust-stochastic decision control method for wind-storage systems that considers internal and external uncertainties. By using a stochastic-robust approach, various internal and external uncertainties faced by the wind-storage system are modeled to fully account for their impact. The energy storage and wind power of the wind-storage system are modeled, as is the day-ahead clearing, and the optimal objective function and safe operation constraints of both models are determined. The model is transformed and solved using an embedded Caro-Kuhn-Tucker condition method and an improved constraint and column generation algorithm. The optimization problem is formulated using Matlab, and the solver obtains the minimum total energy consumption of the wind-storage system. Finally, the operating parameters are evaluated and input into the power grid for decision control.

[0004] The technical solution adopted in this invention is:

[0005] The present invention provides a robust-stochastic decision control method for a wind-storage system that takes into account internal and external uncertainties, comprising the following steps:

[0006] S1. Determine the energy consumption objective function of the wind-storage system based on the uncertainties and internal components of the wind-storage system, and establish the energy consumption model of the wind-storage system.

[0007] S2. Determine the clearing objective function and establish a clearing model based on factors other than the wind storage system.

[0008] S3. Establish a two-layer model of the wind-storage system based on the energy consumption model obtained in step S1 and the clearing model obtained in step S2.

[0009] S4. The embedded Caro-Kun-Tucker condition method and the improved constraint and column generation algorithm are used to optimize and solve the two-layer model of the wind-storage system established in step S3 to obtain the minimum total energy consumption of the wind-storage system.

[0010] Step S1 specifically involves:

[0011] S11. Model the uncertainties outside the wind storage system, including power output fluctuations and real-time energy consumption costs outside the wind storage system.

[0012] S12. Model the uncertainties within the wind storage system, including power output fluctuations within the wind storage system.

[0013] S13. Model the internal components of the wind-storage system, which include the energy storage system and the wind turbine generator.

[0014] S14. Based on the modeling in steps S11-S13, determine the energy consumption objective function of the wind-storage system and establish the energy consumption model of the wind-storage system.

[0015] The energy consumption objective function of the wind-storage system in step S1 is specifically set according to the following formula:

[0016]

[0017] Where s represents the real-time scenario; t represents the time point; q represents the scenario where the day-ahead uncertainty is generated; w represents the label of the wind turbine unit within the wind storage; ρ q With ρ s This represents the probability of generating a single scene from two different scenarios. This represents the unit energy cost of wind curtailment from wind turbines. This indicates the clearing energy cost of the node where the wind-storage system is located; and These represent the day-ahead charging power of the wind-storage system, the day-ahead discharging power of the wind-storage system, the real-time output of the internal wind power, and the difference between the real-time and day-ahead power of the wind-storage system, respectively. This represents the actual predicted value of real-time energy consumption costs in a given scenario. This indicates the actual upper limit of the internal wind power output; This represents the real-time clearing output value of the internal wind power; maxmin represents the maximum and minimum value problem, that is, solving for the optimal state under the worst case.

[0018] The energy consumption model of the wind-storage system in step S1 satisfies the following constraints:

[0019]

[0020]

[0021] Wherein, wo and na represent the code of the external wind turbine and the node number of the wind-storage connection, respectively; Indicates the upper limit of external wind power output; ε represents the historical predicted average value of external wind power output; wo,t,q Indicates historical prediction error; The standard deviation of historical prediction errors; This represents the real-time predicted average. This indicates the real-time prediction error; The standard deviation represents a normal distribution, N() represents a normal distribution; w represents the number of the wind turbine unit inside the wind storage unit; and The flags, 0-1 variables, represent the upper and lower limits of the output of the internal wind turbine units. 0 indicates that the internal fan output has not reached its maximum capacity; 1 indicates that the internal fan output has reached its maximum capacity. 0 indicates that the internal fan output has not reached the lower limit; 1 indicates that the internal fan output has reached its lower limit; and These represent the historical predicted average, historical upper limit, and historical lower limit of wind power output, respectively; Γ1 and Γ2 represent the conservatism coefficients in the dimensions of wind turbine count and time, respectively; e represents the energy storage label; and T represents the label of the last time period. and These represent the real-time charging power and the real-time discharging power of the internal energy storage, respectively. and These represent the maximum charging value and the maximum discharging value of the energy storage, respectively. Indicators representing charging and discharging; This represents the stored energy at time t; This represents the stored energy at time t-1. and These represent the upper and lower limits of the stored energy, respectively; η ec and η ed These represent the charging efficiency and discharging efficiency of energy storage, respectively; E e,0 This represents the initial value of the stored energy; This represents the final value of the stored energy in real-time. and These represent the output of internal wind power, the charging power of energy storage, and the discharging power of energy storage under the day-ahead condition, respectively. This indicates the maximum output value of the internal wind power; This indicates the energy stored in the energy storage system as of the current date. This indicates the energy stored at the end of the current state of energy storage. This indicates the energy stored in the previous time period, representing the current state of the energy storage. With Oacmax These represent the charging energy consumption cost and the upper limit of the charging energy consumption cost of the wind-storage system, respectively. With O admax These represent the discharge energy consumption cost and the upper limit of the discharge energy consumption cost of the wind-storage system, respectively. and These represent the charge and discharge quantities of the wind-storage system, respectively. It is the state variable of the wind-storage system during charging and discharging.

[0022] The clearing objective function in step S2 is specifically set according to the following formula:

[0023]

[0024] Where g represents the ordinary unit number; G represents the number of ordinary units; These represent the energy consumption costs of traditional turbine units, loads, and stand-alone wind power stations, respectively. This indicates the cleared power of a regular generating unit; Indicates the amount of load cleared; This indicates the cleared electricity volume of external independent wind power as of the current day.

[0025] The clearing model established in step S2 satisfies the following constraints:

[0026]

[0027]

[0028] Where d is the load number; This indicates the maximum output capacity of a standard generator unit; and These represent the dual variables of the upper and lower limits of the traditional unit constraints, respectively; and These represent the upper and lower limits of the load clearing volume, respectively. and These represent the dual variables of the upper and lower load limits, respectively; and These represent the upper and lower limits of charging and discharging in the wind-storage system, as well as the dual variables of the upper and lower limit constraints, respectively. This indicates the clearing volume of independent wind farms; Indicates the upper limit of external wind power output. and These represent the dual variables of the upper and lower limits of the wind turbine output constraints, respectively; This represents the phase angle of node n in the system. This represents the phase angle of node m in the system; B represents the upper limit of transmission from branch n to m; nmThis represents the nodal admittance matrix of the system; The dual variable representing the upper limit constraint of transmission from transmission line n to m; and Let represent the dual variables of the upper and lower bound constraints of the phase angle of node n, respectively; Indicates the phase angle of the reference node; Denotes the dual variable of the phase angle constraint at the reference node; d∈Ψ n Indicates the node to which the load is connected; g∈Ψ n This represents the node connected to a regular generator set; a∈Ψ n This represents the nodes connected to the wind-storage system; w∈Ψ n This represents the node connected to an external independent wind turbine; m∈Θ n This indicates that m is a node connected to n; It represents the unit energy consumption value cleared at the day-ahead node of the power system.

[0029] Step S4 specifically involves:

[0030] S41. The two-layer model of the wind-storage system established in step S3 is optimized into a single-layer model using the embedded Carlow-Kuhn-Tucker condition method.

[0031] S42. Solve the single-layer model obtained in step S41 using an improved constraint and column generation algorithm.

[0032] S43. Based on the solution results, obtain the minimum total energy consumption of the wind-storage system.

[0033] The objective function of the single-layer model obtained in step S41 is specifically set according to the following formula:

[0034]

[0035] in, Indicates real-time energy consumption cost; This indicates the current day's energy consumption cost.

[0036] The single-layer model obtained in step S41 satisfies the following constraints:

[0037]

[0038]

[0039] Where n (m∈Θ) represents the node number; This indicates the day-ahead cleared output of external independent wind power. This indicates the real-time output power of the internal wind power system. This indicates the clearing of connection nodes for ordinary generating units; This represents the clearing unit energy consumption value of the day-ahead load connection node; This represents the cleared unit energy consumption value of the current day wind-storage system connection node; This represents the cleared unit energy consumption value of the external independent wind power connection node as of the date of issue; The day-ahead cleared unit energy consumption value of a node in a branch of the power system; This represents the day-ahead cleared unit energy consumption value of another node in a branch of the power system; This represents the unit energy consumption value reported by ordinary generating units; This indicates the unit energy consumption value reported for the load; This indicates the reported unit energy consumption value for charging the wind-storage system; This indicates the unit energy consumption value reported for discharge from the wind-storage system; This indicates the unit energy consumption value reported by the external independent fan; This indicates the clearing load value under the current day-ahead load condition.

[0040] Step S42 specifically involves:

[0041] 1) Divide the objective function of the single-layer model obtained in step S41 into the principal problem. The problem of succubi And add additional constraints. The η q Represents the set of parameters for minimum total energy consumption; the This represents the real-time energy consumption cost for the kth iteration.

[0042] 2) Set the upper bound of the objective function value as LB = -∞ and the lower bound as UB = ∞, and initialize the iteration number k, the convergence constraint parameter v, and the robust uncertainty parameter. Preset convergence limit parameter v.

[0043] 3) Use a solver for the main problem Solving this problem yields the optimal day-ahead energy cost for the main problem. and the optimal set of minimum total energy consumption parameters And utilize the optimal day-ahead energy cost and the optimal set of minimum total energy consumption parameters Update the lower bound to

[0044] 4) Obtain the optimal set of minimum total energy consumption parameters for the main problem obtained in step 3). Substitute into the subproblem The optimal real-time energy cost is obtained by using a solver. And utilize the optimal real-time energy consumption cost Update the upper bound to

[0045] 5) Based on the updated upper and lower bounds, determine Does it meet the following requirements:

[0046] If the conditions are met, stop the iteration and obtain the solution result;

[0047] If the condition is not met, repeat steps 3)-5) and update the iteration count k.

[0048] The innovation of this invention lies in fully considering the uncertainties inside and outside the wind-storage system, the internal component factors, and the factors outside the wind-storage system. It adopts the embedded Caro-Kuhn-Tucker condition method and the improved constraint and column generation algorithm to achieve comprehensive consideration and full coverage of factors. This makes the parameter evaluation of operation more in line with reality, and the lowest energy consumption can bring the greatest energy storage value.

[0049] The beneficial effects of this invention are:

[0050] 1. This invention fully considers the uncertainties inside and outside the wind storage system, the internal component factors, and the factors outside the wind storage system. This reflects the comprehensiveness of the invention and the full coverage of factors, making the evaluation of operating parameters more in line with reality, and the lowest energy consumption obtained can bring the greatest energy storage value.

[0051] 2. This invention fully considers the mutual influence between the wind storage system and the clearing process, so that the energy consumption of the wind storage system is minimized. Attached Figure Description

[0052] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0053] The present invention will now be described in more detail with reference to the accompanying drawings and embodiments. However, the present invention is not limited thereto. For those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention. Contents not described in detail in this specification are prior art known to those skilled in the art.

[0054] like Figure 1 As shown, the embodiments of the present invention include the following steps:

[0055] S1. Determine the energy consumption objective function of the wind-storage system based on the uncertainties and internal components of the wind-storage system, and establish the energy consumption model of the wind-storage system.

[0056] S11. The Monte Carlo random scenario generation method is used to model the uncertainties outside the wind storage system. The uncertainties outside the wind storage system include the power output fluctuations and real-time energy consumption costs outside the wind storage system.

[0057] S12. A robust method is used to model the uncertainties within the wind-storage system, including power output fluctuations within the system.

[0058] S13. Model the internal components of the wind-storage system, which include the energy storage system and the wind turbine.

[0059] S14. Based on the modeling in steps S11-S13, determine the energy consumption objective function of the wind-storage system and establish the energy consumption model of the wind-storage system.

[0060] The energy consumption objective function for the wind-storage system is set according to the following formula:

[0061]

[0062] Where s represents a real-time scenario, S represents the number of real-time scenarios and s∈S; t represents a time point; q represents a scenario generated by day-ahead uncertainty, Q represents the number of scenarios generated by day-ahead uncertainty and q∈Q; w represents the label of the wind turbine unit within the wind storage; ρ q With ρ s This represents the probability of generating a single scene from two different scenarios. This represents the unit energy cost of wind curtailment from wind turbines. This indicates the clearing energy cost of the node where the wind-storage system is located; and These represent the day-ahead charging power of the wind-storage system, the day-ahead discharging power of the wind-storage system, the real-time output of the internal wind power, and the difference between the real-time and day-ahead power of the wind-storage system, respectively. This represents the actual predicted value of real-time energy consumption costs in a given scenario. This indicates the actual upper limit of the internal wind power output; This represents the real-time clearing output value of the internal wind power; maxmin represents the maximum and minimum value problem, that is, solving for the optimal state under the worst case.

[0063] The energy consumption model of the wind-storage system satisfies the following constraints:

[0064]

[0065]

[0066]

[0067] Wherein, wo and na represent the code of the external wind turbine and the node number of the wind-storage connection, respectively; Indicates the upper limit of external wind power output; ε represents the historical predicted average value of external wind power output;wo,t,q This represents the historical prediction error, which follows a normal distribution. The standard deviation of historical prediction errors; This represents the real-time predicted average value; This represents the real-time prediction error, which also conforms to having a mean of 0. The standard deviation represents a normal distribution, N() represents a normal distribution; w represents the number of the wind turbine unit inside the wind storage unit; and The flags, 0-1 variables, represent the upper and lower limits of the output of the internal wind turbine units. 0 indicates that the internal fan output has not reached its maximum capacity; 1 indicates that the internal fan output has reached its maximum capacity. 0 indicates that the internal fan output has not reached the lower limit; 1 indicates that the internal fan output has reached its lower limit; and These represent the historical predicted average, historical upper limit, and historical lower limit of wind power output, respectively; Γ1 and Γ2 represent the conservatism coefficients in the dimensions of wind turbine count and time, respectively; e represents the energy storage label; and T represents the label of the last time period. and These represent the real-time charging power and the real-time discharging power of the internal energy storage, respectively. and These represent the maximum charging value and the maximum discharging value of the energy storage, respectively. Indicators representing charging and discharging; This represents the stored energy at time t; This represents the stored energy at time t-1. and These represent the upper and lower limits of the stored energy, respectively; η ec and η ed These represent the charging efficiency and discharging efficiency of energy storage, respectively; E e,0 This represents the initial value of the stored energy; This represents the final value of the stored energy in real-time. and These represent the output of internal wind power, the charging power of energy storage, and the discharging power of energy storage under the day-ahead condition, respectively. This indicates the maximum output value of the internal wind power; This indicates the energy stored in the energy storage system as of the current date. This indicates the energy stored at the end of the current state of energy storage. This indicates the energy stored in the previous time period, representing the current state of the energy storage. With O acmaxThese represent the charging energy consumption cost and the upper limit of the charging energy consumption cost of the wind-storage system, respectively. With O admax These represent the discharge energy consumption cost and the upper limit of the discharge energy consumption cost of the wind-storage system, respectively. and These represent the charge and discharge quantities of the wind-storage system, respectively. It is the state variable of the charging and discharging of the wind storage system.

[0068] S2. Determine the clearing objective function and establish a clearing model based on factors other than the wind storage system.

[0069] S3. Based on the energy consumption model obtained in step S1 and the clearing model obtained in step S2, establish a two-layer model of the wind-storage system. The energy consumption model obtained in step S1 is the upper-layer model, and the clearing model obtained in step S2 is the lower-layer model.

[0070] The clearing objective function in step S2 is set according to the following formula:

[0071]

[0072] Where g represents the ordinary unit number; G represents the number of ordinary units; These represent the energy consumption costs of traditional turbine units, loads, and stand-alone wind power stations, respectively. This indicates the cleared power of a regular generating unit; Indicates the amount of load cleared; This indicates the cleared electricity volume of external independent wind power as of the current day.

[0073] The established clearing model satisfies the following constraints:

[0074]

[0075] The wind-storage system is connected to the na node of the main grid; d is the load number. This indicates the maximum output capacity of a standard generator unit; and These represent the dual variables of the upper and lower limits of the traditional unit constraints, respectively; and These represent the upper and lower limits of the load clearing volume, respectively. and These represent the dual variables of the upper and lower load limits, respectively; and These represent the upper and lower limits of charging and discharging in the wind-storage system, as well as the dual variables of the upper and lower limit constraints, respectively. This indicates the clearing volume of independent wind farms; Indicates the upper limit of external wind power output. and These represent the dual variables of the upper and lower limits of the wind turbine output constraints, respectively; This represents the phase angle of node n in the system. This represents the phase angle of node m in the system; B represents the upper limit of transmission from branch n to m; nm This represents the nodal admittance matrix of the system; The dual variable representing the upper limit constraint of transmission from transmission line n to m; and Let represent the dual variables of the upper and lower bound constraints of the phase angle of node n, respectively; Indicates the phase angle of the reference node; Denotes the dual variable of the phase angle constraint at the reference node; d∈Ψ n Indicates the node to which the load is connected; g∈Ψ n This represents the node connected to a regular generator set; a∈Ψ n This represents the nodes connected to the wind-storage system; w∈Ψ n This represents the node connected to an external independent wind turbine; m∈Θ n This indicates that m is a node connected to n; It represents the unit energy consumption value cleared at the day-ahead node of the power system.

[0076] S4. The embedded Caro-Kun-Tucker condition method and the improved constraint and column generation algorithm are used to optimize and solve the two-layer model of the wind-storage system established in step S3 to obtain the minimum total energy consumption of the wind-storage system.

[0077] S41. The two-layer model of the wind-storage system established in step S3 is optimized into a single-layer model using the embedded Carlow-Kuhn-Tucker condition method.

[0078] The objective function of the single-layer model obtained in step S41 is set according to the following formula:

[0079]

[0080] in, Indicates real-time energy consumption cost; This indicates the current energy consumption cost.

[0081] The single-layer model obtained in step S41 satisfies the following constraints:

[0082]

[0083]

[0084] Where n (m∈Θ) represents the node number; This indicates the day-ahead cleared output of external independent wind power. This indicates the real-time output power of the internal wind power system. This indicates the clearing of connection nodes for ordinary generating units; This represents the clearing unit energy consumption value of the day-ahead load connection node; This represents the cleared unit energy consumption value of the current day wind-storage system connection node; This represents the cleared unit energy consumption value of external independent wind power connection nodes as of the date of issue; The day-ahead cleared unit energy consumption value of a node in a branch of the power system; This represents the day-ahead cleared unit energy consumption value of another node in a branch of the power system; This represents the unit energy consumption value reported by ordinary generating units; This indicates the unit energy consumption value reported for the load; This indicates the reported unit energy consumption value for charging the wind-storage system; This indicates the unit energy consumption value reported for discharge from the wind-storage system; This indicates the unit energy consumption value reported by the external independent fan; This represents the clearing load value under the current day-ahead load condition. S42. Solve the single-layer model obtained in step S41 using an improved constraint and column generation algorithm;

[0085] 1) Divide the objective function of the single-layer model obtained in step S41 into the principal problem. The problem of succubi And add additional constraints. The η q Represents the set of parameters for minimum total energy consumption; the This represents the real-time energy consumption cost for the kth iteration.

[0086] 2) Set the upper bound of the objective function value as LB = -∞ and the lower bound as UB = ∞, and initialize the iteration number k, the convergence constraint parameter v, and the robust uncertainty parameter. Preset convergence limit parameter v.

[0087] 3) Use a solver for the main problem Solving this problem yields the optimal day-ahead energy cost for the main problem. and the optimal set of minimum total energy consumption parameters And utilize the optimal day-ahead energy cost and the optimal set of minimum total energy consumption parameters Update the lower bound to

[0088] 4) Obtain the optimal set of minimum total energy consumption parameters for the main problem obtained in step 3). Substitute into the subproblem The optimal real-time energy cost is obtained by using a solver. And utilize the optimal real-time energy consumption cost Update the upper bound to

[0089] 5) Based on the updated upper and lower bounds, determine Does it meet the following requirements:

[0090] If the conditions are met, stop the iteration and obtain the solution result;

[0091] If the condition is not met, repeat steps 3)-5) and update the iteration count k.

[0092] The innovation of this invention lies in fully considering the uncertainties inside and outside the wind-storage system, the internal component factors, and the factors outside the wind-storage system. It adopts the embedded Caro-Kuhn-Tucker condition method and the improved constraint and column generation algorithm to achieve comprehensive consideration and full coverage of factors. This makes the parameter evaluation of operation more in line with reality, and the lowest energy consumption can bring the greatest energy storage value.

Claims

1. A robust-stochastic decision control method for a wind-storage system considering internal and external uncertainties, characterized in that the method... include: S1. Determine the energy consumption objective function of the wind storage system and establish the energy consumption model of the wind storage system based on the uncertainties and internal components of the wind storage system. S2. Determine the clearing objective function and establish a clearing model based on factors other than the wind storage system; S3. Establish a two-layer model of the wind-storage system based on the energy consumption model obtained in step S1 and the clearing model obtained in step S2. S4. The embedded Caro-Kuhn-Tucker conditional method and the improved constraint and column generation algorithm are used to optimize and solve the two-layer model of the wind-storage system established in step S3 to obtain the minimum total energy consumption of the wind-storage system. Step S1 specifically involves: S11. Model the uncertainties outside the wind storage system, including power output fluctuations and real-time energy consumption costs outside the wind storage system. S12. Model the uncertainties within the wind storage system, including the power output fluctuations within the wind storage system. S13. Model the internal components of the wind storage system, which include the energy storage system and the wind turbine generator. S14. Based on the modeling in steps S11-S13, determine the energy consumption objective function of the wind storage system and establish the energy consumption model of the wind storage system. The energy consumption objective function of the wind-storage system in step S1 is specifically set according to the following formula: in, Represents a real-time scene; Indicates a point in time; This indicates a scenario where uncertainty arises recently; Indicates the designation of the wind turbine unit inside the wind storage unit; and This represents the probability of generating a single scene from two different scenarios. This represents the unit energy cost of wind curtailment from wind turbines. This indicates the clearing energy cost of the node where the wind-storage system is located; , , and These represent the day-ahead charging power of the wind-storage system, the day-ahead discharging power of the wind-storage system, the real-time output of the internal wind power, and the difference between the real-time and day-ahead power of the wind-storage system, respectively. This represents the actual predicted value of real-time energy consumption costs in a given scenario. This indicates the actual upper limit of the internal wind power output; maxmin represents the maximum and minimum value problem, that is, solving for the optimal state in the worst case. The energy consumption model of the wind-storage system in step S1 satisfies the following constraints: ， ， Wherein, wo and na represent the code of the external wind turbine and the node number of the wind-storage connection, respectively; Indicates the upper limit of external wind power output; This represents the historical forecast average value of external wind power output; Indicates historical prediction error; The standard deviation of historical prediction errors; This represents the real-time predicted average. This indicates the real-time prediction error; The normal distribution representing the standard deviation, Indicates a normal distribution; w represents the number of the wind turbine unit inside the wind storage unit; and The flags, 0-1 variables, represent the upper and lower limits of the output of the internal wind turbine units. 0 indicates that the internal fan output has not reached its maximum capacity; 1 indicates that the internal fan output has reached its maximum capacity. 0 indicates that the internal fan output has not reached the lower limit; 1 indicates that the internal fan output has reached its lower limit; , and These represent the historical predicted average, historical upper limit, and historical lower limit of internal wind power output, respectively. , These represent the conservative coefficients for the number of wind turbines and the time dimension, respectively; e represents the energy storage designation; and T represents the designation of the last time period. and These represent the real-time charging power and the real-time discharging power of the internal energy storage, respectively. and These represent the maximum charging value and the maximum discharging value of the energy storage, respectively. Indicators representing charging and discharging; This represents the stored energy at time t; This represents the stored energy at time t-1. and These represent the upper and lower limits of the stored energy, respectively; and These represent the charging efficiency and discharging efficiency of energy storage, respectively. This represents the initial value of the stored energy; This represents the final value of the stored energy in real-time. , and These represent the output of internal wind power, the charging power of energy storage, and the discharging power of energy storage under the day-ahead condition, respectively. This indicates the maximum output value of the internal wind power; This indicates the energy stored in the energy storage system as of the current date. This indicates the energy stored at the end of the current state of energy storage. This indicates the energy stored in the previous time period, representing the current state of the energy storage. and These represent the charging energy consumption cost and the upper limit of the charging energy consumption cost of the wind-storage system, respectively. and These represent the discharge energy consumption cost and the upper limit of the discharge energy consumption cost of the wind-storage system, respectively. and These represent the charge and discharge quantities of the wind-storage system, respectively. It is the state variable of the wind-storage system during charging and discharging.

2. The robust-stochastic decision control method for a wind-storage system considering internal and external uncertainties according to claim 1, characterized in that: The clearing objective function in step S2 is specifically set according to the following formula: in, G indicates the number of ordinary generating units; G indicates the quantity of ordinary generating units. , , These represent the unit energy consumption value reported by ordinary generating units, the energy consumption cost of the load, and the unit energy consumption value reported by external independent fans, respectively. This indicates the cleared power of a regular generating unit; Indicates the amount of load cleared; This indicates the cleared electricity volume of external independent wind power as of the current day.

3. The robust-stochastic decision control method for a wind-storage system considering internal and external uncertainties according to claim 1, characterized in that: The clearing model established in step S2 satisfies the following constraints: Where d is the load number; This indicates the maximum output capacity of a standard generator unit; and These represent the dual variables of the upper and lower limits of the traditional unit constraints, respectively; and These represent the upper and lower limits of the load clearing volume, respectively. and These represent the dual variables of the upper and lower load limits, respectively; , , and These represent the upper and lower limits of charging and discharging in the wind-storage system, as well as the dual variables of the upper and lower limit constraints, respectively. This indicates the clearing volume of independent wind farms; Indicates the upper limit of external wind power output. and These represent the dual variables of the upper and lower limits of the wind turbine output constraints, respectively; This represents the phase angle of node n in the system. This represents the phase angle of node m in the system; This represents the upper limit of transmission from branch n to m; This represents the nodal admittance matrix of the system; The dual variable representing the upper limit constraint of transmission from transmission line n to m; and Let represent the dual variables of the upper and lower bound constraints of the phase angle of node n, respectively; Indicates the phase angle of the reference node; Represents the dual variable of the phase angle constraint at the reference node; Indicates the node to which the load is connected; This indicates the node to which a regular generator unit is connected; This indicates the nodes connected to the wind storage system; This indicates the node connected to an external independent wind farm; This indicates that m is a node connected to n; It represents the unit energy consumption value cleared at the day-ahead node of the power system.

4. The robust-stochastic decision control method for a wind-storage system considering internal and external uncertainties according to claim 1, characterized in that: Step S4 specifically involves: S41. The two-layer model of the wind-storage system established in step S3 is optimized into a single-layer model using the embedded Carlow-Kuhn-Tucker condition method. S42. Solve the single-layer model obtained in step S41 using an improved constraint and column generation algorithm; S43. Based on the solution results, obtain the minimum total energy consumption of the wind-storage system.

5. A robust-stochastic decision control method for a wind-storage system considering internal and external uncertainties according to claim 4, characterized in that: The objective function of the single-layer model obtained in step S41 is specifically set according to the following formula: in, Indicates real-time energy consumption cost; This indicates the current day's energy consumption cost.

6. A robust-stochastic decision control method for a wind-storage system considering internal and external uncertainties according to claim 4, characterized in that: The single-layer model obtained in step S41 satisfies the following constraints: , , , , , , in, Indicates the node number; This indicates the day-ahead cleared output of external independent wind power. This indicates the real-time output power of the internal wind power system. This indicates the clearing of connection nodes for ordinary generating units; This represents the clearing unit energy consumption value of the day-ahead load connection node; This represents the cleared unit energy consumption value of the current day wind-storage system connection node; This represents the cleared unit energy consumption value of the external independent wind power connection node as of the date of issue; This represents the cleared unit energy consumption value of a node on a specific branch of the power system. This represents the day-ahead cleared unit energy consumption value of another node in a branch of the power system; This represents the unit energy consumption value reported by ordinary generating units; This indicates the unit energy consumption value reported for the load; This indicates the reported unit energy consumption value for charging the wind-storage system; This indicates the unit energy consumption value reported for discharge from the wind-storage system; This indicates the unit energy consumption value reported by the external independent fan; This indicates the clearing load value under the current day-ahead load condition.

7. A robust-stochastic decision control method for a wind-storage system considering internal and external uncertainties according to claim 5, characterized in that: Step S42 specifically involves: 1) Divide the objective function of the single-layer model obtained in step S41 into the principal problem. The problem of succubi And add additional constraints. The Represents the set of parameters for minimum total energy consumption; the This represents the real-time energy cost of the kth iteration; 2) Set the upper bound of the objective function value as and lower bound as The iteration count k, convergence constraint parameter v, and robust uncertainty parameter are initialized. Preset convergence limit parameter v; 3) Use a solver for the main problem Solving this problem yields the optimal day-ahead energy cost for the main problem. and the optimal set of minimum total energy consumption parameters And utilize the optimal daytime energy cost. and the optimal set of minimum total energy consumption parameters Update the lower bound to ; 4) Obtain the optimal set of minimum total energy consumption parameters for the main problem obtained in step 3). Substitute into the subproblem The optimal real-time energy cost is obtained by using a solver. And utilize the optimal real-time energy consumption cost. Update the upper bound to ; 5) Based on the updated upper and lower bounds, determine Does it meet the following requirements: If the conditions are met, stop the iteration and obtain the solution result; If the condition is not met, repeat steps 3)-5) and update the iteration count k.

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