Optimized scheduling method and system for wind-storage combined power generation system

Abstract

The invention discloses an optimal scheduling method for a wind-storage combined power generation system. The method comprises the following steps: acquiring data information of a target power system and the wind-storage combined power generation system; constructing a day-ahead optimization scheduling model by taking the target power system load fluctuation and the minimum energy storage charging and discharging total mileage as targets; constructing an intra-day real-time optimization scheduling model by taking the minimum wind curtailment amount and the minimum adjustment amount of the day-ahead optimization scheduling plan as targets; and taking the day-ahead optimization scheduling model as an upper layer model and the intra-day real-time optimization scheduling model as a lower layer model to carry out combined solution, and completing optimization scheduling of the wind storage combined power generation system. The invention also discloses a system for realizing the optimal scheduling method of the wind storage combined power generation system. According to the method, by obtaining the data information of the target power system and the wind storage combined power generation system, the day-ahead optimization scheduling model and the intra-day real-time optimization scheduling model are constructed and combined solving is carried out, optimization scheduling of the wind storage combined power generation system is achieved, the reliability is higher, and the accuracy is better.

Description

Technical Field

[0001] The present invention belongs to the field of electrical automation, and in particular relates to an optimization scheduling method and system for a wind-storage combined power generation system. Background Art

[0002] With the development of economy and technology and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and life, bringing endless convenience to people's production and life. Therefore, ensuring the stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] At present, with the increasingly serious environmental problems, more and more wind power generation systems are being integrated into the power system and generating electricity. However, the output characteristics of wind power are significantly affected by meteorological conditions and geographical location. It not only shows uncertainties such as randomness, volatility and intermittency, but also presents a relatively significant anti-peaking characteristic, which has a great impact on the economic and safe and stable operation of the power system, and also seriously restricts the safe absorption and penetration of wind power.

[0004] At present, in the context that relying solely on the existing regulation capacity of the power system is unable to meet the safe absorption of a high proportion of wind power, flexible regulation resources represented by energy storage can compensate for the uncertainty and anti-peak characteristics of wind power, and provide new solutions for the friendly grid connection of large-scale wind power.

[0005] However, in the current power system, grid-side applications such as peak load regulation and frequency regulation have global characteristics and need to be optimized and dispatched from the macro perspective of the power grid; while the operation plan of the energy storage configured in the wind farm is often formulated by the wind farm, and the main purpose of formulating its operation plan is to maximize the benefits of the wind-storage joint system. Therefore, the current joint operation and dispatching plan of the power system has the contradiction of different control purposes of the power system and the wind-storage system, which is likely to cause conflicts or confusion in the control process, thus affecting the safe and stable operation of the power system. Summary of the invention

[0006] One of the purposes of the present invention is to provide an optimization scheduling method for a wind-storage combined power generation system with high reliability and good accuracy.

[0007] A second objective of the present invention is to provide a system for realizing the optimization scheduling method of the wind-storage combined power generation system.

[0008] The optimization scheduling method of the wind-storage combined power generation system provided by the present invention comprises the following steps:

[0009] S1. Obtain data information of the target power system and wind-storage combined power generation system;

[0010] S2. Based on the data information obtained in step S1, a day-ahead optimization scheduling model is constructed with the goal of minimizing the load fluctuation of the target power system and the total mileage of energy storage charging and discharging;

[0011] S3. Based on the data information obtained in step S1, a real-time optimization scheduling model is constructed within the day with the goal of minimizing the amount of wind abandonment and the adjustment amount of the day-ahead optimization scheduling plan;

[0012] S4. The constructed day-ahead optimization scheduling model is used as the upper model, and the constructed intraday real-time optimization scheduling model is used as the lower model to perform joint solution;

[0013] S5. According to the solution obtained in step S4, the optimal scheduling of the wind-storage combined power generation system is completed.

[0014] According to the data information obtained in step S1, step S2 constructs a day-ahead optimization scheduling model with the goal of minimizing the load fluctuation of the target power system and the total mileage of energy storage charging and discharging, which specifically includes the following steps:

[0015] According to the data information obtained in step S1, with the goal of minimizing the load fluctuation of the target power system and the total mileage of energy storage charging and discharging, and with the net load balance of each scheduling period, the energy storage charging and discharging status, the energy storage SOC state transfer relationship, the energy storage SOC upper and lower limits, the scheduling period energy storage SOC balance and the energy storage charging and discharging power upper and lower limits as constraints, a day-ahead optimization scheduling model is constructed.

[0016] The constructed day-ahead optimization scheduling model includes the following objective functions:

[0017] The following formula is used as the objective function:

[0018]

[0019] Where P NL,t is the net load power of the tth dispatch period; Kurt(P NL,t ) is the kurtosis coefficient of the net load power curve, and T is the total number of dispatch periods, μ is the average value of net load power and σ is the standard deviation of net load power and β is the set penalty coefficient; P d,t is the energy storage discharge power in the tth scheduling period; P c,t is the energy storage charging power in the tth scheduling period.

[0020] The constructed day-ahead optimization scheduling model includes the following constraints:

[0021] The following formula is used as the constraint condition:

[0022] Net load constraints for each dispatch period:

[0023] P NL,t =P L,t +P c,t -P W,t -P d,t

[0024] Where P L,t is the load forecast value of the tth dispatch period; P W,t is the wind power output forecast value in the tth dispatch period;

[0025] Energy storage charging and discharging state constraints:

[0026] u c,t +u d,t ≤1

[0027] Where u c,t is the energy storage charging state variable in the tth scheduling period. If the energy storage is in the charging state in the tth scheduling period, then u c,t =1, if the energy storage is not charged during the tth dispatch period, then u c,t =0;u d,t is the energy storage discharge state variable in the tth scheduling period. If the energy storage is in the discharge state in the tth scheduling period, then u d,t =1, if the energy storage is not discharged during the tth dispatch period, then u dt =0;

[0028] Energy storage SOC state transition relationship constraints:

[0029]

[0030] Where SOC t is the energy storage SOC value of the tth scheduling period; η c is the energy storage charging efficiency; η d is the energy storage discharge efficiency; ΔT is the length of a single scheduling period; E BN The maximum capacity of energy storage for peak load shifting and valley filling;

[0031] Energy storage SOC upper and lower limit constraints:

[0032] SOC min ≤SOC t ≤SOC max

[0033] Where SOC min is the lower limit of the energy storage SOC value; SOC max is the upper limit of the energy storage SOC value;

[0034] Energy storage SOC balance constraints during the dispatch period:

[0035] SOC1=SOC ini =SOC T

[0036] Where SOC1 is the energy storage SOC value in the first dispatch period; SOC ini is the initial SOC value of the energy storage system when it is dispatched a day before; SOC T is the energy storage SOC value of the Tth scheduling period;

[0037] Energy storage charging and discharging power upper and lower limits:

[0038]

[0039] Where P BN It is the charging and discharging power limit of energy storage.

[0040] Based on the data information obtained in step S1, step S3 constructs a daily real-time optimization scheduling model with the goal of minimizing the amount of wind abandonment and the adjustment amount of the day-ahead optimization scheduling plan, which specifically includes the following steps:

[0041] Based on the data information obtained in step S1, with the goal of minimizing the amount of wind abandonment and the adjustment of the day-ahead optimization scheduling plan, and with the wind abandonment power, the power balance of the wind farm in each time period during the optimization period, the volatility of wind power, the upper and lower limits of the energy storage power adjustment, the energy storage SOC state transition relationship, the initial value of the energy storage SOC and the upper and lower limits of each time period as constraints, a daily real-time optimization scheduling model is constructed.

[0042] The constructed intraday real-time optimization scheduling model includes the following objective functions:

[0043] The following formula is used as the objective function:

[0044]

[0045] Where w1 is the first weight coefficient; N is the total number of time periods in a single optimization cycle; P cu,n is the wind power abandoned in the nth period; w2 is the second weight coefficient; ΔP B,n It is the adjustment amount of energy storage in the nth period to the day-ahead dispatch plan.

[0046] The constructed intraday real-time optimization scheduling model includes the following constraints:

[0047] The following formula is used as the constraint condition:

[0048] Wind power curtailment constraints:

[0049] 0≤P cu,n ≤P W1,n

[0050] Where P W1,nIt is the ultra-short-term predicted output value of the wind farm in the nth period of the day;

[0051] The power balance constraints of the wind farm in each period during the kth optimization cycle are:

[0052] P W1,n +ΔP B,n -P cu,n =P NW,n

[0053] Where P NW,n It is the smoothed intraday ultra-short-term forecast output value of the wind farm in the nth period;

[0054] Wind power fluctuation rate constraints:

[0055] -P R ≤P NW,n-1 -P NW,n ≤P R

[0056] Where P R is the wind power fluctuation rate limit;

[0057] The upper and lower limits of energy storage power adjustment are as follows:

[0058]

[0059] Where P d ' ,t is the day-ahead dispatching plan value of the energy storage discharge power in the nth period in the kth optimization cycle; P c ' ,t is the day-ahead dispatch plan value of the energy storage charging power in the nth period in the kth optimization cycle;

[0060] Energy storage SOC state transition relationship constraints:

[0061]

[0062] The initial value of energy storage SOC and the upper and lower limit constraints for each time period:

[0063]

[0064] Where SOC k is the initial SOC value in the kth optimization cycle.

[0065] The step S4 uses the constructed day-ahead optimization scheduling model as the upper model and the constructed intraday real-time optimization scheduling model as the lower model to perform a joint solution, which specifically includes the following steps:

[0066] The constructed day-ahead optimization scheduling model is used as the upper model, and the constructed intraday real-time optimization scheduling model is used as the lower model, and the Tianniu swarm optimization algorithm is used for joint solution.

[0067] The present invention also provides a system for realizing the optimization scheduling method of the wind-storage combined power generation system, comprising a data acquisition module, a day-ahead scheduling module, an intraday scheduling module, a joint solution module and an optimization scheduling module; the data acquisition module, the day-ahead scheduling module, the intraday scheduling module, the joint solution module and the optimization scheduling module are connected in series in sequence; the data acquisition module is used to acquire data information of the target power system and the wind-storage combined power generation system, and upload the data information to the day-ahead scheduling module; the day-ahead scheduling module is used to construct a day-ahead optimization scheduling module based on the received data information and the acquired data information, with the target power system load fluctuation and the total mileage of energy storage charging and discharging being minimized as the goal. model, and upload the data information to the intraday scheduling module; the intraday scheduling module is used to construct an intraday real-time optimization scheduling model based on the received data information and the acquired data information, with the goal of minimizing the amount of abandoned wind and the adjustment amount of the day-ahead optimization scheduling plan, and upload the data information to the joint solution module; the joint solution module is used to perform a joint solution based on the received data information, with the constructed day-ahead optimization scheduling model as the upper model and the constructed intraday real-time optimization scheduling model as the lower model, and upload the data information to the optimization scheduling module; the optimization scheduling module is used to complete the optimization scheduling of the wind-storage combined power generation system according to the received data information and the obtained solution results.

[0068] The optimization scheduling method and system of the wind-storage combined power generation system provided by the present invention acquires data information of the target power system and the wind-storage combined power generation system, constructs a day-ahead optimization scheduling model and an intraday real-time optimization scheduling model and performs joint solutions, which not only realizes the optimization scheduling of the wind-storage combined power generation system, but also has higher reliability and better accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 The figure is a schematic diagram of the method flow of the present invention.

[0070] Figure 2 Schematic diagram of the functional modules of the system of the present invention. DETAILED DESCRIPTION

[0071] like Figure 1 The method flow diagram of the method of the present invention is shown as follows: The optimization scheduling method of the wind-storage combined power generation system disclosed in the present invention comprises the following steps:

[0072] S1. Obtain data information of the target power system and wind-storage combined power generation system;

[0073] S2. Based on the data information obtained in step S1, a day-ahead optimization scheduling model is constructed with the goal of minimizing the load fluctuation of the target power system and the total mileage of energy storage charging and discharging. Specifically, the model includes the following steps:

[0074] According to the data information obtained in step S1, with the target power system load fluctuation and the minimum total mileage of energy storage charging and discharging as the goal, and with the net load balance of each scheduling period, energy storage charging and discharging status, energy storage SOC state transition relationship, energy storage SOC upper and lower limits, scheduling period energy storage SOC balance and energy storage charging and discharging power upper and lower limits as constraints, a day-ahead optimization scheduling model is constructed;

[0075] In specific implementation, the following formula is used as the objective function:

[0076]

[0077] Where P NL,t is the net load power of the tth dispatch period; Kurt(P NL,t ) is the kurtosis coefficient of the net load power curve, and T is the total number of dispatch periods, μ is the average value of net load power and σ is the standard deviation of net load power and β is the set penalty coefficient; P d,t is the energy storage discharge power in the tth scheduling period; P c,t is the energy storage charging power in the tth scheduling period;

[0078] Among them, the first term of the objective function is related to the flatness of the net load curve. The flatter the net load curve is, the smaller the peak-to-valley difference is, and the smaller its value is. The second term of the objective function is the penalty term. The smaller its value is, the smaller the total mileage of energy storage charging and discharging is. The penalty term is controlled by the penalty factor β, which is preferably set to 200 to avoid excessive use of energy storage and extend the service life of energy storage.

[0079] The following formula is used as the constraint condition:

[0080] Net load constraints for each dispatch period:

[0081] P NL,t =P L,t +P c,t -P W,t -P d,t

[0082] Where P L,t is the load forecast value of the tth dispatch period; P W,t is the wind power output forecast value in the tth dispatch period;

[0083] Energy storage charging and discharging state constraints:

[0084] uc,t +u d,t ≤1

[0085] Where u c,t is the energy storage charging state variable in the tth scheduling period. If the energy storage is in the charging state in the tth scheduling period, then u c,t =1, if the energy storage is not charged during the tth dispatch period, then u c,t =0;u d,t is the energy storage discharge state variable in the tth scheduling period. If the energy storage is in the discharge state in the tth scheduling period, then u d,t =1, if the energy storage is not discharged during the tth dispatch period, then u d,t =0;

[0086] Energy storage SOC state transition relationship constraints:

[0087]

[0088] Where SOC t is the energy storage SOC value of the tth scheduling period; η c is the energy storage charging efficiency; η d is the energy storage discharge efficiency; ΔT is the length of a single scheduling period; E BN The maximum capacity of energy storage for peak load shifting and valley filling;

[0089] Energy storage SOC upper and lower limit constraints:

[0090] SOC min ≤SOC t ≤SOC max

[0091] Where SOC min is the lower limit of the energy storage SOC value; SOC max is the upper limit of the energy storage SOC value;

[0092] Energy storage SOC balance constraints during the dispatch period:

[0093] SOC1=SOC ini =SOC T

[0094] Where SOC1 is the energy storage SOC value in the first dispatch period; SOC ini is the initial SOC value of the energy storage system when it is dispatched a day before; SOC T is the energy storage SOC value of the Tth scheduling period;

[0095] Energy storage charging and discharging power upper and lower limits:

[0096]

[0097] Where P BN is the charging and discharging power limit of energy storage;

[0098] S3. Based on the data information obtained in step S1, a real-time optimization scheduling model is constructed with the goal of minimizing the amount of wind abandonment and the adjustment amount of the day-ahead optimization scheduling plan; specifically, the following steps are included:

[0099] Based on the data information obtained in step S1, with the goal of minimizing the amount of wind abandonment and the adjustment of the day-ahead optimization scheduling plan, and with the wind abandonment power, the power balance of the wind farm in each time period during the optimization period, the volatility of wind power, the upper and lower limits of the energy storage power adjustment, the energy storage SOC state transition relationship, the initial value of the energy storage SOC and the upper and lower limits of each time period as constraints, a real-time optimization scheduling model is constructed within the day;

[0100] In specific implementation, the following formula is used as the objective function:

[0101]

[0102] Where w1 is the first weight coefficient, preferably 0.9; N is the total number of time periods in a single optimization cycle; P cu,n is the wind power abandoned in the nth period; w2 is the second weight coefficient, preferably 0.1; ΔP B,n is the adjustment amount of energy storage in the nth period to the day-ahead dispatch plan

[0103] The following formula is used as the constraint condition:

[0104] Wind power curtailment constraints:

[0105] 0≤P cu,n ≤P W1,n

[0106] Where P W1,n It is the ultra-short-term predicted output value of the wind farm in the nth period of the day;

[0107] The power balance constraints of the wind farm in each period during the kth optimization cycle are:

[0108] P W1,n +ΔP B,n -P cu,n =P NW,n

[0109] Where P NW,n It is the smoothed intraday ultra-short-term forecast output value of the wind farm in the nth period;

[0110] Wind power fluctuation rate constraints:

[0111] -P R ≤P NW,n-1 -P NW,n ≤PR

[0112] Where P R is the wind power fluctuation rate limit;

[0113] The upper and lower limits of energy storage power adjustment are as follows:

[0114]

[0115] Where P d ' ,t is the day-ahead dispatching plan value of the energy storage discharge power in the nth period in the kth optimization cycle; P c ' ,t is the day-ahead dispatch plan value of the energy storage charging power in the nth period in the kth optimization cycle;

[0116] Energy storage SOC state transition relationship constraints:

[0117]

[0118] The initial value of energy storage SOC and the upper and lower limit constraints for each time period:

[0119]

[0120] Where SOC k is the initial SOC value in the kth optimization cycle;

[0121] S4. The constructed day-ahead optimization scheduling model is used as the upper model, and the constructed intraday real-time optimization scheduling model is used as the lower model to perform a joint solution; specifically, the steps include:

[0122] In specific implementation, the preferred solution is: the day-ahead dispatch plan is formulated once every 24 hours, divided into 96 time periods, each 15 minutes as a time period; the intraday dispatch plan is formulated once every 3 minutes, a total of K = 480 times a day, each time the power adjustment amount for the next 15 minutes is formulated, but only the plan for the first time period is executed; the actual charging and discharging power instruction of the energy storage is the sum of the day-ahead dispatch plan and the intraday power adjustment amount;

[0123] The constructed day-ahead optimization scheduling model is used as the upper model, and the constructed intraday real-time optimization scheduling model is used as the lower model, and the Tianniu swarm optimization algorithm is used for joint solution;

[0124] S5. According to the solution obtained in step S4, the optimal scheduling of the wind-storage combined power generation system is completed.

[0125] like Figure 2The figure shows a schematic diagram of the functional modules of the system of the present invention: the system disclosed in the present invention for realizing the optimized dispatching method of the wind-storage combined power generation system comprises a data acquisition module, a day-ahead dispatching module, an intraday dispatching module, a joint solution module and an optimized dispatching module; the data acquisition module, the day-ahead dispatching module, the intraday dispatching module, the joint solution module and the optimized dispatching module are connected in series in sequence; the data acquisition module is used to acquire data information of the target power system and the wind-storage combined power generation system, and upload the data information to the day-ahead dispatching module; the day-ahead dispatching module is used to construct a method for optimizing the dispatching of the wind-storage combined power generation system according to the received data information, based on the acquired data information, with the target power system load fluctuation and the total mileage of energy storage charging and discharging being minimized as the goal. A day-ahead optimization dispatching model is constructed, and the data information is uploaded to the intraday dispatching module; the intraday dispatching module is used to construct an intraday real-time optimization dispatching model based on the received data information, based on the acquired data information, with the goal of minimizing the amount of abandoned wind and the adjustment amount of the day-ahead optimization dispatching plan, and upload the data information to the joint solution module; the joint solution module is used to perform a joint solution based on the received data information, with the constructed day-ahead optimization dispatching model as the upper model and the constructed intraday real-time optimization dispatching model as the lower model, and upload the data information to the optimization dispatching module; the optimization dispatching module is used to complete the optimization dispatching of the wind-storage combined power generation system according to the received data information and the obtained solution results.

Claims

1. An optimization scheduling method for a wind-storage combined power generation system, comprising the following steps: S1. Obtain data information of the target power system and wind-storage combined power generation system; S2. Based on the data information obtained in step S1, a day-ahead optimization scheduling model is constructed with the goal of minimizing the load fluctuation of the target power system and the total mileage of energy storage charging and discharging; S3. Based on the data information obtained in step S1, a real-time optimization scheduling model is constructed within the day with the goal of minimizing the amount of wind abandonment and the adjustment amount of the day-ahead optimization scheduling plan; S4. The constructed day-ahead optimization scheduling model is used as the upper model, and the constructed intraday real-time optimization scheduling model is used as the lower model to perform joint solution; S5. According to the solution obtained in step S4, the optimal scheduling of the wind-storage combined power generation system is completed.

2. The optimization scheduling method of the wind-storage combined power generation system according to claim 1 is characterized in that According to the data information obtained in step S1, step S2 constructs a day-ahead optimization scheduling model with the goal of minimizing the load fluctuation of the target power system and the total mileage of energy storage charging and discharging, which specifically includes the following steps: According to the data information obtained in step S1, with the goal of minimizing the load fluctuation of the target power system and the total mileage of energy storage charging and discharging, and with the net load balance of each scheduling period, the energy storage charging and discharging status, the energy storage SOC state transfer relationship, the energy storage SOC upper and lower limits, the scheduling period energy storage SOC balance and the energy storage charging and discharging power upper and lower limits as constraints, a day-ahead optimization scheduling model is constructed.

3. The optimization scheduling method of the wind-storage combined power generation system according to claim 2 is characterized in that The constructed day-ahead optimization scheduling model includes the following objective functions: The following formula is used as the objective function: Where P NL,t is the net load power of the tth dispatch period; Kurt(P NL,t ) is the kurtosis coefficient of the net load power curve, and T is the total number of dispatch periods, μ is the average value of net load power and σ is the standard deviation of net load power and β is the set penalty coefficient; P d,t is the energy storage discharge power in the tth scheduling period; P c,t is the energy storage charging power in the tth scheduling period.

4. The optimization scheduling method of the wind-storage combined power generation system according to claim 3 is characterized in that The constructed day-ahead optimization scheduling model includes the following constraints: The following formula is used as the constraint condition: Net load constraints for each dispatch period: P NL,t =P L,t +P c,t -P W,t -P d,t Where P L,t is the load forecast value of the tth dispatch period; P W,t is the wind power output forecast value in the tth dispatch period; Energy storage charging and discharging state constraints: in c,t +in d,t ≤1 Where u c,t is the energy storage charging state variable in the tth scheduling period. If the energy storage is in the charging state in the tth scheduling period, then u c,t =1, if the energy storage is not charged during the tth dispatch period, then u c,t =0;u d,t is the energy storage discharge state variable in the tth scheduling period. If the energy storage is in the discharge state in the tth scheduling period, then u d,t =1, if the energy storage is not discharged during the tth dispatch period, then u d,t =0; Energy storage SOC state transition relationship constraints: Where SOC t is the energy storage SOC value of the tth scheduling period; η c Charging efficiency for energy storage; η d is the energy storage discharge efficiency; ΔT is the length of a single scheduling period; E BN The maximum capacity of energy storage for peak load shifting and valley filling; Energy storage SOC upper and lower limit constraints: SOC min ≤SOC t ≤SOC max Where SOC min is the lower limit of the energy storage SOC value; SOC max is the upper limit of the energy storage SOC value; Energy storage SOC balance constraints during the dispatch period: SOC1=SOC ini =SOC T Where SOC1 is the energy storage SOC value in the first scheduling period; SOC ini is the initial SOC value of the energy storage system when it is dispatched a day before; SOC T is the energy storage SOC value of the Tth scheduling period; Energy storage charging and discharging power upper and lower limits: Where P BN It is the charging and discharging power limit of energy storage.

5. The optimization scheduling method of the wind-storage combined power generation system according to claim 1 is characterized in that Based on the data information obtained in step S1, step S3 constructs a daily real-time optimization scheduling model with the goal of minimizing the amount of wind abandonment and the adjustment amount of the day-ahead optimization scheduling plan, which specifically includes the following steps: Based on the data information obtained in step S1, with the goal of minimizing the amount of wind abandonment and the adjustment of the day-ahead optimization scheduling plan, and with the wind abandonment power, the power balance of the wind farm in each time period during the optimization period, the volatility of wind power, the upper and lower limits of the energy storage power adjustment, the energy storage SOC state transition relationship, the initial value of the energy storage SOC and the upper and lower limits of each time period as constraints, a daily real-time optimization scheduling model is constructed.

6. The optimization scheduling method of the wind-storage combined power generation system according to claim 5 is characterized in that The constructed intraday real-time optimization scheduling model includes the following objective functions: The following formula is used as the objective function: Where w1 is the first weight coefficient; N is the total number of time periods in a single optimization cycle; P cu,n is the wind power abandoned in the nth period; w2 is the second weight coefficient; ΔP B,n It is the adjustment amount of energy storage in the nth period to the day-ahead dispatch plan.

7. The optimization scheduling method of the wind-storage combined power generation system according to claim 6 is characterized in that The constructed intraday real-time optimization scheduling model includes the following constraints: The following formula is used as the constraint condition: Wind power curtailment constraints: 0≤P cu,n ≤P W1,n Where P W1,n It is the ultra-short-term predicted output value of the wind farm in the nth period of the day; The power balance constraints of the wind farm in each period during the kth optimization cycle are: P W1,n +ΔP B,n -P cu,n =P NW,n Where P NW,n It is the smoothed intraday ultra-short-term forecast output value of the wind farm in the nth period; Wind power fluctuation rate constraints: -P R ≤P NW,n-1 -P NW,n ≤P R Where P R is the wind power fluctuation rate limit; The upper and lower limits of energy storage power adjustment are as follows: Where P d ' ,t is the day-ahead dispatching plan value of the energy storage discharge power in the nth period in the kth optimization cycle; P c ' ,t is the day-ahead dispatch plan value of the energy storage charging power in the nth period in the kth optimization cycle; Energy storage SOC state transition relationship constraints: The initial value of energy storage SOC and the upper and lower limit constraints for each time period: Where SOC k is the initial SOC value in the kth optimization cycle.

8. The optimization scheduling method of the wind-storage combined power generation system according to claim 1 is characterized in that The step S4 uses the constructed day-ahead optimization scheduling model as the upper model and the constructed intraday real-time optimization scheduling model as the lower model to perform a joint solution, which specifically includes the following steps: The constructed day-ahead optimization scheduling model is used as the upper model, and the constructed intraday real-time optimization scheduling model is used as the lower model, and the Tianniu swarm optimization algorithm is used for joint solution.

9. A system for implementing the optimal dispatching method of the wind-storage combined power generation system according to any one of claims 1 to 8, characterized in that It includes a data acquisition module, a day-ahead dispatch module, an intraday dispatch module, a joint solution module and an optimization dispatch module; the data acquisition module, the day-ahead dispatch module, the intraday dispatch module, the joint solution module and the optimization dispatch module are connected in series in sequence; the data acquisition module is used to obtain data information of the target power system and the wind-storage combined power generation system, and upload the data information to the day-ahead dispatch module; The day-ahead dispatching module is used to construct a day-ahead optimization dispatching model based on the received data information, with the goal of minimizing the load fluctuation of the target power system and the total mileage of energy storage charging and discharging, and upload the data information to the intraday dispatching module; The intraday scheduling module is used to build an intraday real-time optimization scheduling model based on the received data information and the acquired data information, with the goal of minimizing the amount of wind abandonment and the adjustment of the day-ahead optimization scheduling plan, and upload the data information to the joint solution module; The joint solution module is used to perform joint solution based on the received data information, using the constructed day-ahead optimization scheduling model as the upper model and the constructed intraday real-time optimization scheduling model as the lower model, and upload the data information to the optimization scheduling module; The optimization scheduling module is used to complete the optimization scheduling of the wind-storage combined power generation system according to the received data information and the obtained solution results.

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