Rolling optimization scheduling method for wind-fire-storage combined system based on multi-time-scale nesting and dynamic collaboration

Abstract

The invention discloses a multi-time-scale nested and dynamic collaborative rolling optimization scheduling method for a wind-fire-storage combined system, and relates to the field of power scheduling. The invention aims to solve the problems that the power dispatching response speed is slow and the optimal benefit cannot be obtained in the existing method for participating in the power dispatching of the wind-thermal storage combined system. The method comprises the following steps: generating day-ahead optimization results of each time period of the next day by using a 24-hour day-ahead scheduling model of the wind-fire-storage combined system; taking the day-ahead optimization result as a boundary, establishing an intra-day correction model of a 4-hour rolling time window, and obtaining an output correction instruction of each time period in the current intra-day scheduling window; constructing a 15-minute-level real-time checking model, and obtaining a real-time dynamic scheduling instruction; and updating a subsequent operation state according to the real-time dynamic scheduling instruction, translating a scheduling window backwards for a period of time, reconstructing a rolling time window and repeatedly iteratively updating the real-time dynamic scheduling instruction, and finally completing dynamic tracking and correction of a full-period operation scheme. The method is used for realizing power dispatching.

Description

Technical Field

[0001] The present invention relates to the field of power dispatching, and particularly relates to a rolling optimal dispatching method for a combined wind-fire-energy storage system with multi-time-scale nesting and dynamic coordination. Background Art

[0002] With the large-scale grid connection of new energy and the deepening of power market reform, the combined operation of wind turbines, thermal power units and energy storage power stations has become an important means to improve the flexibility and economy of the system. However, under the background of the coordinated operation of the electric energy market and the frequency regulation market, traditional power dispatching methods face many challenges. From the perspective of the output characteristics of wind power, due to the randomness and intermittency of wind energy itself, the output of wind power shows strong uncertainty. In this case, relying solely on the day-ahead dispatching method is difficult to cope with real-time power deviations, which easily leads to an increase in frequency regulation costs and losses in power market revenues. Especially when the proportion of renewable energy gradually increases, the volatility of wind power makes the real-time dispatching problem more complex and difficult to predict. At the same time, there are significant differences in the operating characteristics of thermal power units and energy storage power stations. Although thermal power units have a large regulation capacity, their regulation speed is slow and the response lags; while energy storage power stations can respond quickly, but are limited by the state of charge and capacity. Therefore, existing dispatching models often fail to achieve dynamic coordination among the three, resulting in the system being unable to give full play to their respective advantages, thus affecting the overall operating efficiency. This coordination problem is particularly prominent under high load or large fluctuations in wind power output. Therefore, the current research on the participation of the combined wind-fire-energy storage system in power dispatching is very important.

[0003] Currently, the relevant research on the participation of the combined wind-fire-energy storage system in power dispatching mostly focuses on the optimal dispatching of a single time scale or a single market participation strategy. Some research constructs a day-ahead dispatching plan based on deterministic wind power prediction, but does not consider the dynamic correction problem of prediction errors in the intra-day and real-time stages, resulting in the dispatching plan being prone to deviate from the actual situation and unable to cope with the rapidly changing wind power output, thus leading to a slow response speed of power dispatching. Some other research attempts to introduce stochastic optimization or robust optimization to handle the uncertainty of wind power prediction, but these methods usually have the problem of too high model complexity and lack a flexible rolling adjustment mechanism, making it difficult to cope with the dynamic changes of the market and the system in actual operation, resulting in a slow response speed of power dispatching. In terms of participating in the frequency regulation market, existing technologies mostly adopt the mode of independent provision of frequency regulation services by thermal power or energy storage to decouple and optimize the electric energy and frequency regulation markets, ignoring the spatio-temporal correlation between the two markets, resulting in the inability to obtain the optimal benefit. Summary of the Invention

[0004] The object of the present invention is to solve the problems that the existing method for the combined wind-fire-energy storage system to participate in power dispatching has a slow response speed for power dispatching and cannot obtain the optimal benefit, and a rolling optimization dispatching method for the combined wind-fire-energy storage system with multi-time-scale nesting and dynamic coordination is proposed.

[0005] A rolling optimization dispatching method for the combined wind-fire-energy storage system with multi-time-scale nesting and dynamic coordination, comprising:

[0006] S1. Construct a 24-hour day-ahead dispatching model for the combined wind-fire-energy storage system, and solve the 24-hour day-ahead dispatching model for the combined wind-fire-energy storage system to generate the day-ahead optimization results for each time period of the next day;

[0007] S2. Taking the day-ahead optimization results as the boundary, establish an intra-day correction model with a 4-hour rolling time window based on the 24-hour day-ahead dispatching model for the combined wind-fire-energy storage system, and use the intra-day correction model with a 4-hour rolling time window to obtain the output correction instructions for each time period within the current intra-day dispatching window;

[0008] S3. For the starting time period of the dispatching window, construct a 15-minute real-time checking model, and use the 15-minute real-time checking model to obtain real-time dynamic dispatching instructions;

[0009] S4. Update the subsequent operating status according to the real-time dynamic dispatching instructions, shift the dispatching window backward by one time period, reconstruct the rolling time window and repeat the execution of S3 to iteratively update the real-time dynamic dispatching instructions;

[0010] S5. Repeat the execution of S1-S4, with a progressive rolling time window, and complete the dynamic tracking and correction of the full-time operating plan through multiple rounds of iterative calculations.

[0011] Further, the 24-hour day-ahead dispatching model for the combined wind-fire-energy storage system in S1 is specifically:

[0012]

[0013] In fact, t is the time period label, T is the total number of time periods, is the benefit of the combined wind-fire-energy storage system in the electricity energy market at time period t, is the benefit of the combined wind-fire-energy storage system in the frequency regulation market at time period t, F g,t is the operating cost of the thermal power unit at time period t; F e,t is the operating cost of the energy storage power station at time period t; F oc,t is the total opportunity cost of the combined wind-fire-energy storage system at time period t, p s is the probability of the occurrence of the s-th scenario; is the electricity price in the electricity energy market at time period t; is the total bid power of the combined wind-fire-energy storage system in the electricity energy market at time period t in the s-th scenario, They are the bidding powers of the wind turbine generator, thermal power generator, and energy storage power station in the electricity energy market during the t-th period in the s-th scenario respectively, and Δt is the time interval between adjacent periods. is the revenue of the combined wind-thermal-energy storage system in the energy market during the t-th period in the s-th scenario. is the revenue of the combined wind-thermal-energy storage system in the frequency regulation market during the t-th period in scenario s. They are the frequency regulation capacity compensation price and frequency regulation mileage compensation price in the frequency regulation market during the t-th period respectively. is the frequency regulation bidding capacity of the combined wind-thermal-energy storage system in the frequency regulation market during the t-th period in the s-th scenario. is the net frequency regulation mileage during the t-th period in the s-th scenario; S t,k+1,s 、S t,k,s They are the regulation command signals during the (k + 1)-th and k-th frequency regulation signal periods during the t-th period in the s-th scenario respectively. They are the frequency regulation bidding capacities of the wind turbine generator, thermal power generator, and energy storage power station in the frequency regulation market during the t-th period in the s-th scenario respectively. is the comprehensive frequency regulation performance index in the s-th scenario, and K is the total number of frequency regulation signal periods.

[0014] Furthermore, the scenarios include:

[0015] In the first scenario when s = 1: Low wind power output and low load demand are specifically: P w ≤0.3P w,rate and P D ≤0.7P D,peak ;

[0016] In the second scenario when s = 2: Low wind power output and high load demand are specifically: P w ≤0.3P w,rate and P D ≥0.9P D,peak ;

[0017] In the third scenario when s = 3: Medium wind power output and balanced load demand are specifically: 0.3P w,rate <P w <0.8P w,rate and 0.7P D,peak <P D <0.9P D,peak ;

[0018] In the fourth scenario when s = 4: High wind power output and low load demand are specifically: P w ≥0.8P w,rate and P D ≤0.7P D,peak ;

[0019] In the fifth scenario when s = 5: High wind power output and high load demand are specifically: Pw ≥0.8P w,rate and P D ≥0.9P D,peak ;

[0020] wherein, P w is the wind power output, P w,rate is the rated power of the wind turbine, P D is the load demand, P D,peak is the historical peak load of the regional power grid.

[0021] Furthermore, the operating cost of the thermal power unit in period t is as follows:

[0022]

[0023] wherein, δ g is the start-up and shut-down cost of the thermal power unit; B g,t-1,s , B g,t,s are 0-1 state variables, representing the start-up and shut-down states of the thermal power unit in periods t-1 and t in the s-th scenario respectively; a g , b g , c g are the consumption characteristic coefficients of the thermal power unit; P g,t,s is the power generation of the thermal power unit in period t in the s-th scenario.

[0024] Furthermore, the operating cost of the energy storage power station in period t is as follows:

[0025]

[0026] wherein, ΔE k,t,s is the energy change in the k-th frequency modulation signal period in period t in the s-th scenario, is the initial purchase cost of the energy storage battery; is the maximum charge and discharge times of the battery at 100% depth of discharge; E max is the maximum allowable capacity of the energy storage power station.

[0027] Furthermore, the total opportunity cost of the wind-thermal-storage combined system in period t is as follows:

[0028]

[0029] wherein, are the opportunity costs of the wind turbine, thermal power unit and energy storage power station participating in the electric energy and frequency modulation markets in period t in the s-th scenario respectively; are the bid capacities of the wind turbine, thermal power unit and energy storage power station in the electric energy market when they do not participate in the frequency modulation market in period t in the s-th scenario respectively.

[0030] Furthermore, the constraints for solving the 24-hour day-ahead scheduling model of the wind-fire-storage integrated system are as follows:

[0031] System power balance constraint, specifically:

[0032]

[0033] where N w 、N g 、N e are the numbers of wind turbines, thermal power plants, and energy storage power stations in the system respectively; P w,t,s is the generated power of the wind turbine at time t in the s-th scenario; are the discharging and charging powers of the energy storage battery at time t in the s-th scenario respectively; P d,t,s is the load demand at node d at time t in the s-th scenario, and D is the total number of time nodes at time t;

[0034] The system frequency regulation demand constraint is as follows:

[0035]

[0036] where are the frequency regulation capacity demand and frequency regulation mileage demand of the system at time t in the s-th scenario respectively;

[0037] The output constraint of the wind turbine is as follows:

[0038]

[0039] where is the predicted maximum generated power of the wind turbine in the electricity energy market at time t in the s-th scenario;

[0040] The frequency regulation capacity constraint of the wind turbine, specifically:

[0041]

[0042] where are the reserved powers for the up and down frequency regulation of the wind turbine at time t in the s-th scenario respectively;

[0043] The output, ramp rate, start-stop time constraints of the thermal power plant are as follows:

[0044]

[0045] where are the maximum and minimum outputs of the thermal power plant respectively; are the upward and downward ramp rates of the thermal power plant respectively; T on,g,t,s 、T off,g,t,s are the continuous operation duration and continuous shutdown duration of the thermal power plant at time t in the s-th scenario respectively; They are the minimum continuous operation duration and continuous shutdown duration allowed for thermal power units respectively.

[0046] The frequency regulation capacity constraints of thermal power units are as follows:

[0047]

[0048] Among them, They are the reserved capacities for the upward and downward frequency regulation of the thermal power unit at time t in the s-th scenario respectively.

[0049] The charge-discharge power and state of charge constraints of the energy storage power station are as follows:

[0050]

[0051] E e,min ≤E e,t,s ≤E e,max

[0052] E e,t,s =E e,t-1,s +ΔE e,t,s

[0053]

[0054] Among them, are 0-1 state variables, representing the discharge and charge states of the energy storage power station at time t in the s-th scenario respectively; They are the minimum discharge power and charge power of the energy storage power station respectively; They are the maximum discharge power and charge power of the energy storage power station respectively; E e,min 、E e,max They are the minimum and maximum allowable state of charge of the energy storage power station respectively; E e,t-1,s 、E e,t,s They are the state of charge of the energy storage power station at time t-1 and t in the s-th scenario respectively; ΔE e,t,s is the change in the state of charge of the energy storage power station at time t in the s-th scenario; η dis 、η ch They are the discharge and charge efficiencies of the energy storage power station respectively; E e,0,s 、E e,T,s They are the state of charge of the energy storage power station at the start time and end time of each day in the s-th scenario respectively;

[0055] The frequency regulation capacity constraints of the energy storage power station are as follows:

[0056]

[0057] Among them, They are the reserved capacities for the upward and downward frequency regulation of the energy storage power station at time t in the s-th scenario respectively.

[0058] Further, the day-ahead scheduling optimization results for each time period on the next day specifically include: the bidding power of the wind turbine generator in the electricity energy market at time period t in the s-th scenario of the day-ahead Optimal solution, the frequency regulation bidding capacity of the wind turbine generator in the frequency regulation market at time period t in the s-th scenario of the day-ahead Optimal solution, the bidding power of the thermal power generator in the electricity energy market at time period t in the s-th scenario of the day-ahead Optimal solution, the frequency regulation bidding capacity of the thermal power generator in the frequency regulation market at time period t in the s-th scenario of the day-ahead Optimal solution, the start-stop state B of the thermal power generator at time period t in the s-th scenario of the day-ahead g,t,s Optimal solution, the bidding power of the energy storage power station in the electricity energy market at time period t in the s-th scenario of the day-ahead Optimal solution, the frequency regulation bidding capacity of the wind turbine generator in the energy storage power station at time period t in the s-th scenario of the day-ahead Optimal solution, the state of charge E of the energy storage power station at time period t in the s-th scenario of the day-ahead e,t,s Optimal solution.

[0059] Further, in S2, with the day-ahead optimization results as the boundary, an intraday correction model with a 4-hour rolling time window is established based on the 24-hour day-ahead scheduling model of the wind-thermal-storage combined system, and the output correction instructions for each time period within the current intraday scheduling window are obtained by using the intraday correction model with a 4-hour rolling time window, specifically as follows:

[0060] S201. With the day-ahead optimization results as the boundary, the objective function of the intraday correction model with a 4-hour rolling time window is established based on the objective function of the 24-hour day-ahead scheduling model of the wind-thermal-storage combined system, specifically as follows:

[0061]

[0062] Among them, F w,t is the deviation penalty cost of the wind turbine generator at time period t in the current intraday scheduling, and ρ w is the deviation penalty cost coefficient for the mismatch between the bidding capacity and the actual output of the wind turbine generator at time period t in the current intraday scheduling in the electricity energy market; is the actual output of the wind turbine generator at time period t in the current intraday scheduling in the s-th scenario, and F w,t,s is the deviation penalty cost of the wind turbine generator at time period t in the current intraday scheduling in the s-th scenario;

[0063] S202. Solve the objective function of the intraday correction model with a 4-hour rolling time window to obtain the output correction instructions for each time period within the current intraday scheduling window, including: the bidding power of the wind turbine generator in the electricity energy market at time period t in the s-th scenario of the intraday Optimal solution, the frequency regulation bidding capacity of the wind turbine generator in the frequency regulation market at time period t in the s-th scenario of the intraday Optimal solution, the bidding power of thermal power units in the electric energy market at time period t in the s-th scenario within a day Optimal solution, the frequency regulation bidding capacity of thermal power units in the frequency regulation market at time period t in the s-th scenario within a day Optimal solution, the start-stop state of thermal power units at time period t in the s-th scenario within a day Optimal solution, the bidding power of energy storage power stations in the electric energy market at time period t in the s-th scenario within a day Optimal solution, the frequency regulation bidding capacity of wind turbines in the energy storage power station at time period t in the s-th scenario within a day Optimal solution, the state of charge of energy storage power stations at time period t in the s-th scenario within a day Optimal solution.

[0064] Furthermore, for the starting time period of the scheduling window in S3, a 15-minute real-time verification model is constructed, and real-time dynamic scheduling instructions are obtained by using the 15-minute real-time verification model. Specifically:[[]]

[0065] S301. Construct the objective function of the 15-minute real-time verification model based on the intra-day correction model with a 4-hour rolling time window:[[]]

[0066]

[0067] Among them,[[]] are the revenues of the wind-fire-energy storage combined system in the electric energy and frequency regulation markets during the frequency regulation signal period k within the current real-time scheduling time period t respectively; F g,t,k and F e,t,k are the operating costs of thermal power units and energy storage power stations during the frequency regulation signal period k within the current real-time scheduling time period t respectively; F oc,t,k is the opportunity cost of the wind-fire-energy storage combined system during the frequency regulation signal period k within the current real-time scheduling time period t; F w,t,k is the deviation penalty cost of wind turbines during the frequency regulation signal period k within the current real-time scheduling time period t; γ is the frequency regulation error penalty cost coefficient; ε t,k is the frequency regulation error during the frequency regulation signal period k within the current real-time scheduling time period t.

[0068] S302. Add the following constraints to the 15-minute real-time verification model:[[]]

[0069]

[0070] Among them,[[]] is the bidding capacity of wind turbines, thermal power units and energy storage power stations in the frequency regulation market during the current real-time scheduling time period t in the s-th scenario;

[0071] S303. Obtain real-time dynamic dispatch instructions based on the 15-minute real-time verification model constraint conditions added in S302 and the 15-minute real-time verification model established in S301, including: the bidding power of the wind turbine generator in the electricity energy market at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the frequency regulation bidding capacity of the wind turbine generator in the frequency regulation market at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the bidding power of the thermal power generator in the electricity energy market at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the frequency regulation bidding capacity of the thermal power generator in the frequency regulation market at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the frequency regulation bidding capacity of the thermal power generator in the frequency regulation market at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the start-stop state of the thermal power generator at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the bidding power of the energy storage power station in the electricity energy market at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the frequency regulation bidding capacity of the wind turbine generator in the energy storage power station at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the state of charge of the energy storage power station at the k-th moment in the t-th period of the s-th real-time scenario The optimal solution of the optimal solution.

[0072] The beneficial effects of the present invention are as follows:

[0073] First, improve dispatch flexibility: The present invention constructs a multi-stage dispatch model for day-ahead, intra-day, and real-time, fully considering the uncertainty of wind power output and market price fluctuations, and can dynamically adjust according to the specific conditions of different stages. Through rolling optimization dispatch, the dispatch plan can be re-reviewed and optimized at each time period, effectively combining the operating characteristics of wind turbine generators, thermal power generators, and energy storage power stations, greatly improving the dispatch flexibility of the combined wind-thermal-energy storage system in the electricity energy and frequency regulation markets, and better adapting to the complex and changeable power market environment;

[0074] Second, enhance real-time response ability: Based on the intra-day correction model, correct the output of each time period within the current intra-day dispatch window, and continuously update the real-time verification model in a rolling manner at the real-time dispatch stage. Optimize the dispatch of subsequent time periods through the updated real-time correction results. This mechanism enables the system to quickly respond to real-time power deviations, timely adjust the operating states of wind turbine generators, thermal power generators, and energy storage power stations, enhance the real-time response speed of the system to rapid changes in wind power output and frequent market fluctuations, and ensure the stable operation of the power system;

[0075] III. Improving the overall system revenue: On the basis of considering the spatio-temporal correlation between the electric energy and the frequency regulation market, the capacity complementary potential of the combined wind-fire-storage frequency regulation is fully exploited through optimal dispatching, avoiding the revenue loss caused by decoupled optimization of the two markets. It can accurately coordinate the operation of each part, effectively reduce the frequency regulation cost, increase the revenue of the power market, significantly improve the overall economic efficiency of the combined wind-fire-storage system, and provide strong support for the efficient operation of the power market. Description of the Drawings

[0076] Figure 1 It is the flow chart of the present invention;

[0077] Figure 2 It is the solution flow chart of the present invention;

[0078] Figure 3 It is the schematic diagram of the principle of the present invention. Detailed Implementation Modes

[0079] Detailed Implementation Mode 1: As Figures 1-3 shown, the specific process of a rolling optimal dispatching method for a combined wind-fire-storage system with multi-time scale nesting and dynamic coordination in this implementation mode is as follows:

[0080] S1. Build a 24-hour day-ahead dispatching model for the combined wind-fire-storage system, consider the uncertainty of wind power output and load fluctuations, and solve the 24-hour day-ahead dispatching model of the combined wind-fire-storage system to generate the optimization results for each time period of the next day. Specifically:

[0081] S101. Build the objective function of the 24-hour day-ahead dispatching model for the combined wind-fire-storage system:

[0082]

[0083] Actually, t is the time period label, T is the total number of time periods, is the revenue of the combined wind-fire-storage system in the energy market at time period t, is the revenue of the combined wind-fire-storage system in the frequency regulation market at time period t, F g,t is the operating cost of the thermal power unit at time period t; F e,t is the operating cost of the energy storage power station at time period t; F oc,t is the total opportunity cost of the combined wind-fire-storage system at time period t;

[0084] Under the scenario of considering the uncertainty of wind power output and load fluctuations, five scenarios are adopted for the combined wind-fire-storage system composed of wind turbines, thermal power units and energy storage power stations. Among them: the first scenario corresponds to low wind power output and low load demand, the second scenario corresponds to low wind power output and high load demand, the third scenario corresponds to medium wind power output and balanced load demand, the fourth scenario corresponds to high wind power output and low load demand, and the fifth scenario corresponds to high wind power output and high load demand.

[0085] Wind power output classification standard:

[0086] High output: ≥ 80% of the rated capacity (e.g., for a 2 MW unit, the output ≥ 1.6 MW);

[0087] Medium output: between 30% - 80% of the rated capacity;

[0088] Low output: < 30% of the rated capacity;

[0089] Load demand classification standard:;

[0090] High load: ≥ 90% of the peak load of the regional power grid;

[0091] Balanced load: 70% - 90% of the peak load;

[0092] Low load: < 70% of the peak load;

[0093] The specific scenario division standard satisfies:

[0094] When the wind power output P w satisfies P w ≥ 0.8P w,rate it is determined as high wind power output;

[0095] When 0.3P w,rate <P w <0.8P w,rate it is determined as medium wind power output;

[0096] When P w ≤ 0.3P w,rate it is determined as low wind power output;

[0097] When the load demand P D satisfies P D ≥ 0.9P D,peak it is determined as high load demand;

[0098] When 0.7P D,peak <P D <0.9P D,peak it is determined as balanced load demand;

[0099] When P D ≤ 0.7P D,peak it is determined as low load demand;

[0100] Among them, P w,rate is the rated power of the wind turbine, and P D,peak is the historical peak load of the regional power grid."

[0101] For the five scenarios, the expression of the electricity energy market revenue is:

[0102]

[0103] where p s is the probability of the s-th scenario occurring; is the electricity price in the electricity energy market at time period t; is the total bidding power of the wind-thermal-energy storage integrated system in the electricity energy market at time period t in the s-th scenario; are the bidding powers of the wind turbine, thermal power unit, and energy storage power station in the electricity energy market at time period t in the s-th scenario respectively; Δt is the time interval between adjacent time periods.

[0104] The expression for the frequency regulation market revenue is:

[0105]

[0106] In the formula: are the frequency regulation capacity compensation price and frequency regulation mileage compensation price in the frequency regulation market at time period t respectively; is the frequency regulation bidding capacity of the wind-thermal-energy storage integrated system in the frequency regulation market at time period t in the s-th scenario; is the net frequency regulation mileage at time period t in the s-th scenario; S t,k+1,s 、S t,k,s are the adjustment command signals in the k + 1-th and k-th frequency regulation signal cycles at time period t in the s-th scenario respectively, are the frequency regulation bidding capacities of the wind turbine, thermal power unit, and energy storage power station in the frequency regulation market at time period t in the s-th scenario respectively; is the comprehensive frequency regulation performance index in the s-th scenario, and K is the total number of frequency regulation signal cycles.

[0107] The expression for the operating cost of the thermal power unit is:

[0108]

[0109] where δ g is the start-up and shut-down cost of the thermal power unit; B g,t-1,s 、B g,t,s are 0-1 state variables, representing the start-up and shut-down states of the thermal power unit at time periods t - 1 and t in the s-th scenario respectively, 0 indicates that the thermal power unit is shut down, and 1 indicates that the thermal power unit is started up; a g 、b g 、c g are the consumption characteristic coefficients of the thermal power unit; P g,t,s is the power generation of the thermal power unit at time period t in the s-th scenario.

[0110] The expression for the operating cost of the energy storage power station is:

[0111]

[0112] Among them, ΔE k,t,s is the energy change of the k-th frequency modulation signal period in the t-th time period of the s-th scenario; is the initial acquisition cost of the energy storage battery; is the maximum charge and discharge times of the battery at 100% depth of discharge; E max is the maximum allowable capacity of the energy storage power station.

[0113] The expression of the opportunity cost of the combined wind-fire-energy storage system is:

[0114]

[0115] Among them, are respectively the opportunity costs of the wind turbine, thermal power unit and energy storage power station participating in the electric energy and frequency modulation markets in the t-th time period of the s-th scenario; are respectively the bidding capacities of the wind turbine, thermal power unit and energy storage power station in the electric energy market when they do not participate in the frequency modulation market in the t-th time period of the s-th scenario.

[0116] S102. Construct the constraint conditions of the 24-hour day-ahead scheduling model, specifically:

[0117] The constraint conditions for constructing the 24-hour day-ahead scheduling model include: system power balance constraint, system frequency modulation demand constraint, wind turbine output constraint, wind turbine frequency modulation capacity constraint, thermal power unit output, ramp rate, start-stop time constraint, energy storage power station charge-discharge power, state of charge constraint, energy storage power station frequency modulation capacity constraint;

[0118] The system power balance constraint is specifically:

[0119]

[0120] Among them, N w 、N g 、N e are respectively the numbers of wind turbines, thermal power units and energy storage power stations in the system; P w,t,s is the power generation of the wind turbine in the t-th time period of the s-th scenario; are respectively the discharge and charge powers of the energy storage battery in the t-th time period of the s-th scenario; P d,t,s is the load demand of node d in the t-th time period of the s-th scenario, and D is the total number of event nodes in the t-th time period.

[0121] The system frequency modulation demand constraint is as follows:

[0122]

[0123] Among them, are respectively the frequency modulation capacity demand and frequency modulation mileage demand of the system in the t-th time period of the s-th scenario.

[0124] The output constraints of the wind turbine are as follows:

[0125]

[0126] Wherein, is the predicted maximum power generation of the wind turbine in the electricity energy market at time t in the s-th scenario.

[0127] The frequency regulation capacity constraints of the wind turbine are specifically:

[0128]

[0129] Wherein, are the reserved powers for the upward and downward frequency regulation of the wind turbine at time t in the s-th scenario respectively;

[0130] The output, ramping, start-stop time constraints of the thermal power unit are as follows:

[0131]

[0132] Wherein, are the maximum and minimum outputs of the thermal power unit respectively; are the upward and downward ramping rates of the thermal power unit respectively; T on,g,t,s 、T off,g,t,s are the continuous operation duration and continuous shutdown duration of the thermal power unit at time t in the s-th scenario respectively; are the minimum allowable continuous operation duration and continuous shutdown duration of the thermal power unit respectively.

[0133] The frequency regulation capacity constraints of the thermal power unit are as follows:

[0134]

[0135] Wherein, are the reserved capacities for the upward and downward frequency regulation of the thermal power unit at time t in the s-th scenario respectively.

[0136] The charge-discharge power and state of charge constraints of the energy storage power station are as follows:

[0137]

[0138] E e,min ≤E e,t,s ≤E e,max

[0139] E e,t,s =E e,t-1,s +ΔE e,t,s

[0140]

[0141] E e,0,s = E e,T,s

[0142] wherein, is a 0-1 state variable, representing the discharging and charging states of the energy storage power station in the t-th period of the s-th scenario respectively. 0 represents the discharging state, and 1 represents the charging state; are respectively the minimum discharging power and charging power of the energy storage power station; are respectively the maximum discharging power and charging power of the energy storage power station; E e,min and E e,max are respectively the minimum and maximum charge levels allowed for the energy storage power station; E e,t-1,s and E e,t,s are respectively the charge levels of the energy storage power station in the (t-1)-th and t-th periods of the s-th scenario; ΔE e,t,s is the change in the charge level of the energy storage power station in the t-th period of the s-th scenario; η dis and η ch are respectively the discharging and charging efficiencies of the energy storage power station; E e,0,s and E e,T,s are respectively the charge level of the energy storage power station at the start of each day and the charge level at the end of each day in the s-th scenario.

[0143] The frequency regulation capacity constraint of the energy storage power station is as follows:

[0144]

[0145] wherein, are respectively the reserved capacities for the upward and downward frequency regulation of the energy storage power station in the t-th period of the s-th scenario.

[0146] S103. Solve the objective function of the 24-hour day-ahead scheduling model of the wind-fire-energy storage combined system according to the constraint conditions of the 24-hour day-ahead scheduling model constructed in S102, and obtain the day-ahead scheduling optimization results for each period of the next day under different scenarios, including: the bidding power of the wind turbine in the electricity energy market in the t-th period of the s-th day-ahead scenario the frequency regulation bidding capacity of the wind turbine in the frequency regulation market in the t-th period of the s-th day-ahead scenario the bidding power of the thermal power unit in the electricity energy market in the t-th period of the s-th day-ahead scenario the frequency regulation bidding capacity of the thermal power unit in the frequency regulation market in the t-th period of the s-th day-ahead scenario the start-stop state B of the thermal power unit in the t-th period of the s-th day-ahead scenario g,t,s , the bidding power of the energy storage power station in the electricity energy market in the t-th period of the s-th day-ahead scenario the frequency regulation bidding capacity of the wind turbine in the energy storage power station in the t-th period of the s-th day-ahead scenario the charge level E of the energy storage power station in the t-th period of the s-th day-ahead scenarioe,t,s Optimal solution

[0147] S2. Taking the day-ahead optimization result as the boundary, establish an intraday correction model with a 4-hour rolling time window based on the objective function of the 24-hour day-ahead scheduling model of the wind-fire-energy storage combined system, and use the intraday correction model with a 4-hour rolling time window to obtain the output correction instructions for each period within the current intraday scheduling window, specifically as follows:

[0148] S201. Taking the day-ahead optimization result as the boundary, establish the objective function of the intraday correction model with a 4-hour rolling time window based on the objective function of the 24-hour day-ahead scheduling model of the wind-fire-energy storage combined system, specifically as follows:

[0149]

[0150] where, F w,t is the deviation penalty cost of the wind turbine at time t in the current intraday scheduling;

[0151] For the five scenarios, the expression for the additional deviation penalty cost of the wind turbine is:

[0152]

[0153] where, ρ w is the deviation penalty cost coefficient for the mismatch between the bid capacity and the actual output of the wind turbine at time t in the current intraday scheduling in the electricity energy market; is the actual output of the wind turbine at time t in the current intraday scheduling in the sth scenario.

[0154] S202. Obtain the output correction instructions for each period within the current intraday scheduling window through dynamic optimization calculation, including: the optimal bid power of the wind turbine in the electricity energy market at time t in the sth scenario of the intraday optimal solution, the optimal bid capacity of the wind turbine in the frequency regulation market at time t in the sth scenario of the intraday optimal solution, the optimal bid power of the thermal power unit in the electricity energy market at time t in the sth scenario of the intraday optimal solution, the optimal bid capacity of the thermal power unit in the frequency regulation market at time t in the sth scenario of the intraday optimal solution, the optimal start-stop state of the thermal power unit at time t in the sth scenario of the intraday optimal solution, the optimal bid power of the energy storage power station in the electricity energy market at time t in the sth scenario of the intraday optimal solution, the optimal bid capacity of the wind turbine in the energy storage power station for frequency regulation at time t in the sth scenario of the intraday optimal solution, the optimal state of charge of the energy storage power station at time t in the sth scenario of the intraday optimal solution.

[0155] S3. For the starting period of the scheduling window, construct a 15 - minute real - time verification model, refine and correct the operating parameters, and obtain real - time dynamic scheduling instructions, specifically as follows:

[0156] S301. Based on the intra - day correction model with a 4 - hour rolling time window, construct the objective function of the 15 - minute real - time verification model:

[0157]

[0158] Among them, are the revenues of the combined wind - fire - storage system in the electricity energy and frequency regulation markets during the frequency modulation signal period k in the current real - time scheduling period t; F g,t,k and F e,t,k are the operating costs of the thermal power unit and the energy storage power station during the frequency modulation signal period k in the current real - time scheduling period t respectively; F oc,t,k is the opportunity cost of the combined wind - fire - storage system during the frequency modulation signal period k in the current real - time scheduling period t; F w,t,k is the deviation penalty cost of the wind turbine during the frequency modulation signal period k in the current real - time scheduling period t; γ is the frequency modulation error penalty cost coefficient; ε t,k is the frequency modulation error during the frequency modulation signal period k in the current real - time scheduling period t.

[0159] S302. For five scenarios, add constraints to the real - time verification model, specifically as follows:

[0160] Fast frequency modulation bidding constraints for the combined wind - fire - storage system:

[0161]

[0162] Among them, is the bidding capacity of the wind turbine, thermal power unit, and energy storage power station in the frequency regulation market during the frequency modulation signal period k in the current real - time scheduling period t in the s - th scenario.

[0163] S303. Based on the constraints of the 15 - minute real - time verification model added in S302 and the 15 - minute real - time verification model established in S301, refine and correct the operating parameters at the k - th moment during the current real - time scheduling period t, and obtain real - time dynamic scheduling instructions, including: the optimal bidding power of the wind turbine in the electricity energy market at the k - th moment during the t - th period in the s - th scenario The optimal solution, the optimal bidding capacity of the wind turbine in the frequency regulation market at the k - th moment during the t - th period in the s - th scenario The optimal solution, the optimal bidding power of the thermal power unit in the electricity energy market at the k - th moment during the t - th period in the s - th scenario The optimal solution, the optimal bidding capacity of the thermal power unit in the frequency regulation market at the k - th moment during the t - th period in the s - th scenario The optimal solution, the optimal bidding capacity of the thermal power unit in the frequency regulation market at the k - th moment during the t - th period in the s - th scenario Optimal solution, start-stop status of thermal power units at the k-th moment in the t-th period of the s-th scenario in real time Optimal solution, bid power of the energy storage power station in the electricity energy market at the k-th moment in the t-th period of the s-th scenario in real time Optimal solution, frequency regulation bid capacity of wind turbines at the energy storage power station at the k-th moment in the t-th period of the s-th scenario in real time Optimal solution, state of charge of the energy storage power station at the k-th moment in the t-th period of the s-th scenario in real time Optimal solution of the optimal solution.

[0164] S4. Modify and update the subsequent operation status according to the real-time dynamic scheduling instruction, shift the scheduling window backward by one period, reconstruct the rolling time window, and repeat the execution of S3 to iteratively update the real-time dynamic scheduling instruction.

[0165] S5. Repeat the execution of S1 - S4, with a progressive rolling time window, and complete the dynamic tracking and closed-loop correction of the full-period operation plan through multiple rounds of iterative calculations.

[0166] In summary, through a rolling optimization scheduling method for a wind-fire-storage integrated system with multi-time-scale nesting and dynamic coordination proposed by the present invention, it is possible to effectively solve the challenges brought by the differences in the operating characteristics of wind turbines, thermal power units, and energy storage power stations in the case of wind power output uncertainty and load demand fluctuations. This method improves the flexibility and economy of the system through multi-time-scale nesting and dynamic rolling optimization scheduling strategies, and at the same time can coordinate the coordinated operation of the electricity energy market and the frequency regulation market, effectively reducing the real-time scheduling cost and increasing the power market revenue.

[0167] The present invention separately establishes optimization scheduling models for the day-ahead, intraday, and real-time stages to fully consider the differences in the operating characteristics of the wind-fire-energy storage integrated system in different stages and the complex interaction relationships between the electric energy and frequency regulation markets. In the day-ahead stage, comprehensively considering the uncertainty of wind power output and the fluctuation of load demand, with the maximization of the overall system benefit as the optimization goal, a day-ahead scheduling model is constructed to optimize and formulate the generation plans and frequency regulation capacity allocations for each time period of the next day, providing a basic framework for the subsequent stages. Although it is difficult to accurately capture real-time changes in this stage, it can plan resources from a macroscopic level and give play to the advantages of the combined operation of wind, fire, and energy storage. In the intraday stage, based on the day-ahead scheduling optimization results, an intraday correction model is constructed for the situation within the intraday time window. This model focuses on the dynamic correction of wind power prediction errors during the day. Through intraday scheduling optimization, the generation plans and frequency regulation capacity are further adjusted to cope with the changes in wind power output and load during the day, reduce the deviation from the actual situation, make the scheduling plan more in line with real-time requirements, and at the same time take into account the coupling relationship between the electric energy market and the frequency regulation market to explore the collaborative potential of the combined operation of wind, fire, and energy storage. In the real-time stage, based on the optimization results at the starting time period within the intraday scheduling time window, a real-time verification model is constructed to optimize the real-time scheduling for the first time period. This stage adopts a shorter time scale to quickly respond to real-time power deviations, utilizes the fast response ability of the energy storage power station and the regulation capacity advantage of thermal power units to quickly handle emergency frequency deviation events, and ensures the stability and reliability of the system. At the same time, based on the real-time scheduling optimization results, the operating states of subsequent time periods are updated, and the scheduling process is advanced in a rolling manner to achieve flexible response to the dynamic changes of the market and the system. The present invention does not simply consider the economic indicators of different stages in the objective function, but deeply analyzes the specific constraint conditions and variable differences in each stage, such as the influence of factors such as wind power prediction accuracy, thermal power unit ramp rate, and the state of charge of the energy storage power station in different stages, and at the same time fully considers the connections and influences between the decisions in different stages to form an organic whole. In addition, the present invention fully considers the differences in time scales of the day-ahead, intraday, and real-time stages. In the day-ahead stage, a longer optimization step size (such as 24 hours) is adopted to balance the computational complexity and macroscopic planning requirements; in the intraday stage, a moderate time step size (such as 4 hours) is adopted to balance flexibility and adjustment costs; in the real-time stage, a shorter optimization step size (such as 15 minutes) is adopted to ensure the fast response ability to real-time changes, thereby ensuring the rationality and effectiveness of the scheduling method.

[0168] Those of ordinary skill in the art can understand that all or part of the steps in the above embodiments can be driven by program instructions to relevant hardware. This program can be stored in a computer-readable storage medium, and such storage media include but are not limited to: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, and optical discs, etc.

Claims

1. A rolling optimal scheduling method for a combined wind-fire-energy storage system with multi-time-scale nesting and dynamic coordination, characterized in that The specific process of the method is as follows: S1. Construct a 24-hour day-ahead scheduling model for the thermal-wind-storage integrated system, solve the 24-hour day-ahead scheduling model for the thermal-wind-storage integrated system to generate the day-ahead optimization results for each period of the next day; S2. Taking the day-ahead optimization results as the boundary, establish an intraday correction model with a 4-hour rolling time window based on the 24-hour day-ahead scheduling model of the thermal-wind-storage integrated system, and use the intraday correction model with a 4-hour rolling time window to obtain the output correction instructions for each period within the current intraday scheduling window; S3. For the starting period of the scheduling window, construct a 15-minute real-time verification model, and use the 15-minute real-time verification model to obtain real-time dynamic scheduling instructions; S4. Update the subsequent operating status according to the real-time dynamic scheduling instructions, shift the scheduling window backward by one period, reconstruct the rolling time window and repeat S3 to iteratively update the real-time dynamic scheduling instructions; S5. Repeat S1-S4, with a progressive rolling time window, and complete the dynamic tracking and correction of the full-period operation plan through multiple rounds of iterative calculations.

2. The rolling optimization scheduling method for a combined wind-fire-energy storage system with multi-time scale nesting and dynamic coordination according to claim 1, wherein: The 24-hour day-ahead scheduling model of the thermal-wind-storage integrated system in S1 is specifically as follows: In fact, t is the label of the time period, and T is the total number of time periods. is the revenue of the wind - fire - energy storage integrated system in the electricity energy market during period t. is the revenue of the wind - fire - energy storage integrated system in the frequency regulation market during period t, F g,t is the operating cost of the thermal power unit during period t; F e,t is the operating cost of the energy storage power station during period t; F oc,t is the total opportunity cost of the wind - fire - energy storage integrated system during period t, p s is the probability of the occurrence of the s - th scenario; is the electricity price in the electricity energy market during period t; is the total bidding power of the wind - fire - energy storage integrated system in the electricity energy market during period t in the s - th scenario, are the bidding powers of the wind turbine, thermal power unit, and energy storage power station in the electricity energy market during period t in the s - th scenario respectively. Δt is the time interval between adjacent time periods. is the revenue of the wind - fire - energy storage integrated system in the energy market during period t in the s - th scenario, is the revenue of the wind - fire - energy storage integrated system in the frequency regulation market during period t in scenario s, are the frequency regulation capacity compensation price and frequency regulation mileage compensation price in the frequency regulation market during period t respectively; is the frequency regulation bidding capacity of the wind - fire - energy storage integrated system in the frequency regulation market during period t in the s - th scenario; is the net frequency regulation mileage during period t in the s - th scenario; S t,k+1,s 、S t,k,s are the regulation command signals in the (k + 1)-th and k - th frequency regulation signal cycles during period t in the s - th scenario respectively, are the frequency regulation bidding capacities of the wind turbine, thermal power unit, and energy storage power station in the frequency regulation market during period t in the s - th scenario respectively; is the comprehensive frequency regulation performance index in the s - th scenario, and K is the total number of frequency regulation signal cycles.

3. A rolling optimal scheduling method for a wind-fire-storage integrated system with multi-time-scale nesting and dynamic coordination according to claim 2, characterized in that: The scenarios include: For the first scenario when s = 1: Low wind power output and low load demand are specifically: P w ≤0.3P w,rate and P D ≤0.7P D,peak ; The second scenario when s = 2: low wind power output and high load demand, specifically: P w ≤0.3P w,rate and P D ≥0.9P D,peak ; The third scenario when s = 3: Medium wind power output and balanced load demand, specifically: 0.3P w,rate <P w <0.8P w,rate And 0.7P D,peak <P D <0.9P D,peak ; Fourth scenario when s = 4: High wind power output and low load demand, specifically: P w ≥0.8P w,rate and P D ≤0.7P D,peak ; The fifth scenario when s = 5: High wind power output and high load demand, specifically: P w ≥0.8P w,rate and P D ≥0.9P D,peak ; Among them, P w is the wind power output, P w,rate is the rated power of the wind turbine, P D is the load demand, P D,peak is the historical peak load of the regional power grid.

4. A rolling optimization scheduling method for a combined wind-fire-energy storage system with multi-time scale nesting and dynamic coordination according to claim 3, characterized in that: The operating cost of the thermal power unit in period t is as follows: Among them, δ g is the start-up and shut-down cost of the thermal power unit; B g,t-1,s , B g,t,s are 0-1 state variables, representing the start-up and shut-down states of the thermal power unit at t-1 and t in the s-th scenario respectively; a g , b g , c g are the consumption characteristic coefficients of the thermal power unit; P g,t,s is the power generation of the thermal power unit at t in the s-th scenario.

5. A rolling optimization scheduling method for a wind-fire-storage integrated system with multi-time-scale nesting and dynamic coordination according to claim 4, characterized in that: The operating cost of the energy storage power station in period t is as follows: Among them, ΔE k,t,s is the energy change of the k-th frequency modulation signal period in the t-th time period of the s-th scenario, is the initial purchase cost of the energy storage battery; is the maximum charge-discharge times of the battery at 100% depth of discharge; E max is the maximum allowable capacity of the energy storage power station.

6. A rolling optimal scheduling method for a combined wind-fire-energy storage system with multi-time scale nesting and dynamic coordination according to claim 5, characterized in that: The total opportunity cost of the thermal-wind-storage integrated system in period t is as follows: Among them, are the opportunity costs of the wind turbine generator, thermal power generator, and energy storage power station participating in the electric energy and frequency regulation markets during the t-th period in the s-th scenario, respectively; are the bidding capacities of the wind turbine generator, thermal power generator, and energy storage power station in the electric energy market when they do not participate in the frequency regulation market during the t-th period in the s-th scenario, respectively.

7. A rolling optimization scheduling method for a wind-fire-storage integrated system with multi-time-scale nesting and dynamic coordination according to claim 6, characterized in that: The constraints for solving the 24-hour day-ahead scheduling model of the thermal-wind-storage integrated system are as follows: The system power balance constraint is specifically: Among them, N w , N g , N e are the numbers of wind turbines, thermal power units and energy storage power stations in the system respectively; P w,t,s is the power generation of the wind turbine at time t in the s-th scenario; are the discharging and charging powers of the energy storage battery at time t in the s-th scenario respectively; P d,t,s is the load demand of node d at time t in the s-th scenario, and D is the total number of time nodes in time period t; The system frequency regulation demand constraint is as follows: Among them, are respectively the frequency regulation capacity demand and the frequency regulation mileage demand of the system in the t-th period of the s-th scenario; The output constraint of the wind turbine is as follows: Among them, is the predicted maximum power generation of the wind turbine in the electricity energy market at time period t in the s-th scenario; The frequency regulation capacity constraint of the wind turbine is specifically: Among them, are the reserved powers for the up and down frequency regulation of the wind turbine at the t-th time period in the s-th scenario respectively; The output, ramping, start-stop time constraints of the thermal power unit are as follows: wherein, are the maximum and minimum outputs of the thermal power unit, respectively; are the upward and downward ramping rates of the thermal power unit, respectively; T on,g,t,s and T off,g,t,s are the continuous operation duration and continuous shutdown duration of the thermal power unit at time t in the s-th scenario, respectively; are the minimum allowable continuous operation duration and continuous shutdown duration of the thermal power unit, respectively. The frequency regulation capacity constraint of the thermal power unit is as follows: Among them, are the reserved capacities for the upward and downward frequency regulation of the thermal power unit in the t-th period of the s-th scenario, respectively. The charge-discharge power and state of charge constraints of the energy storage power station are as follows: E e,min ≤ E e,t,s ≤ E e,max E e,t,s = E e,t-1,s + ΔE e,t,s E e,0,s = E e,T,s Among them, is a 0-1 state variable, representing the discharging and charging states of the energy storage power station at time t in the s-th scenario; P e dis,min , P e ch，min are respectively the minimum discharging power and charging power of the energy storage power station; P e dis,max , P e ch，max are respectively the maximum discharging power and charging power of the energy storage power station; E e,min , E e,max are respectively the minimum and maximum charge levels allowed for the energy storage power station; E e,t-1,s , E e,t,s are respectively the charge levels of the energy storage power station at time t-1 and t in the s-th scenario; ΔE e,t,s is the change in the charge level of the energy storage power station at time t in the s-th scenario; η dis , η ch are respectively the discharging and charging efficiencies of the energy storage power station; E e,0,s , E e,T,s are respectively the charge level of the energy storage power station at the start time of each day and the charge level at the end time of each day in the s-th scenario; The frequency regulation capacity constraint of the energy storage power station is as follows: Among them, are the reserved capacities for the upper and lower frequency regulation of the energy storage power station in the t-th time period of the s-th scenario, respectively.

8. A rolling optimal scheduling method for a combined wind-fire-energy storage system with multi-time-scale nesting and dynamic coordination according to claim 7, characterized in that: The specific results of the day-ahead dispatching optimization for each time period on the next day include: the bidding power of the wind turbine in the electricity energy market at time t in the s-th scenario of the day-ahead Optimal solution, the frequency regulation bidding capacity of the wind turbine in the frequency regulation market at time t in the s-th scenario of the day-ahead Optimal solution, the bidding power of the thermal power unit in the electricity energy market at time t in the s-th scenario of the day-ahead Optimal solution, the frequency regulation bidding capacity of the thermal power unit in the frequency regulation market at time t in the s-th scenario of the day-ahead Optimal solution, the start-stop status B of the thermal power unit at time t in the s-th scenario of the day-ahead g,t,s Optimal solution, the bidding power of the energy storage power station in the electricity energy market at time t in the s-th scenario of the day-ahead Optimal solution, the frequency regulation bidding capacity of the wind turbine in the energy storage power station at time t in the s-th scenario of the day-ahead Optimal solution, the state of charge E of the energy storage power station at time t in the s-th scenario of the day-ahead e,t,s Optimal solution.

9. A rolling optimization scheduling method for a wind-fire-storage integrated system with multi-time-scale nesting and dynamic coordination according to claim 8, characterized in that: In S2, taking the day-ahead optimization results as the boundary, establish an intraday correction model with a 4-hour rolling time window based on the 24-hour day-ahead scheduling model of the thermal-wind-storage integrated system, and use the intraday correction model with a 4-hour rolling time window to obtain the output correction instructions for each period within the current intraday scheduling window, specifically: S201. Taking the day-ahead optimization results as the boundary, establish the objective function of the intraday correction model with a 4-hour rolling time window based on the objective function of the 24-hour day-ahead scheduling model of the thermal-wind-storage integrated system, specifically: Among them, F w,t is the deviation penalty cost of the wind turbine unit during the t-th period of the current day-ahead scheduling, and ρ w is the deviation penalty cost coefficient for the mismatch between the bidding capacity and the actual output of the wind turbine unit during the t-th period of the current day-ahead scheduling in the electricity energy market; is the actual output of the wind turbine unit during the t-th period of the current day-ahead scheduling in the s-th scenario, and F w,t,s is the deviation penalty cost of the wind turbine unit during the t-th period of the current day-ahead scheduling in the s-th scenario; S202. Solve the objective function of the intraday correction model for the 4-hour rolling time window to obtain the output correction instructions for each period within the current intraday scheduling window, including: the bidding power of the wind turbine in the electricity energy market at the t-th period in the s-th intraday scenario Optimal solution, the frequency regulation bidding capacity of the wind turbine in the frequency regulation market at the t-th period in the s-th intraday scenario Optimal solution, the bidding power of the thermal power unit in the electricity energy market at the t-th period in the s-th intraday scenario Optimal solution, the frequency regulation bidding capacity of the thermal power unit in the frequency regulation market at the t-th period in the s-th intraday scenario Optimal solution, the start-stop state of the thermal power unit at the t-th period in the s-th intraday scenario Optimal solution, the bidding power of the energy storage power station in the electricity energy market at the t-th period in the s-th intraday scenario Optimal solution, the frequency regulation bidding capacity of the wind turbine in the energy storage power station at the t-th period in the s-th intraday scenario Optimal solution, the state of charge of the energy storage power station at the t-th period in the s-th intraday scenario Optimal solution.

10. A rolling optimization scheduling method for a wind-fire-storage integrated system with multi-time-scale nesting and dynamic coordination according to claim 9, characterized in that: In S3, for the starting period of the scheduling window, construct a 15-minute real-time verification model, and use the 15-minute real-time verification model to obtain real-time dynamic scheduling instructions, specifically; S301. Based on the intraday correction model with a 4-hour rolling time window, construct the objective function of the 15-minute real-time verification model: Among them, are the revenues of the wind - fire - energy storage integrated system in the electricity energy and frequency regulation market during the t - th period of the current real - time scheduling with the frequency modulation signal period k; F g,t,k and F e,t,k are the operating costs of the thermal power unit and the energy storage power station during the t - th period of the current real - time scheduling with the frequency modulation signal period k; F oc,t,k is the opportunity cost of the wind - fire - energy storage integrated system during the t - th period of the current real - time scheduling with the frequency modulation signal period k; F w,t,k is the deviation penalty cost of the wind turbine during the t - th period of the current real - time scheduling with the frequency modulation signal period k; γ is the frequency modulation error penalty cost coefficient; ε t,k is the frequency modulation error during the t - th period of the current real - time scheduling with the frequency modulation signal period k; S302. Add the following constraint conditions for the 15-minute real-time verification model: Among them, is the bidding capacity of the wind turbine, thermal power unit and energy storage power station in the frequency regulation market during the current real-time scheduling period t in the s-th scenario for the frequency regulation signal period k. S303. Obtain real-time dynamic scheduling instructions based on the 15-minute real-time verification model constraint conditions added in S302 and the 15-minute real-time verification model established in S301, including: the bidding power of the wind turbine generator in the electricity energy market at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the frequency regulation bidding capacity of the wind turbine generator in the frequency regulation market at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the bidding power of the thermal power unit in the electricity energy market at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the frequency regulation bidding capacity of the thermal power unit in the frequency regulation market at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the frequency regulation bidding capacity of the thermal power unit in the frequency regulation market at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the start-stop state of the thermal power unit at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the bidding power of the energy storage power station in the electricity energy market at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the frequency regulation bidding capacity of the wind turbine generator in the energy storage power station at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution, the state of charge of the energy storage power station at the k-th moment in the t-th period of the s-th real-time scenario Optimal solution of the optimal solution.

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