Independent micro-grid multi-time scale coordinated scheduling method based on dynamic feedback correction

Abstract

The invention provides an independent micro-grid multi-time-scale coordinated scheduling method based on dynamic feedback correction, and relates to the technical field of micro-grid scheduling, comprising the following steps: establishing an independent micro-grid system model, and establishing an operation model of each device; establishing a short-term scheduling model, and constructing a target function by taking economy optimization as a target; establishing an ultra-short-term scheduling model, and selecting a micro-gas turbine or a storage battery to perform power deviation compensation through a virtual overrun punishment method by taking a short-term scheduling result as a reference; setting power adjusting boundaries of the micro-gas turbine and the storage battery; according to the method, the running state and the adjusting capacity of each unit are predicted in advance in short-term scheduling, and the micro gas turbine is prevented from being frequently started and stopped or running in a low-power state for a long time through dynamic feedback correction, so that the output power of other equipment is optimized, and the operation and maintenance cost of the system is minimized; the utilization rate of renewable energy sources by the micro-grid is improved, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid scheduling, and particularly to an independent microgrid multi-time scale coordinated scheduling method based on dynamic feedback correction. Background Art

[0002] A microgrid is a small power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, loads, monitoring and protection devices, etc. It can achieve friendly grid connection interaction and autonomous operation, effectively solve the flexible and efficient application of distributed power sources, and solve problems such as the grid connection of a large number and diverse forms of distributed power sources. It is an effective way to realize an active distribution network;

[0003] At present, the optimal scheduling of microgrids mainly adopts a single-time scale static scheduling method, which is difficult to adapt to the real-time fluctuations of renewable energy and loads. There are problems such as frequent start-stop of micro gas turbines in the microgrid, overcharge and over-discharge of energy storage systems, low utilization rate of renewable energy, and insufficient scheduling robustness. Some studies have tried to improve through multi-time scale scheduling or model predictive control (MPC) methods, but there are still problems such as loose connection between ultra-short-term scheduling and short-term scheduling, lack of consideration of economic penalties in ultra-short-term scheduling, insufficient consideration of economy in power deviation compensation strategies, lack of a dynamic correction mechanism for the operating state of micro gas turbines, inability to effectively avoid their frequent start-stop or low-power operation, ineffective combination of virtual penalty cost optimization of the coordinated output of energy storage and micro gas turbines, and high overall operating costs. Therefore, the present invention proposes an independent microgrid multi-time scale coordinated scheduling method based on dynamic feedback correction to solve the problems existing in the prior art. Summary of the Invention

[0004] In view of the above problems, the present invention proposes an independent microgrid multi-time scale coordinated scheduling method based on dynamic feedback correction. This independent microgrid multi-time scale coordinated scheduling method based on dynamic feedback correction avoids the frequent start-stop or long-term low-power operation of micro gas turbines, optimizes the utilization rate of renewable energy, reduces the system operating cost, and improves the power supply reliability and stability of the independent microgrid through multi-time scale scheduling strategies and dynamic feedback correction technologies.

[0005] To achieve the object of the present invention, the present invention is realized through the following technical solutions: An independent microgrid multi-time scale coordinated scheduling method based on dynamic feedback correction, comprising the following steps:

[0006] S1: Build an independent microgrid system model and establish the operation models of each device;

[0007] S2: Establish a short-term scheduling model and construct an objective function with the goal of optimal economy;

[0008] S3: Establish an ultra-short-term scheduling model. Based on the short-term scheduling results, select a micro gas turbine or a battery for power deviation compensation through the virtual over-limit penalty method;

[0009] S4: Set the power adjustment boundaries of the micro gas turbine and the battery, calculate the virtual over-limit penalty cost according to the unit operation cost, and select the optimal power compensation method;

[0010] S5: Control the smooth connection between short-term scheduling and ultra-short-term scheduling through interpolation;

[0011] S6: Establish the operating constraints of the microgrid. Based on the above models and constraints, use the CPLEX solver to solve, obtain the optimal scheduling plan for each unit of the microgrid, and output the scheduling results.

[0012] The further improvement lies in that: in the above S1, the independent microgrid system model includes a photovoltaic panel, a wind turbine, a micro gas turbine, and a battery, and the operation models of each device include a photovoltaic power generation model, a wind power generation model, a micro gas turbine model, and a battery energy storage system model.

[0013] The further improvement lies in that: the photovoltaic power generation model is:

[0014]

[0015] Where, P PV is the output power of the photovoltaic panel, G t is the solar irradiance, G STC is the irradiance under standard test conditions, is the temperature coefficient -0.0047, T STC is the surface temperature of the photovoltaic panel under standard test conditions of 25 °C, P STC is the output power of the photovoltaic panel under standard measurement;

[0016] The wind power generation model is:

[0017]

[0018] Where, v t is the instantaneous wind speed, v in , v r and v out are the cut-in wind speed, rated wind speed and cut-out wind speed respectively, and P r is the rated power of the wind turbine.

[0019] The further improvement lies in that: the micro gas turbine is:

[0020]

[0021] In the formula, is the fuel mass flow rate of the micro gas turbine at time t; is the fuel density; is the lower heating value of the fuel of the micro gas turbine; is the power generation efficiency of the micro gas turbine at time t;

[0022] The battery energy storage system model is as follows:

[0023]

[0024] where SOC (t + 1) and SOC (t) are the state of charge of the battery at times t + 1 and t, and are the charge and discharge power of the battery, and are the charge and discharge efficiencies of the battery.

[0025] The further improvement lies in: In S2, a short-term scheduling model is established. With the goal of optimal economy, considering the equipment depreciation cost, maintenance cost, fuel cost, and environmental cost, an objective function is constructed. The objective function of the short-term optimal scheduling is:

[0026]

[0027] where C T is the total daily operating cost of the independent microgrid; C E (t) is the environmental cost of the microgrid at time t; i is the serial number of the wind turbine, photovoltaic panel, micro gas turbine, and battery; C d,t (t) and C m,t (t) are the depreciation cost and maintenance cost of each micro power source at time t respectively; C m,t (t) is the fuel cost of the micro gas turbine at time t; k S is the status factor of the micro gas turbine, k S = 1 represents the startup of the micro gas turbine; C S is the startup cost of the micro gas turbine;

[0028] Depreciation cost:

[0029]

[0030] where C INS,i is the installation cost of the i-th micro power source; k i is the capacity factor of each micro power source, which is the ratio of the annual actual total power generation to the annual theoretical total power generation of each micro power source; P rated is the rated power; P i (t) is the output of each micro power source at time t; D R,i is the depreciation rate of the i-th micro power source; n iis the service life of the i-th micro-power source;

[0031] Maintenance cost:

[0032]

[0033] Among them, C UM,i is the maintenance cost per unit power generation of each micro-source;

[0034] Environmental cost:

[0035]

[0036] Among them, is the output power of the micro gas turbine at time t in short-term scheduling; and are the emissions of CO2 and NO per unit power generation of the micro gas turbine respectively X ; and are the environmental governance costs under the unit emissions of CO2 and NO respectively X ;

[0037] Fuel cost:

[0038]

[0039] Among them, C NG is the unit price of natural gas; η MT is the efficiency of the micro gas turbine; L NG is the lower calorific value of natural gas;

[0040] The constraint conditions for short-term optimal scheduling are:

[0041] Output range of power generation equipment:

[0042]

[0043] Among them, and are the outputs of the photovoltaic panel and the wind turbine at time t in short-term scheduling respectively; is the minimum output power when the micro gas turbine is working;

[0044] Battery constraint:

[0045]

[0046] Among them, 、 are the charging and discharging powers of the battery at time t in short-term scheduling respectively; η ch 、η dis are the charging efficiency and discharging efficiency of the battery respectively; U ch (t), Udis (t) are the state flags for the charging and discharging of the battery at time t, U ch (t) = 1 represents that the battery is in the charging state, U dis (t) = 1 represents that the battery is in the discharging state;

[0047] Power balance:

[0048]

[0049] Among them, is the load power at time t in the short-term dispatch; is the output power of the battery at time t in the short-term dispatch.

[0050] The further improvement lies in that in the said S3, the objective function of the ultra-short-term optimal dispatch is:

[0051]

[0052]

[0053]

[0054]

[0055] Among them, C V is the total virtual overlimit penalty cost; C MV , C BV and C WV are the virtual overlimit penalty costs of the micro gas turbine, battery, and wind curtailment respectively; k M , k B and k W are the virtual overlimit penalty cost coefficients of the micro gas turbine, battery, and wind curtailment respectively; P CWT (t) is the wind curtailment volume;

[0056] The constraint conditions of the ultra-short-term optimal dispatch are:

[0057] Micro gas turbine ramp rate:

[0058]

[0059] Among them, is the output power of the micro gas turbine at time t in the ultra-short-term dispatch;

[0060] Conditions for the penalty of the micro gas turbine power overlimit:

[0061]

[0062] Conditions for the penalty of the battery power overlimit:

[0063]

[0064] Load loss of power rate:

[0065]

[0066] Wherein, is the ultra-short-term load power at time t; and are the outputs of the wind turbine and the photovoltaic panel at time t in the ultra-short-term scheduling respectively; LOLP is the set value of the load loss of power rate.

[0067] The further improvement lies in that: S5 includes the following steps:

[0068] Obtaining the short-term scheduling result: First, obtain the result of the short-term scheduling, including the output plan of each micro-power source and the start-stop state of the micro gas turbine;

[0069] Determining the ultra-short-term scheduling reference value: Utilize the short-term scheduling result to determine the reference reference value of the ultra-short-term scheduling by the interpolation method;

[0070] Calculating the power deviation: Calculate the power deviation between the predicted values of renewable energy and load and the reference value in the ultra-short-term scheduling;

[0071] Adjusting the power of the micro gas turbine and the battery: According to the power deviation, determine the outputs of the micro gas turbine and the battery by optimization to meet the load demand of the microgrid;

[0072] Dynamic adjustment: In real-time control, according to the ramp rate of the micro gas turbine and the state of the battery, dynamically adjust the start-stop time and output of the micro gas turbine, and the charge-discharge state of the battery.

[0073] The further improvement lies in that: In S6, establish the operating constraint conditions of the microgrid, including power balance constraint, equipment output range constraint, battery state of charge constraint, and start-stop interval and ramp rate constraint of the micro gas turbine. Among them, the constraint conditions for short-term optimal scheduling are: power balance constraint, equipment output range constraint, battery state of charge constraint; the constraint conditions for ultra-short-term optimal scheduling are: micro gas turbine ramp rate constraint, conditions for micro gas turbine power overlimit penalty, conditions for battery power overlimit penalty, load loss of power rate.

[0074] The further improvement lies in that: In S6, output the scheduling result, including the start-stop state of the micro gas turbine, the output of each device, the system operation cost, and the utilization rate of renewable energy.

[0075] The beneficial effects of the present invention are:

[0076] 1. The present invention predicts in advance the operating states and adjustment capabilities of each unit in short-term scheduling, and uses dynamic feedback correction to avoid frequent start-stop of micro gas turbines or long-term operation at low power states, thereby optimizing the output power of other devices to minimize the system operation and maintenance costs, improving the utilization rate of renewable energy in the microgrid, and reducing the operating costs.

[0077] 2. The present invention makes adjustments around the short-term scheduling results in ultra-short-term scheduling, punishes the wind abandonment phenomenon, and selects micro gas turbines or batteries for over-limit power compensation through virtual over-limit punishment methods. This method increases the utilization rate of renewable energy, reduces the wind abandonment phenomenon, improves the consumption capacity of renewable energy in the microgrid, and obtains better cost and environmental benefits.

[0078] 3. The present invention realizes the coordinated optimization of scheduling at different time scales, improves the accuracy of scheduling. The ultra-short-term scheduling is based on the short-term scheduling results, and combines the ramp rate of the micro gas turbine and the fluctuations of renewable energy and load, so that the ultra-short-term scheduling makes adjustments around the optimization results of the short-term scheduling, thereby enhancing the anti-interference ability and power supply reliability of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 is a schematic flow chart of the present invention;

[0080] Figure 2 is a schematic diagram of the key nodes of dynamic feedback correction and the key nodes of over-limit cost punishment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0081] In order to deepen the understanding of the present invention, the following will further elaborate on the present invention in combination with embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0082] Embodiment 1

[0083] According to Figure 1 、 2 shown, this embodiment proposes an independent microgrid multi-time scale coordinated scheduling method based on dynamic feedback correction. Among them, the independent microgrid system includes a wind turbine, a photovoltaic panel, a micro gas turbine (micro GT), a battery, and a load unit. The implementation steps of short-term scheduling are as follows:

[0084] Data Input and Prediction: Collect short-term wind power prediction values, photovoltaic power prediction values, and electrical load prediction values; generate an initial short-term scheduling plan based on the prediction data to determine the initial output schemes of the micro gas turbine and the battery. To smooth the prediction data between different time scales, cubic spline interpolation is used for multi-scale fusion, and the interpolation time window is set to ±30 minutes. During the interpolation process, the weights of adjacent moments are distributed according to the distance from the central moment t, meeting the symmetry requirements.

[0085] Dynamic Feedback Correction: Based on the prediction of wind speed, light, and load demand, with the goal of optimal economy and power balance and safety stability as constraints, dynamic optimization is carried out. The micro gas turbine plays a core role in the independent microgrid. Renewable energy and load have random volatility, and unreasonable scheduling will lead to frequent start-stop of the micro gas turbine, resulting in increased start-stop costs and reduced service life. Although restricting its minimum start-stop time can avoid frequent start-stop of the micro gas turbine, it may also cause it to operate at low power for a long time, increasing fuel consumption and mechanical wear. Therefore, this paper predicts the working states of each unit in the independent microgrid in advance, and uses the dynamic feedback correction method to adjust the operating state of the micro gas turbine and correct the working modes of other devices to improve the microgrid's ability to respond to disturbances. During the dynamic optimization of short-term scheduling, not only does the result of the previous moment affect the result of the next moment, but the result of the next moment also affects the result of the previous moment through dynamic feedback correction. Taking the start-stop ramp rate and start-stop interval of the micro gas turbine as constraints, if the micro gas turbine starts and stops frequently, the optimization process pauses and the current scheduling plan is corrected. The correction of the working state of the micro gas turbine will cause the adjustment of the battery state.

[0086] According to the operating characteristics of the micro gas turbine, its start-stop interval is set to more than 2 hours. In short-term scheduling, if the working state of the micro gas turbine shows the situation of "start-stop-start" or "stop-start-stop", it indicates that the micro gas turbine switches frequently between the start and stop states. The following formula is used to judge whether the micro gas turbine has frequent start-stop phenomena.

[0087]

[0088] Among them, B is the state matrix of the micro gas turbine during frequent start-stop, 1 is the start state, and 0 is the stop state; A is the current state matrix of the micro gas turbine; S t is the start-stop state of the micro gas turbine; t is the scheduling time scale; C is the current state of charge matrix of the battery, S oc (t) is the state of charge of the battery at time t; E is the identity matrix.

[0089] The key nodes of dynamic feedback correction are as Figure 2 shown. The trigger condition for correcting the state of the micro gas turbine is set to the start-stop interval being less than 2 hours. When the continuous start-stop interval ΔT is detected MGWhen it is less than 2 hours, the system determines that there is frequent start-stop behavior and triggers the state correction logic. The mathematical expression of the battery compensation strategy is as follows:

[0090]

[0091] represents the net load power compensated by the battery when the micro gas turbine is not running. Among them, 、 、 are the load, photovoltaic and wind power at time 𝑡 respectively, is the dispatching output of the micro gas turbine at this moment. The battery only compensates when there is a power gap.

[0092] Correction of the "start-stop-start" state: Force the state of the micro gas turbine to continuous operation and store the excess power through the battery; if the battery capacity is insufficient, reduce the wind power output.

[0093] Correction of the "stop-start-stop" state: Adjust the state of the micro gas turbine to continuous shutdown, and the battery compensates for the power deviation; if the battery power is insufficient, start the micro gas turbine in advance for charging.

[0094] Embodiment 2

[0095] According to Figure 1 、 2 shown, this embodiment proposes an independent microgrid multi-time scale coordinated dispatching method based on dynamic feedback correction. Among them, the ultra-short-term dispatching is based on the short-term dispatching result. In order to minimize the wind curtailment as much as possible and at the same time compensate for the power deviation between the two time scales caused by the fluctuations of renewable energy and load, the virtual overlimit penalty method is used to select the micro gas turbine and the battery for overlimit power balance adjustment. The key nodes of the overlimit cost penalty are as Figure 2 shown. Set the power adjustment boundaries P OB and P OMT of the battery and the micro gas turbine. When the net power fluctuation in the ultra-short-term dispatching exceeds the adjustment boundary set based on the short-term dispatching result, the micro gas turbine and the battery are used for overlimit power compensation. According to the unit operation costs of the micro gas turbine and the battery, linear functions with two different coefficients are used to describe their virtual overlimit penalty costs respectively.

[0096] The method of selecting the micro gas turbine or the battery to achieve power compensation is as follows:

[0097]

[0098] In the formula: ΔP(t) is the deviation power to be compensated at time t; and are the outputs of the battery and the micro gas turbine for deviation compensation at time t respectively; J B and JMT They are the start flag bits of the storage battery and the micro gas turbine respectively, J B = 1 means using the storage battery for power deviation compensation, J MT = 1 means using the micro gas turbine to compensate for power deviation.

[0099] Embodiment III

[0100] According to Figure 1 、 2 As shown, this embodiment proposes an independent microgrid multi-time scale coordinated scheduling method based on dynamic feedback correction. Among them, the coordination of short-term and ultra-short-term scheduling is achieved through time scale connection and system stability guarantee. The short-term scheduling (1-hour scale) result provides a benchmark value for the ultra-short-term scheduling (15-minute scale); the ultra-short-term scheduling dynamically adjusts the start and stop times of the micro gas turbine within a ±30-minute time window around the benchmark value. It is restricted that the micro gas turbine and the storage battery cannot stop simultaneously to ensure voltage / frequency stability; the state of charge of the storage battery is adjusted in advance through dynamic feedback to avoid insufficient energy storage in extreme cases.

[0101] The start time of the micro gas turbine in the ultra-short-term scheduling can be judged by the following formula:

[0102]

[0103] Among them, is the maximum ramp rate of the micro gas turbine; t m is the start time of the micro gas turbine, and m can take 1, 2, 3, 4.

[0104] The best stop time of the micro gas turbine can be judged by the following formula:

[0105]

[0106] In the formula, t n is the stop time of the micro gas turbine, and n can take 10, 11, 12, 13, 14.

[0107] Embodiment IV

[0108] According to Figure 1 、 2 As shown, this embodiment proposes an independent microgrid multi-time scale coordinated scheduling method based on dynamic feedback correction. Among them, the objective function of short-term optimal scheduling:

[0109]

[0110] Among them, C T is the total daily operating cost of the independent microgrid; C E (t) is the environmental cost of the microgrid at time t; i is the serial number of the wind turbine, photovoltaic panel, micro gas turbine, and storage battery; C d,t(t) and C m,t (t) are the depreciation cost and maintenance cost of each micro - power source at time t; C m,t (t) is the fuel cost of the micro - gas turbine at time t; k S is the state factor of the micro - gas turbine, k S = 1 represents that the micro - gas turbine starts; C S is the start - up cost of the micro - gas turbine.

[0111] Depreciation cost:

[0112]

[0113] Among them, C INS,i is the installation cost of the i - th micro - power source; k i is the capacity factor of each micro - power source, which is the ratio of the annual actual total power generation to the annual theoretical total power generation of each micro - power source; P rated is the rated power; P i (t) is the output of each micro - power source at time t; D R,i is the depreciation rate of the i - th micro - power source; n i is the service life of the i - th micro - power source.

[0114] Maintenance cost:

[0115]

[0116] Among them, C UM,i is the maintenance cost per unit power generation of each micro - source.

[0117] Environmental cost:

[0118]

[0119] Among them, is the output power of the micro - gas turbine at time t in short - term scheduling; and are the emissions of CO2 and NO X per unit power generation of the micro - gas turbine respectively; and are the emissions of CO2 and NO X per unit respectively, and the environmental governance cost under this unit emission.

[0120] Fuel cost:

[0121]

[0122] Among them, C NG is the unit price of natural gas; η MT is the efficiency of the micro - gas turbine; L NG is the lower calorific value of natural gas.

[0123] Constraints for short-term optimal scheduling:

[0124] Output range of power generation equipment:

[0125]

[0126] Among them, and are the outputs of the photovoltaic panels and wind turbines at time t in the short-term scheduling, respectively; is the minimum output power when the micro gas turbine is working.

[0127] Battery constraints:

[0128]

[0129] Among them, , are the charging and discharging powers of the battery at time t in the short-term scheduling, respectively; η ch , η dis are the charging efficiency and discharging efficiency of the battery, respectively; U ch (t), U dis (t) are the charging and discharging state flags of the battery at time t, respectively. U ch (t) = 1 represents that the battery is in the charging state, and U dis (t) = 1 represents that the battery is in the discharging state.

[0130] Power balance:

[0131]

[0132] Among them, is the load power at time t in the short-term scheduling; is the output of the battery at time t in the short-term scheduling.

[0133] Objective function of ultra-short-term optimal scheduling:

[0134]

[0135]

[0136]

[0137]

[0138] Among them, C V is the total virtual overlimit penalty cost; C MV , C BV and C WV are the virtual overlimit penalty costs of the micro gas turbine, battery, and curtailed wind, respectively; k M , kB and k W are the virtual over - limit penalty cost coefficients for the micro - gas turbine, battery, and curtailed wind respectively, and their values are set based on economic trade - offs; P CWT (t) is the amount of curtailed wind.

[0139] Constraints for ultra - short - term optimal scheduling:

[0140] Ramp rate of the micro - gas turbine:

[0141]

[0142] Among them, is the output power of the micro - gas turbine at time t in ultra - short - term scheduling.

[0143] Conditions for over - limit penalty of the micro - gas turbine power:

[0144]

[0145] Conditions for over - limit penalty of the battery power:

[0146]

[0147] Load loss rate:

[0148]

[0149] Among them, is the ultra - short - term load power at time t; and are the outputs of the wind turbine and photovoltaic panel at time t in ultra - short - term scheduling respectively; LOLP is the set value of the load loss rate.

[0150] The present invention predicts in advance the operating states and adjustment capabilities of each unit in short - term scheduling, and uses dynamic feedback correction to avoid frequent start - stop of the micro - gas turbine or long - time operation at low power states. Furthermore, it optimizes the output powers of other devices to minimize the system operation and maintenance costs, improves the utilization rate of renewable energy in the micro - grid, and reduces the operating costs. And the present invention adjusts around the short - term scheduling results in ultra - short - term scheduling, punishes the phenomenon of curtailed wind, and selects the micro - gas turbine or battery to compensate for the over - limit power through the virtual over - limit penalty method. This method increases the utilization rate of renewable energy, reduces the phenomenon of curtailed wind, improves the consumption capacity of renewable energy in the micro - grid, and obtains good cost and environmental benefits. At the same time, the present invention realizes the coordinated optimization of different time - scale scheduling, improves the accuracy of scheduling. The ultra - short - term scheduling is based on the short - term scheduling results, combines the ramp rate of the micro - gas turbine and the fluctuations of renewable energy and load, so that the ultra - short - term scheduling adjusts around the optimization results of short - term scheduling, thereby enhancing the anti - interference ability and power supply reliability of the micro - grid.

[0151] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. An independent microgrid multi-time scale coordinated scheduling method based on dynamic feedback correction, characterized in that It includes the following steps: S1: Build an independent microgrid system model and establish the operation models of various devices; S2: Establish a short-term scheduling model, aiming at the optimal economy, and construct an objective function; S3: Establish an ultra-short-term scheduling model. Based on the short-term scheduling results, select a micro gas turbine or a battery for power deviation compensation through the virtual overlimit penalty method; S4: Set the power adjustment boundaries of the micro gas turbine and the battery, calculate the virtual overlimit penalty cost according to the unit operation cost, and select the optimal power compensation method; S5: Control the smooth connection between the short-term scheduling and the ultra-short-term scheduling through the interpolation method; S6: Establish the operating constraints of the microgrid. Based on the above models and constraints, use the CPLEX solver to solve, obtain the optimal scheduling plan of each unit of the microgrid, and output the scheduling results.

2. The multi-time scale coordinated scheduling method for an independent microgrid based on dynamic feedback correction according to claim 1, characterized in that: In the above S1, the independent microgrid system model includes a photovoltaic panel, a wind turbine, a micro gas turbine, and a battery. The operation models of various devices include a photovoltaic power generation model, a wind power generation model, a micro gas turbine model, and a battery energy storage system model.

3. The multi-time scale coordinated scheduling method for an independent microgrid based on dynamic feedback correction according to claim 2, characterized in that: The photovoltaic power generation model is: ， Among them, P PV is the output power of the photovoltaic panel, G t is the solar irradiance, G STC is the irradiance under standard test conditions, is the temperature coefficient -0.0047, T STC is the surface temperature of the photovoltaic panel under standard test conditions, 25 °C, P STC is the output power of the photovoltaic panel under standard measurement; The wind power generation model is: ， Among them, v t is the instantaneous wind speed, v in , v r and v out are the cut-in wind speed, rated wind speed and cut-out wind speed respectively, and P r is the rated power of the wind turbine.

4. The multi-time scale coordinated dispatching method of the independent microgrid based on dynamic feedback correction according to claim 2, characterized in that: The micro gas turbine is: ， Wherein, is the fuel mass flow rate of the micro gas turbine at time t; is the fuel density; is the lower calorific value of the fuel of the micro gas turbine; is the power generation efficiency of the micro gas turbine at time t; The battery energy storage system model is: ， Among them, SOC (t + 1) and SOC (t) are the state of charge of the battery at times t + 1 and t, and are the charge and discharge power of the battery, and are the charge and discharge efficiency of the battery.

5. The multi-time scale coordinated scheduling method for an independent microgrid based on dynamic feedback correction according to claim 1, characterized in that: In the above S2, establish a short-term scheduling model, aiming at the optimal economy, and comprehensively consider the equipment depreciation cost, maintenance cost, fuel cost, and environmental cost to construct an objective function. The objective function of the short-term optimal scheduling is: ， Among them, C T is the total daily operating cost of the independent microgrid; C E (t) is the environmental cost of the microgrid at time t; i is the serial number of the wind turbine, photovoltaic panel, micro gas turbine, and battery; C d,t (t) and C m,t (t) are the depreciation cost and maintenance cost of each micro power source at time t, respectively; C m,t (t) is the fuel cost of the micro gas turbine at time t; k S is the state factor of the micro gas turbine, k S = 1 represents the startup of the micro gas turbine; C S is the startup cost of the micro gas turbine; Depreciation cost: ， Among them, C INS,i is the installation cost of the i-th micro-power source; k i is the capacity factor of each micro-power source, which is the ratio of the annual actual total power generation of each micro-power source to the annual theoretical total power generation; P rated is the rated power; P i (t) is the output of each micro-power source at time t; D R,i is the depreciation rate of the i-th micro-power source; n i is the service life of the i-th micro-power source; Maintenance cost: ， Among them, C UM,i is the maintenance cost at the power generation power of each micro-source unit; Environmental cost: ， Among them, is the output power of the micro gas turbine at time t in short-term scheduling; and are the emissions of CO2 and NOx respectively under the unit power generation of the micro gas turbine; X emissions; and are the environmental governance costs under the unit emissions of CO2 and NOx respectively; X emissions; Fuel cost: ， Among them, C NG is the unit price of natural gas; η MT is the efficiency of the micro gas turbine; L NG is the low calorific value of natural gas; The constraint conditions of the short-term optimal scheduling are: Output range of the power generation equipment: ， Among them, and are the outputs of the photovoltaic panels and wind turbines at time t in the short-term scheduling, respectively; is the minimum output power when the micro gas turbine is working; Battery constraint: ， Among them, and are the charging and discharging powers of the battery at the short-term scheduling time t, respectively; η ch and η dis are the charging efficiency and discharging efficiency of the battery, respectively; U ch (t) and U dis (t) are the charging and discharging state flag bits of the battery at time t, respectively. U ch (t) = 1 represents that the battery is in the charging state, and U dis (t) = 1 represents that the battery is in the discharging state; Power balance: ， Among them, is the load power at time t in short-term scheduling; is the output of the battery at time t in short-term scheduling.

6. The multi-time scale coordinated scheduling method for an independent microgrid based on dynamic feedback correction according to claim 1, characterized in that: In the above S3, the objective function of the ultra-short-term optimal scheduling is: ， Among them, C V is the total virtual over-limit penalty cost; C MV , C BV and C WV are the virtual over-limit penalty costs of the micro gas turbine, the battery, and the curtailed wind respectively; k M , k B and k W are the virtual over-limit penalty cost coefficients of the micro gas turbine, the battery, and the curtailed wind respectively; P CWT (t) is the curtailed wind volume; The constraint conditions of the ultra-short-term optimal scheduling are: Ramp rate of the micro gas turbine: ， Among them, is the output power of the micro gas turbine at time t in the ultra-short-term scheduling; Conditions for power overlimit penalty of the micro gas turbine: ， Conditions for power overlimit penalty of the battery: ， Load power outage rate: ， Among them, is the ultra-short-term load power at time t; and are the outputs of the wind turbine and the photovoltaic panel at time t in the ultra-short-term scheduling respectively; LOLP is the set value of the load loss of power rate.

7. The multi-time scale coordinated scheduling method for an independent microgrid based on dynamic feedback correction according to claim 1, characterized in that: The above S5 includes the following steps: Obtaining short-term scheduling results: First, obtain the short-term scheduling results, including the output plans of each micro power source and the start-stop status of the micro gas turbine; Determining the ultra-short-term scheduling reference value: Use the short-term scheduling results to determine the reference value of the ultra-short-term scheduling through the interpolation method; Calculating the power deviation: Calculate the power deviation between the predicted values of renewable energy and load and the reference value in the ultra-short-term scheduling; Adjusting the power of the micro gas turbine and the battery: According to the power deviation, optimize to determine the output of the micro gas turbine and the battery to meet the load demand of the microgrid; Dynamic adjustment: In real-time control, according to the ramp rate of the micro gas turbine and the state of the battery, dynamically adjust the start-stop time and output of the micro gas turbine, and the charge-discharge state of the battery.

8. The multi-time-scale coordinated scheduling method for an independent microgrid based on dynamic feedback correction according to claim 1, characterized in that: In the above S6, establish the operating constraints of the microgrid, including power balance constraints, equipment output range constraints, battery state of charge constraints, and micro gas turbine start-stop interval and ramp rate constraints. Among them, the constraint conditions of the short-term optimal scheduling are: power balance constraints, equipment output range constraints, battery state of charge constraints; the constraint conditions of the ultra-short-term optimal scheduling are: micro gas turbine ramp rate constraint, conditions for power overlimit penalty of the micro gas turbine, conditions for power overlimit penalty of the battery, load power outage rate.

9. The multi-time scale coordinated scheduling method for an independent microgrid based on dynamic feedback correction according to claim 1, characterized in that: In S6, output the scheduling result, including the start / stop status of the micro gas turbine, the output of each device, the system operation cost, and the utilization rate of renewable energy.

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