An industrial park micro-grid high reliability optimization scheduling method under adverse weather

Abstract

This invention relates to a high-reliability optimized scheduling method for industrial park microgrids under severe weather conditions, belonging to the field of power system operation optimization technology. This method addresses the uncertainties in wind power, photovoltaic, and load power changes and affected periods under severe weather conditions such as typhoons. It corrects for the predicted power of wind power, photovoltaic, and load, and, combined with the microgrid's operating status, constructs robust optimization models for grid-connected and off-grid operation of the industrial park microgrid, respectively, to solve for the operating strategy. This invention overcomes the problem of traditional methods not considering the impact of severe weather, achieving highly economical and reliable operation of industrial park microgrids.

Description

Technical Field

[0001] This invention belongs to the field of power system operation optimization technology, specifically relating to a high-reliability optimized scheduling method for industrial park microgrids under severe weather conditions. Background Technology

[0002] Deploying wind power, photovoltaic power, and other new energy sources, along with energy storage, in industrial parks is an important direction for the development of distributed power sources (see schematic diagram of industrial park microgrid). Figure 1 This approach can improve the self-sufficiency rate of electricity for work and daily life in industrial parks, achieving clean power supply. By optimizing wind, solar, and energy storage power generation plans and power exchange plans with the external grid, industrial park microgrids can reduce electricity costs and even achieve profitability. Furthermore, they can maintain islanded operation for a certain period after a fault, reducing economic losses. Existing industrial park microgrid optimization and dispatch methods typically only consider strategy optimization under normal operating conditions, with less consideration for the impact of severe weather such as typhoons, high temperatures, and solar eclipses on operating strategies. This method considers the impact of severe weather on boundary conditions such as renewable energy and load power, and the operating status of the industrial park microgrid, as well as the uncertainty of severe weather prediction. It conducts optimized dispatch under both grid-connected and off-grid conditions to achieve high-reliability operation of the industrial park microgrid under severe weather conditions. Summary of the Invention

[0003] The purpose of this invention is to provide a high-reliability optimized scheduling method for industrial park microgrids under severe weather conditions. This method is based on the wind, solar and load prediction correction curves under severe weather conditions to carry out robust optimized scheduling of microgrids under grid-connected and off-grid conditions, and obtains an operation strategy that meets both economic and reliability requirements.

[0004] To achieve the above objectives, the technical solution of the present invention is: a high-reliability optimized scheduling method for industrial park microgrids under severe weather conditions, comprising the following steps:

[0005] Step 1: Obtain the data required for calculation, including power parameters, tie line parameters, renewable energy forecast data, and load forecast data;

[0006] Step 2: Obtain severe weather warning information, including the type of severe weather and its start and end times;

[0007] Step 3: Based on severe weather warning information, revise the wind power, solar power, and load power forecast curves;

[0008] Step 4: Determine the microgrid's operating status. If the microgrid is in grid-connected mode, construct a robust optimization model for the grid-connected operation strategy with the goal of optimal economic efficiency. If the microgrid is in off-grid mode, construct a robust optimization model for off-grid operation with the goal of maximizing the power supply time for important loads.

[0009] Step 5: Solve the optimization model to obtain the operating strategy.

[0010] In one embodiment of the present invention, in step 1, the power supply parameters, tie line parameters, renewable energy forecast data, and load forecast data are specifically as follows:

[0011] 1) Power parameters: including the capacity and power parameters of various power sources such as wind power, photovoltaic power, and energy storage;

[0012] 2) Tie line parameters: Maximum transmission power of the tie line connecting the industrial park microgrid and the distribution network;

[0013] 3) New energy forecast data: forecasted hourly power generation capacity of wind power and photovoltaic power;

[0014] 4) Load forecast data: hourly power consumption forecast of the industrial park microgrid.

[0015] In one embodiment of the present invention, in step 2, the types of severe weather include: typhoon, high temperature, and solar eclipse.

[0016] In one embodiment of the present invention, step 3 considers the different impacts of various severe weather events, including typhoons, high temperatures, and solar eclipses, on wind power, photovoltaics, and load curves, and also considers the uncertainty of predicting the start and end times of severe weather events.

[0017] In one embodiment of the present invention, step 3 is specifically implemented as follows:

[0018] Obtain the power correction curves for severe weather, and denote the wind power, photovoltaic, and load power prediction curves as P, respectively. t W,pre P t S ,pre P t L,pre The corresponding power correction curves are ΔP t W ΔP t S ΔP t L ;

[0019] Typhoons cause drastic changes in wind speed in the microgrid area, which in turn leads to changes in wind power generation capacity. These changes are highly random, and the wind power correction curve can be considered to be a series of data that follow a normal distribution.

[0020] ΔP t W ~N(0,σ 2 (1)

[0021]

[0022] In the formula, σ is the variance of the normal distribution, which can be set according to the typhoon warning situation, and P W For wind power installed capacity, generate wind power correction curve according to equation (1), and the generated curve should meet the upper and lower limit requirements of equation (2);

[0023] Meanwhile, the cooling brought by the typhoon will lead to a decrease in electricity load. It is assumed that the electricity load will decrease proportionally, i.e.:

[0024]

[0025] In the formula, k L This is the load power scaling factor, with a value ranging from 0 to 1;

[0026] The impact of typhoons on photovoltaic power is relatively small; therefore, the corrected photovoltaic power during a typhoon can be considered to be 0, i.e.:

[0027]

[0028] High temperatures will lead to increased load and photovoltaic power, while wind power will decrease. It can be assumed that under the influence of high temperatures, the predicted power of wind power, photovoltaic power, and load will be scaled proportionally, and the corresponding power correction curves are as follows:

[0029]

[0030] Where, k W k S These are the scaling factors for wind power and solar power, respectively, P. S For photovoltaic installed capacity, the photovoltaic power correction should also meet the upper and lower limits of equation (8);

[0031] A solar eclipse primarily affects photovoltaic (PV) output; during the eclipse, PV output drops to a minimum value p. S The photovoltaic power correction curve is then:

[0032]

[0033] The impact of the solar eclipse on wind power and load capacity can be ignored, and the power curve during the eclipse is assumed to be 0, i.e.:

[0034]

[0035] Once the power correction curve is obtained, adjustments can be made to wind power, solar power, and load power based on the predicted periods of severe weather; using These represent the periods during which wind power, solar power, and load are affected by severe weather, respectively. A value of 1 indicates impact, and a value of 0 indicates no impact. The impact period should be continuous. For wind power, the impact period is... The time of the start of the influence is denoted as The end time of the effect is recorded as The intermediate time of influence is denoted as Then the duration of the effect between, The value is 1 for the period and 0 for the rest of the time. Similarly, for photovoltaics and loads, the affected time periods are respectively...

[0036] Due to the uncertainty in predicting the duration of severe weather impacts, the start and end times may differ from the predicted times; that is, the actual start time of the impact may not be... Instead At some point in the vicinity, while the actual end time of the impact may not be... Instead At some point in the vicinity; when considering t W Assuming severe weather occurs frequently, considering the uncertainty of the onset / end of the weather, the affected period is... Expressed as follows:

[0037]

[0038]

[0039] In the formula, This is the predicted start time when photovoltaic and load power will be affected by severe weather. t represents the predicted end time of the impact of severe weather on photovoltaic and load power. S t L Γ represents the midpoint of the predicted impact of severe weather on photovoltaic and load power. W,1 ,Γ S,1 ,Γ L,1 Γ represents the maximum possible offset at the start time of the impact on wind power, photovoltaic power, and load power, respectively. W,2 ,Γ S,2 ,Γ L,2 These represent the maximum possible offset at the end of the affected time for wind power, photovoltaic power, and load power, respectively.

[0040] The power curves P after wind power, photovoltaic power, and load correction are as follows. t W,fix P t S,fix P t L,fix Represented as:

[0041]

[0042] In one embodiment of the present invention, in step 4, a robust optimization model for grid-connected operation and a robust optimization model for off-grid operation are constructed according to the microgrid's operating status, so as to achieve the lowest cost operation under grid-connected conditions and high reliability operation under off-grid conditions under the influence of uncertain factors.

[0043] In one embodiment of the present invention, the robust optimization model for grid-connected operation is specifically constructed as follows:

[0044] The optimization objective of the grid-connected robust optimization model is to achieve the lowest operating cost under all possible severe weather start and end times, while also returning the worst-case operating cost. Let X be the set of all decision variables, including wind power generation, photovoltaic power generation, energy storage charging and discharging power, and tie-line switching power; and U be the set of all uncertain variables, including... The objective function is then expressed as follows:

[0045]

[0046] In the formula, c in c out These represent the unit cost of electricity purchase and the unit revenue from electricity sales, respectively. t L,in P t L,out These refer to the power purchased via the tie line and the power sold via the tie line, respectively.

[0047] Operational constraints include:

[0048] 1) Wind power operation constraints:

[0049]

[0050] In the formula, P t W Indicates wind power generation capacity;

[0051] 2) Constraints on photovoltaic operation:

[0052]

[0053] In the formula, P t S Indicates photovoltaic power generation capacity;

[0054] 3) Energy storage operation constraints:

[0055]

[0056] Among them, P t Stor,c P t Stor,d , Q Stor , These are, respectively, energy storage charging power, discharging power, upper power limit, charging state, discharging state, storage capacity, lower storage capacity limit, and upper storage capacity limit; η q η c η d These are the self-loss coefficient, charging efficiency, and discharging efficiency of the energy storage, respectively, with M being a maximum constant. A value of 1 indicates charging. A value of 1 indicates discharge;

[0057] If the severe weather warning is for high temperatures or a solar eclipse, then the lower limit of energy storage capacity will be... Q P Take 5%; if the severe weather warning is a typhoon, considering the impact of faults and other factors during the typhoon, the microgrid may enter off-grid operation mode, and some electricity needs to be reserved for use during off-grid operation, the lower limit of energy storage capacity. Q P Take 50%;

[0058] 4) Transmission power constraints of tie lines:

[0059]

[0060] In the formula, P L,max This represents the maximum transmission power of the tie line;

[0061] 5) Power balance constraints:

[0062]

[0063] 6) Uncertainty constraints on predicted power of wind, solar and load: Equations (12)-(20).

[0064] In one embodiment of the present invention, the robust optimization model for off-grid operation is specifically constructed as follows:

[0065] The objective of the off-grid robust optimization model is to maintain power supply to critical loads for the longest possible duration under all possible severe weather conditions, while also providing the worst-case continuous power supply time. The objective function is then expressed as:

[0066]

[0067] In the formula, This indicates the load supply status. If the power generation capacity of wind, solar, and energy storage can meet the power needs of important loads, the value is 1; otherwise, the value is 0. Therefore... The following constraints must be met:

[0068]

[0069] Where, δ LThe important load proportion coefficient is assumed to be a fixed value, representing the proportion of important loads to the total load of the microgrid. For equation (38), when the microgrid's power generation capacity can meet the power consumption of important loads, the expression within the parentheses is 0. Under the action of the objective function... Take 1; if the microgrid's power generation capacity cannot meet the power consumption of important loads, the expression in parentheses is less than 0, under the constraint of equation (38), Set to 0;

[0070] The constraints include:

[0071] 1) Wind power operation constraints: i.e., equation (22);

[0072] 2) Photovoltaic operation constraints: i.e., equation (23);

[0073] 3) Energy storage operation constraints: i.e., equations (24) to (31);

[0074] The present invention also provides a high-reliability optimized scheduling system for industrial park microgrids under severe weather conditions, including a memory, a processor, and computer program instructions stored in the memory and executable by the processor. When the processor executes the computer program instructions, it can implement the steps described above.

[0075] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions that can be executed by a processor, wherein when the processor executes the computer program instructions, it can implement the steps of the method described above.

[0076] Compared with the prior art, the present invention has the following beneficial effects: The method first obtains severe weather warning information, and corrects the wind power, photovoltaic and load forecast curves based on the severe weather warning information, and then monitors the microgrid operation status in real time. Based on the monitoring results, it constructs and solves grid-connected / off-grid optimized scheduling models to achieve high economic efficiency and high reliability operation under the influence of various severe weather such as typhoons, high temperatures and solar eclipses. Attached Figure Description

[0077] Figure 1 This is a schematic diagram of the microgrid structure in the industrial park.

[0078] Figure 2 Optimization flowchart for microgrid scheduling in industrial parks;

[0079] Figure 3 This is a diagram illustrating the periods affected by severe weather. Detailed Implementation

[0080] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0081] This invention provides a high-reliability optimized scheduling method for industrial park microgrids under severe weather conditions, comprising the following steps:

[0082] Step 1: Obtain the data required for calculation, including power parameters, tie line parameters, renewable energy forecast data, and load forecast data;

[0083] Step 2: Obtain severe weather warning information, including the type of severe weather and its start and end times;

[0084] Step 3: Based on severe weather warning information, revise the wind power, solar power, and load power forecast curves;

[0085] Step 4: Determine the microgrid's operating status. If the microgrid is in grid-connected mode, construct a robust optimization model for the grid-connected operation strategy with the goal of optimal economic efficiency. If the microgrid is in off-grid mode, construct a robust optimization model for off-grid operation with the goal of maximizing the power supply time for important loads.

[0086] Step 5: Solve the optimization model to obtain the operating strategy.

[0087] The following is a detailed implementation process of the present invention.

[0088] This invention addresses the challenge of operational decision-making for industrial park microgrids under severe weather conditions by proposing a highly reliable optimized scheduling method for industrial park microgrids under such conditions. The proposed method considers the impact of various severe weather events such as typhoons, high temperatures, and solar eclipses, as well as the uncertainty of prediction, and takes into account both the requirements for economical grid-connected operation and reliable off-grid operation.

[0089] Reference Figure 2 A highly reliable optimized scheduling method for industrial park microgrids under severe weather conditions is described in the following steps:

[0090] Step 1: Obtain the data required for calculation, including power supply parameters, tie-line parameters, renewable energy forecast data, load forecast data, etc., specifically including:

[0091] 1) Power parameters: including the capacity and power of various power sources such as wind power, photovoltaic, and energy storage;

[0092] 2) Tie line parameters: upper limit of transmission power of the tie line connecting the industrial park microgrid and the distribution network, etc.

[0093] 3) New energy forecast data: forecasted hourly power generation capacity of wind power and photovoltaic power;

[0094] 4) Load forecast data: hourly power consumption forecast of the industrial park microgrid.

[0095] Step 2: Obtain severe weather warning information, including the type of severe weather, start and end times, etc.

[0096] Among them, severe weather types include: typhoons, high temperatures, and solar eclipses.

[0097] Step 3: Based on severe weather warning information, revise the wind power, solar power, and load power prediction curves.

[0098] Specifically, the severe weather power correction curve is first obtained, and the wind power, photovoltaic, and load power prediction curves are denoted as P0, P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, P11, P2 ... t W,pre P t S,pre P t L,pre The power correction curves are ΔP t W ΔP t S ΔP t L .

[0099] Typhoons cause drastic changes in wind speed in the microgrid area, which in turn leads to changes in wind power generation capacity. These changes are highly random, and the wind power curve can be considered to represent a series of data that follow a normal distribution.

[0100] ΔP t W ~N(0,σ 2 (1)

[0101]

[0102] In the formula, σ is the variance of the normal distribution, which can be set according to the typhoon warning situation, and P W The installed capacity of wind power is given. A wind power correction curve is generated according to equation (1), and the generated curve should meet the upper and lower limit requirements of equation (2).

[0103] Meanwhile, the cooling brought by the typhoon will lead to a decrease in electricity consumption. Assuming the electricity consumption decreases proportionally, that is:

[0104]

[0105] In the formula, k L This is the load power scaling factor, with a value ranging from 0 to 1.

[0106] The impact of typhoons on photovoltaic power is relatively small; therefore, the corrected photovoltaic power during a typhoon can be considered to be 0, i.e.:

[0107]

[0108] High temperatures will lead to increased load and photovoltaic power, while wind power will decrease. It can be assumed that under the influence of high temperatures, the predicted power of wind power, photovoltaic power, and load will be scaled proportionally, with the corrected power as follows:

[0109]

[0110] Where, k W k S These are the scaling factors for wind power and solar power, respectively, P. S The photovoltaic installed capacity. The photovoltaic power correction should also meet the upper and lower limits of equation (8).

[0111] A solar eclipse primarily affects photovoltaic (PV) output. During the eclipse, PV output drops to a minimum value p. S The photovoltaic power correction curve is then:

[0112]

[0113] The impact of the solar eclipse on wind power and load capacity can be ignored, and the power curve during the eclipse is assumed to be 0, i.e.:

[0114]

[0115] Once the correction curve is obtained, adjustments can be made to wind power, solar power, and load power based on the predicted periods of severe weather. These represent the periods during which wind power, solar power, and load are affected by severe weather, respectively. A value of 1 indicates impact, while a value of 0 indicates no impact. The impact period should be continuous. Taking wind power as an example, the impact period... The curve is as follows Figure 3 As shown. The start time of the influence is denoted as... The end time of the effect is recorded as The intermediate time of influence is denoted as Then the duration of the effect between, The value is 1 for the time period and 0 for the rest of the time period.

[0116] Furthermore, due to the uncertainty in predicting the duration of severe weather impacts, the start and end times may deviate from the predicted times. That is, the actual start time of the impact may not be... Instead At some point in the vicinity, while the actual end time of the impact may not be... Instead At some point in the vicinity. Considering t... W Assuming severe weather occurs frequently, considering the uncertainty of the onset / end of the weather, the affected period is... It can be expressed by the following formula:

[0117]

[0118] In the formula, This is the predicted start time when photovoltaic and load power will be affected by severe weather. t represents the predicted end time of the impact of severe weather on photovoltaic and load power. S t L Γ represents the midpoint of the predicted impact of severe weather on photovoltaic and load power. W,1 ,Γ S,1 ,Γ L,1 Γ represents the maximum possible offset at the start time of the impact on wind power, photovoltaic power, and load power, respectively. W,2 ,Γ S,2 ,Γ L,2 These represent the maximum possible offset at the end of the period when wind power, photovoltaic power, and load power are affected, respectively.

[0119] The power curves P after wind power, photovoltaic power, and load correction are as follows. t W,fix P t S,fix P t L,fix It can be represented as:

[0120]

[0121] Step 4: Determine the microgrid's operating status. If the microgrid is in a grid-connected state, construct a robust optimization model for the microgrid's grid-connected operation strategy with the goal of optimal economic efficiency. If the microgrid is in an off-grid state, construct a robust optimization model for the microgrid's off-grid operation with the goal of maximizing the power supply time for important loads.

[0122] Specifically, the grid-connected operation optimization model is as follows:

[0123] The optimization objective is to achieve the lowest operating cost under all possible severe weather start and end times. It also returns the worst-case operating cost. Let X be the set of all decision variables (including wind power generation, photovoltaic power generation, energy storage charging and discharging power, tie-line switching power, etc.), and U be the set of all uncertain variables, including... The objective function is then expressed as follows:

[0124]

[0125] In the formula, c in c out These represent the unit cost of electricity purchase and the unit revenue from electricity sales, respectively. t L,in P t L,outThese refer to the power purchased by the tie line and the power sold by the tie line, respectively.

[0126] Operational constraints include:

[0127] 1) Wind power operation constraints:

[0128]

[0129] In the formula, P t W This indicates the power generation capacity of wind power.

[0130] 2) Constraints on photovoltaic operation:

[0131]

[0132] In the formula, P t S This indicates the power output of photovoltaic power generation.

[0133] 3) Energy storage operation constraints:

[0134]

[0135]

[0136] Among them, P t Stor,c P t Stor,d , Q Stor , These are, respectively, energy storage charging power, discharging power, upper power limit, charging state, discharging state, storage capacity, lower storage capacity limit, and upper storage capacity limit; η q η c η d Let be the self-loss coefficient of energy storage, the charging efficiency, and the discharging efficiency, respectively, and M be a maximum constant. A value of 1 indicates charging. A value of 1 indicates discharge.

[0137] If the severe weather warning is for high temperatures or a solar eclipse, then the lower limit of energy storage capacity will be... Q P A smaller value, such as 5%, can be chosen. If the severe weather warning is a typhoon, considering the impact of faults and other factors during the typhoon, the microgrid may enter off-grid operation. Therefore, some electricity needs to be reserved for off-grid operation, and the lower limit of energy storage capacity should be set. Q P A larger value can be taken, such as 50%.

[0138] 4) Transmission power constraints of tie lines:

[0139]

[0140] In the formula, PL,max This represents the maximum transmission power of the tie line.

[0141] 5) Power balance constraints:

[0142]

[0143] 6) Uncertainty constraints on predicted power of wind, solar and load: Equations (12)-(20).

[0144] The off-grid operation optimization model is as follows:

[0145] The operational objective is to maintain power supply to critical loads for the longest possible period under off-grid conditions during all possible periods of severe weather. The worst-case scenario of continuous power supply time is also given. Therefore, the expression for the objective function is:

[0146]

[0147] In the formula, This indicates the load supply status. If the power generation capacity of wind, solar, and energy storage can meet the power needs of important loads, the value is 1; otherwise, the value is 0. Therefore, The following constraints must be met:

[0148]

[0149] Where, δ L Here is the critical load proportion coefficient, assuming that the proportion of critical loads to the total load of the microgrid is a fixed value. For equation (38), when the microgrid's power generation capacity can meet the electricity demand of critical loads, the expression within the parentheses is 0. Under the influence of the objective function, Take 1; if the microgrid's power generation capacity cannot meet the power consumption of important loads, the expression in parentheses is less than 0, under the constraint of equation (38), Take 0.

[0150] The constraints include:

[0151] 1) Wind power operation constraints: i.e., equation (22).

[0152] 2) Photovoltaic operation constraints: i.e., equation (23).

[0153] 3) Energy storage operation constraints: i.e. Equations (24) to (31).

[0154] Step 5: Solve the optimization model to obtain the operating strategy. The resulting decision includes time-by-time wind power generation plans, photovoltaic power generation plans, energy storage charging and discharging plans, and tie-line power purchase and sale plans (grid-connected only).

[0155] The present invention also provides a high-reliability optimized scheduling system for industrial park microgrids under severe weather conditions, including a memory, a processor, and computer program instructions stored in the memory and executable by the processor. When the processor executes the computer program instructions, it can implement the steps described above.

[0156] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions that can be executed by a processor, wherein when the processor executes the computer program instructions, it can implement the steps of the method described above.

[0157] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0158] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0159] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0160] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0161] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A high-reliability optimized scheduling method for industrial park microgrids under severe weather conditions, characterized in that, Includes the following steps: Step 1: Obtain the data required for calculation, including power parameters, tie line parameters, renewable energy forecast data, and load forecast data; Step 2: Obtain severe weather warning information, including the type of severe weather and its start and end times; Step 3: Based on severe weather warning information, revise the wind power, solar power, and load power forecast curves; Step 4: Determine the microgrid's operating status. If the microgrid is in grid-connected mode, construct a robust optimization model for the grid-connected operation strategy with the goal of optimal economic efficiency. If the microgrid is in off-grid mode, construct a robust optimization model for off-grid operation with the goal of maximizing the power supply time for important loads. Step 5: Solve the optimization model to obtain the operating strategy; The robust optimization model for microgrid grid-connected operation strategy is constructed as follows: The optimization objective of the robust optimization model for microgrid grid-connected operation strategy is to achieve the lowest operating cost under all possible severe weather start and end periods, while also returning the worst-case possible operating cost; let be... The set of all decision variables includes wind power generation, photovoltaic power generation, energy storage charging and discharging power, and tie-line switching power. For the set of all uncertain variables, including , , The objective function is then expressed as follows: （21） In the formula, , These are the unit cost of electricity purchase and the unit revenue from electricity sales, respectively. , These refer to the power purchased via the tie line and the power sold via the tie line, respectively. Operational constraints include: 1) Wind power operation constraints: （22） In the formula, Indicates wind power generation capacity; 2) Constraints on photovoltaic operation: （23） In the formula, Indicates photovoltaic power generation capacity; 3) Energy storage operation constraints: （24） （25） （26） （27） （28） （29） （30） （31） in, , , , , , , , These are energy storage charging power, discharging power, power limit, charging status, discharging status, energy storage capacity, energy storage capacity lower limit, and energy storage capacity upper limit; , , These are the self-loss coefficient, charging efficiency, and discharging efficiency of energy storage, respectively. It is a very large constant; A value of 1 indicates charging. A value of 1 indicates discharge; If the severe weather warning is for high temperatures or a solar eclipse, then the lower limit of energy storage capacity will be... Take 5%; if the severe weather warning is a typhoon, considering the impact of fault factors during the typhoon, the microgrid may enter off-grid operation mode, and some electricity needs to be reserved for use during off-grid operation, the lower limit of energy storage capacity. Take 50%; 4) Transmission power constraints of tie lines: （32） （33） In the formula, This represents the maximum transmission power of the tie line; 5) Power balance constraints: （34） 6) Uncertainty constraints on predicted power of wind, solar, and load; The robust optimization model for off-grid operation of microgrids is constructed as follows: The operational objective of the robust optimization model for off-grid operation of a microgrid is to maintain the power supply to critical loads for the longest possible period under all possible severe weather conditions, while also providing the worst-case continuous power supply time. The objective function is then expressed as follows: （35） In the formula, This indicates the load supply status. If the power generation capacity of wind, solar, and energy storage can meet the power needs of important loads, the value is 1; otherwise, the value is 0. Therefore... The following constraints must be met: （36） （37） （38） in, The important load proportion coefficient is assumed to be a fixed value, representing the proportion of important loads to the total load of the microgrid. For equation (38), when the microgrid's power generation capacity can meet the power consumption of important loads, the expression within the parentheses is 0. Under the action of the objective function, Take 1; if the microgrid's power generation capacity cannot meet the power consumption of important loads, the expression in parentheses is less than 0, under the constraint of equation (38), Set to 0; The constraints include: 1) Wind power operation constraints: i.e., equation (22); 2) Photovoltaic operation constraints: i.e., equation (23); 3) Energy storage operation constraints: i.e. Equations (24) to (31).

2. The high-reliability optimized scheduling method for industrial park microgrids under severe weather conditions according to claim 1, characterized in that, In step 1, the power supply parameters, tie-line parameters, renewable energy forecast data, and load forecast data are as follows: 1) Power parameters: including the capacity and power parameters of various power sources such as wind power, photovoltaic power, and energy storage; 2) Tie line parameters: Maximum transmission power of the tie line connecting the industrial park microgrid and the distribution network; 3) New energy forecast data: forecasted hourly power generation capacity of wind power and photovoltaic power; 4) Load forecast data: hourly power consumption forecast of the industrial park microgrid.

3. The high-reliability optimized scheduling method for industrial park microgrids under severe weather conditions according to claim 1, characterized in that, In step 2, the types of severe weather include: typhoons, high temperatures, and solar eclipses.

4. The high-reliability optimized scheduling method for industrial park microgrids under severe weather conditions according to claim 1, characterized in that, In step 3, the different impacts of various severe weather events, including typhoons, high temperatures, and solar eclipses, on wind power, photovoltaics, and load curves are considered, as well as the uncertainty of the prediction of the start and end times of severe weather.

5. The high-reliability optimized scheduling method for industrial park microgrids under severe weather conditions according to claim 4, characterized in that, Step 3 is implemented as follows: Obtain the power correction curves for severe weather, and denote the wind power, solar power, and load power prediction curves as follows: , , The corresponding power correction curves are as follows: , , ; Typhoons cause drastic changes in wind speed in the microgrid area, which in turn leads to changes in wind power generation capacity. These changes are highly random, and the wind power correction curve can be considered to be a series of data that follow a normal distribution. （1） （2） In the formula, The variance is a normal distribution and can be set according to the typhoon warning situation. For wind power installed capacity, generate wind power correction curve according to equation (1), and the generated curve should meet the upper and lower limit requirements of equation (2); Meanwhile, the cooling brought by the typhoon will lead to a decrease in electricity load. It is assumed that the electricity load will decrease proportionally, i.e.: （3） In the formula, This is the load power scaling factor, with a value ranging from 0 to 1; The impact of typhoons on photovoltaic power is relatively small; therefore, the corrected photovoltaic power during a typhoon can be considered to be 0, i.e.: （4） High temperatures will lead to increased load and photovoltaic power, while wind power will decrease. It can be assumed that under the influence of high temperatures, the predicted power of wind power, photovoltaic power, and load will be scaled proportionally, and the corresponding power correction curves are as follows: （5） （6） （7） （8） in, , These are the scaling factors for wind power and solar power, respectively. For photovoltaic installed capacity, the photovoltaic power correction should also meet the upper and lower limits of equation (8); A solar eclipse primarily affects photovoltaic (PV) output; during the eclipse, PV output drops to a minimum. The photovoltaic power correction curve is then: （9） The impact of the solar eclipse on wind power and load capacity can be ignored, and the power curve during the eclipse is assumed to be 0, i.e.: （10） （11） Once the power correction curve is obtained, adjustments can be made to wind power, solar power, and load power based on the predicted periods of severe weather; using , , These represent the periods during which wind power, solar power, and load are affected by severe weather, respectively. A value of 1 indicates impact, and a value of 0 indicates no impact. The impact period should be continuous. For wind power, the impact period is... The time when the influence begins is recorded as The time when the influence ends is recorded as The intermediate time affected is recorded as This affects the duration. between, The value is 1 for the period and 0 for the rest of the time. Similarly, for photovoltaics and loads, the affected time periods are respectively... , ; Due to the uncertainty in predicting the duration of severe weather impacts, the start and end times may differ from the predicted times; that is, the actual start time of the impact may not be... , but At some point in the vicinity, while the actual end time of the impact may not be... , but At some point nearby; in the opinion Assuming severe weather occurs frequently, and considering the uncertainty of its occurrence or termination, the affected period is... , , Expressed as follows: （12） （13） （14） （15） （16） （17） In the formula, , This is the predicted start time when photovoltaic and load power will be affected by severe weather. , This refers to the predicted end time when the impact of severe weather on photovoltaic and load power will cease. , This represents the midpoint of the predicted impact of severe weather on photovoltaic and load power. , , These represent the maximum possible offset at the start time when wind power, solar power, and load power are affected, respectively. , , These represent the maximum possible offset at the end of the affected time for wind power, photovoltaic power, and load power, respectively. The power curves after wind power, photovoltaic power, and load correction. , , Represented as: （18） （19） （20）。 6. The high-reliability optimized scheduling method for industrial park microgrids under severe weather conditions according to claim 5, characterized in that, In step 4, robust optimization models for grid-connected operation and off-grid operation are constructed according to the microgrid's operating status, respectively, to achieve the lowest cost operation under grid-connected conditions and high reliability operation under off-grid conditions under the influence of uncertain factors.

7. A high-reliability optimized dispatching system for industrial park microgrids under severe weather conditions, characterized in that, It includes a memory, a processor, and computer program instructions stored in the memory and executable by the processor, which, when executed by the processor, enable the implementation of the steps of the method as described in any one of claims 1-6.

8. A computer-readable storage medium having stored thereon computer program instructions executable by a processor, wherein when the processor executes the computer program instructions, it is able to implement the steps of the method as described in any one of claims 1-6.

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