A robust iterative optimization method for day-ahead operation strategy of power system under extreme weather

Abstract

The application provides an extreme weather power system day-ahead operation strategy robust iterative optimization method, including the following steps; step S1, obtaining calculation required data, including power supply parameters, section parameters, load data, new energy prediction data, historical prediction data; step S2, considering operation risks under extreme weather, constructing wind power output uncertainty set and section state uncertainty set; step S3, considering reserve demand, constructing a day-ahead operation optimization model with the minimum total cost as an objective function; step S4, considering in-station energy storage, constructing a wind power field operation optimization model under extreme weather; step S5, based on the uncertainty set, constructing an operation strategy robust optimization checking model under extreme weather; step S6, iteratively optimizing power generation plans and outputting calculation results; the application can obtain an operation strategy meeting economy and reliability through iterative optimization of day-ahead power generation plan decision and extreme scenario checking.

Description

Technical Field

[0001] This invention relates to the field of power system operation optimization technology, and in particular to a robust iterative optimization method for day-ahead operation strategy of power system under extreme weather conditions. Background Technology

[0002] As the proportion of installed capacity from renewable energy sources increases, the uncertainty of their output has become a key factor to consider when formulating day-ahead power generation plans. Power generation plans based on predicted renewable energy power output need to be able to handle the actual renewable energy generation scenarios of the following day to avoid phenomena such as wind curtailment, solar curtailment, or load shedding. Based on the magnitude of the deviation, this method classifies prediction deviations into two categories (see Appendix). Figure 1 There are two types of prediction errors: one is the prediction error under normal weather conditions, which has a relatively small deviation; the other is the prediction error under extreme weather conditions, which has a larger deviation. Under extreme weather conditions (such as typhoons), wind power output increases or decreases significantly, but the timing and magnitude of these changes are difficult to predict accurately, resulting in a large power prediction error during these periods, which increases the difficulty of operation.

[0003] Existing methods typically only consider Type I prediction bias, and the resulting operational strategies cannot adapt to operational needs under extreme weather conditions. Furthermore, extreme weather events such as typhoons can cause line faults, leading to a decrease in transmission capacity at critical sections, ultimately affecting the absorption and supply of renewable energy.

[0004] This method constructs an uncertainty set of wind power output and cross-sectional state under extreme weather conditions. Through iterative optimization of operation decision-making and extreme scenario verification, it obtains operation results that meet the requirements of operational economy and reliability under extreme scenarios. Summary of the Invention

[0005] This invention proposes a robust iterative optimization method for day-ahead operation strategy of power system under extreme weather conditions. Through iterative optimization of day-ahead power generation plan decision-making and extreme scenario verification, an operation strategy that meets both economic and reliability requirements can be obtained.

[0006] The present invention adopts the following technical solution.

[0007] A robust iterative optimization method for day-ahead operation strategy of power system under extreme weather conditions includes the following steps: Step S1: Obtain the data required for calculation, including power source parameters, cross-sectional parameters, load data, renewable energy forecast data, and historical forecast data;

[0008] Step S2: Considering the operational risks under extreme weather conditions, construct the uncertain set of wind power output and the uncertain set of cross-sectional conditions;

[0009] Step S3: Considering backup demand, construct a day-ahead operation optimization model with the objective function of minimizing total cost; Step S4: Considering on-site energy storage, construct a wind farm operation optimization model under extreme weather conditions.

[0010] Step S5: Based on the uncertain set, construct a robust optimization and verification model for the operation strategy under extreme weather conditions;

[0011] Step S6: Iteratively optimize the power generation plan and output the calculation results.

[0012] The power parameters include the unit capacity, number of units, technical and economic parameters of the units, and power output parameters of each power plant; the types of power plants include thermal power plants, hydropower plants, nuclear power plants, wind power plants, photovoltaic power plants, pumped storage power plants, and energy storage power plants.

[0013] The cross-sectional parameters include the upper limit of transmission power for each cross-section and the derating of transmission power under extreme weather conditions.

[0014] The load data includes the next-day forecast load for each region;

[0015] The new energy forecast data includes the next-day forecast power of each wind farm and the next-day forecast power of each photovoltaic power station.

[0016] The historical forecast data includes the historical day-ahead forecast power and actual power of each wind farm, and the historical day-ahead forecast power and actual power of each photovoltaic power station.

[0017] The uncertainty set constructed in step S2 is used to describe the operational risks of limited cross-sectional transmission power, sudden changes in wind power generation time, and prediction deviations in the amount of such changes under extreme weather conditions.

[0018] In step S2, the uncertainty set is the set of uncertain factors. Under extreme weather conditions in step S2, the uncertain factors affecting the operation of the power system include:

[0019] Factor 1: Faults reduce the power transmission capacity of cross sections, thereby affecting the power balance in various regions;

[0020] Factor 2: The prediction of the timing and magnitude of sudden changes in wind farm power has a large deviation, which increases the difficulty of regulation and thus leads to the risk of wind and solar power curtailment or load shedding.

[0021] In step S2, the method for establishing the uncertain set of cross-sectional states for factor one is as follows: If the transmission power limit of cross-section l between region A and region B is... In extreme weather conditions, transmission power may decrease due to line faults. The set of uncertain cross-sectional states is then:

[0022]

[0023] Among them, the auxiliary variable Z l Indicates whether the transmission power limit of section l has decreased, Γ LThe total number of sections that control the limit of transmission power reduction, where L is the set of sections;

[0024] In step S2, the method for establishing the uncertain set of wind power output for factor two is as follows: Let the predicted power of the wind farm be w. Decomposed into a baseline curve with smaller fluctuations and power fluctuation amplitude That is, expressed as a formula:

[0025]

[0026]

[0027]

[0028] in, This indicates whether the predicted wind power at that moment fluctuates significantly relative to the baseline curve, P. W T and T represent the wind farm set and time set, respectively;

[0029] Under extreme weather conditions, the actual generating power of a wind farm may deviate, with variations in magnitude and timing. The uncertainty set is modeled as follows:

[0030]

[0031] in, n represents the actual generating capacity of the wind farm. w Indicates the predicted time of power mutation. Used to control the offset of the time of mutation. This represents the deviation in the actual power fluctuation amplitude. This is used to control the deviation value of the mutation amplitude; that is, in Factor Two, the actual power mutation may be inconsistent with the prediction. For example, if the mutation time is inconsistent, the difference in the number of moments between the actual mutation time and the predicted mutation time shall be less than [a certain value]. If the mutation power is inconsistent, then the actual mutation value Compared with predicted mutation value The difference is less than

[0032] The day-ahead optimization model established in step S3 sets reserve constraints for different regions, taking into account the regional power regulation capacity and cross-sectional regulation capacity to jointly address the upstream and downstream reserve requirements caused by the prediction deviation of new energy power.

[0033] In step S3, to meet the basic requirements of electricity load and renewable energy consumption, the day-ahead operation optimization model uses the objective function of minimizing system operating cost. It considers basic constraints such as unit operation constraints, cross-sectional operation constraints, and regional power balance constraints, and introduces upper and lower reserve constraints to address the impact of renewable energy power prediction errors under conventional conditions. The specific expression of this model is as follows:

[0034] The objective function is:

[0035]

[0036] Among them, P T For the collection of thermal power plants, These represent the power generation capacity, number of units in operation, and number of units shut down for a thermal power plant (n), respectively. These represent the unit power generation cost, single start-up cost, and single shutdown cost of a thermal power plant, respectively.

[0037] The constraints include:

[0038] Constraint 1: Thermal Power Plant Operation Constraints

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046] in, For the lower and upper limits of the output of a single unit, u n,t The number of operating aircraft units. Let n be the climbing rate. n This represents the total number of generating units. Minimum startup time and minimum downtime;

[0047] Nuclear power plant operation constraints are set with reference to those for thermal power plants;

[0048] Constraint 2: Hydropower Operation Constraints

[0049]

[0050]

[0051] in, These represent the lower and upper limits of the output of hydropower plant n, respectively. For the power generation capacity of hydropower plants, This represents the amount of electricity that can be generated the following day.

[0052] Constraint 3: Operational Constraints of New Energy Power Stations

[0053]

[0054]

[0055] in, These are wind power and photovoltaic power generation plans, Forecasting power generation for photovoltaic power plants;

[0056] To ensure the full absorption of renewable energy power generation, power generation plans for renewable energy power plants are set according to the predicted output.

[0057] Constraint 4: Operational constraints of pumped storage power stations and energy storage:

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066] in, These are, respectively, pumped storage / energy storage charging power, discharging power, upper limit of power, charging status, discharging status, energy storage capacity, lower limit of energy storage capacity, and upper limit of energy storage capacity; Here, denoted as the self-loss coefficient, charging efficiency, and discharging efficiency of the pumped storage / energy storage system, respectively, and M is a maximum constant. A value of 1 indicates charging. A value of 1 indicates discharge;

[0067] Constraint 5, Sectional Operation Constraint, is expressed by the following formula:

[0068]

[0069] in, Power transmitted across section l;

[0070] Constraint 6, Regional Power Balance, expressed by the formula:

[0071]

[0072] in, These are the collections of thermal power plants, hydropower plants, wind farms, photovoltaic power stations, and pumped storage / energy storage power stations within region a. Let L represent the set of cross-sections with region a as the receiving end and the sending end, respectively. a,t The transmission power of the external transmission lines connected to region a;

[0073] Constraint 7, Alternate Constraint, is expressed by the following formula:

[0074]

[0075]

[0076] The left side of the above formula represents the upper and lower reserve capacity that the power source and connected lines in the region can provide, respectively; the right side represents the upper and lower reserve demand caused by the error in the prediction of new energy sources.

[0077] The backup constraint indicates that the obtained operating strategy must be able to adapt to prediction deviations with small magnitudes and not cause wind and solar power curtailment.

[0078] in, These are the upward deviation coefficients for wind power forecast power, upward deviation coefficients for photovoltaic power forecast power, downward deviation coefficients for wind power forecast power, and downward deviation coefficients for photovoltaic power forecast power, which should be determined in conjunction with historical forecast data. These are wind power installed capacity and photovoltaic installed capacity, respectively. These are the reserve coefficients on the cross-section and the reserve coefficients below the cross-section, respectively. The capacity to provide upper and lower reserves for power plants should be determined based on the power plant's output status and regulation range, namely:

[0079]

[0080]

[0081] in, These represent the power plant's upward and downward ramp rates, respectively. These are the upper and lower limits of power plant output, respectively.

[0082] The cross-section is provided with upward and downward backup capabilities, respectively, which are determined by the cross-section's transmission power value and transmission power limit, i.e.:

[0083]

[0084]

[0085] The wind farm operation optimization model constructed in step S4 prioritizes the regulation role of energy storage within the new energy power station under extreme weather conditions, and serves as a new boundary condition for the optimized operation of the entire system.

[0086] In step S4, the role of energy storage in wind farms in mitigating predicted power deviations under extreme weather conditions is considered. When there is a significant deviation between the actual generated power and the predicted power, to reduce the assessment costs caused by the deviation, wind farms (i.e., wind power plants) prioritize the use of internal energy storage to ensure that the combined output of the wind turbines and energy storage within the site is as close as possible to the predicted value. The specific method is as follows:

[0087] In actual transmittance is In this case, the optimization model for each wind farm is as follows:

[0088] The objective function is to minimize the sum of deviation-considered costs and internal energy storage operating costs, i.e.:

[0089]

[0090] in, The cost of assessing unit power deviation. The wind farm is equipped with energy storage charging power and discharge power respectively. Operating cost per unit of electricity for energy storage;

[0091] Operational constraints include:

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101] in, The following parameters are specified for each wind farm: energy storage capacity, energy loss coefficient, charging efficiency, discharging efficiency, lower limit of energy storage capacity, and upper limit of energy storage capacity. The wind farm is configured with energy storage charging status, discharging status, and energy storage capacity.

[0102] In step S5, the robust optimization verification model for the operation strategy under extreme weather conditions is used to verify whether the operation strategy obtained from the model in step S3 can cope with the uncertainties caused by extreme weather, such as the decrease in cross-sectional transmission capacity and the sudden changes in the timing and amplitude of wind power generation, to achieve reliable power supply under uncertain scenarios. The objective function of this model is the maximum sum of load deviations under all uncertain scenarios, representing the worst possible outcome caused by the uncertain scenario. The objective function is expressed by the formula:

[0103]

[0104] Where X is the set of variables to be solved, including the power generation capacity of each power plant, reserve capacity, start-up and shutdown status variables, and cross-sectional transmission power. These are the power generation slack variables and load slack variables, used to reflect the power balance constraints on the generation and load sides, i.e., the insufficiency of upward and downward reserve capacity; the specific constraints of the robust optimization verification model for operation strategies under extreme weather conditions are as follows:

[0105] In stage A and step S5, the start-up and shutdown status of the thermal power plant is determined, so the operating constraint formula for the thermal power plant is:

[0106]

[0107] Constraint B, wind farm operation constraints are expressed by equations (34)-(43);

[0108] Constraint C, the cross-sectional operation constraint, is expressed by the formula as follows:

[0109]

[0110] Constraint D, the regional power balance constraint, is expressed by the formula as follows:

[0111]

[0112]

[0113] Step S6 involves iterative updates of day-ahead operation strategy solution and extreme weather operation strategy feasibility verification to finally obtain a set of operation strategies that meet the requirements of operation economy, power supply reliability, and new energy consumption under extreme weather conditions.

[0114] The extreme weather events mentioned include typhoons;

[0115] When performing iterative optimization of the power generation plan, the method includes the following steps;

[0116] Step A1: Solve the current running optimization model constructed in step S3. If the solution is successful, save the solution results and proceed to step A2; if the solution fails, proceed to step A4.

[0117] Step A2: Based on the thermal power state variable parameters obtained in Step A1, solve the verification model constructed in Step S5. If the objective function is 0, the verification is passed, and proceed to Step A5; if the objective function is not 0, the verification is failed, and proceed to Step A3.

[0118] Step A3, based on variables The solution results are used to adjust the standby constraint parameters, and step A1 is recalculated; specifically, if This indicates insufficient upward adjustment ability, which leads to an increase. Simultaneously reduce like This indicates insufficient downward adjustment capability, therefore increase Simultaneously reduce Step A4: Adjust the external power L a,t Parameters, Restore to the initial value and return to step A1;

[0119] Step A5: Output the calculation results and end the iteration.

[0120] The method described in this invention constructs an uncertainty set of wind power output and cross-sectional state under extreme weather conditions, and through iterative optimization of day-ahead power generation plan decision-making and extreme scenario verification, it can obtain operating results that meet the requirements of operational economy and reliability under extreme scenarios.

[0121] This invention addresses operational risks such as the timing and magnitude prediction errors of sudden wind power surges under extreme weather conditions, and the reduction in cross-sectional transmission power limits. It constructs an uncertainty set and then iteratively optimizes the day-ahead operation strategy through day-ahead power generation plan optimization and reliability verification under extreme weather conditions. This method overcomes the problem of traditional methods failing to consider prediction errors of sudden wind power surges, improving the economic efficiency of power generation plans while ensuring reliable power supply under extreme weather conditions and full absorption of renewable energy. Attached Figure Description

[0122] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0123] Appendix Figure 1 This is a schematic diagram of wind power prediction deviation mentioned in the background technology;

[0124] Appendix Figure 2 This is a schematic diagram of the operation optimization method of the present invention;

[0125] Appendix Figure 3 This is a schematic diagram of the decomposition of sudden power changes in a wind farm;

[0126] Appendix Figure 4 This is a schematic diagram of the iterative process for performing power generation plan optimization according to the present invention. Detailed Implementation

[0127] like Figure 2 As shown, a robust iterative optimization method for day-ahead operation strategy of power system under extreme weather conditions includes the following steps;

[0128] Step S1: Obtain the data required for calculation, including power parameters, cross-sectional parameters, load data, new energy forecast data, and historical forecast data;

[0129] Step S2: Considering the operational risks under extreme weather conditions, construct the uncertain set of wind power output and the uncertain set of cross-sectional conditions;

[0130] Step S3: Considering backup demand, construct a day-ahead operation optimization model with the objective function of minimizing total cost; Step S4: Considering on-site energy storage, construct a wind farm operation optimization model under extreme weather conditions.

[0131] Step S5: Based on the uncertain set, construct a robust optimization and verification model for the operation strategy under extreme weather conditions;

[0132] Step S6: Iteratively optimize the power generation plan and output the calculation results.

[0133] The power parameters include the unit capacity, number of units, technical and economic parameters of the units, and power output parameters of each power plant; the types of power plants include thermal power plants, hydropower plants, nuclear power plants, wind power plants, photovoltaic power plants, pumped storage power plants, and energy storage power plants.

[0134] The cross-sectional parameters include the upper limit of transmission power for each cross-section and the derating of transmission power under extreme weather conditions.

[0135] The load data includes the next-day forecast load for each region;

[0136] The new energy forecast data includes the next-day forecast power of each wind farm and the next-day forecast power of each photovoltaic power station.

[0137] The historical forecast data includes the historical day-ahead forecast power and actual power of each wind farm, and the historical day-ahead forecast power and actual power of each photovoltaic power station.

[0138] The uncertainty set constructed in step S2 is used to describe the operational risks of limited cross-sectional transmission power, sudden changes in wind power generation time, and prediction deviations in the amount of such changes under extreme weather conditions.

[0139] In step S2, the uncertainty set is the set of uncertain factors. Under extreme weather conditions in step S2, the uncertain factors affecting the operation of the power system include:

[0140] Factor 1: Faults reduce the power transmission capacity of cross sections, thereby affecting the power balance in various regions;

[0141] Factor 2: The prediction of the timing and magnitude of sudden changes in wind farm power has a large deviation, which increases the difficulty of regulation and thus leads to the risk of wind and solar power curtailment or load shedding.

[0142] In step S2, the method for establishing the uncertain set of cross-sectional states for factor one is as follows: If the transmission power limit of cross-section l between region A and region B is... In extreme weather conditions, transmission power may decrease due to line faults. The set of uncertain cross-sectional states is then:

[0143]

[0144] Among them, the auxiliary variable Z l Indicates whether the transmission power limit of section l has decreased, Γ L The total number of sections that control the limit of transmission power reduction, where L is the set of sections;

[0145] In step S2, the method for establishing the uncertain set of wind power output for factor two is as follows: Let the predicted power of the wind farm be w. Decomposed into a baseline curve with smaller fluctuations and power fluctuation amplitude That is, expressed as a formula:

[0146]

[0147]

[0148]

[0149] in, This indicates whether the predicted wind power at that moment fluctuates significantly relative to the baseline curve, P. W T and T represent the wind farm set and time set, respectively;

[0150] Under extreme weather conditions, the actual generating power of a wind farm may deviate, with variations in magnitude and timing. The uncertainty set is modeled as follows:

[0151]

[0152] in, n represents the actual generating capacity of the wind farm. w Indicates the predicted time of power mutation. Used to control the offset of the time of mutation. This represents the deviation in the actual power fluctuation amplitude. This is used to control the deviation value of the mutation amplitude; that is, in Factor Two, the actual power mutation may be inconsistent with the prediction. For example, if the mutation time is inconsistent, the difference in the number of moments between the actual mutation time and the predicted mutation time shall be less than [a certain value]. If the mutation power is inconsistent, then the actual mutation value Compared with predicted mutation value The difference is less than

[0153] The day-ahead optimization model established in step S3 sets reserve constraints for different regions, taking into account the regional power regulation capacity and cross-sectional regulation capacity to jointly address the upstream and downstream reserve requirements caused by the prediction deviation of new energy power.

[0154] In step S3, to meet the basic requirements of electricity load and renewable energy consumption, the day-ahead operation optimization model uses the objective function of minimizing system operating cost. It considers basic constraints such as unit operation constraints, cross-sectional operation constraints, and regional power balance constraints, and introduces upper and lower reserve constraints to address the impact of renewable energy power prediction errors under conventional conditions. The specific expression of this model is as follows:

[0155] The objective function is:

[0156]

[0157] Among them, P T For the collection of thermal power plants, These represent the power generation capacity, number of units in operation, and number of units shut down for a thermal power plant (n), respectively. These represent the unit power generation cost, single start-up cost, and single shutdown cost of a thermal power plant, respectively.

[0158] The constraints include:

[0159] Constraint 1: Thermal Power Plant Operation Constraints

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166]

[0167] in, For the lower and upper limits of the output of a single unit, u n,t The number of operating aircraft units. Let n be the climbing rate. n This represents the total number of generating units. Minimum startup time and minimum downtime;

[0168] Nuclear power plant operation constraints are set with reference to those for thermal power plants;

[0169] Constraint 2: Hydropower Operation Constraints

[0170]

[0171]

[0172] in, These represent the lower and upper limits of the output of hydropower plant n, respectively. For the power generation capacity of hydropower plants, This represents the amount of electricity that can be generated the following day.

[0173] Constraint 3: Operational Constraints of New Energy Power Stations

[0174]

[0175]

[0176] in, These are wind power and photovoltaic power generation plans, Forecasting power generation for photovoltaic power plants;

[0177] To ensure the full absorption of renewable energy power generation, power generation plans for renewable energy power plants are set according to the predicted output.

[0178] Constraint 4: Operational constraints of pumped storage power stations and energy storage:

[0179]

[0180]

[0181]

[0182]

[0183]

[0184]

[0185]

[0186]

[0187] in, These are, respectively, pumped storage / energy storage charging power, discharging power, upper limit of power, charging status, discharging status, energy storage capacity, lower limit of energy storage capacity, and upper limit of energy storage capacity; Here, denoted as the self-loss coefficient, charging efficiency, and discharging efficiency of the pumped storage / energy storage system, respectively, and M is a maximum constant. A value of 1 indicates charging. A value of 1 indicates discharge;

[0188] Constraint 5, Sectional Operation Constraint, is expressed by the following formula:

[0189]

[0190] in, Power transmitted across section l;

[0191] Constraint 6, Regional Power Balance, expressed by the formula:

[0192]

[0193] in, These are the collections of thermal power plants, hydropower plants, wind farms, photovoltaic power stations, and pumped storage / energy storage power stations within region a. Let L represent the sets of cross-sections with region a as the receiving and sending ends, respectively. a,t The transmission power of the external transmission lines connected to region a;

[0194] Constraint 7, Alternate Constraint, is expressed by the following formula:

[0195]

[0196]

[0197] The left side of the above formula represents the upper and lower reserve capacity that the power source and connected lines in the region can provide, respectively; the right side represents the upper and lower reserve demand caused by the error in the prediction of new energy sources.

[0198] The backup constraint indicates that the obtained operating strategy must be able to adapt to prediction deviations with small magnitudes and not cause wind and solar power curtailment.

[0199] in, These are the upward deviation coefficients for wind power forecast power, upward deviation coefficients for photovoltaic power forecast power, downward deviation coefficients for wind power forecast power, and downward deviation coefficients for photovoltaic power forecast power, which should be determined in conjunction with historical forecast data. These are wind power installed capacity and photovoltaic installed capacity, respectively. These are the reserve coefficients on the cross-section and the reserve coefficients below the cross-section, respectively. The capacity to provide upper and lower reserves for power plants should be determined based on the power plant's output status and regulation range, namely:

[0200]

[0201]

[0202] in, These represent the power plant's upward and downward ramp rates, respectively. These are the upper and lower limits of power plant output, respectively.

[0203] The cross-section is provided with upward and downward backup capabilities, respectively, which are determined by the cross-section's transmission power value and transmission power limit, i.e.:

[0204]

[0205]

[0206] The wind farm operation optimization model constructed in step S4 prioritizes the regulation role of energy storage within the new energy power station under extreme weather conditions, and serves as a new boundary condition for the optimized operation of the entire system.

[0207] In step S4, the role of energy storage in wind farms in mitigating predicted power deviations under extreme weather conditions is considered. When there is a significant deviation between the actual generated power and the predicted power, to reduce the assessment costs caused by the deviation, wind farms (i.e., wind power plants) prioritize the use of internal energy storage to ensure that the combined output of the wind turbines and energy storage within the site is as close as possible to the predicted value. The specific method is as follows:

[0208] In actual transmittance is In this case, the optimization model for each wind farm is as follows:

[0209] The objective function is to minimize the sum of deviation-considered costs and internal energy storage operating costs, i.e.:

[0210]

[0211] in, The cost per unit of electricity deviation is used for assessment. The wind farm is equipped with energy storage charging power and discharge power respectively. Operating cost per unit of electricity for energy storage;

[0212] Operational constraints include:

[0213]

[0214]

[0215]

[0216]

[0217]

[0218]

[0219]

[0220]

[0221]

[0222] in, The following parameters are specified for each wind farm: energy storage capacity, energy loss coefficient, charging efficiency, discharging efficiency, lower limit of energy storage capacity, and upper limit of energy storage capacity. The wind farm is configured with energy storage charging status, discharging status, and energy storage capacity.

[0223] In step S5, the robust optimization verification model for the operation strategy under extreme weather conditions is used to verify whether the operation strategy obtained from the model in step S3 can cope with the uncertainties caused by extreme weather, such as the decrease in cross-sectional transmission capacity and the sudden changes in the timing and amplitude of wind power generation, to achieve reliable power supply under uncertain scenarios. The objective function of this model is the maximum sum of load deviations under all uncertain scenarios, representing the worst possible outcome caused by the uncertain scenario. The objective function is expressed by the formula:

[0224]

[0225] Where X is the set of variables to be solved, including the power generation capacity of each power plant, reserve capacity, start-up and shutdown status variables, and cross-sectional transmission power. These are the power generation slack variables and load slack variables, used to reflect the power balance constraints on the generation and load sides, i.e., the insufficiency of upward and downward reserve capacity; the specific constraints of the robust optimization verification model for operation strategies under extreme weather conditions are as follows:

[0226] In stage A and step S5, the start-up and shutdown status of the thermal power plant is determined, so the operating constraint formula for the thermal power plant is:

[0227]

[0228] Constraint B, the wind farm operation constraints are expressed by equations (34)-(43);

[0229] Constraint C, the cross-sectional operation constraint, is expressed by the formula as follows:

[0230]

[0231] Constraint D, the regional power balance constraint, is expressed by the formula as follows:

[0232]

[0233]

[0234] Step S6 involves iterative updates of day-ahead operation strategy solution and extreme weather operation strategy feasibility verification to finally obtain a set of operation strategies that meet the requirements of operation economy, power supply reliability, and new energy consumption under extreme weather conditions.

[0235] The extreme weather events mentioned include typhoons;

[0236] When performing iterative optimization of the power generation plan, the method includes the following steps;

[0237] Step A1: Solve the current running optimization model constructed in step S3. If the solution is successful, save the solution results and proceed to step A2; if the solution fails, proceed to step A4.

[0238] Step A2: Based on the thermal power state variable parameters obtained in Step A1, solve the verification model constructed in Step S5. If the objective function is 0, the verification is passed, and proceed to Step A5; if the objective function is not 0, the verification is failed, and proceed to Step A3.

[0239] Step A3, based on variables The solution results are used to adjust the standby constraint parameters, and step A1 is recalculated; specifically, if This indicates insufficient upward adjustment ability, which leads to an increase. Simultaneously reduce like This indicates insufficient downward adjustment capability, therefore increase Simultaneously reduce Step A4: Adjust the external power L a,t Parameters, Restore to the initial value and return to step A1;

[0240] Step A5: Output the calculation results and end the iteration.

Claims

1. A robust iterative optimization method for day-ahead operation strategy of power system under extreme weather conditions, characterized in that: Includes the following steps: Step S1: Obtain the data required for calculation, including power parameters, cross-sectional parameters, load data, new energy forecast data, and historical forecast data; Step S2: Considering the operational risks under extreme weather conditions, construct the uncertain set of wind power output and the uncertain set of cross-sectional conditions; Step S3: Considering backup requirements, construct a day-ahead operation optimization model with the objective function of minimizing total cost; Step S4: Considering on-site energy storage, construct an optimization model for wind farm operation under extreme weather conditions; Step S5: Based on the uncertain set, construct a robust optimization and verification model for the operation strategy under extreme weather conditions; Step S6: Iteratively optimize the power generation plan and output the calculation results; In step S4, the role of wind farm-built energy storage in dealing with predicted power deviations under extreme weather conditions is considered. When there is a large deviation between the actual generated power and the predicted power, in order to reduce the assessment costs caused by the deviation, the wind farm will prioritize the use of internal energy storage so that the sum of the output of the wind turbines and energy storage inside the station is as close as possible to the predicted value. In step S5, the robust optimization verification model for the operation strategy under extreme weather conditions is used to verify whether the operation strategy obtained from the model in step S3 can cope with the uncertainties caused by extreme weather, such as the decrease in cross-sectional transmission capacity and the sudden changes in the timing and amplitude of wind power generation, to achieve reliable power supply under uncertain scenarios. The objective function of this model is the maximum sum of load deviations under all uncertain scenarios, representing the worst possible outcome caused by the uncertain scenario. The objective function is expressed by the formula: （44） in, The set of variables to be solved includes the power generation capacity of each power plant, reserve capacity, start-up and shutdown status variables, and cross-sectional transmission power. , These are the power generation slack variable and the load slack variable, respectively, used to reflect the power balance constraints on the power generation and load sides, that is, the insufficiency of upward and downward reserve capacity; Step S6 involves iterative updates of day-ahead operation strategy solution and extreme weather operation strategy feasibility verification to finally obtain a set of operation strategies that meet the requirements of operation economy, power supply reliability, and new energy consumption under extreme weather conditions. The extreme weather events mentioned include typhoons; When performing iterative optimization of the power generation plan, the method includes the following steps; Step A1: Solve the current running optimization model constructed in step S3. If the solution is successful, save the solution results and proceed to step A2; if the solution fails, proceed to step A4. Step A2: Based on the thermal power state variable parameters obtained in Step A1, solve the verification model constructed in Step S5; if the objective function is 0, the verification is passed and proceed to Step A5; if the objective function is not 0, the verification is not passed and proceed to Step A3. Step A3, based on variables , The solution results are used to adjust the standby constraint parameters, and step A1 is recalculated; specifically, if This indicates insufficient upward adjustment ability, which increases At the same time reduce ;like This indicates insufficient downward adjustment capability, thus increasing... At the same time reduce ; Step A4: Adjust the external power The parameters, , , , Restore to the initial value and return to step A1; Step A5: Output the calculation results and end the iteration; set up This represents the actual generating capacity of the wind farm. , These are the upward deviation coefficient and downward deviation coefficient of wind power forecast, respectively, which should be determined in conjunction with historical forecast data. , These are the reserve coefficients on the cross-section and the reserve coefficients below the cross-section, respectively.

2. The robust iterative optimization method for day-ahead operation strategy of power system under extreme weather conditions according to claim 1, characterized in that: The power parameters include the unit capacity, number of units, technical and economic parameters of the units, and power output parameters of each power plant; the types of power plants include thermal power plants, hydropower plants, nuclear power plants, wind power plants, photovoltaic power plants, pumped storage power plants, and energy storage power plants. The cross-sectional parameters include the transmission power limit of each cross-section and the transmission power derating under extreme weather conditions. The load data includes the next-day forecast load for each region; The new energy forecast data includes the next-day forecast power of each wind farm and the next-day forecast power of each photovoltaic power station. The historical forecast data includes the historical day-ahead forecast power and actual power of each wind farm, and the historical day-ahead forecast power and actual power of each photovoltaic power station.

3. The robust iterative optimization method for day-ahead operation strategy of power system under extreme weather conditions according to claim 1, characterized in that: The uncertainty set constructed in step S2 is used to describe the operational risks of limited cross-sectional transmission power, sudden changes in wind power generation time, and prediction deviations in the amount of such changes under extreme weather conditions.

4. The robust iterative optimization method for day-ahead operation strategy of power system under extreme weather conditions according to claim 3, characterized in that: In step S2, the uncertainty set is the set of uncertain factors. Under extreme weather conditions in step S2, the uncertain factors affecting the operation of the power system include: Factor 1: Faults reduce the power transmission capacity of cross sections, thereby affecting the power balance in various regions; Factor 2: The prediction of the timing and magnitude of sudden changes in wind farm power has a large deviation, which increases the difficulty of regulation and thus leads to the risk of wind and solar power curtailment or load shedding. In step S2, the method for establishing the uncertain set of cross-sectional states for factor one is as follows: If the transmission power limit of cross-section l between region A and region B is... In extreme weather conditions, transmission power may decrease due to line faults. The set of uncertain cross-sectional states is then: （1） Among them, auxiliary variables This indicates whether the transmission power limit of section l has decreased. The total number of sections where the transmission power limit decreases. A set of cross-sections; In step S2, the method for establishing the uncertain set of wind power output for factor two is as follows: Let the wind farm Predicted power Decomposed into a baseline curve with smaller fluctuations and power fluctuation amplitude That is, expressed as a formula: （2） （3） （4） in, This indicates whether the predicted wind power output at that moment fluctuates significantly relative to the baseline curve. , These are the wind farm set and the time set, respectively. Under extreme weather conditions, the actual generating power of a wind farm may deviate, with variations in magnitude and timing. The uncertainty set is modeled as follows: （5） in, This represents the actual generating capacity of the wind farm. Indicates the predicted time of power mutation. Used to control the offset of the mutation time. This represents the deviation in the actual power fluctuation amplitude. This is used to control the deviation value of the mutation amplitude; that is, in Factor Two, the actual power mutation may be inconsistent with the prediction. For example, if the mutation time is inconsistent, the difference in the number of moments between the actual mutation time and the predicted mutation time shall be less than [a certain value]. If the mutation power is inconsistent, the actual mutation value will be... Compared with predicted mutation value The difference is less than .

5. A robust iterative optimization method for day-ahead operation strategy of power system under extreme weather conditions as described in claim 4, characterized in that: The day-ahead optimization model established in step S3 sets reserve constraints for different regions, taking into account the regional power regulation capacity and cross-sectional regulation capacity to jointly address the upstream and downstream reserve requirements caused by the prediction deviation of new energy power.

6. The robust iterative optimization method for day-ahead operation strategy of power system under extreme weather conditions according to claim 5, characterized in that: In step S3, to meet the basic requirements of electricity load and renewable energy consumption, the day-ahead operation optimization model uses the objective function of minimizing system operating cost. It considers basic constraints such as unit operation constraints, cross-sectional operation constraints, and regional power balance constraints, and introduces upper and lower reserve constraints to address the impact of renewable energy power prediction errors under conventional conditions. The specific expression of this model is as follows: The objective function is: （6） in, For the collection of thermal power plants, , , thermal power plants Power generation capacity, number of units in operation, number of units shut down , , thermal power plants Unit power generation cost, cost per start-up, cost per shutdown.

7. A robust iterative optimization method for day-ahead operation strategy of power system under extreme weather conditions as described in claim 6, characterized in that: The wind farm operation optimization model constructed in step S4 prioritizes the regulation role of energy storage within the new energy power station under extreme weather conditions, and serves as a new boundary condition for the optimized operation of the entire system.

8. A robust iterative optimization method for day-ahead operation strategy of power system under extreme weather conditions, as described in claim 7, is characterized in that: In step S4, the specific method is as follows: In actual transmittance is In this case, the optimization model for each wind farm is as follows: The objective function is to minimize the sum of deviation-considered costs and internal energy storage operating costs, i.e.: （34） in, The cost of assessing unit power deviation. , The wind farm is equipped with energy storage charging power and discharge power respectively. The operating cost per unit of electricity stored.

9. A robust iterative optimization method for day-ahead operation strategy of power system under extreme weather conditions, as described in claim 8, is characterized in that: In step S5, the constraints of the robust optimization verification model for operating strategies under extreme weather conditions are as follows: Constraint A, the stage of step S5, the thermal power plant start-up and shutdown status is determined, thermal power plant operation constraints; Constraint B: Wind farm operation constraints; Constraint C: Cross-sectional operational constraints; Constraint D: Regional power balance constraint.

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