Power grid toughness enhancement method of hydrogen storage system collaborative response under heterogeneous uncertainty

Abstract

The invention discloses a power grid toughness enhancement method for hydrogen storage system collaborative response under heterogeneous uncertainty. The method specifically comprises the following steps: step 1, constructing a mathematical model of a heterogeneous uncertain set of power transmission line faults, energy output fluctuation and load changes of a power grid system under a typhoon scene; step 2, bringing the mathematical models of the day-ahead market of the power transmission line, the real-time unit combination and the hydrogen storage system into the mathematical model of the heterogeneous uncertain set in the step 1, and establishing a two-stage robust optimization model; and step 3, solving the two-stage robust optimization model obtained in the step 2 by combining column and constraint generation iteration solving technology to obtain a power grid toughness enhancement decision. Aiming at the problem that an existing power grid toughness enhancement strategy generally only depends on an emergency means of a single disaster prevention and reduction type unit combination and is difficult to realize multi-level response in an extreme event, the method can flexibly coordinate resource configuration under disturbance of typhoon disasters, and the toughness of a power system is remarkably enhanced.

Description

Technical Field

[0001] This invention relates to the field of power system planning and dispatch control technology, specifically to a method for enhancing grid resilience in the coordinated response of hydrogen storage systems under heterogeneous uncertainties. Background Technology

[0002] In recent years, influenced by global climate change, extreme natural disasters such as typhoons and torrential rains have become more frequent, posing a serious threat to the safe and stable operation of the power grid. Meanwhile, the large-scale integration of new energy sources and energy storage devices has led to multi-source and heterogeneous uncertainties in the power grid operating environment, making it difficult for traditional control methods to achieve efficient coordinated responses. Hydrogen storage systems, due to their bidirectional energy regulation and high flexibility, have significant advantages in enhancing grid resilience. Therefore, researching grid resilience enhancement methods that consider the coordinated response of hydrogen storage systems under heterogeneous uncertainties is of great significance for achieving the safe, stable, and sustainable operation of the power grid.

[0003] Currently, research on enhancing grid resilience is mostly based on deterministic models, formulating disaster prevention and mitigation strategies for different stages, such as pre-disaster prevention, disaster response, and post-disaster recovery. However, these methods typically optimize only a single stage, lacking a comprehensive consideration of the interactions and synergies between different stages, making it difficult to achieve resilience enhancement throughout the entire process. Furthermore, existing technologies largely rely on the regulation of single energy sources or independent energy storage systems, achieving rapid recovery in disaster scenarios through operational optimization. However, they fail to fully consider the coupling relationships between multi-source heterogeneous uncertainties such as renewable energy fluctuations, load changes, and differences in disaster impact, resulting in insufficient resource response flexibility and low synergy efficiency, making it difficult to meet the actual needs of grid resilience optimization in complex disaster scenarios. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method for enhancing grid resilience of hydrogen storage systems under heterogeneous uncertainties through coordinated response, comprising:

[0005] Step 1: Construct a mathematical model of the heterogeneous uncertain set of transmission line faults, energy output fluctuations, and load changes in the power grid system under typhoon scenarios;

[0006] Step 2: Incorporate the mathematical model of the day-ahead market and real-time unit combination of the transmission line and the hydrogen storage system into the mathematical model of the heterogeneous uncertain set mentioned in Step 1, and establish a two-stage robust optimization model.

[0007] Step 3: Using column and constraint generation iterative solution techniques, solve the two-stage robust optimization model obtained in Step 2 to obtain the power grid resilience enhancement decision.

[0008] Preferably, the specific process of step 1 is as follows:

[0009] Step 1.1 The power grid system consists of towers and conductors in transmission lines. The conductors in the transmission lines are divided into multiple segments in space to form multiple conductor segments. Based on the vulnerability curves of the towers in the transmission lines and each conductor segment, the failure probability of the towers in the transmission lines and the failure probability of the conductors in the transmission lines under the typhoon path are calculated.

[0010] Step 1.2: Based on the discrete Markov process, the failure probability of the tower and the failure probability of the conductor, the overall failure probability of the transmission line is obtained;

[0011] Step 1.3: Based on the overall fault probability of the transmission line, determine the time-varying characteristics of the transmission line's operating state;

[0012] Step 1.4: Based on the time-varying characteristics of the operation status of the transmission line, construct a mathematical model of the heterogeneous uncertain set of transmission line faults, energy output fluctuations and load changes under the typhoon scenario.

[0013] Preferably, the failure probability of the tower satisfies the following formula:

[0014]

[0015] in, The probability of tower failure at a given wind speed; Let the expected mechanical strain of the tower be under a given damage level. The variance coefficient of the tower under a given damage level; Typhoon path middle The equivalent wind speed at the tower at any given moment. This represents the failure probability of the tower. Pi is the mathematical constant of a circle.

[0016] Preferably, the failure probability of the conductor satisfies the following formula:

[0017]

[0018] in, For the conductor in the transmission line The equivalent wind speed at the section; For the conductor number Rainfall speed at the section; Indicates the conductor number The coefficient of wind speed influence on the segment; Indicates the conductor number The coefficient of the section affected by rainfall; Indicates the conductor number The coefficient of the segment's influence by other factors; For the conductor number Design parameters for a broken wire fault at standard wind speed; For the conductor number Section on the design parameters for a broken wire fault related to standard rainfall speed; For the conductor number The length of the segment This represents the probability of failure of the conductor.

[0019] Preferably, the overall failure probability of the transmission line satisfies the following formula:

[0020]

[0021] in, A collection of power transmission towers. A collection of wires. Let L be a set of transmission lines, and L be a specific transmission line. Let T be the time interval, and T be the overall scheduling period. Let be the overall failure probability of the transmission line at time t. This represents the probability of failure of the conductor. This represents the failure probability of the tower.

[0022] Preferably, the mathematical model of the heterogeneous uncertain set includes: a mathematical model of transmission line faults, a box-type uncertain set mathematical model of renewable energy output fluctuations, and a box-type uncertain set mathematical model of load fluctuations;

[0023] The mathematical model for transmission line faults includes four constraints: Constraint 1: Passing the line fault probability threshold Determine each time interval Downline fault status Constraint 2: Construct logical constraints for the transition between line fault states and operating states based on the obtained line fault states; Constraint 3: A transmission line is considered to have only one fault within a scheduling cycle; Constraint 4: Line faults in the power grid system satisfy the Nk criterion of N elements - k faults, which aims to ensure that the power grid system can still operate safely and stably when any k line faults occur simultaneously among N elements.

[0024] The mathematical model for the transmission line fault satisfies the following formula:

[0025]

[0026] in, The values ​​represent the fault status of the line, with 1 indicating a transmission line fault and the other indicating a normal status. Let be the overall failure probability of the transmission line at time t; Let represent the operating state of the conductor at time t. This represents the operating state of the conductor at time t-1; M represents the number of lines; M represents the maximum allowable number of line faults. The four sets of constraints are constructed; T is the overall scheduling period; Let L be a set of transmission lines;

[0027] The box-shaped uncertainty set mathematical model for the fluctuation of renewable energy output satisfies the following formula:

[0028]

[0029] in, For wind farm Predicted output within the time interval; For wind farm The actual output during the time interval; For wind farm Within the range of increased output fluctuations over time intervals For wind farm Within the range of reduced output fluctuations over time intervals; 0-1 variables; 0-1 variables; Let W be a set of wind farms; A set of box-shaped uncertainties constraining the fluctuations in renewable energy output;

[0030] The box-shaped uncertainty set mathematical model of the load fluctuation satisfies the following formula:

[0031]

[0032] in, The load is the predicted value over the time interval; This represents the actual load value over the time interval; This represents the range of load fluctuations during that time interval. This represents the range of load reduction during that time interval; 0-1 variables; The variables are 0-1; d represents a load node, and D is the load set; For the set of box-type uncertain constraints for load fluctuations.

[0033] Preferably, the specific process of step 2 is as follows:

[0034] Step 2.1: Based on the established unit combination's on / off logic constraints, minimum on / off time constraints, and initial state constraints of thermal power units, a mathematical model for the first stage is established with the goal of minimizing the operating cost of the power grid system.

[0035] Step 2.2: Based on the real-time operation constraints of thermal power units, hydrogen storage system operation constraints, grid power flow and balance constraints, unit tripping and load shedding constraints, and wind farm output constraints in the established unit combination, a mathematical model for the second stage is established with the goal of minimizing the penalty cost of grid system network operation.

[0036] Step 2.3: Based on the mathematical models of the first and second stages established in Steps 2.1 and 2.2, incorporate the mathematical model of the heterogeneous uncertain set in Step 1 to establish a two-stage robust optimization model.

[0037] Preferably, the mathematical model of the first stage satisfies the following formula:

[0038]

[0039] in, The unit cost of starting up a thermal power unit; The unit cost of shutting down a thermal power unit; This represents the unit cost of no-load operation of a thermal power unit; g is the thermal power unit number, and G is the set of all thermal power units. , , All are 0-1 variables; The operating status of thermal power units. The thermal power unit is in operation. This indicates that the thermal power unit is in a shutdown state; This is the start-up indicator variable for thermal power units. Indicates the time of thermal power unit Perform the power-on operation; This is the shutdown indicator variable for thermal power units. This indicates that the thermal power unit will be shut down during this period; For time intervals; To minimize the operating costs of the power grid system; For the operating costs of the power grid system;

[0040] The logic constraints for starting and stopping thermal power units are as follows:

[0041]

[0042]

[0043] The minimum start-up and shutdown time constraints for thermal power units are:

[0044]

[0045]

[0046] in, This represents the minimum number of operating hours for a thermal power unit. is the shortest shutdown hour for thermal power units; T is the overall dispatching cycle;

[0047] The initial state constraints of the thermal power unit are:

[0048]

[0049] in, This represents the initial operating state of the thermal power unit. This represents the remaining operating time of the thermal power unit. This represents the remaining shutdown time for the thermal power unit.

[0050] Preferably, the mathematical model for the second stage satisfies the following formula:

[0051]

[0052] in, The unit cost of power output for thermal power units; The unit penalty cost for power grid shutdown caused by a typhoon; The unit penalty cost for power grid load shedding caused by a typhoon; For thermal power unit g in time interval Those who have made meritorious contributions; Let g be the number of units to be switched off at time t. This represents the load shedding amount at a load node at that moment; G is the set of thermal power units; T is the overall dispatching cycle; d represents a certain load node; and D is the load set. The penalty cost for the operation of the power grid system network, To minimize the penalty costs of operating the power grid system network;

[0053] The real-time operating constraints of thermal power units are:

[0054]

[0055]

[0056]

[0057] in, This represents the lower limit of the actual output of thermal power units. This represents the upper limit of the actual output of thermal power units. This indicates the hourly uphill rate of a thermal power unit; This indicates the hourly downhill ramp rate of the thermal power unit; Indicates the ramp limit for thermal power units; Indicates the ramp limit for shutting down thermal power units; This refers to the operating status of the thermal power unit. This is the start-up indicator variable for thermal power units; This is the shutdown indicator variable for thermal power units;

[0058] The operating constraints of the hydrogen storage system are:

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] in, The amount of hydrogen produced by the energy hub electrolyzer of the power grid system within a time interval; The electro-hydrogen energy conversion efficiency of the electrolyzer; The correlation coefficient for the lower heating value of hydrogen; This is the maximum power of the electrolytic cell. This is the minimum power of the electrolytic cell. This refers to the actual power of the electrolytic cell; These are the operating parameters of the compressor; This refers to the compression ratio of the compressor. The adiabatic index of hydrogen gas is given. This represents the lower limit of the compressor's power. This is the upper limit of the compressor's power. This refers to the actual power of the compressor. For hydrogen storage systems The state of hydrogen at any given moment. For hydrogen storage systems The state of hydrogen at any given moment; The amount of hydrogen injected into the hydrogen storage tank. The loss coefficient of the hydrogen storage tank. This represents the maximum hydrogen storage capacity of the hydrogen storage tank. This is the lower limit of the hydrogen state. This represents the upper limit of the hydrogen state; This refers to the electrical power obtained by converting hydrogen gas into electricity through a fuel cell. The efficiency coefficient of the fuel cell; The efficiency of hydrogen-to-electricity conversion; This represents the lower limit of hydrogen consumption. This represents the upper limit for hydrogen consumption. This represents the actual value of hydrogen consumption. Let I be a hydrogen storage system, and let I be the set of hydrogen storage systems.

[0068] Power flow and balance constraints in the power grid:

[0069]

[0070]

[0071]

[0072] in, This refers to the maximum transmission capacity of the transmission line. This represents the actual transmission power of the power transmission line. and The power angle at both ends of a certain transmission line; The susceptance of the transmission line; This represents the operating state of the conductor at time t; Let L be a set of transmission lines; This refers to the power flow on the transmission line;

[0073] Cutting machine and load shearing constraints:

[0074]

[0075]

[0076] in, In order to be in Forecasted load values ​​for thermal power units at any given time;

[0077] Wind farm output constraints:

[0078]

[0079] in, For wind farm The actual output at time t after real-time adjustment; For wind farm The actual output at time t; Let W be a single wind farm, and W be the set of wind farms.

[0080] Preferably, the two-stage robust optimization model satisfies the following standard mathematical form:

[0081]

[0082] in, Simplified to : Represents the operating cost of the power grid system. Simplified to : Represents the penalty cost of operating the power grid system network; The first stage of the constraint set includes the thermal power unit start-up logic constraint, the thermal power unit minimum start-up time constraint, and the thermal power unit initial state constraint. It is a heterogeneous set of uncertainties; The second stage of the constraint set includes real-time operation constraints of thermal power units, operation constraints of hydrogen storage systems, power flow and balance constraints of the power grid, constraints on unit tripping and load shedding, and output constraints of wind farms. It is an uncertain variable; This is the left-hand coefficient matrix of the first-stage constraints. This is the right-hand coefficient vector of the first-stage constraints. This is the left-hand coefficient matrix of the second-stage constraints. This is the right-hand coefficient vector of the second-stage constraints. The left-hand coefficient matrix of the uncertainty set constraint, The right-hand side coefficient vector represents the uncertainty set constraint.

[0083] Compared with the prior art, the beneficial effects of the present invention are:

[0084] By introducing a collaborative response mechanism for hydrogen storage systems and achieving coordinated and optimized operation of multi-energy systems under heterogeneous uncertainty environments, the load shedding of power systems caused by extreme weather events such as typhoons can be significantly reduced, and the power supply guarantee capability of critical busbars can be improved. This method effectively enhances the recovery capability and operational resilience of power systems under damaged conditions, solving the problems of insufficient load supply and slow recovery of traditional power systems under disaster impacts, thereby achieving rapid self-healing and improved resilience of power systems. Attached Figure Description

[0085] Figure 1 This is a flowchart of a method for enhancing the grid resilience of a hydrogen storage system under heterogeneous uncertainty, according to the present invention.

[0086] Figure 2 This is a schematic diagram of the 24-node power system of the integrated hydrogen storage system of the present invention;

[0087] Figure 3 This is a schematic diagram of load shedding for the grid resilience enhancement method of the present invention that does not employ the cooperative response of hydrogen storage systems under heterogeneous uncertainty;

[0088] Figure 4 This is a schematic diagram of the load shedding method of the present invention, which uses the coordinated response of a hydrogen storage system under heterogeneous uncertainty to enhance grid resilience. Detailed Implementation

[0089] like Figure 1 As shown, the present invention provides a method for enhancing grid resilience of a hydrogen storage system under heterogeneous uncertainty through coordinated response, comprising the following steps:

[0090] Step 1: Construct a mathematical model of the heterogeneous uncertain set of transmission line faults, energy output fluctuations, and load changes in the power grid system under typhoon scenarios;

[0091] Step 2: Incorporate the mathematical model of the day-ahead market and real-time unit combination of the transmission line and the hydrogen storage system into the mathematical model of the heterogeneous uncertain set mentioned in Step 1, and establish a two-stage robust optimization model.

[0092] Step 3: Using column and constraint generation iterative solution techniques, solve the two-stage robust optimization model obtained in Step 2 to obtain the power grid resilience enhancement decision.

[0093] In steps 1–3 above, based on the typhoon scenario, a mathematical model (heterogeneous uncertainty set) is first constructed, which includes a heterogeneous uncertainty set containing transmission line faults, energy output fluctuations, and load changes. Then, the models of the day-ahead market of transmission lines, real-time unit combination, and hydrogen storage system are coupled with this uncertainty set to form a two-stage robust optimization model. Finally, the column constraint generation method is used to solve the model and obtain the grid resilience enhancement decision.

[0094] Furthermore, the specific process of step 1 is as follows:

[0095] Step 1.1 The power grid system consists of towers and conductors in transmission lines. The conductors in the transmission lines are divided into multiple segments in space to form multiple conductor segments. Based on the vulnerability curves of the towers in the transmission lines and each conductor segment, the failure probability of the towers in the transmission lines and the failure probability of the conductors in the transmission lines under the typhoon path are calculated.

[0096] Step 1.2: Based on the discrete Markov process, the failure probability of the tower and the failure probability of the conductor, the overall failure probability of the transmission line is obtained;

[0097] Step 1.3: Based on the overall fault probability of the transmission line, determine the time-varying characteristics of the transmission line's operating state;

[0098] Step 1.4: Based on the time-varying characteristics of the operation status of the transmission line, construct a mathematical model of the heterogeneous uncertain set of transmission line faults, energy output fluctuations and load changes under the typhoon scenario.

[0099] In steps 1.1-1.4 above, the transmission line conductors are first divided into multiple segments according to space, and the failure probability under the typhoon path is calculated by combining the vulnerability curves of the towers and each conductor segment. Then, by using a discrete Markov process, the failure probabilities of the towers and conductors are fused to obtain the overall failure probability of the transmission line, and the time-varying changes of its operating state are plotted accordingly. Finally, based on this time-varying characteristic, a heterogeneous uncertainty set including transmission line failures, energy output fluctuations and load changes is constructed.

[0100] Furthermore, the failure probability of the tower satisfies the following formula:

[0101]

[0102] in, The probability of tower failure at a given wind speed; Let the expected mechanical strain of the tower be under a given damage level. The variance coefficient of the tower under a given damage level; Typhoon path middle The equivalent wind speed at the tower at any given time can be estimated based on rainfall and wind speed information. This represents the failure probability of the tower. Pi is the mathematical constant of a circle.

[0103] In the above steps, the failure probability of the tower is calculated by combining the failure probability under a given wind speed, the expected mechanical strain and variance coefficients for different damage levels, and the equivalent wind speed estimated based on the typhoon path.

[0104] Furthermore, the failure probability of the conductor satisfies the following formula:

[0105]

[0106] in, For the conductor in the transmission line The equivalent wind speed at the section; For the conductor number Rainfall speed at the section; Indicates the conductor number The coefficient of wind speed influence on the segment; Indicates the conductor number The coefficient of the section affected by rainfall; Indicates the conductor number The coefficient of the segment's influence by other factors; For the conductor number Design parameters for a broken wire fault at standard wind speed; For the conductor number Section on the design parameters for a broken wire fault related to standard rainfall speed; For the conductor number The length of the segment This represents the probability of failure of the conductor.

[0107] The failure probability of the conductor in the above steps is calculated by combining the equivalent wind speed, rainfall speed, different influence coefficients, and design parameters for line breakage in each segment with the length.

[0108] Furthermore, the overall failure probability of the transmission line satisfies the following formula:

[0109]

[0110] in, A collection of power transmission towers. A collection of wires. Let L be a set of transmission lines, and L be a specific transmission line. Let T be the time interval, and T be the overall scheduling period. Let be the overall failure probability of the transmission line at time t. This represents the probability of failure of the conductor. This represents the failure probability of the tower.

[0111] The overall failure probability of the aforementioned transmission line is calculated by combining the failure probabilities of its towers and conductors at various times, thus reflecting the time-varying failure risk of the line throughout the entire dispatch cycle. Therefore, step 1.3 uses this probability to characterize the time-varying characteristics of the transmission line's operating state, that is, transmission lines at different spatial locations and at different temporal times all have corresponding failure probabilities.

[0112] Furthermore, the mathematical model of the heterogeneous uncertain set mentioned in step 1.4 includes: a mathematical model of transmission line faults, a box-type uncertain set mathematical model of renewable energy output fluctuations, and a box-type uncertain set mathematical model of load fluctuations;

[0113] The mathematical model for transmission line faults includes four constraints: Constraint 1: Passing the line fault probability threshold Determine each time interval Downline fault status Constraint 2: Construct logical constraints for the transition between line fault states and operating states based on the obtained line fault states; Constraint 3: A transmission line is considered to have only one fault within a scheduling cycle; Constraint 4: Line faults in the power grid system satisfy the Nk criterion of N elements - k faults, which aims to ensure that the power grid system can still operate safely and stably when any k line faults occur simultaneously among N elements.

[0114] The mathematical model for the transmission line fault satisfies the following formula:

[0115]

[0116] in, The values ​​represent the fault status of the line, with 1 indicating a transmission line fault and the other indicating a normal status. Let be the overall failure probability of the transmission line at time t; Let represent the operating state of the conductor at time t. This represents the operating state of the conductor at time t-1; M represents the number of lines; M represents the maximum allowable number of line faults. The four sets of constraints are constructed; T is the overall scheduling period; Let L be a set of transmission lines;

[0117] The box-shaped uncertainty set mathematical model for the fluctuation of renewable energy output satisfies the following formula:

[0118]

[0119] in, For wind farm Predicted output within the time interval; For wind farm The actual output during the time interval; For wind farm Within the range of increased output fluctuations over time intervals For wind farm Within the range of reduced output fluctuations over time intervals; The variable is 0-1, used to determine whether there is a fluctuation in wind power output. 1 indicates that there is a fluctuation in output, and 0 otherwise. The variable is 0-1, used to determine whether there is a fluctuation in wind power output. 1 indicates that there is a fluctuation in output, and 0 otherwise. Let W be a set of wind farms; A set of box-shaped uncertainties constraining the fluctuations in renewable energy output;

[0120] The box-shaped uncertainty set mathematical model of the load fluctuation satisfies the following formula:

[0121]

[0122] in, The load is the predicted value over the time interval; This represents the actual load value over the time interval; This represents the range of load fluctuations during that time interval. This represents the range of load reduction during that time interval; The variable is 0-1, used to determine whether there is a fluctuation in load increase; 1 indicates a fluctuation in load increase, otherwise it is 0. The variable is 0-1, used to determine whether there is a fluctuation in load reduction. 1 indicates that there is a fluctuation in load reduction, and 0 indicates otherwise; d represents a certain load node, and D is the load set; This represents a set of box-shaped uncertainties related to load fluctuations. In summary, this forms a unified set of heterogeneous uncertainties. This provides uncertainty input for the construction of subsequent resilience optimization models.

[0123] In step 1.4 above, by unifying and expressing the mathematical model of transmission line faults, the box-type uncertainty set mathematical model of renewable energy output fluctuations and load fluctuations into a mathematical model of heterogeneous uncertainty sets, a systematic characterization of multi-source uncertainties under typhoon scenarios is achieved, providing uncertainty input for subsequent power grid resilience enhancement optimization models.

[0124] Furthermore, the specific process of step 2 is as follows:

[0125] Step 2.1: Based on the established unit combination's on / off logic constraints, minimum on / off time constraints, and initial state constraints of thermal power units, a mathematical model for the first stage is established with the goal of minimizing the operating cost of the power grid system.

[0126] Step 2.2: Based on the real-time operation constraints of thermal power units, hydrogen storage system operation constraints, grid power flow and balance constraints, unit tripping and load shedding constraints, and wind farm output constraints in the established unit combination, a mathematical model for the second stage is established with the goal of minimizing the penalty cost of grid system network operation.

[0127] Step 2.3: Based on the mathematical models of the first and second stages established in Steps 2.1 and 2.2, incorporate the mathematical model of the heterogeneous uncertain set in Step 1 to establish a two-stage robust optimization model.

[0128] In steps 2.1–2.3 above, a two-stage robust optimization model for grid resilience dispatch under typhoon scenarios is finally established by constructing a first-stage model containing unit combination decision-making and a second-stage model characterizing real-time operation constraints, and integrating them with the heterogeneous uncertainty set formed in step 1.

[0129] Furthermore, the mathematical model of the first stage satisfies the following formula:

[0130]

[0131] in, The unit cost of starting up a thermal power unit; The unit cost of shutting down a thermal power unit; This represents the unit cost of no-load operation of a thermal power unit; g is the thermal power unit number, and G is the set of all thermal power units. , , All are 0-1 variables; The operating status of thermal power units. The thermal power unit is in operation. This indicates that the thermal power unit is in a shutdown state; This is the start-up indicator variable for thermal power units. Indicates the time of thermal power unit Perform the power-on operation; This is the shutdown indicator variable for thermal power units. This indicates that the thermal power unit will be shut down during this period; For time intervals; To minimize the operating costs of the power grid system; For the operating costs of the power grid system;

[0132] The logic constraints for starting and stopping thermal power units are as follows:

[0133]

[0134]

[0135] The minimum start-up and shutdown time constraints for thermal power units are:

[0136]

[0137]

[0138] in, This represents the minimum number of operating hours for a thermal power unit. is the shortest shutdown hour for thermal power units; T is the overall dispatching cycle;

[0139] The initial state constraints of the thermal power unit are:

[0140]

[0141] in, This represents the initial operating state of the thermal power unit. This represents the remaining operating time of the thermal power unit. The remaining shutdown time of the thermal power unit; this constraint is the initial state constraint of the thermal power unit. It stipulates that after the initial moment, during the entire period spanning the sum of the remaining start-up time and shutdown time of the unit, the start-up and shutdown state of each unit must remain unchanged from its initial value, thereby ensuring the continuity of the physical operating characteristics of the unit and meeting its minimum start-up and shutdown cycle requirements.

[0142] In step 2.1 above, with the goal of minimizing the operating cost of the power grid system, the constraints include the switching logic constraints of thermal power units in the unit combination, the minimum switching time constraints of thermal power units, and the initial state constraints of thermal power units, thus establishing the mathematical model for the first stage.

[0143] Furthermore, the mathematical model for the second stage satisfies the following formula:

[0144]

[0145] in, The unit cost of power output for thermal power units; The unit penalty cost for power grid shutdown caused by a typhoon; The unit penalty cost for power grid load shedding caused by a typhoon; For thermal power unit g in time interval Those who have made meritorious contributions; Let g be the number of units to be switched off at time t. This represents the load shedding amount at a load node at that moment; G is the set of thermal power units; T is the overall dispatching cycle; d represents a certain load node; and D is the load set. The penalty cost for the operation of the power grid system network, To minimize the penalty costs of operating the power grid system network;

[0146] The real-time operating constraints of thermal power units are:

[0147]

[0148]

[0149]

[0150] in, This represents the lower limit of the actual output of thermal power units. This represents the upper limit of the actual output of thermal power units. This indicates the hourly uphill rate of a thermal power unit; This indicates the hourly downhill ramp rate of the thermal power unit; Indicates the ramp limit for thermal power units; Indicates the ramp limit for shutting down thermal power units; This refers to the operating status of the thermal power unit. This is the start-up indicator variable for thermal power units; This is the shutdown indicator variable for thermal power units;

[0151] The operating constraints of the hydrogen storage system are:

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159]

[0160] in, The amount of hydrogen produced by the energy hub electrolyzer of the power grid system within a time interval; The electro-hydrogen energy conversion efficiency of the electrolyzer; The correlation coefficient for the lower heating value of hydrogen; This is the maximum power of the electrolytic cell. This is the minimum power of the electrolytic cell. This refers to the actual power of the electrolytic cell; These are the operating parameters of the compressor, which are related to factors such as operating temperature, specific heat capacity of hydrogen, and operating efficiency. This refers to the compression ratio of the compressor. The adiabatic index of hydrogen gas is given. This represents the lower limit of the compressor's power. This is the upper limit of the compressor's power. This refers to the actual power of the compressor. Let t represent the state of hydrogen in the hydrogen storage system. The amount of hydrogen injected into the hydrogen storage tank. The loss coefficient of the hydrogen storage tank. This represents the maximum hydrogen storage capacity of the hydrogen storage tank. This is the lower limit of the hydrogen state. This represents the upper limit of the hydrogen state; This refers to the electrical power obtained by converting hydrogen gas into electricity through a fuel cell. The efficiency coefficient of the fuel cell; The efficiency of hydrogen-to-electricity conversion; This represents the lower limit of hydrogen consumption. This represents the upper limit for hydrogen consumption. This represents the actual value of hydrogen consumption. Let I be a hydrogen storage system, and let I be the set of hydrogen storage systems.

[0161] Power flow and balance constraints in the power grid:

[0162]

[0163]

[0164] in, This refers to the maximum transmission capacity of the transmission line. This represents the actual transmission power of the power transmission line. and The power angle at both ends of a certain transmission line; The susceptance of the transmission line; This represents the operating state of the conductor at time t; Let L be a set of transmission lines; the constraint indicates that when the line is operating normally, there is a constraint relationship between the transmission power of the line and the power angle of the nodes, and the constraint does not take effect when the transmission line is faulty.

[0165] Cutting machine and load shearing constraints:

[0166]

[0167]

[0168] in, In order to be in The load forecast value of the thermal power unit at any time, the constraint indicates that the amount of thermal power unit cut-off due to the typhoon does not exceed the actual output of the thermal power unit, and the amount of load cut-off due to the typhoon does not exceed the actual demand of the load.

[0169] Wind farm output constraints:

[0170]

[0171] in, For wind farm The actual output at time t after real-time adjustment; For wind farm The actual output at time t; Let W be a single wind farm, and W be the set of wind farms. The constraint states that the actual power output of a wind farm should not exceed the actual predicted output for that period.

[0172] In step 2.2 above, with the goal of minimizing the penalty cost of the power grid system network operation, the constraints include the real-time operation constraints of thermal power units in the unit combination, the operation constraints of the hydrogen storage system, the power flow and balance constraints of the power grid, the constraints of unit tripping and load shedding, and the output constraints of the wind farm, and a mathematical model for the second stage is established.

[0173] Through the two-stage modeling described above, step 2 achieves the coordinated optimization of unit scheduling and hydrogen storage system under multi-source heterogeneous uncertainty of the power grid. Combining the heterogeneous uncertainty set established in step 1, a two-stage robust optimization model can be further constructed. This two-stage robust optimization model satisfies the following standard mathematical form:

[0174]

[0175] in, Simplified to : Represents the operating cost of the power grid system. Simplified to : Represents the penalty cost of operating the power grid system network; The first stage of the constraint set includes the thermal power unit start-up logic constraint, the thermal power unit minimum start-up time constraint, and the thermal power unit initial state constraint. It is a heterogeneous set of uncertainties; The second stage of the constraint set includes real-time operation constraints of thermal power units, operation constraints of hydrogen storage systems, power flow and balance constraints of the power grid, constraints on unit tripping and load shedding, and output constraints of wind farms. It is an uncertain variable; This is the left-hand coefficient matrix of the first-stage constraints. This is the right-hand coefficient vector of the first-stage constraints. This is the left-hand coefficient matrix of the second-stage constraints. This is the right-hand coefficient vector of the second-stage constraints. The left-hand coefficient matrix of the uncertainty set constraint, The right-hand side coefficient vector of the uncertainty set constraint

[0176] Furthermore, in step 3, combining column and constraint generation iterative solution techniques, the two-stage robust optimization model obtained in step 2 is solved to obtain the power grid resilience enhancement decision. The specific process is as follows:

[0177] Step 3.1: Using a combination of column and constraint generation iterative solution technology, the two-stage robust optimization model is decomposed into a main problem and sub-problems. The main problem determines the first-stage decision, and the sub-problems search for the most unfavorable uncertainty scenario.

[0178] Step 3.2: By iteratively solving the main problem and subproblems, cut constraints are generated and the upper and lower bounds are updated until convergence to the preset accuracy threshold, thus obtaining the optimal resilience enhancement decision.

[0179] Furthermore, the two-stage robust optimization model is decomposed into a main problem and sub-problems, including:

[0180] The main problem is responsible for determining the decision-making and scheduling plan for the first phase, which can be expressed in the following form:

[0181]

[0182] Among them, auxiliary variables Used for the second phase problem The relaxation representation is used so that the main problem can be solved in the form of mixed integer programming. This is the coefficient vector of the decision variables in the first stage; This is the coefficient vector of the decision variables in the second stage; This is the right-hand coefficient vector of the second-stage constraints. The left-hand coefficient matrix of the uncertainty set constraint, Let be the set of real numbers.

[0183] Given the current decision options for the main problem, construct and solve subproblems to search for the worst-case scenario under multi-source heterogeneous uncertainty, which can be expressed as:

[0184]

[0185] in, It's about decision-making. The dual variables are obtained. The subproblem is transformed into a single-layer maximization problem by dualizing the inner problem of the second-stage decision; the dual variables obtained from solving the subproblem and the worst-case scenario are used to generate cut constraints on the main problem.

[0186] Furthermore, iteratively solving the subproblems of the main problem and returning the cutting plane includes:

[0187] The main problem and subproblems are updated iteratively until the objective function value obtained from the main problem is taken as the lower bound (LB) and the objective function value obtained from the subproblems is taken as the upper bound (UB), and the difference or relative gap between the two converges to within the preset accuracy threshold.

[0188] The iterative solution process generated by the columns and constraints can decompose the original two-stage robust optimization problem into multiple alternating main / subproblem solving tasks, thereby significantly improving computational efficiency while ensuring solution accuracy, and finally obtaining the optimal power grid resilience enhancement decision under multi-source heterogeneous uncertainty conditions.

[0189] Step 3 above, through the iterative solution process generated by the columns and constraints, can decompose the original two-stage robust optimization problem into multiple alternating solutions of main / subproblems, thereby significantly improving computational efficiency while ensuring solution accuracy, and finally obtaining the optimal power grid resilience enhancement decision under multi-source heterogeneous uncertainty conditions.

[0190] As can be seen, this invention addresses the problem that existing power grid resilience enhancement strategies generally rely solely on emergency measures involving a single disaster prevention and mitigation unit combination, making it difficult to achieve multi-level responses under extreme events. It proposes a multi-strategy resilience enhancement method that comprehensively considers source-grid interaction. This method can flexibly coordinate resource allocation under typhoon disaster disturbances, reduce the high cost losses caused by unit and load shedding, and significantly enhance the resilience of the power system.

[0191] To illustrate the method of the present invention, as a further example of the present invention, please refer to [link / reference]. Figure 2 This embodiment relates to an IEEE 24-node reliability testing system. The power generation system comprises 32 generating units with a power range of 12-400 MW. The transmission system comprises 24 load / generator nodes connected by 38 lines or autotransformers, with voltage levels of 138 kV and 230 kV. The IEEE 24-node reliability testing system is projected onto a 150km*200km area (30.52ºN-32.32ºN, 87.68ºW-89.25ºW) of a transmission grid, assuming consistent typhoon parameters at all components of the system. The system includes two wind farms, connected to the grid via bus 2 and bus 7, respectively. A hydrogen storage power station is connected to buses 9, 16, and 23, and its operating parameters are shown in Table 1.

[0192] Table 1 Parameters of the Energy Storage System

[0193]

[0194] During the solution process, the lower and upper bounds of the main problem and subproblems are converged with a preset threshold of 1%, and the error accuracy of the Cplex solver used to solve the main problem and subproblems is set to 0.1%. The parameters of the hydrogen storage system and the above system model parameters are set according to Table 1, and the method in Example 1 is simulated.

[0195] The simulation results are as follows:

[0196] from Figure 3 It can be seen that, without considering the hydrogen storage system, varying degrees of load shedding occurred at busbars 8, 12, and 18 starting at 8:00 AM, indicating that the typhoon's landfall at 8:00 AM caused significant damage to the power system. Figure 4 It can be seen that, considering the operating conditions of the hydrogen storage system, load shedding only occurs between 9:00 and 17:00; after 17:00, the system load fully recovers, and no load shedding occurs. (Comparison) Figure 3 and Figure 4It can be observed that the method proposed in this invention can significantly reduce the load shedding at buses 8, 12, and 18. The above simulation results verify that the grid resilience enhancement method proposed in this invention, which considers the coordinated response of the hydrogen storage system under heterogeneous uncertainties, can effectively alleviate the problem of insufficient load supply caused by typhoons and achieve a significant improvement in the resilience level of the power system.

[0197] Finally, those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.

[0198] This invention is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for enhancing the resilience of a power grid under heterogeneous uncertainty with a hydrogen storage system collaborative response, characterized in that, The application relates to a two-stage robust optimization model for enhancing the resilience of a power grid under a typhoon scenario. The application comprises the following steps: Step 1: constructing a mathematical model of a heterogeneous uncertain set of power transmission line faults, energy output fluctuations and load changes under a typhoon scenario; Step 2: incorporating the mathematical models of the power transmission line day-ahead market, real-time unit commitment and hydrogen storage system into the mathematical model of the heterogeneous uncertain set in step 1 to establish a two-stage robust optimization model; 2. The method of claim 1, wherein, Step 3: combining columns and constraints to generate an iterative solution technique to solve the two-stage robust optimization model obtained in step 2 to obtain a decision for enhancing the resilience of the power grid. The specific process of step 1 is as follows: Step 1.1: the power grid system comprises towers and conductors in a power transmission line, the conductors in the power transmission line are divided into multiple sections according to space to form multiple conductor sections; Based on the vulnerability curves of the towers and each conductor section in the power transmission line, the failure probability of the towers in the power transmission line and the failure probability of the conductors in the power transmission line under a typhoon path are calculated; Step 1.2: based on a discrete Markov process, the failure probability of the towers and the failure probability of the conductors, the overall failure probability of the power transmission line is obtained; Step 1.3: based on the overall failure probability of the power transmission line, the time-varying characteristics of the operating state of the power transmission line are determined; 3. The method of claim 2, wherein, Step 1.4: based on the time-varying characteristics of the operating state of the power transmission line, a mathematical model of a heterogeneous uncertain set of power transmission line faults, energy output fluctuations and load changes under a typhoon scenario is constructed. wherein, is the failure probability of the tower at a given wind speed; is the expected mechanical fatigue period of the tower at a given damage level, is the coefficient of variation of the tower at a given damage level; is the typhoon path in the equivalent wind speed at the tower at the instant of time, is the failure probability of the tower, is the circle constant.

4. The method of claim 2, wherein, The failure probability of the towers satisfies the following formula: in, For the conductor in the transmission line The equivalent wind speed at the section; For the conductor number Rainfall speed at the section; Indicates the first conductor The coefficient of wind speed influence on the segment; Indicates the first conductor The coefficient of the section affected by rainfall; Indicates the conductor number The coefficient of the segment's influence by other factors; For the conductor number Design parameters for a broken wire fault at standard wind speed; For the conductor number Section on the design parameters for a broken wire fault related to standard rainfall speed; For the conductor number The length of the segment This represents the probability of failure of the conductor.

5. The method of claim 2, wherein, The failure probability of the conductors satisfies the following formula: wherein, is a set of transmission towers, is a set of conductors, is a certain transmission line, L is a set of transmission lines, is a time instant, T is the overall dispatching period, is the overall failure probability of transmission line t at time instant, is the failure probability of a conductor, is the failure probability of a tower.

6. The method of claim 2, wherein, The overall failure probability of the power transmission line satisfies the following formula: The mathematical model of the transmission line fault includes four constraints: constraint one: passing through the line fault probability threshold determining each time interval the fault state of the lower line ; constraint two: constructing the logical constraint of the line fault state and the operating state transition from the obtained line fault state; constraint three: a transmission line is considered to fail only once in a dispatching cycle; constraint four: the line fault of the power grid system satisfies the N-k criterion of N elements-k faults, that is, aiming to ensure that when any k line faults occur in N elements at the same time, the power grid system can still safely and stably operate; The mathematical model of the heterogeneous uncertain set comprises a mathematical model of power transmission line faults, a box-type uncertain set mathematical model of renewable energy output fluctuations and a box-type uncertain set mathematical model of load fluctuations; wherein, is the fault state of the line, 1 represents the transmission line fault, and vice versa, is the normal state; is the total fault probability of the transmission line at time t; is the operating state of the conductor at time t, is the operating state of the conductor at time t-1; is the number of lines; M is the maximum number of allowed line faults; is the four constraint sets constructed; T is the total scheduling period; is a certain transmission line, L is the set of transmission lines; The mathematical model of the power transmission line faults satisfies the following formula: wherein, for a wind farm forecasted output at a time interval; for a wind farm actual output at a time interval; for a wind farm range of increased output fluctuation at a time interval, for a wind farm range of decreased output fluctuation at a time interval; is a 0-1 variable; is a 0-1 variable; is a certain wind farm, W is a set of wind farms; is a set of box uncertainty constraints for renewable energy output fluctuation; The box-type uncertain set mathematical model of the renewable energy output fluctuations satisfies the following formula: wherein, is the forecast value of the load over the time interval; is the actual value of the load over the time interval; is the fluctuation range of the load increase over the time interval, is the range of the load decrease over the time interval; is a 0-1 variable; is a 0-1 variable; d denotes a certain load node, and D is a load set; is a set of box uncertainty constraints of the load fluctuation.

7. The method of claim 1, wherein, The box-type uncertain set mathematical model of the load fluctuations satisfies the following formula: The specific process of step 2 is as follows: Step 2.1: based on the established switching logic constraints, minimum switching time constraints and initial state constraints of thermal power units in unit commitment, a mathematical model of the first stage is established with the minimum operating cost of the power grid system as the target; Step 2.2: based on the established real-time operation constraints of thermal power units in unit commitment, hydrogen storage system operation constraints, power flow and balance constraints of the power grid, unit shutdown and load shedding constraints and wind farm output constraints, a mathematical model of the second stage is established with the minimum penalty cost of the network operation of the power grid system as the target; 8. The method of claim 7, wherein, Step 2.3: based on the mathematical models of the first stage and the second stage established in steps 2.1 and 2.2, the mathematical model of the heterogeneous uncertain set in step 1 is incorporated to establish a two-stage robust optimization model. wherein, is the start-up unit cost of the thermal power unit; is the shut-down unit cost of the thermal power unit; represents the unit cost of the no-load operation of the thermal power unit; g is the thermal power unit number, and G is the set of all thermal power units; , , are all 0-1 variables; is the operation state of the thermal power unit, is the operation state of the thermal power unit, represents the shutdown state of the thermal power unit; is the start-up instruction variable of the thermal power unit, represents that the thermal power unit performs the start-up operation at the time ; is the shut-down instruction variable of the thermal power unit, represents that the thermal power unit performs the shut-down operation in the time interval; is the time interval; is the minimum grid system operation cost; is the grid system operation cost; The mathematical model of the first stage satisfies the following formula: The switching logic constraints of the thermal power units are as follows: wherein, is the shortest start-up hours of the thermal power unit; is the shortest shut-down hours of the thermal power unit; T is the total scheduling period; The minimum switching time constraints of the thermal power units are as follows: wherein, is the initial operation state of the thermal power unit; is the remaining start-up time of the thermal power unit; is the remaining shut-down time of the thermal power unit.

9. The method of claim 7, wherein, The initial state constraints of the thermal power units are as follows: The mathematical model of the second stage satisfies the following formula: wherein, is the unit cost of the power output of thermal power units; is the unit penalty cost of the grid tripping caused by typhoon; is the unit penalty cost of the grid load shedding caused by typhoon; is the active power output of the thermal power unit g in the time interval ; is the amount of tripping of the thermal power unit g at time t, represents the amount of load shedding at the load node at the time; G is the set of thermal power units; T is the total scheduling period; d represents a certain load node, and D is the set of loads; is the penalty cost of the network operation of the grid system, is the minimum penalty cost of the network operation of the grid system; The real-time operation constraints of the thermal power unit are: wherein, is a lower bound of the actual output of the thermal power unit; is an upper bound of the actual output of the thermal power unit; represents the hourly upward ramp rate of the thermal power unit; represents the hourly downward ramp rate of the thermal power unit; represents the ramp limit value for starting the thermal power unit; represents the ramp limit value for stopping the thermal power unit; is the operating state of the thermal power unit; is the start-up instruction variable of the thermal power unit; is the shut-down instruction variable of the thermal power unit; The hydrogen storage system operation constraints are: wherein, is the hydrogen production amount of the energy hub electrolyzer of the power grid system in the time interval; is the electricity-hydrogen energy conversion efficiency of the electrolyzer; is the hydrogen low heating value correlation coefficient; is the maximum power of the electrolyzer, is the minimum power of the electrolyzer, is the actual power of the electrolyzer; is the operating parameter of the compressor; is the compression ratio of the compressor; is the hydrogen adiabatic index, is the lower limit of the compressor power, is the upper limit of the compressor power, is the actual power of the compressor; is the hydrogen storage system is the hydrogen state at the time instant, is the hydrogen storage system is the hydrogen state at the time instant; is the hydrogen amount injected into the hydrogen storage tank, is the loss coefficient of the hydrogen storage tank, is the maximum hydrogen storage amount of the hydrogen storage tank, is the lower limit of the hydrogen state, is the upper limit of the hydrogen state; is the electric power obtained by converting hydrogen through a fuel cell; is the efficiency coefficient of the fuel cell; is the hydrogen-electricity energy conversion efficiency; is the lower limit of the hydrogen consumption amount, is the upper limit of the hydrogen consumption amount, is the actual value of the hydrogen consumption amount; is a certain hydrogen storage system, and I is a set of hydrogen storage systems; The power flow and balance constraints of the power grid are: wherein, is the maximum transmission capacity of the transmission line; is the actual transmission power of the transmission line; and is the power angle at both ends of a certain transmission line; is the susceptance of the transmission line; is the operating state of the conductor at time t; is a certain transmission line, L is a set of transmission lines; is the power flow on the transmission line; The generator tripping and load shedding constraints are: wherein, is the predicted value of the load of the thermal power unit at the time instant is the predicted value of the load of the thermal power unit at the time instant The wind farm output constraints are: wherein, for a wind farm actual output at time t after real-time adjustment; for a wind farm actual output at time t; for a single wind farm, W for a set of wind farms.

10. The method of claim 6, wherein, The two-stage robust optimization model meets the following mathematical standard form: wherein, is simplified as represents the cost of grid system operation, is simplified as represents the penalty cost of grid system network operation; is the constraint set of the first stage, i.e., the logical constraints of the thermal power unit switching, the minimum switching time constraints of the thermal power unit and the initial state constraints of the thermal power unit; is the set of heterogeneous uncertainties; is the constraint set of the second stage, i.e., the real-time operation constraints of the thermal power unit, the operation constraints of the hydrogen storage system, the power flow and balance constraints of the grid, the unit and load shedding constraints, the wind farm output constraints; is the uncertain variable; is the left side coefficient matrix of the first stage constraints, is the right side coefficient vector of the first stage constraints, is the left side coefficient matrix of the second stage constraints, is the right side coefficient vector of the second stage constraints, is the left side coefficient matrix of the uncertainty set constraints, is the right side coefficient vector of the uncertainty set constraints.

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