Future state power flow generation method and system of power system

Abstract

The invention discloses a future state power flow generation method of a power system. The method comprises the steps of obtaining data information of a target power system; constructing a source-load-energy storage output model of the target power system, wherein the source-load uncertainty and the energy storage operation state are considered in the source-load-energy storage output model; using a general power flow generation method to generate future state power flow data of the target power system in the next time period; the steps are repeated, and generation of the future-state power flow of the target power system is completed. The invention also discloses a system for realizing the future state power flow generation method of the power system. According to the method, the generation of the future-state power flow of the power system is realized, the reliability is higher, and the accuracy is better.

Description

Technical Field

[0001] This invention belongs to the field of electrical automation, and specifically relates to a method and system for generating future power flow in a power system. Background Technology

[0002] With economic and technological development and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and daily life, bringing endless convenience. Therefore, ensuring a stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] Currently, with the continuous increase in the proportion of new energy sources and the increasing complexity of power system load characteristics, the uncertainty of power system operation has significantly increased. The randomness of new energy power generation systems and the changing characteristics of power system loads, this dual uncertainty of source and load makes it difficult for traditional power flow calculation methods based on deterministic assumptions to accurately reflect the operating status of the power grid in future periods, thus affecting the effectiveness of power grid security assessment and operation decisions.

[0004] However, existing future power flow generation schemes often ignore the dynamic constraints of energy storage operation characteristics. Therefore, the future power flow generated by these schemes is difficult to truly reflect the grid flexibility level, and the reliability and accuracy of the schemes are poor. Summary of the Invention

[0005] One of the objectives of this invention is to provide a method for generating future power flow in a power system that is both highly reliable and accurate.

[0006] The second objective of this invention is to provide a system for implementing the future power flow generation method of the power system.

[0007] The future power flow generation method for a power system provided by this invention includes the following steps:

[0008] S1. Obtain data information about the target power system;

[0009] S2. Based on the data obtained in step S1, construct a source-load-storage output model of the target power system that takes into account source-load uncertainties and energy storage operation status;

[0010] S3. Based on the power flow data of the target power system, use a general power flow generation method to generate the future power flow data of the target power system in the next time period;

[0011] S4. Repeat step S3 to generate the future power flow of the target power system.

[0012] Step S1, which involves acquiring data information about the target power system, specifically includes the following steps:

[0013] Acquire data information from the target power system;

[0014] The data information includes the target power system's grid structure data, power generation plan data, maintenance plan data, load forecast data, interconnection plan data, and energy storage operation status data.

[0015] Step S2, which involves constructing a source-load-storage output model of the target power system based on the data obtained in step S1, includes the following steps:

[0016] Based on the historical operating data of photovoltaic power plants in the target power system, and based on the mean and standard deviation data of irradiance, a power output probability model of photovoltaic power plants in the target power system is constructed.

[0017] Based on the historical power output data of wind farms in the target power system, a power output probability model of wind farms in the target power system is constructed based on the Weibull distribution and the cumulative distribution function CDF.

[0018] Based on the historical load data of the target power system, a load probability model of the target power system is constructed.

[0019] Based on historical electrical energy storage data and historical thermal energy storage data of the target power system, and using probability density estimation, we construct probability models for electrical energy storage output and thermal energy storage output of the target power system.

[0020] Step S2 specifically includes the following steps:

[0021] Construct a probabilistic output model for photovoltaic power plants in the target power system:

[0022] The following formula is used as the output probability model for the photovoltaic power station in the target power system:

[0023] In the formula Let be the first shape parameter of the output probability model of the i-th photovoltaic power station; Let be the average solar irradiance of the i-th photovoltaic power station; Let be the standard deviation of the illuminance of the i-th photovoltaic power station; Let be the second shape parameter of the output probability model of the i-th photovoltaic power station; Let be the power output probability density function of the i-th photovoltaic power station at time t; For the Gamma function in parameters The value at; For the Gamma function in parameters The value at; For the Gamma function in parameters The value at; Let be the predicted photovoltaic output of the i-th photovoltaic power station at time t; The maximum photovoltaic output of the i-th photovoltaic power station; Let be the number of photovoltaic panels in the i-th photovoltaic power station; Let be the area of ​​a single photovoltaic panel in the i-th photovoltaic power station; Let be the photovoltaic efficiency of the i-th photovoltaic power station;

[0024] Construct a power output probability model for wind farms in the target power system:

[0025] Establish a time-series photovoltaic power output probability model that follows a Weibull distribution:

[0026] In the formula Let the wind speed of the x-th wind farm at time t be... The wind speed probability density function at that time; Let be the shape parameter of the Weibull distribution for the x-th wind farm; Let be the Weibull distribution scale parameter for the x-th wind farm; Let be the wind speed of the x-th wind farm at time t;

[0027] The cumulative distribution function (CDF) is used to indicate that the wind speed at the x-th wind farm is less than or equal to The probability of:

[0028] In the formula Let the wind speed random variable of the x-th wind farm be... The probability density function at time; for Regarding wind speed Integral factors;

[0029] Express the output power of a wind turbine as a function of wind speed. Relevant linear functions:

[0030] In the formula Let the wind speed of the x-th wind farm at time t be... A linear function expression for the output power of a wind turbine generator; Let be the cut-in wind speed of the wind turbines in the x-th wind farm; Let x be the rated output power of the x-th wind farm; Let x be the rated output wind speed of the wind turbines in the x-th wind farm; Let be the cut-out wind speed of the wind turbine in the x-th wind farm;

[0031] Finally, the power output probability model of the wind farm in the target power system is obtained:

[0032] In the formula Let x be the probability density function when the output of the x-th wind farm is 0. The output of the x-th wind farm in the interval The probability density at that time; Let x be the rated output power of the x-th wind farm; Let x be the output power of the wind turbine in the x-th wind farm; The output of the x-th wind farm is equal to its rated output power. The probability density at that time;

[0033] Construct a load probability model for the target power system:

[0034] The following formula is used as the load probability model for the target power system:

[0035] In the formula Let be the probability density function of the load prediction deviation at time t; Let be the standard deviation of the load forecast, and , This is the average load forecast. The standard deviation is the percentage of the load output. The load forecast deviation value at time t; This is the average of the load forecast;

[0036] Construct probabilistic models for the output of electrical energy storage and thermal energy storage in the target power system:

[0037] The following formula is used as the probabilistic model for the output power of electrical energy storage:

[0038] In the formula Let be the remaining energy storage capacity of the j-th energy storage station at time t; Let be the energy storage self-loss coefficient of the j-th energy storage station; The energy storage charging efficiency of the j-th energy storage station; The energy storage charging power of the j-th energy storage station at time t; Let be the energy storage discharge power of the j-th energy storage station at time t; Let be the energy storage discharge efficiency of the j-th energy storage power station; The time interval between two adjacent scheduling periods; Let be the initial remaining energy storage capacity of the j-th energy storage station; The target remaining power of the j-th energy storage station at the end of the entire scheduling cycle; This represents the lower limit of the remaining energy storage capacity of the j-th energy storage station relative to its total capacity. Let be the energy storage capacity of the j-th energy storage station; This represents the upper limit of the remaining energy storage capacity of the j-th energy storage station relative to its total capacity. Let j be a binary variable indicating whether the j-th energy storage station is in a charging state. This indicates that the j-th energy storage station is in a charging state. This indicates that the j-th energy storage station is not in a charging state; Let j be a binary variable indicating whether the j-th energy storage station is in a discharging state. This indicates that the j-th energy storage station is in a discharging state. This indicates that the j-th energy storage station is not in a discharging state;

[0039] The following formula is used as the probabilistic model for thermal energy storage output:

[0040] In the formula For the heat released or absorbed by the l-th thermal energy storage site during time period t; For the first The lower limit of heat release per unit dispatch time for a thermal energy storage site; For the first The upper limit of heat absorption by a thermal energy storage station within a unit dispatch time.

[0041] Step S3, based on the power flow data of the target power system, uses a general power flow generation method to generate future power flow data of the target power system in the next time period, specifically including the following steps:

[0042] Based on the power flow data of the target power system, a general power flow generation method is used to generate the future power flow data of the target power system in the next time period.

[0043] In general power flow generation methods, a power flow sample is defined as the combination of all node-injected power and node voltages when the power system is in steady state, denoted as:

[0044] In the formula The injected active power is for node p, and , Let be the voltage amplitude at node p. Let be the voltage magnitude at node q. Let be the real part of the mutual admittance of nodes p and q. Let p be the voltage phase angle that leads q. Let be the imaginary part of the mutual admittance of nodes p and q; Inject reactive power into node p, and ; Let be the voltage phase angle at node p; The total number of nodes;

[0045] The initial voltage value is generated using the following formula:

[0046] In the formula This is the initial value of the generated voltage; The initial voltage value generation function is set and used to input the model. Converted into initial estimates of node voltage magnitude and phase angle that satisfy grid operation constraints; Input for the model; express Safety constraints;

[0047] Based on the generated initial voltage value The Newton-Raphson method is used to perform iterative optimization of power flow calculation, generating future power flow data of the target power system in the next time period.

[0048] Step S4 involves repeating step S3 to generate the future power flow of the target power system, specifically including the following steps:

[0049] Based on the technical solution in step S3, the time-series rolling calculation method is adopted, and after several iterations of calculation, the future power flow of the target power system is generated.

[0050] This invention also provides a system for implementing the future power flow generation method of the power system, comprising a data acquisition module, a model building module, a power flow calculation module, and a power flow generation module; the data acquisition module, model building module, power flow calculation module, and power flow generation module are connected in series; the data acquisition module is used to acquire data information of the target power system and upload the data information to the model building module; the model building module is used to construct a source-load-storage output model of the target power system considering source-load uncertainty and energy storage operation status based on the received data information and the acquired data information, and upload the data information to the power flow calculation module; the power flow calculation module is used to generate future power flow data of the target power system in the next time period based on the power flow data of the target power system using a general power flow generation method, and upload the data information to the power flow generation module; the power flow generation module is used to repeat the work of the power flow calculation module based on the received data information to complete the generation of the future power flow of the target power system.

[0051] The present invention provides a method and system for generating future power flow in a power system. By constructing a source-load-storage output model that takes into account source-load uncertainty and energy storage operation status, and iteratively generating power flow data based on a general power flow generation method, it not only realizes the generation of future power flow in the power system, but also has higher reliability and better accuracy. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0053] Figure 2 This is a schematic diagram of the functional modules of the system of the present invention. Detailed Implementation

[0054] like Figure 1 The diagram shown is a flowchart of the method of the present invention: The future power flow generation method for a power system disclosed in this invention includes the following steps:

[0055] S1. Obtain data information of the target power system; specifically including the following steps:

[0056] Acquire data information from the target power system;

[0057] The data information includes the target power system's grid structure data, power generation plan data, maintenance plan data, load forecast data, interconnection plan data, and energy storage operation status data;

[0058] S2. Based on the data obtained in step S1, construct a source-load-storage output model for the target power system that takes into account source-load uncertainties and energy storage operating status; including the following steps:

[0059] Based on the historical operating data of photovoltaic power plants in the target power system, and based on the mean and standard deviation data of irradiance, a power output probability model of photovoltaic power plants in the target power system is constructed.

[0060] The following formula is used as the output probability model for the photovoltaic power station in the target power system:

[0061] In the formula Let be the first shape parameter of the output probability model of the i-th photovoltaic power station; Let be the average solar irradiance of the i-th photovoltaic power station; Let be the standard deviation of the illuminance of the i-th photovoltaic power station; Let be the second shape parameter of the output probability model of the i-th photovoltaic power station; Let be the power output probability density function of the i-th photovoltaic power station at time t; For the Gamma function in parameters The value at; For the Gamma function in parameters The value at; For the Gamma function in parameters The value at; Let be the predicted photovoltaic output of the i-th photovoltaic power station at time t; The maximum photovoltaic output of the i-th photovoltaic power station; Let be the number of photovoltaic panels in the i-th photovoltaic power station; Let be the area of ​​a single photovoltaic panel in the i-th photovoltaic power station; Let be the photovoltaic efficiency of the i-th photovoltaic power station;

[0062] Based on the historical power output data of wind farms in the target power system, a power output probability model of wind farms in the target power system is constructed based on the Weibull distribution and the cumulative distribution function CDF.

[0063] Establish a time-series photovoltaic power output probability model that follows a Weibull distribution:

[0064] In the formula Let the wind speed of the x-th wind farm at time t be... The wind speed probability density function at that time; Let be the shape parameter of the Weibull distribution for the x-th wind farm; Let be the Weibull distribution scale parameter for the x-th wind farm; Let be the wind speed of the x-th wind farm at time t;

[0065] The cumulative distribution function (CDF) is used to indicate that the wind speed at the x-th wind farm is less than or equal to The probability of:

[0066] In the formula Let the wind speed random variable of the x-th wind farm be... The probability density function at time; for Regarding wind speed Integral factors;

[0067] Express the output power of a wind turbine as a function of wind speed. Relevant linear functions:

[0068] In the formula Let the wind speed of the x-th wind farm at time t be... A linear function expression for the output power of a wind turbine generator; Let be the cut-in wind speed of the wind turbines in the x-th wind farm; Let x be the rated output power of the x-th wind farm; Let x be the rated output wind speed of the wind turbines in the x-th wind farm; Let be the cut-out wind speed of the wind turbine in the x-th wind farm;

[0069] Finally, the power output probability model of the wind farm in the target power system is obtained:

[0070] In the formula Let x be the probability density function when the output of the x-th wind farm is 0. The output of the x-th wind farm in the interval The probability density at that time; Let x be the rated output power of the x-th wind farm; Let x be the output power of the wind turbine in the x-th wind farm; The output of the x-th wind farm is equal to its rated output power. The probability density at that time;

[0071] Based on the historical load data of the target power system, a load probability model of the target power system is constructed.

[0072] The following formula is used as the load probability model for the target power system:

[0073] In the formula Let be the probability density function of the load prediction deviation at time t; Let be the standard deviation of the load forecast, and , This is the average load forecast. The standard deviation is the percentage of the load output. The load forecast deviation value at time t; This is the average of the load forecast;

[0074] Based on historical electrical energy storage data and historical thermal energy storage data of the target power system, and based on probability density estimation, we construct the electrical energy storage output probability model and the thermal energy storage output probability model of the target power system.

[0075] The following formula is used as the probabilistic model for the output power of electrical energy storage:

[0076] In the formula Let be the remaining energy storage capacity of the j-th energy storage station at time t; Let be the energy storage self-loss coefficient of the j-th energy storage station; The energy storage charging efficiency of the j-th energy storage station; The energy storage charging power of the j-th energy storage station at time t; Let be the energy storage discharge power of the j-th energy storage station at time t; Let be the energy storage discharge efficiency of the j-th energy storage power station; The time interval between two adjacent scheduling periods; Let be the initial remaining energy storage capacity of the j-th energy storage station; The target remaining power of the j-th energy storage station at the end of the entire scheduling cycle; This represents the lower limit of the remaining energy storage capacity of the j-th energy storage station relative to its total capacity. Let be the energy storage capacity of the j-th energy storage station; This represents the upper limit of the remaining energy storage capacity of the j-th energy storage station relative to its total capacity. Let j be a binary variable indicating whether the j-th energy storage station is in a charging state. This indicates that the j-th energy storage station is in a charging state. This indicates that the j-th energy storage station is not in a charging state; Let j be a binary variable indicating whether the j-th energy storage station is in a discharging state. This indicates that the j-th energy storage station is in a discharging state. This indicates that the j-th energy storage station is not in a discharging state;

[0077] The following formula is used as the probabilistic model for thermal energy storage output:

[0078] In the formula For the heat released or absorbed by the l-th thermal energy storage site during time period t; For the first The lower limit of heat release per unit dispatch time for a thermal energy storage site; For the first The upper limit of heat absorption by a thermal energy storage station per unit dispatch time;

[0079] S3. Based on the power flow data of the target power system, use a general power flow generation method to generate the future power flow data of the target power system in the next time period; specifically, this includes the following steps:

[0080] Based on the power flow data of the target power system, a general power flow generation method is used to generate the future power flow data of the target power system in the next time period.

[0081] In general power flow generation methods, a power flow sample is defined as the combination of all node-injected power and node voltages when the power system is in steady state, denoted as:

[0082] In the formula The injected active power is for node p, and , Let be the voltage amplitude at node p. Let be the voltage magnitude at node q. Let be the real part of the mutual admittance of nodes p and q. Let p be the voltage phase angle that leads q. Let be the imaginary part of the mutual admittance of nodes p and q; Inject reactive power into node p, and ; Let be the voltage phase angle at node p; The total number of nodes;

[0083] The initial voltage value is generated using the following formula:

[0084] In the formula This is the initial value of the generated voltage; The initial voltage value generation function is set and used to input the model. Converted into initial estimates of node voltage magnitude and phase angle that satisfy grid operation constraints; Input for the model; express Safety constraints;

[0085] Based on the generated initial voltage value The Newton-Raphson method is used to perform iterative optimization of power flow calculation, generating future power flow data of the target power system in the next time period, specifically including the injected power, steady-state voltage value of all nodes, and derived parameters such as line power and loss in this state;

[0086] S4. Repeat step S3 to generate the future power flow of the target power system; specifically including the following steps:

[0087] Based on the technical solution in step S3, the time-series rolling calculation method is adopted, and after several iterations of calculation, the future power flow of the target power system is generated.

[0088] Future-state trends are represented as:

[0089] In the formula This is the future power flow vector for the (t+1)th time period, which includes state variables such as voltage magnitude and voltage phase angle of all nodes in the (t+1)th time period. Calculate the power flow function; The source (excluding conventional thermal power units)-load-storage input vector for the (t+1)th time period contains the photovoltaic output, wind power output, load, electrical energy storage, and thermal energy storage values ​​predicted by the time series probability model for the (t+1)th time period. This represents the steady-state voltage value during the t-th time period. Let be the voltage amplitude at the p-th node during the (t+1)-th time period; Let be the voltage phase angle of the p-th node in the (t+1)-th time period.

[0090] like Figure 2The diagram shows the functional modules of the system of the present invention: The system for generating future power flow in the power system disclosed in this invention includes a data acquisition module, a model building module, a power flow calculation module, and a power flow generation module; the data acquisition module, model building module, power flow calculation module, and power flow generation module are connected in series; the data acquisition module is used to acquire data information of the target power system and upload the data information to the model building module; the model building module is used to construct a source-load-storage output model of the target power system considering source-load uncertainty and energy storage operation status based on the received data information and the acquired data information, and upload the data information to the power flow calculation module; the power flow calculation module is used to generate future power flow data of the target power system in the next time period based on the power flow data of the target power system using a general power flow generation method, and upload the data information to the power flow generation module; the power flow generation module is used to repeat the work of the power flow calculation module based on the received data information to complete the generation of the future power flow of the target power system.

Claims

1. A method for generating future power flow in a power system, comprising the following steps: S1. Obtain data information about the target power system; S2. Based on the data obtained in step S1, construct a source-load-storage output model of the target power system that takes into account source-load uncertainties and energy storage operation status; S3. Based on the power flow data of the target power system, use a general power flow generation method to generate the future power flow data of the target power system in the next time period; S4. Repeat step S3 to generate the future power flow of the target power system.

2. The method for generating future power flow in a power system according to claim 1, characterized in that... Step S1, which involves acquiring data information about the target power system, specifically includes the following steps: Acquire data information from the target power system; The data information includes the target power system's grid structure data, power generation plan data, maintenance plan data, load forecast data, interconnection plan data, and energy storage operation status data.

3. The method for generating future power flow in a power system according to claim 2, characterized in that... Step S2, which involves constructing a source-load-storage output model of the target power system based on the data obtained in step S1, includes the following steps: Based on the historical operating data of photovoltaic power plants in the target power system, and based on the mean and standard deviation data of irradiance, a power output probability model of photovoltaic power plants in the target power system is constructed. Based on the historical power output data of wind farms in the target power system, a power output probability model of wind farms in the target power system is constructed based on the Weibull distribution and the cumulative distribution function CDF. Based on the historical load data of the target power system, a load probability model of the target power system is constructed. Based on historical electrical energy storage data and historical thermal energy storage data of the target power system, and using probability density estimation, we construct probability models for electrical energy storage output and thermal energy storage output of the target power system.

4. The method for generating future power flow in a power system according to claim 3, characterized in that... Step S2 specifically includes the following steps: Construct a probabilistic output model for photovoltaic power plants in the target power system: The following formula is used as the output probability model for the photovoltaic power station in the target power system: In the formula Let be the first shape parameter of the output probability model of the i-th photovoltaic power station; Let be the average solar irradiance of the i-th photovoltaic power station; Let be the standard deviation of the illuminance of the i-th photovoltaic power station; Let be the second shape parameter of the output probability model of the i-th photovoltaic power station; Let be the power output probability density function of the i-th photovoltaic power station at time t; For the Gamma function in parameters The value at; For the Gamma function in parameters The value at; For the Gamma function in parameters The value at; Let be the predicted photovoltaic output of the i-th photovoltaic power station at time t; The maximum photovoltaic output of the i-th photovoltaic power station; Let be the number of photovoltaic panels in the i-th photovoltaic power station; Let be the area of ​​a single photovoltaic panel in the i-th photovoltaic power station; Let be the photovoltaic efficiency of the i-th photovoltaic power station; Construct a power output probability model for wind farms in the target power system: Establish a time-series photovoltaic power output probability model that follows a Weibull distribution: In the formula Let the wind speed of the x-th wind farm at time t be... The wind speed probability density function at that time; Let be the shape parameter of the Weibull distribution for the x-th wind farm; Let be the Weibull distribution scale parameter for the x-th wind farm; Let be the wind speed of the x-th wind farm at time t; The cumulative distribution function (CDF) is used to indicate that the wind speed at the x-th wind farm is less than or equal to The probability of: In the formula Let the wind speed random variable of the x-th wind farm be... The probability density function at time; for Regarding wind speed Integral factors; Express the output power of a wind turbine as a function of wind speed. Relevant linear functions: In the formula Let the wind speed of the x-th wind farm at time t be... A linear function expression for the output power of a wind turbine generator; Let be the cut-in wind speed of the wind turbines in the x-th wind farm; Let x be the rated output power of the x-th wind farm; Let x be the rated output wind speed of the wind turbines in the x-th wind farm; Let be the cut-out wind speed of the wind turbine in the x-th wind farm; Finally, the power output probability model of the wind farm in the target power system is obtained: In the formula Let x be the probability density function when the output of the x-th wind farm is 0. The output of the x-th wind farm in the interval The probability density at that time; Let x be the rated output power of the x-th wind farm; Let x be the output power of the wind turbine in the x-th wind farm; The output of the x-th wind farm is equal to its rated output power. The probability density at that time; Construct a load probability model for the target power system: The following formula is used as the load probability model for the target power system: In the formula Let be the probability density function of the load prediction deviation at time t; Let be the standard deviation of the load forecast, and , This is the average load forecast. It represents the percentage of standard deviation in load output; The load forecast deviation value at time t; This represents the average of the load forecasts; Construct probabilistic models for the output of electrical energy storage and thermal energy storage in the target power system: The following formula is used as the probabilistic model for the output power of electrical energy storage: In the formula Let be the remaining energy storage capacity of the j-th energy storage station at time t; Let be the energy storage self-loss coefficient of the j-th energy storage station; The energy storage charging efficiency of the j-th energy storage station; The energy storage charging power of the j-th energy storage station at time t; Let be the energy storage discharge power of the j-th energy storage station at time t; Let be the energy storage discharge efficiency of the j-th energy storage power station; The time interval between two adjacent scheduling periods; Let be the initial remaining energy storage capacity of the j-th energy storage station; The target remaining power of the j-th energy storage station at the end of the entire scheduling cycle; This represents the lower limit of the remaining energy storage capacity of the j-th energy storage station relative to its total capacity. Let be the energy storage capacity of the j-th energy storage station; This represents the upper limit of the remaining energy storage capacity of the j-th energy storage station relative to its total capacity. Let j be a binary variable indicating whether the j-th energy storage station is in a charging state. This indicates that the j-th energy storage station is in a charging state. This indicates that the j-th energy storage station is not in a charging state; Let j be a binary variable indicating whether the j-th energy storage station is in a discharging state. This indicates that the j-th energy storage station is in a discharging state. This indicates that the j-th energy storage station is not in a discharging state; The following formula is used as the probabilistic model for thermal energy storage output: In the formula For the heat released or absorbed by the l-th thermal energy storage site during time period t; For the first The lower limit of heat release per unit dispatch time for a thermal energy storage site; For the first The upper limit of heat absorption by a thermal energy storage station within a unit dispatch time.

5. The method for generating future power flow in a power system according to claim 4, characterized in that... Step S3, based on the power flow data of the target power system, uses a general power flow generation method to generate future power flow data of the target power system in the next time period, specifically including the following steps: Based on the power flow data of the target power system, a general power flow generation method is used to generate the future power flow data of the target power system in the next time period. In general power flow generation methods, a power flow sample is defined as the combination of all node-injected power and node voltages when the power system is in steady state, denoted as: In the formula The injected active power is for node p, and , Let be the voltage amplitude at node p. Let be the voltage magnitude at node q. Let be the real part of the mutual admittance of nodes p and q. Let p be the voltage phase angle that leads q. Let be the imaginary part of the mutual admittance of nodes p and q; Inject reactive power into node p, and ; Let be the voltage phase angle at node p; The total number of nodes; The initial voltage value is generated using the following formula: In the formula This is the initial value of the generated voltage; The initial voltage value generation function is set and used to input the model. Converted into initial estimates of node voltage magnitude and phase angle that satisfy grid operation constraints; Input for the model; express Safety constraints; Based on the generated initial voltage value The Newton-Raphson method is used to perform iterative optimization of power flow calculation, generating future power flow data of the target power system in the next time period.

6. The method for generating future power flow in a power system according to claim 5, characterized in that... Step S4 involves repeating step S3 to generate the future power flow of the target power system, specifically including the following steps: Based on the technical solution in step S3, the time-series rolling calculation method is adopted, and after several iterations of calculation, the future power flow of the target power system is generated.

7. A system for implementing the future power flow generation method of the power system according to any one of claims 1 to 6, characterized in that... It includes a data acquisition module, a model building module, a power flow calculation module, and a power flow generation module; the data acquisition module, model building module, power flow calculation module, and power flow generation module are connected in series; the data acquisition module is used to acquire data information of the target power system and upload the data information to the model building module; The model building module is used to construct a source-load-storage output model of the target power system that takes into account source-load uncertainties and energy storage operation status based on the received data information and the acquired data information, and upload the data information to the power flow calculation module. The power flow calculation module is used to generate future power flow data of the target power system in the next time period based on the received data information and the power flow data of the target power system, using a general power flow generation method, and then upload the data information to the power flow generation module. The power flow generation module is used to repeat the work of the power flow calculation module based on the received data information to generate the future power flow of the target power system.

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