Step-by-step optimization generation system and method for power grid safety control strategy

Abstract

The invention discloses a power grid safety control strategy step-by-step optimization generation system, which comprises an optimization function solving module for constructing target optimization functions of unit power adjustment amount, gas turbine start-stop, empty charge line loop closing and load transfer demand step by step according to a set power grid safety control strategy rule; the optimal solution is solved in combination with a corresponding security constraint rule; the power grid safety verification module updates a power grid section file according to a solving result, and power grid risk assessment is carried out by utilizing load flow calculation and N-1 calculation methods; and after the verification is passed, the disposal strategy generation module generates disposal strategy text description according to the plan text generation rule. Through step-by-step optimization and a multi-target intelligent algorithm, the problems of single-target optimization, high calculation complexity, lack of systematic integration and the like in the prior art are solved, the power grid safety control strategy can be efficiently and accurately generated, the requirement of real-time operation of the power grid is met, and the safety and reliability of the power grid are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system security control, and specifically refers to a system and method for step-by-step optimization of power grid security control strategies. Background Art

[0002] With the continuous expansion of the power grid scale and the increasing complexity, the difficulty of handling power grid faults has increased accordingly. The safe operation of the power system faces many challenges. Ensuring the safe and stable operation of the power grid under various working conditions is an important goal of power system research and operation. When a fault or abnormal operation occurs in the power grid, it is necessary to generate effective security control strategies in a timely manner to avoid risks such as power flow over-limit and voltage instability, and ensure reliable power supply.

[0003] The existing methods for generating power grid security control strategies are mainly optimized based on a single goal. For example, only the reduction of generation cost or the stability of the power grid is concerned, thus ignoring the comprehensive requirements in multiple aspects such as power grid security, economy, and low carbon, resulting in poor effects of the generated control strategies in practical applications. At the same time, although the existing technologies can handle simple power grid faults to a certain extent and generate corresponding control strategies through preset rules and models, due to the high computational complexity of the existing multi-objective optimization methods when dealing with complex power grid operation constraints and multi-objective balance, it is difficult to meet the requirements of real-time power grid operation. Especially in large-scale power grids, the existing optimization algorithms often require a long calculation time and cannot quickly generate effective control strategies. In addition, the existing technologies lack systematic integration and step-by-step optimization of different control measures, resulting in poor effects of the generated control strategies in practical applications.

[0004] Therefore, inventing a system for step-by-step optimization of power grid security control strategies, which can solve the problems of single-objective optimization, high computational complexity, and lack of systematic integration in the existing technologies, and can efficiently and accurately generate power grid security control strategies to meet the requirements of real-time power grid operation has become an urgent technical problem to be solved. Summary of the Invention

[0005] The purpose of the present invention is to provide a system for step-by-step optimization of power grid security control strategies. The present invention can solve the problems of single-objective optimization, high computational complexity, and lack of systematic integration, and can efficiently and accurately generate power grid security control strategies to meet the requirements of real-time power grid operation.

[0006] To achieve this purpose, a system for step-by-step optimization of power grid security control strategies designed by the present invention includes:

[0007] The optimization function solving module respectively constructs the objective optimization function of the unit power adjustment amount and the first safety constraint rule according to the set grid security control strategy rules, adds the load loss penalty optimization function and the unit adjustment cost optimization function in the objective optimization function of the unit power adjustment amount, and combines the first safety constraint rule to solve the minimum value of the objective optimization function of the unit power adjustment amount to obtain the unit power adjustment amount;

[0008] The grid security verification module updates the grid section file for grid risk calculation according to the unit power adjustment amount to obtain the updated first grid section file, and performs grid risk calculation using the power flow calculation method and the N-1 calculation method of the network analysis module in the intelligent control system based on the updated first grid section file to obtain the risk assessment result based on the unit power adjustment amount;

[0009] The disposal strategy generation module is used to generate the text description of the disposal strategy based on the unit power adjustment amount according to the pre - plan text generation rule when the risk assessment result based on the unit power adjustment amount meets the verification standard set by the current grid.

[0010] Preferably, when the risk assessment result based on the unit power adjustment amount does not meet the verification standard set by the current grid: the optimization function solving module respectively constructs the start - stop target optimization function of the gas turbine and the second safety constraint rule according to the set grid security control strategy rules, adds the load loss penalty optimization function in the start - stop target optimization function of the gas turbine, the unit adjustment cost optimization function in the start - stop target optimization function of the gas turbine, and the start - stop cost optimization function of the gas turbine in the start - stop target optimization function of the gas turbine, and combines the second safety constraint rule to solve the minimum value of the start - stop target optimization function of the gas turbine to obtain the start - stop setting value of the gas turbine;

[0011] The grid security verification module updates the grid section file for grid risk calculation according to the start - stop setting value of the gas turbine to obtain the updated second grid section file, and performs grid risk calculation using the power flow calculation method and the N-1 calculation method of the network analysis module in the intelligent control system based on the updated second grid section file to obtain the risk assessment result based on the start - stop setting value of the gas turbine;

[0012] The disposal strategy generation module is used to generate the text description of the disposal strategy based on the unit power adjustment amount and the start - stop setting value of the gas turbine according to the pre - plan text generation rule when the risk assessment result based on the start - stop setting value of the gas turbine meets the verification standard set by the current grid.

[0013] Preferably, when the risk assessment result based on the start-stop set value of the gas turbine does not meet the verification standard set by the current power grid: the optimization function solving module respectively constructs the closed-loop target optimization function of the no-load charging line and the third safety constraint rule according to the set power grid safety control strategy rules, adds the load loss penalty optimization function in the closed-loop target optimization function of the no-load charging line, the unit adjustment cost optimization function in the closed-loop target optimization function of the no-load charging line, the start-stop cost optimization function of the gas turbine in the closed-loop target optimization function of the no-load charging line, and the closed-loop penalty optimization function of the no-load charging line in the closed-loop target optimization function of the no-load charging line, and solves the minimum value of the closed-loop target optimization function of the no-load charging line in combination with the third safety constraint rule to obtain the closed-loop set value of the no-load charging line;

[0014] The power grid safety verification module updates the power grid section file for power grid risk calculation according to the closed-loop set value of the no-load charging line to obtain the updated third power grid section file, and performs power grid risk calculation according to the updated third power grid section file by using the power flow calculation method and N-1 calculation method of the network analysis module in the intelligent control system to obtain the risk assessment result based on the closed-loop set value of the no-load charging line;

[0015] The disposal strategy generation module is used to generate a text description of the disposal strategy based on the unit power adjustment amount, the start-stop set value of the gas turbine, and the closed-loop set value of the no-load charging line according to the pre-plan text generation rule when the risk assessment result of the closed-loop set value of the no-load charging line meets the verification standard set by the current power grid.

[0016] Preferably, when the risk assessment result based on the closed-loop set value of the no-load charging line does not meet the verification standard set by the current power grid: the optimization function solving module respectively constructs the load transfer demand target optimization function and the fourth safety constraint rule according to the set power grid safety control strategy rules, adds the load loss penalty optimization function in the load transfer demand target optimization function, the unit adjustment cost optimization function in the load transfer demand target optimization function, the start-stop cost optimization function of the gas turbine in the load transfer demand target optimization function, the closed-loop penalty optimization function of the no-load charging line in the load transfer demand target optimization function, and the transfer penalty optimization function in the load transfer demand target optimization function, and solves the minimum value of the closed-loop target optimization function of the no-load charging line in combination with the third safety constraint rule to obtain the load transfer demand set value;

[0017] The power grid safety verification module updates the power grid section file for power grid risk calculation according to the load transfer demand set value to obtain the updated fourth power grid section file, and performs power grid risk calculation according to the updated power grid section file by using the power flow calculation method and N-1 calculation method of the network analysis module in the intelligent control system to obtain the risk assessment result based on the load transfer demand set value;

[0018] The disposal strategy generation module is used to generate a text description of the disposal strategy based on the unit power adjustment amount, the gas turbine start / stop setting value, the closed-loop setting value of the unloaded line, and the load transfer demand setting value according to the pre-plan text generation rule when the risk assessment result of the load transfer demand setting value meets the checking standard set by the current power grid.

[0019] Preferably, the set power grid safety control strategy rules are summarized according to the key points of power grid fault disposal and the set fault disposal rules. The specific method is as follows: According to the fault data, change the network topology structure to generate the network topology after the fault; test the power supply of the equipment that generated the fault data. If the test power supply is successful, no subsequent operation is required; if the test power supply is not successful, adjust the output of key units according to the output range and sensitivity index of thermal and hydropower, and judge whether there is a line over-limit situation and a power supply gap in the system; adjust the start / stop of gas turbine units and judge whether there is a line over-limit situation and a power supply gap in the system; adjust the unloaded line and judge whether there is an unloaded line connection to the node with a power supply gap or a line over-limit situation; perform load transfer and provide the transferable path and power, and judge whether there is a power supply gap at the corresponding node. If so, transfer the load of the node to other transformer nodes; finally, if there is still a load loss, put forward the load emergency control demand and generate a fault response pre-plan through the pre-plan template.

[0020] Advantages of the present invention: The present invention proposes a step-by-step optimization generation system for grid security control strategies. Adopting the idea of step-by-step optimization, the process of generating grid security control strategies is decomposed into multiple steps. In each step, the optimization function and security constraint rules are independently modeled. This step-by-step processing method avoids the high computational complexity brought by simultaneously processing all complex problems, making the optimization problem in each step simpler and capable of being quickly solved to meet the requirements of real-time grid operation. Through the multi-objective optimization function, multiple operating indicators such as grid security, economy, and low carbon are comprehensively considered. In different steps, control measures such as unit adjustment, gas turbine start-stop, charging line loop closing, and load transfer are respectively optimized to ensure that the generated control strategies can not only ensure the safe and stable operation of the grid, but also meet other requirements such as the economy and low carbon of the grid, realizing the comprehensive optimized operation of the grid. By using intelligent algorithms to solve the optimization function, a better solution can be found in a relatively short time. Through the combination of step-by-step optimization and intelligent algorithms, the generated control strategies are more accurate and reliable, can effectively cope with grid faults and abnormal conditions, and reduce the grid operation risk. Through the step-by-step optimization method, various control measures such as unit adjustment, gas turbine start-stop, charging line loop closing, and load transfer are systematically integrated. This systematic integration can effectively cope with complex grid fault scenarios and ensure that the generated control strategies have high adaptability and practicality in actual applications. The present invention solves problems such as single-objective optimization, high computational complexity, and lack of systematic integration in the prior art through step-by-step optimization and multi-objective intelligent algorithms, can efficiently and accurately generate grid security control strategies, meet the requirements of real-time grid operation, and help improve the security and reliability of the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a structural schematic diagram of the present invention;

[0022] Figure 2 is a flow schematic diagram of the present invention;

[0023] Figure 3 is a rule diagram of the security control strategy of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Therefore, the detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0025] The following further elaborates on the present invention with reference to the accompanying drawings and specific embodiments:

[0026] Embodiment 1

[0027] A step-by-step optimization generation system for grid security control strategies, as Figure 1 shown, includes:

[0028] The optimization function solving module constructs an objective optimization function for the unit power adjustment amount and a first security constraint rule according to the set grid security control strategy rules, adds the load loss penalty optimization function in the objective optimization function for the unit power adjustment amount and the unit adjustment cost optimization function in the objective optimization function for the unit power adjustment amount, and solves the minimum value of the objective optimization function for the unit power adjustment amount in combination with the first security constraint rule to obtain the unit power adjustment amount;

[0029] The grid security verification module updates the grid section file for grid risk calculation according to the unit power adjustment amount to obtain the updated first grid section file, and performs grid risk calculation using the power flow calculation method and the N-1 calculation method of the network analysis module in the intelligent control system based on the updated first grid section file to obtain a risk assessment result based on the unit power adjustment amount;

[0030] The disposal strategy generation module is used to generate a text description of the disposal strategy based on the unit power adjustment amount according to the pre-plan text generation rule when the risk assessment result based on the unit power adjustment amount meets the verification standard set for the current grid.

[0031] In the above technical solution, when the risk assessment result based on the unit power adjustment amount does not meet the verification standard set for the current grid:

[0032] The optimization function solving module constructs the start-stop target optimization function of the gas turbine and the second safety constraint rule respectively according to the set grid security control strategy rules, adds the load loss penalty optimization function in the start-stop target optimization function of the gas turbine, the unit adjustment cost optimization function in the start-stop target optimization function of the gas turbine, and the start-stop cost optimization function of the gas turbine in the start-stop target optimization function of the gas turbine, and combines the second safety constraint rule to solve the minimum value of the start-stop target optimization function of the gas turbine to obtain the start-stop setting value of the gas turbine;

[0033] The grid security verification module updates the grid section file for grid risk calculation according to the start-stop setting value of the gas turbine to obtain the updated second grid section file, and performs grid risk calculation using the power flow calculation method and N-1 calculation method of the network analysis module in the intelligent control system according to the updated second grid section file to obtain the risk assessment result based on the start-stop setting value of the gas turbine;

[0034] The disposal strategy generation module is used to generate a text description of the disposal strategy based on the unit power adjustment amount and the start-stop setting value of the gas turbine according to the pre-plan text generation rule when the risk assessment result based on the start-stop setting value of the gas turbine meets the verification standard set by the current grid.

[0035] In the above technical solution, when the risk assessment result based on the start-stop setting value of the gas turbine does not meet the verification standard set by the current grid:

[0036] The optimization function solving module constructs the loop closing target optimization function of the unloaded line and the third safety constraint rule respectively according to the set grid security control strategy rules, adds the load loss penalty optimization function in the loop closing target optimization function of the unloaded line, the unit adjustment cost optimization function in the loop closing target optimization function of the unloaded line, the start-stop cost optimization function of the gas turbine in the loop closing target optimization function of the unloaded line, and the loop closing penalty optimization function of the unloaded line in the loop closing target optimization function of the unloaded line, and combines the third safety constraint rule to solve the minimum value of the loop closing target optimization function of the unloaded line to obtain the loop closing setting value of the unloaded line;

[0037] The grid security verification module updates the grid section file for grid risk calculation according to the loop closing setting value of the unloaded line to obtain the updated third grid section file, and performs grid risk calculation using the power flow calculation method and N-1 calculation method of the network analysis module in the intelligent control system according to the updated third grid section file to obtain the risk assessment result based on the loop closing setting value of the unloaded line;

[0038] The disposal strategy generation module is used to generate a text description of the disposal strategy based on the unit power adjustment amount, the start-stop setting value of the gas turbine, and the loop closing setting value of the unloaded line according to the pre-plan text generation rule when the risk assessment result of the loop closing setting value of the unloaded line meets the verification standard set by the current grid.

[0039] In the above technical solution, when the risk assessment result based on the loop closing setting value of the no-load charging line does not meet the checking standard set by the current power grid:

[0040] The optimization function solving module constructs an optimization function for the load transfer demand target and the fourth safety constraint rule respectively according to the set power grid safety control strategy rules, adds the load loss penalty optimization function in the load transfer demand target optimization function, the unit adjustment cost optimization function in the load transfer demand target optimization function, the gas turbine start-stop cost optimization function in the load transfer demand target optimization function, the loop closing penalty optimization function of the no-load charging line in the load transfer demand target optimization function, and the transfer penalty optimization function in the load transfer demand target optimization function, and combines the third safety constraint rule to solve the minimum value of the loop closing target optimization function of the no-load charging line to obtain the load transfer demand setting value;

[0041] The power grid safety verification module updates the power grid section file used for power grid risk calculation according to the load transfer demand setting value to obtain the updated fourth power grid section file, and performs power grid risk calculation based on the updated power grid section file using the power flow calculation method and the N-1 calculation method of the network analysis module in the intelligent control system to obtain the risk assessment result based on the load transfer demand setting value;

[0042] The disposal strategy generation module is used to generate a text description of the disposal strategy based on the unit power adjustment amount, the gas turbine start-stop setting value, the loop closing setting value of the no-load charging line, and the load transfer demand setting value according to the pre-plan text generation rule when the risk assessment result of the load transfer demand setting value meets the checking standard set by the current power grid.

[0043] In the above technical solution, the power grid section file refers to the basic data file used to describe the power grid operation state, equipment parameters, topological structure, and power flow distribution, etc. in the operation and dispatch of the power system. By analyzing the information in the section file, the safety, stability, and economy of the power grid are evaluated, and the operation mode is adjusted as needed; the format and content of the power grid section file may vary according to the specific requirements of the power grid dispatching agency and the software used, but its core content (such as topological structure, power flow distribution, and safety constraints) is similar in different power grids, so it has a certain generality at the technical level.

[0044] In the above technical solution, the network analysis module in the intelligent control system is an important part of the power grid intelligent control system, mainly used for real-time monitoring, analysis, and evaluation of the operation state of the power grid to ensure the safe and stable operation of the power grid.

[0045] In the above technical solution, the power flow calculation method is one of the tools in power system analysis, which is used to solve the non-linear equations of the power grid, determine the voltage amplitudes, phase angles of each node, and the line power distribution and losses.

[0046] In the above technical solution, the N-1 calculation method is one of the tools in power system security analysis, which is mainly used to evaluate the operating state of the power grid when a certain device (such as a line, transformer) fails, and ensure that the power grid can still operate stably after a single device failure.

[0047] In the above technical solution, the risk assessment result includes the states of all devices in the current power grid, and it can be determined from the result whether there is a risk of over-limit for devices, sections or buses.

[0048] In the above technical solution, the set checking standard is used to judge whether the risk assessment result contains risk situations. If there are power grid risks in the risk assessment result, it does not meet the set checking standard of the current power grid and the check fails; if there are no power grid risks in the risk assessment result, it meets the set checking standard of the current power grid and the check passes.

[0049] In the above technical solution, as Figure 3 shown, through step-by-step optimization, the system can gradually adjust the control strategy according to the severity of the fault, improve the ability to respond to complex faults, further enrich the control means of the system, reduce the impact of faults on the operation of the power grid, realize the closed-loop control from fault detection to final disposal, and improve the integrity and reliability of the system.

[0050] In the above technical solution, the set power grid safety control strategy rules are summarized according to the key points of power grid fault handling and the set fault handling rules. The specific method is as follows:

[0051] According to the fault data, change the network topology structure to generate the network topology after the fault; try to send power to the device that generated the fault data. If the power transmission is successful, no subsequent operations are required; if the power transmission is not successful, adjust the output of key units according to the output range and sensitivity index of thermal and hydro power, and judge whether there are line over-limit situations and power supply gap situations in the system; adjust the start and stop of gas turbine units, and judge whether there are line over-limit situations and power supply gap situations in the system; adjust the no-load charging line, and judge whether there is a no-load charging line connection for the node with a power supply gap or whether there is a line over-limit situation; perform load transfer, provide the transferable path and power, and judge whether there is a power supply gap for the corresponding node. If there is, transfer the load of the node to other transformer nodes; finally, if there is still load loss, put forward the load emergency control demand and generate a fault response plan through the plan template.

[0052] In the above technical solution, the key points for power grid fault handling and the fault handling rules are as follows: In a power system, to reduce the reactive power transmission on the line, the measure of local reactive power compensation is generally adopted. Therefore, the DC power flow can be used to quickly calculate the power flow and meet a small error with the actual power flow. However, considering that the units need to be quickly adjusted and the node voltage level needs to be adjusted when a fault occurs, if the unit sensitivity cost is adopted, it needs to be recalculated when the grid structure changes. Therefore, directly using the AC linear power flow considering line losses, when a fault occurs in the actual power system, the measures that can be taken include adjusting the unit output, starting and stopping the units, regulating the energy storage behavior, closing and opening the loop operation, and so on.

[0053] In the above technical solution, the objective optimization function for the unit power adjustment amount is min(f1, f2), the objective optimization function for the gas turbine start-stop is min(f1, f2, f3), the objective optimization function for the energizing of the unloaded line and closing the loop is min(f1, f2, f3, f4), and the objective optimization function for the load transfer demand is min(f1, f2, f3, f4, f5). Among them, the specific calculation formulas for the load loss penalty optimization function f1, the unit adjustment cost optimization function f2, the gas turbine start-stop cost optimization function f3, the energizing of the unloaded line and closing the loop penalty optimization function f4, and the transfer penalty optimization function f5 are as follows:

[0054]

[0055]

[0056] Among them, c pen represents the load loss penalty cost in the fault handling stage; represents the load power supply gap of node i at time t; L k , respectively represent the sets of the unloaded line and its opposite-side line; T eme is the post-fault handling duration; u g,t represents the unit start-stop status quantity at time t; c g represents the unit active power adjustment cost per unit; ΔP g,t represents the unit active power adjustment amount at time t; s g represents the unit start-stop cost; Δu g,t represents the change in the unit start-stop status quantity at time t; represents the penalty coefficient for the energizing of the unloaded line and the disconnection of the load transfer line. The original state of the unloaded line is 0, and the load transfer line is 1; represents the change in the input status of the corresponding line at time t.

[0057] In the above technical solution, through multi-objective optimization, the optimization functions at each stage are described in detail, the optimization objectives and constraint conditions of the system are clarified, and the system can balance various operation indexes of the power grid at different stages, improving the comprehensive performance of the system.

[0058] In the above technical solution, the first safety constraint condition includes line power flow constraint condition, node power constraint condition, thermal power output and ramp constraint condition, voltage constraint condition, energy storage constraint condition, new energy output constraint condition, line capacity constraint condition; the second safety constraint condition includes line power flow constraint condition, node power constraint condition, thermal power output and ramp constraint condition, voltage constraint condition, energy storage constraint condition, new energy output constraint condition, line capacity constraint condition, start-stop state change constraint condition; the third safety constraint condition includes line power flow constraint condition, node power constraint condition, thermal power output and ramp constraint condition, voltage constraint condition, energy storage constraint condition, new energy output constraint condition, line capacity constraint condition, start-stop state change constraint condition, no-load charging line constraint condition; the fourth safety constraint condition includes line power flow constraint condition, node power constraint condition, thermal power output and ramp constraint condition, voltage constraint condition, energy storage constraint condition, new energy output constraint condition, line capacity constraint condition, start-stop state change constraint condition, no-load charging line constraint condition, load transfer constraint condition.

[0059] In the above technical solution, by describing the safety constraint conditions at each stage in detail, it is ensured that the optimization result of the system meets the safety requirements of power grid operation, and risks such as over-limit can be avoided during the optimization process, ensuring the safe operation of the power grid.

[0060] In the above technical solution, the specific calculation formulas for the line power flow constraint condition, node power constraint condition, thermal power output and ramp constraint condition, voltage constraint condition, energy storage constraint condition, new energy output constraint condition, line capacity constraint condition, start-stop state change constraint condition, no-load charging line constraint condition, and load transfer constraint condition are as follows:

[0061] (1) Line power flow constraint condition

[0062]

[0063] Among them, P ij,t , Q ij,t respectively represent the active and reactive components of the branch power flow at nodes i and j at time t; g ij , b ij respectively represent the capacitive coupling conductance and capacitive coupling susceptance at nodes i and j; v i,t , v j,t respectively represent the voltage amplitudes of nodes i and j at time t; θ ij,t represents the phase angle difference between nodes i and j at time t; the right side of the equation In it, L represents the power grid line, and the quadratic term of θ on the right side of the equation represents the loss term, which is used to improve the accuracy of the AC power flow linearization formula and provide a reference for the subsequent optimization of the unit adjustment amount;

[0064] In extreme disaster scenarios, there is a risk of failure in the main grid lines. After a serious failure occurs, load transfer and restoration of the fault path are often carried out after the disaster. Therefore, the large M method is used to improve the above constraints:

[0065]

[0066] Among them, M is a very large constant, which is used to ensure that when the line i, j fails, the voltage amplitudes and phase angles of the two end nodes will not be forced to be equal; represents a 0-1 variable. In this chapter, it is first considered that in the extreme scenario of the main grid, the switching state of the line i, j does not change during the entire day-ahead scheduling period. "1" represents normal operation, and vice versa represents a fault;

[0067] In the actual power grid and according to the characteristics of the particle swarm intelligence algorithm for dealing with equality constraints, we can process the power flow constraints and load loss penalties:

[0068]

[0069] Among them, α P represents the active power coefficient, represents the post-fault power of nodes i, j at time t, represents the positive power adjustment amount of nodes i, j at time t, represents the negative power adjustment amount of nodes i, j at time t, S ij,max represents the maximum apparent power capacity between nodes i, j. In this way, it can not only meet the short-term over-limit of the power flow to a certain extent in practice, but also reduce the over-limit degree through the penalty coefficient M. Therefore, the load loss penalty can also be extended to the over-limit penalty;

[0070] (2) Node power constraint conditions

[0071]

[0072] Among them, P ii,t , Q ii,t respectively represent the active and reactive power components of the grounding branch of node i at time t; v i,t represents the voltage amplitude of node i at time t; g ii , b ii respectively represent the capacitive coupling conductance and capacitive coupling susceptance of node i; B represents the set of all electrical energy storage systems; P g,i,t represents the output of the thermal power unit at node i at time t; P d,i,t , Qd,i,t respectively represent the active and reactive power demands of node i at time t; is expressed as the discharging power of the electric energy storage at node i at time t; is expressed as the charging power of the electric energy storage at node i at time t; P w,i,t is expressed as the wind power at node i at time t; represents the reactive power slack term of node i at time t. Since reactive power can be adjusted locally by switching capacitors and reactors, a slack term is added here to prevent non - satisfaction of the equality constraint, and the upper limit of reactive power adjustment Q is set d,max ;

[0073] (3) Thermal power output and ramp - rate constraint conditions

[0074]

[0075] Among them, represent the upper and lower limits of the active power output of the thermal power unit; u g,t represents the unit start - stop status quantity at time t; represent the upper and lower limits of the reactive power output of the thermal power unit. In practice, at a rate of 3% of the installed capacity per minute, in order to reduce the constraint conditions, when formulating the pre - adjustment measures of the fault handling plan in advance, the adjustment range of the entire unit output can meet the requirements within the fault handling time. Therefore, the ramp - rate constraint is not considered in the pre - adjustment model;

[0076] (4) Voltage constraint conditions

[0077] v min ≤v i,t ≤v max

[0078] θ min ≤θ ij,t ≤θ max

[0079] Among them, v max 、v min respectively represent the upper and lower limits of the node voltage amplitude; θ max 、θ min respectively represent the upper and lower limits of the node voltage phase - angle difference;

[0080] (5) Energy storage constraint conditions

[0081]

[0082] Among them, respectively represent the charging and discharging powers of the electric energy storage at time t; are 0 - 1 variables, respectively representing the charging and discharging status bits of the electric energy storage at time t; respectively represent the maximum charging and discharging powers of the electric energy storage; Eb,0 Indicates the initial capacity of the electrical energy storage, E b,t Indicates the capacity of the electrical energy storage at time t; η b Indicates the working efficiency of the electrical energy storage, assuming the same charging and discharging efficiency; Δt represents the time interval; E b,max Indicates the maximum capacity of the electrical energy storage; the associated equality constraint represents that the initial and final storage capacities are the same within the scheduling period T;

[0083] (6) New energy output constraint conditions

[0084]

[0085] Among them, P p,t Indicates the photovoltaic output at time t; Indicates the predicted photovoltaic output for the day ahead at time t; Indicates the amount of curtailed light at time t; P w,t Indicates the wind power output at time t; Indicates the predicted wind power output for the day ahead at time t; Indicates the amount of curtailed wind at time t;

[0086] (7) Line capacity constraint conditions

[0087] (P ij,t ) 2 +(Q ij,t ) 2 ≤(S ij,max ) 2

[0088] Among them, P ij,t Indicates the active power component of the power flow flowing through line i, j at time t; Q ij,t Indicates the reactive power component of the power flow flowing through line i, j at time t; S ij,max Indicates the upper limit of the line transmission capacity. For the convenience of linear solution, the above formula can be simplified to:

[0089]

[0090] (8) No-load charging line constraint conditions

[0091] The no-load charging line set refers to the lines in the power grid where one end of the line breaker is closed and the other end is open, and the lines are in a standby state:

[0092]

[0093] Among them, Indicates the input state of line i, j at time t;

[0094] (9) Load transfer constraint conditions

[0095] The load transfer line set refers to the lines with positive active power being transmitted at the other end of the transformer nodes below 220 kV connected to the closed end of the no-load charging line in order to reduce the solution difficulty:

[0096]

[0097] Among them, represents the input state of line i and j at time t; solving multiple objective functions to obtain the optimal solution or the set of optimal solutions, that is, obtaining the optimal security control adjustment strategy;

[0098] (10) Start-stop state change constraint conditions

[0099] Δu g,t =|u g,t -u g,t-1 | ∑Δu g,t ≤1

[0100] Among them, Δu g,t represents the change amount of the unit start-stop state quantity at time t.

[0101] In the above technical solution, the solution of the unit power adjustment amount, the start-stop setting value of the gas turbine, the closed-loop setting value of the no-load charging line, and the load transfer demand setting value can adopt multi-objective particle swarm optimization algorithm, non-dominated sorting genetic algorithm, ant colony algorithm, etc. Among them, using the multi-objective particle swarm optimization (Multi-objective Particle Swarm Optimization, MOPSO) algorithm, based on the original single-objective particle swarm, the specific method to obtain the Pareto optimal solution of the objective function is as follows:

[0102] The Pareto optimal solution refers to a solution in the decision space where no other solution can simultaneously improve the values of all objective functions; the Pareto optimal solution set (Pareto Optimal Set) refers to the set of all Pareto optimal solutions; this set is usually called the Pareto front (Pareto Front) or Pareto boundary (Pareto Boundary); the Pareto optimal front is the projection of the Pareto optimal solution set in the objective space, that is, the curve or surface formed in the objective function value space, and this front represents all possible solutions that trade off multiple objectives; the dominance relationship is one of the important conditions for judging the Pareto optimal solution. In multi-objective problems, the quality of solutions is judged using the dominance relationship of solutions. For a multi-objective problem min F(x)=(f1(x),...f s (x)) T , there are two solutions x1 and x2, satisfying:

[0103]

[0104] Since x1 is not worse than x2 in each objective and is better than x2 in a certain objective, such a relationship is called x1 dominates x2; otherwise, there is no dominance relationship between x1 and x2.

[0105] Multi-objective particle swarm optimization is modified based on the original single-objective particle swarm. The optimal solution of multi-objective optimization is no longer unique. An external archive is added to store the obtained non-dominated solutions. When updating the individual historical optimum and the population global optimum, instead of just comparing sizes, the fitness between each objective is used to determine the dominance relationship. When updating the velocity vector, the global optimum position is selected from the external archive. To avoid the multi-objective particle swarm algorithm from converging to the local optimum solution, other measures need to be taken to ensure the diversity of solutions.

[0106] In the above technical solution, the solution of the unit power adjustment amount, the start-stop setting value of the gas turbine, the loop closing setting value of the no-load charging line, and the load transfer demand setting value can be solved by using the multi-objective particle swarm optimization algorithm. During the solution process, the crowding entropy method is adopted, considering the distribution entropy and crowding distance of the particle swarm. The specific method is as follows:

[0107]

[0108] c ij = dl ij + du ij

[0109] E ij = -[dl ij log2(dl ij / c ij ) + du ij log2(du ij / c ij )] / (f j max - f j min )

[0110] Among them, m is the number of objective functions; dl ij and du ij respectively represent the distances between the j-th objective function of the i-th particle and the adjacent particles below and above; c ij is an intermediate variable, representing the addition of the other two variables, without a separate meaning; E ij is the distribution entropy of the i-th particle along the j-th objective function in the objective space; and respectively represent the maximum and minimum values of the particle swarm on the j-th objective function;

[0111] It can be seen from this that the smaller the crowding entropy, the denser the solutions around the solution. Since there are no lower or upper adjacent solutions in a certain direction for the boundary solutions of each objective function, it is necessary to define their crowding entropy values separately. Noticing the particularity of the boundary solutions, they should not be deleted during the update of the external archive, and the surrounding areas of the boundary solutions are more worthy of search. Therefore, for the boundary solutions of each objective function, an infinite crowding entropy is directly assigned to meet the expected goals;

[0112] To ensure population diversity, crossover and mutation operations are introduced during the population iteration process. Particles with relatively high fitness under multiple objectives are selected for single-point mutation operations. The dimension of the particle is randomly selected and fragments are exchanged to regenerate new particles.

[0113] In the above technical solution, through an efficient optimization algorithm, the solution process of the system is clarified, enabling the system to find a better solution within a relatively short time and improving the practicality and reliability of the system.

[0114] In the above technical solution, the selected crossover operation is single-point crossover. Particles with relatively high fitness under multiple objectives are selected for single-point mutation operations. The dimension of the particle is randomly selected and fragments are exchanged to regenerate new particles; the selected mutation operation in this paper is Gaussian mutation. Points are projected at sparse positions in the external archive to generate a thickening set, which is added to the update process of the archive. The specific operations are as follows:

[0115] The crowding entropy method is used to identify sparse positions. Note that due to the assignment of infinite crowding entropy to the boundary solutions, the boundary solutions must participate in the mutation point projection operation;

[0116] Centered on the solution at the selected sparse position, points are projected according to the Gaussian distribution with variance σ in its corresponding decision space. Here, the variance σ is taken as one-sixth of the width of each dimension of the decision space, and the randomly generated solutions are added to the thickening point set. For example:

[0117] x=(x1,x2)∈D, D = [-12,0]×[0,6], then

[0118] Combining the improvements in these two sections, the update method of the external archive is modified. Based on the position update of the particle swarm and the original update of the external archive, a mutation point set and a Gaussian thickening point set are added to improve the search ability of the particle swarm.

[0119] In the above technical solution, the specific method for generating the text description of the disposal strategy according to the pre-plan text generation rules is as follows:

[0120] The compilation of power system pre-plans should follow the terminology specifications, equipment dispatching naming, and relevant dispatching regulations defined in GB / T 2900.50, GB / T 33590.2, and DL / T 861, and specifically includes the following parts:

[0121] (1) Pre - plan number: Record the pre - plan time in the form of year - month - day (YYYYMMDD), record according to the region division, and list the generated pre - plan serial number;

[0122] (2) Pre - plan name: Fault information + supplementary other fault information;

[0123] (3) Pre - plan summary: Compilation time, compiler, participating dispatching department, changes in operation mode before and after the fault, main risk warnings after the fault;

[0124] (4) Operation mode before the fault: Key lines with greater fault impact, key power flow sections, power generation and load levels, system reserve conditions, etc.;

[0125] (5) Operation mode after the fault: Key lines with greater fault impact, key power flow sections, power generation and load levels, system reserve conditions, abnormal operation states caused by the fault (such as power flow section over - limit, load loss, system voltage and frequency over - limit, insufficient system reserve, etc.);

[0126] (6) Fault handling measures: Compile in sequence according to the degree of emergency state faced by the power grid using the unified national standard terms as described in the pre - plan generation part, and clarify the operating unit and specific measures; among them, the specific fault handling measures can be divided into three stages: emergency control stage, mode adjustment stage, and fault recovery stage;

[0127] (7) Information notification: Write the copy - to units of this pre - plan, convey downward, and report the fault information upward.

[0128] In the above - mentioned technical solution, an example of a fault pre - plan for generating a disposal strategy text description according to the pre - plan text generation rules is shown in Table 1 below:

[0129]

[0130]

[0131] Table 1 Example of Fault Pre - plan

[0132] In the above - mentioned technical solution, by following national standards and industry specifications such as GB / T 2900.50, GB / T 33590.2, and DL / T 861, the standardization and normalization of pre - plan compilation are ensured, making the pre - plan universal and consistent in different regions and different power grids, ensuring that the power grid can take measures quickly and accurately when a fault occurs, reducing the impact of the fault on the safe operation of the power grid, thereby improving the overall safety of the power grid and facilitating quick understanding and implementation in actual operation.

[0133] Example 2

[0134] A method for step-by-step optimization generation of power grid security control strategies, as Figure 2 shown, summarizes the security control strategy rules according to the key points of power grid fault handling and fault handling rules; constructs an optimization function and constraint rules according to the security control strategy rules; uses a multi-objective particle swarm algorithm for solution to obtain a security control strategy; calls the function of the network analysis model to check the security control strategy; generates a text description of the disposal strategy according to the pre-plan text generation rules.

[0135] The power grid security control strategy generation method includes the following steps:

[0136] The optimization function solving module respectively constructs an objective optimization function for the unit power adjustment amount and a first security constraint rule according to the set power grid security control strategy rules, adds the load loss penalty optimization function and the unit adjustment cost optimization function in the objective optimization function for the unit power adjustment amount, and combines the first security constraint rule to solve the minimum value of the objective optimization function for the unit power adjustment amount to obtain the unit power adjustment amount;

[0137] The power grid security verification module updates the power grid section file for power grid risk calculation according to the unit power adjustment amount to obtain the updated first power grid section file, and performs power grid risk calculation based on the updated first power grid section file by using the power flow calculation method and N-1 calculation method of the network analysis module in the intelligent control system to obtain a risk assessment result based on the unit power adjustment amount;

[0138] The disposal strategy generation module is used to generate a text description of the disposal strategy based on the unit power adjustment amount according to the pre-plan text generation rules when the risk assessment result based on the unit power adjustment amount meets the verification standard set for the current power grid.

[0139] Embodiment 3

[0140] A computer program product includes a computer program, and when the computer program is executed by a processor, it implements the steps of the method described in Embodiment 2.

[0141] The content not detailed in this specification belongs to the prior art well-known to those skilled in the art.

[0142] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0143] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate a system for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0144] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction system that implements the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0145] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: after reading the present invention, those skilled in the art can still make various changes, modifications, or equivalent replacements to the specific implementation manners of the invention, but these changes, modifications, or equivalent replacements are all within the protection scope of the pending claims of the invention.

Claims

1. A step-by-step optimization generation system for grid security control strategies, characterized in that, It includes: The optimization function solving module respectively constructs the objective optimization function of the unit power adjustment amount and the first safety constraint rule according to the set grid security control strategy rules, adds the load loss penalty optimization function and the unit adjustment cost optimization function in the objective optimization function of the unit power adjustment amount, and combines the first safety constraint rule to solve the minimum value of the objective optimization function of the unit power adjustment amount to obtain the unit power adjustment amount; The grid security verification module updates the grid section file for grid risk calculation according to the unit power adjustment amount to obtain the updated first grid section file, and uses the power flow calculation method and the N-1 calculation method of the network analysis module in the intelligent control system to perform grid risk calculation based on the updated first grid section file to obtain the risk assessment result based on the unit power adjustment amount; The disposal strategy generation module is used to generate the text description of the disposal strategy based on the unit power adjustment amount according to the rule of the pre-plan text when the risk assessment result based on the unit power adjustment amount meets the verification standard set by the current grid.

2. The step-by-step optimization generation system for a power grid security control strategy according to claim 1, characterized in that: When the risk assessment result based on the unit power adjustment amount does not meet the verification standard set by the current grid: The optimization function solving module respectively constructs the start-stop target optimization function of the gas turbine and the second safety constraint rule according to the set grid security control strategy rules, adds the load loss penalty optimization function, the unit adjustment cost optimization function and the start-stop cost optimization function of the gas turbine in the start-stop target optimization function of the gas turbine, and combines the second safety constraint rule to solve the minimum value of the start-stop target optimization function of the gas turbine to obtain the start-stop setting value of the gas turbine; The grid security verification module updates the grid section file for grid risk calculation according to the start-stop setting value of the gas turbine to obtain the updated second grid section file, and uses the power flow calculation method and the N-1 calculation method of the network analysis module in the intelligent control system to perform grid risk calculation based on the updated second grid section file to obtain the risk assessment result based on the start-stop setting value of the gas turbine; The disposal strategy generation module is used to generate the text description of the disposal strategy based on the unit power adjustment amount and the start-stop setting value of the gas turbine according to the rule of the pre-plan text when the risk assessment result based on the start-stop setting value of the gas turbine meets the verification standard set by the current grid.

3. A step-by-step optimization generation system for a power grid security control strategy according to claim 2, characterized in that: When the risk assessment result based on the start-stop setting value of the gas turbine does not meet the verification standard set by the current grid: The optimization function solving module respectively constructs the closed-loop target optimization function of the no-load charging line and the third safety constraint rule according to the set grid security control strategy rules, adds the load loss penalty optimization function, the unit adjustment cost optimization function, the start-stop cost optimization function of the gas turbine and the closed-loop penalty optimization function of the no-load charging line in the closed-loop target optimization function of the no-load charging line, and combines the third safety constraint rule to solve the minimum value of the closed-loop target optimization function of the no-load charging line to obtain the closed-loop setting value of the no-load charging line; The power grid safety verification module updates the power grid section file for power grid risk calculation according to the inrush line closing setting value to obtain the updated third power grid section file, and performs power grid risk calculation according to the updated third power grid section file by using the power flow calculation method and N-1 calculation method of the network analysis module in the intelligent control system to obtain the risk assessment result based on the inrush line closing setting value; The disposal strategy generation module is used to generate a text description of the disposal strategy based on the unit power adjustment amount, gas turbine start-stop setting value and inrush line closing setting value according to the pre-plan text generation rule when the risk assessment result of the inrush line closing setting value meets the verification standard set by the current power grid.

4. A step-by-step optimization generation system for a power grid security control strategy according to claim 3, characterized in that: When the risk assessment result based on the inrush line closing setting value does not meet the verification standard set by the current power grid: The optimization function solving module constructs a load transfer demand target optimization function and a fourth safety constraint rule respectively according to the set power grid safety control strategy rules, adds the load loss penalty optimization function, unit adjustment cost optimization function, gas turbine start-stop cost optimization function, inrush line closing penalty optimization function and transfer penalty optimization function in the load transfer demand target optimization function, and solves the minimum value of the inrush line closing target optimization function in combination with the third safety constraint rule to obtain the load transfer demand setting value; The power grid safety verification module updates the power grid section file for power grid risk calculation according to the load transfer demand setting value to obtain the updated fourth power grid section file, and performs power grid risk calculation according to the updated power grid section file by using the power flow calculation method and N-1 calculation method of the network analysis module in the intelligent control system to obtain the risk assessment result based on the load transfer demand setting value; The disposal strategy generation module is used to generate a text description of the disposal strategy based on the unit power adjustment amount, gas turbine start-stop setting value, inrush line closing setting value and load transfer demand setting value according to the pre-plan text generation rule when the risk assessment result of the load transfer demand setting value meets the verification standard set by the current power grid.

5. The step-by-step optimization generation system for a power grid safety control strategy according to claim 4, wherein: The set power grid safety control strategy rules are summarized according to the key points of power grid fault disposal and the set fault disposal rules, and the specific method is: According to the fault data, change the network topology structure to generate the network topology after the fault; try to send power to the equipment that generates the fault data. If the power transmission is successful, no subsequent operation is required; otherwise, continue with the subsequent operations; adjust the output of key units according to the output range and sensitivity index of thermal and hydropower, and judge whether there is a line over-limit situation and a power supply gap in the system; adjust the start and stop of gas turbine units, and judge whether there is a line over-limit situation and a power supply gap in the system; adjust the inrush line, and judge whether there is an inrush line connection at the node with a power supply gap or whether there is a line over-limit situation; Perform load transfer, provide transferable paths and power, and determine whether there is a power supply gap at the corresponding node. If there is, transfer the load of the node to other transformer nodes; finally, if there is still load loss, put forward the load emergency control requirements and generate a fault response plan through the plan template.

6. The step-by-step optimization generation system for a power grid security control strategy according to claim 4, characterized in that: The objective optimization function for the unit power adjustment amount is min(f1, f2), the objective optimization function for the start / stop of the gas turbine is min(f1, f2, f3), the objective optimization function for the loop closing of the no-load charging line is min(f1, f2, f3, f4), and the objective optimization function for the load transfer demand is min(f1, f2, f3, f4, f5). Among them, the specific calculation formulas for the load loss penalty optimization function f1, the unit adjustment cost optimization function f2, the gas turbine start / stop cost optimization function f3, the no-load charging line loop closing penalty optimization function f4, and the transfer penalty optimization function f5 are as follows: Among them, c pen represents the penalty cost of load loss during the fault handling stage; represents the load power supply gap of node i at time t; L k and are the sets of no-load charging lines and their opposite-side lines respectively; T eme is the post-fault handling duration; u g,t represents the unit start-stop state quantity of the unit at time t; c g represents the unit active power adjustment cost; ΔP g,t represents the active power adjustment quantity of the unit at time t; s g represents the unit start-stop cost; Δu g,t represents the change in the unit start-stop state quantity at time t; represents the penalty coefficients for the no-load charging line to be put into operation and the load transfer line to be disconnected. The original state of the no-load charging line is 0, and that of the load transfer line is 1; represents the change in the input state of the corresponding line at time t.

7. A step-by-step optimization generation system for a power grid security control strategy according to claim 4, characterized in that: The first safety constraint conditions include line power flow constraint conditions, node power constraint conditions, thermal power output and ramp constraint conditions, voltage constraint conditions, energy storage constraint conditions, new energy output constraint conditions, and line capacity constraint conditions; the second safety constraint conditions include line power flow constraint conditions, node power constraint conditions, thermal power output and ramp constraint conditions, voltage constraint conditions, energy storage constraint conditions, new energy output constraint conditions, line capacity constraint conditions, and start / stop state change constraint conditions; the third safety constraint conditions include line power flow constraint conditions, node power constraint conditions, thermal power output and ramp constraint conditions, voltage constraint conditions, energy storage constraint conditions, new energy output constraint conditions, line capacity constraint conditions, start / stop state change constraint conditions, and no-load charging line constraint conditions; The fourth safety constraint conditions include line power flow constraint conditions, node power constraint conditions, thermal power output and ramp constraint conditions, voltage constraint conditions, energy storage constraint conditions, new energy output constraint conditions, line capacity constraint conditions, start / stop state change constraint conditions, no-load charging line constraint conditions, and load transfer constraint conditions.

8. A step-by-step optimization generation system for a power grid security control strategy according to claim 4, characterized in that: The solutions for the unit power adjustment amount, the start / stop setting value of the gas turbine, the loop closing setting value of the no-load charging line, and the load transfer demand setting value can be solved using a multi-objective particle swarm optimization algorithm. During the solution process, the crowding entropy method is used to consider the distribution entropy and crowding distance of the particle swarm. The specific method is as follows: c ij = dl ij + du ij E ij = -[dl ij log2(dl ij / c ij ) + du ij log2(du ij / c ij )] / (f j max -f j min ) where m is the number of objective functions; dl ij and du ij represent the distances between the j-th objective function of the i-th particle and the adjacent particles below and above respectively; c ij is an intermediate variable, representing the addition of two other variables and having no separate meaning; E ij is the distribution entropy of the i-th particle along the j-th objective function in the objective space; and represent the maximum and minimum values of the particle swarm on the j-th objective function respectively; During the population iteration process, crossover and mutation operations are introduced. Select particles with relatively large fitness under multiple objectives for single-point mutation operations. Randomly select the dimension of the particle and exchange segments to generate new particles.

9. A method for step-by-step optimization and generation of a power grid security control strategy, characterized in that, It includes the following steps: According to the set grid security control strategy rules, respectively construct the objective optimization function for the unit power adjustment amount and the first safety constraint rule. Add the load loss penalty optimization function and the unit adjustment cost optimization function in the objective optimization function for the unit power adjustment amount, and combine the first safety constraint rule to solve the minimum value of the objective optimization function for the unit power adjustment amount to obtain the unit power adjustment amount; Update the power grid section file for power grid risk calculation according to the unit power adjustment amount to obtain the updated first power grid section file, and perform power grid risk calculation based on the updated first power grid section file using the power flow calculation method and N-1 calculation method of the network analysis module in the intelligent control system to obtain the risk assessment result based on the unit power adjustment amount; When the risk assessment result based on the unit power adjustment amount meets the verification standard set by the current power grid, generate the text description of the disposal strategy based on the unit power adjustment amount according to the pre-plan text generation rule.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it realizes the steps of the method described in claim 9.

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