A source network load storage rapid response control method and system

Abstract

The application discloses a source network load storage fast response control method and system, relates to the technical field of power grid control, and comprises the following steps: collecting real-time data of power sources, power grids, loads and energy storage systems, and determining a correction value according to error terms of the power sources, the power grids, the loads and the energy storage systems; corresponding prediction models are used to predict the predicted states of the power sources, the power grids, the loads and the energy storage systems at a first time node, and the actual states of the power sources, the power grids, the loads and the energy storage systems at the first time node are monitored; whether the deviation between the actual state and the predicted state of the first time node is within a reasonable range is compared through the correction value, and a deviation amount is obtained; a particle swarm algorithm is formulated according to the deviation amount, the optimal solution of the control of the power sources, the power grids, the loads and the energy storage systems is calculated based on the particle swarm algorithm, and the power sources, the power grids, the loads and the energy storage systems at a second time node are controlled. The response speed of the power sources, the power grids, the loads and the energy storage systems is improved, and the safe and stable operation of the system is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid control, and more particularly to a source-grid-load-storage fast response control method and system. BACKGROUND

[0002] The source-grid-load-storage fast response control is a control strategy for coping with transient changes and emergency situations in the power system. It comprehensively applies real-time data acquisition, prediction models, distributed control, energy storage technology, communication technology, emergency state stability control and other background technologies. Through real-time monitoring of various system parameters by high-precision sensors, the future state is predicted using prediction models, real-time control and optimization are performed using distributed and intelligent algorithms, energy storage technology is used for rapid adjustment, communication technology ensures rapid exchange of information between system components, and emergency state stability control copes with possible system instability. This comprehensive technical solution aims to improve the fast response capability, robustness and reliability of the power system.

[0003] In the prior art, the optimal solution of the response of the source-grid-load-storage is difficult to find, or needs to be calculated for a long time, resulting in a long response time and poor fast response performance.

[0004] Therefore, how to improve the fast response performance is a technical problem to be solved at present. SUMMARY

[0005] The present application provides a source-grid-load-storage fast response control method to solve the technical problem of slow response speed in the prior art. The method comprises:

[0006] Collecting real-time data of the power source, power grid, load and energy storage system, and determining a correction value according to the error term of the power source, power grid, load and energy storage system;

[0007] Monitoring the actual state of the first time node of the power source, power grid, load and energy storage system by a plurality of prediction models corresponding to the predicted state of the first time node of the power source, power grid, load and energy storage system;

[0008] Obtaining a deviation amount by comparing whether the deviation of the actual state and the predicted state of the first time node is within a reasonable range through the correction value;

[0009] Formulating a particle swarm algorithm according to the deviation amount, calculating the control optimal solution of the power source, power grid, load and energy storage system based on the particle swarm algorithm, and controlling the power source, power grid, load and energy storage system of the second time node.

[0010] In some embodiments of the present application, the correction value is determined according to the error term of the power source, power grid, load and energy storage system, comprising:

[0011] The error term includes a quality error term and a time delay error term.

[0012] determining each quality error term and time delay error term of the power supply, power grid, load and energy storage system;

[0013] calculating error influence quantity of each quality error term and error influence quantity of time delay error term, and ranking the error influence quantities according to their sizes;

[0014] determining a first interval size according to the range size of the error influence quantity of the quality error term, and determining a second interval size according to the range size of the error influence quantity of the time delay error term;

[0015] dividing the error influence quantity range of the quality error term based on the first interval size to obtain multiple intervals, and assigning different weights to each interval;

[0016] dividing the error influence quantity range of the time delay error term based on the second interval size to obtain multiple intervals, and assigning different weights to each interval;

[0017] determining interval influence quantity of each interval, and comparing the interval influence quantity with the corresponding ranking to determine whether the interval influence quantity meets an interval influence quantity threshold;

[0018] retaining the quality error term and the time delay error term that meet the interval influence quantity threshold, and calculating the error influence quantity sum of the retained quality error term and time delay error term;

[0019] determining a correction value according to the error influence quantity sum of the quality error term and the error influence quantity sum of the time delay error term.

[0020] In some embodiments of the present application, the actual state of the first time node of the power supply, power grid, load and energy storage system is monitored, including:

[0021] defining the power supply performance state based on the real-time data of the power supply system;

[0022] defining the power grid stability state and load state based on the real-time data of the power grid system;

[0023] defining the power demand state based on the real-time data of the load system;

[0024] defining the power storage state and power usage state based on the real-time data of the energy storage system.

[0025] In some embodiments of the present application, the deviation quantity is obtained by comparing whether the deviation between the actual state and the predicted state of the first time node is within a reasonable range through the correction value, including:

[0026] Quantify power supply performance state, power grid stability state and load state, power demand state, power storage state and power usage state, respectively calculate the deviation part of the actual state and the predicted state of these states, and correspondingly obtain the first deviation, the second deviation, the third deviation and the fourth deviation;

[0027] According to the proof value, determine the deviation interval corresponding to the first deviation, the second deviation, the third deviation and the fourth deviation;

[0028] The first deviation, the second deviation, the third deviation and the fourth deviation are all within the corresponding deviation interval, then the deviation between the actual state and the predicted state is within a reasonable range, otherwise, the deviation between the actual state and the predicted state is not within a reasonable range.

[0029] In some embodiments of the application, by comparing the deviation between the actual state and the predicted state of the first time node through the proof value whether it is within a reasonable range, the deviation amount is obtained, and the method further comprises:

[0030] If the deviation between the actual state and the predicted state of the first time node is within a reasonable range, the deviation amount is 0;

[0031] Otherwise, according to the deviation between the first deviation, the second deviation, the third deviation and the fourth deviation and the corresponding deviation interval, the total deviation is determined, and the deviation amount is obtained according to the total deviation and the conversion coefficient.

[0032] In some embodiments of the application, a particle swarm algorithm is formulated according to the deviation amount, comprising:

[0033] Determine the common control target of the power supply, the power grid, the load and the energy storage system;

[0034] Define the variables of the power supply, the power grid, the load and the energy storage system, and initialize the particle swarm;

[0035] Set the fitness function according to the common control target, and update the particle position and velocity, and determine the control optimal solution through multiple iterations.

[0036] In some embodiments of the application, the common control target of the power supply, the power grid, the load and the energy storage system is determined, comprising:

[0037] Determine all control targets, and mark the control targets with a response speed correlation degree exceeding a first threshold as first echelon targets;

[0038] Mark the control targets with a response speed correlation degree exceeding a second threshold and not exceeding the first threshold as second echelon targets;

[0039] Take the first echelon targets and the second echelon targets as the common control targets.

[0040] In some embodiments of the present application, the fitness function is set according to the common control target, including:

[0041] The constraint conditions of the power supply, power grid, load and energy storage system are set, and the constraint penalty term of the first echelon target and the second echelon target is formulated according to the constraint conditions;

[0042] The fitness function is established according to the constraint penalty term;

[0043]

[0044] Wherein, F(x i , y j ) is the fitness function, β1 is the conversion coefficient corresponding to the first echelon target, n is the number of the first echelon target, α i is the first weight corresponding to the i-th first echelon target, x i is the size of the i-th first echelon target, c1 is the constraint penalty term of the first echelon target, β2 is the conversion coefficient corresponding to the second echelon target, m is the number of the first echelon target, γ j is the second weight corresponding to the j-th second echelon target, y j is the size of the j-th second echelon target, and c2 is the constraint penalty term of the second echelon target.

[0045] Correspondingly, the present application also provides a source-grid-load-energy storage fast response control system, comprising:

[0046] The first module is configured to collect real-time data of the power supply, power grid, load and energy storage system, and determine the correction value according to the error term of the power supply, power grid, load and energy storage system;

[0047] The second module is configured to predict the state of the power supply, power grid, load and energy storage system at the first time node through a plurality of prediction models, and monitor the actual state of the power supply, power grid, load and energy storage system at the first time node;

[0048] The third module is configured to compare the deviation between the actual state and the predicted state of the first time node through the correction value, and obtain the deviation amount;

[0049] The fourth module is configured to formulate a particle swarm algorithm according to the deviation amount, calculate the control optimal solution of the power supply, power grid, load and energy storage system based on the particle swarm algorithm, and control the power supply, power grid, load and energy storage system at the second time node.

[0050] By applying the above technical solution, real-time data of the power supply, power grid, load and energy storage system are collected, and the correction value is determined according to the error term of the power supply, power grid, load and energy storage system; the first time node of the power supply, power grid, load and energy storage system is monitored by a plurality of prediction models corresponding to the predicted state of the power supply, power grid, load and energy storage system; whether the deviation between the actual state and the predicted state of the first time node is within a reasonable range is obtained by comparing the correction value, and the deviation amount is obtained; the particle swarm algorithm is formulated according to the deviation amount, and the control optimal solution of the power supply, power grid, load and energy storage system is calculated based on the particle swarm algorithm, so as to control the power supply, power grid, load and energy storage system of the second time node. In this way, the response speed of the power supply, power grid, load and energy storage system is improved, and the safe and stable operation of the system is ensured. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0052] Figure 1 A flowchart of a source network load storage fast response control method according to an embodiment of the present application is shown;

[0053] Figure 2 A structure diagram of a source network load storage fast response control system according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0055] The present application provides a source network load storage fast response control method, as shown in Figure 1 The method comprises the following steps:

[0056] In step S101, real-time data of the power supply, power grid, load and energy storage system are collected, and the correction value is determined according to the error term of the power supply, power grid, load and energy storage system.

[0057] In this embodiment, there is a complex relationship between the power supply, power grid, load, and energy storage system. They work together to achieve balance and stability in the power system. Here is a description of the relationship between the four systems and their working interaction principles:

[0058] Power supply system:

[0059] The power supply system includes traditional generators, renewable energy sources (such as wind power and photovoltaic power), and energy storage systems.

[0060] The main task of the power supply system is to provide electricity, but its output power may be affected by factors such as weather changes and fuel supply.

[0061] Power grid system:

[0062] The power grid is a comprehensive system that connects power supply, load, and energy storage systems, including transmission lines, substations, etc.

[0063] The power grid transmits electricity from the power supply to the load through transmission lines and maintains the stability of voltage and frequency in the entire system.

[0064] Load system:

[0065] The load system represents the power demand of users, including various electrical equipment and facilities.

[0066] The load system has real-time and dynamic power demand for the power grid, which may change with user behavior, seasonal changes, and other factors.

[0067] Energy storage system:

[0068] The energy storage system can store electricity and release it when needed to balance the difference between power supply and load.

[0069] The charge and discharge state of the energy storage system directly affects the power balance of the system, which can provide additional power or absorb excess power in a short period of time.

[0070] Working interaction principles:

[0071] Power supply and power grid:

[0072] The power supply system delivers the generated electricity to the load through the power grid. The power supply needs to be adjusted according to the voltage and frequency of the power grid to meet the load demand.

[0073] Power grid and load:

[0074] The power grid maintains stable operation by adjusting voltage and frequency and distributes electricity generated by the power supply to the load. The power grid needs to adjust transmission lines, substations, and other equipment in real time according to changes in the load.

[0075] Load and power grid:

[0076] The change of load directly affects the working state of power and grid. When the load is high, the power needs to provide more power, and the grid needs to adjust the transmission line. When the load is low, the power may need to adjust the output power, and the grid needs to adapt to the reduced power demand.

[0077] Energy storage system and power grid / load:

[0078] The energy storage system can balance the power difference between the power and the load by charging or discharging. In the case of large load fluctuation or large power fluctuation, the energy storage system can provide fast adjustment capability.

[0079] In this embodiment, the determination of the correction value according to the error terms of the power, the grid, the load and the energy storage system means that the threshold value of the subsequent prediction and the actual comparison is determined according to the quality error and the time delay error of the data.

[0080] In some embodiments of the present application, the determination of the correction value according to the error terms of the power, the grid, the load and the energy storage system includes:

[0081] The error terms include quality error terms and time delay error terms;

[0082] Determine each quality error term and time delay error term of the power, the grid, the load and the energy storage system;

[0083] Calculate the error influence amount of each quality error term and the error influence amount of each time delay error term, and sort the error influence amounts according to the size;

[0084] Determine the first interval size according to the size of the error influence amount range of the quality error term, and determine the second interval size according to the size of the error influence amount range of the time delay error term;

[0085] Divide the error influence amount range of the quality error term based on the first interval size to obtain multiple intervals, and assign different weights to each interval;

[0086] Divide the error influence amount range of the time delay error term based on the second interval size to obtain multiple intervals, and assign different weights to each interval;

[0087] Determine the interval influence amount of each interval, and compare the interval influence amount with the corresponding ranking to determine whether it meets the interval influence amount threshold;

[0088] Retain the quality error terms and time delay error terms that meet the interval influence amount threshold, and calculate the error influence amount sum of the retained quality error terms and time delay error terms;

[0089] Determine the correction value according to the error influence amount sum of the quality error term and the error influence amount sum of the time delay error term.

[0090] In this embodiment, the quality error term, for example, a sensor, is used to measure each system parameter, and its accuracy and precision directly affect the quality of real-time data. The time delay error term, for example, communication delay, in a distributed system, data acquisition may involve communication between multiple nodes. Communication delay is a factor in data transmission process, which may cause real-time data lag and desynchronization.

[0091] In this embodiment, the ranking order of error influence quantity is different, and there are different interval influence quantity thresholds corresponding to them.

[0092] In this embodiment, the correction value is determined according to the sum of the error influence quantity of the quality error term and the sum of the error influence quantity of the time delay error term. Two different sums correspond to different correction values.

[0093] Step S102, through the corresponding prediction model of multiple prediction models, the predicted state of the first time node of the power supply, power grid, load and energy storage system is predicted, and the actual state of the first time node of the power supply, power grid, load and energy storage system is monitored.

[0094] In some embodiments of the present application, monitoring the actual state of the first time node of the power supply, power grid, load and energy storage system comprises:

[0095] Defining the power supply performance state based on the real-time data of the power supply system;

[0096] Defining the power grid stability state and load state based on the real-time data of the power grid system;

[0097] Defining the power demand state based on the real-time data of the load system;

[0098] Defining the power storage state and power usage state based on the real-time data of the energy storage system.

[0099] In this embodiment, the actual state and the predicted state are of the same category, only different in quantity.

[0100] In this embodiment, defining the state of the power supply, power grid, load and energy storage system is a key step in system modeling, which reflects the operation of the system at a certain time. Each system component has a set of key parameters, and the combination of these parameters constitutes the state of the system. The following:

[0101] Power supply state:

[0102] The power supply state can include the output power, voltage, current and other parameters of the power supply. These parameters reflect the current working state and performance of the power supply.

[0103] Power grid state:

[0104] The grid state includes information such as the voltage, frequency, and load of the transmission line of the power grid. These parameters reflect the stability and load condition of the power grid.

[0105] The load state includes parameters such as the current and power demand of the load. These parameters describe the actual power demand of the load on the system.

[0106] The energy storage system state may include parameters such as the state of charge, state of discharge, and capacity of the energy storage device. These parameters reflect the current energy storage capacity and utilization of the energy storage system.

[0107] The energy storage system state may include parameters such as the state of charge, state of discharge, and capacity of the energy storage device. These parameters reflect the current energy storage capacity and utilization of the energy storage system.

[0108] The energy storage system state may include parameters such as the state of charge, state of discharge, and capacity of the energy storage device. These parameters reflect the current energy storage capacity and utilization of the energy storage system.

[0109] In some embodiments of the present application, the deviation amount is obtained by comparing whether the deviation between the actual state and the predicted state of the first time node is within a reasonable range through the calibration value.

[0110] In some embodiments of the present application, the deviation amount is obtained by comparing whether the deviation between the actual state and the predicted state of the first time node is within a reasonable range through the calibration value.

[0111] The power supply performance state, the grid stability state, the load state, the energy demand state, the energy storage state, and the energy usage state are quantified, and the deviation between the actual state and the predicted state of each of these states is calculated to obtain a first deviation, a second deviation, a third deviation, and a fourth deviation.

[0112] The first deviation, the second deviation, the third deviation, and the fourth deviation are determined to correspond to a deviation interval according to the calibration value.

[0113] If the first deviation, the second deviation, the third deviation, and the fourth deviation are all within the corresponding deviation interval, then the deviation between the actual state and the predicted state is within a reasonable range, otherwise, the deviation between the actual state and the predicted state is not within a reasonable range.

[0114] In some embodiments of the present application, the deviation amount is obtained by comparing whether the deviation between the actual state and the predicted state of the first time node is within a reasonable range through the calibration value.

[0115] If the deviation between the actual state and the predicted state of the first time node is within a reasonable range, then the deviation amount is 0.

[0116] Otherwise, a total deviation is determined according to the deviation between the first deviation, the second deviation, the third deviation, and the fourth deviation and the corresponding deviation interval, and the deviation amount is obtained according to the total deviation and the conversion coefficient.

[0117] Step S104: Formulate the particle swarm optimization algorithm based on the deviation, calculate the optimal control solution of the power supply, power grid, load and energy storage system based on the particle swarm optimization algorithm, and control the power supply, power grid, load and energy storage system at the second time node.

[0118] In this embodiment, the particle swarm optimization algorithm includes the following contents:

[0119] Determine the optimization objective of the problem:

[0120] Determine the optimization objective of the system, such as maximizing system efficiency, minimizing cost, reducing carbon emissions, etc. In this scheme, the main optimization target of fast response is selected, which should be consistent with the nature and operation demand of the system.

[0121] Define system variables:

[0122] Determine the system variables involved in optimization, which may include power output, load demand, energy storage system charge and discharge state, etc. These variables constitute the search space of the particle swarm optimization algorithm.

[0123] Initialize the particle swarm:

[0124] Randomly generate a group of particles, each particle representing a possible solution, i.e. a potential system state. Each particle has a position (variable value) and speed.

[0125] Calculate the fitness function:

[0126] Define the fitness function to evaluate the degree of compliance of each particle to the system target. The fitness function should be consistent with the selected optimization target.

[0127] Update particle position and speed:

[0128] Update the position and speed of each particle according to the fitness function. This involves adjusting the particle according to its own experience and global optimal solution.

[0129] Iterative optimization:

[0130] Through multiple iterations, the position and speed of the particles are constantly updated, gradually approaching the optimal solution. Each iteration considers individual experience and group experience to guide the search process.

[0131] Stopping criterion:

[0132] Set the stopping criterion, such as reaching the maximum number of iterations, the value of the objective function being close enough to the optimal solution, etc. When the stopping criterion is met, stop the iteration.

[0133] Output the optimal solution:

[0134] According to the result of the algorithm running, the optimal system state, i.e., the optimal solution, is output. These optimal solutions correspond to the adjustment strategies of the power supply, power grid, load and energy storage system to achieve the optimization target.

[0135] In some embodiments of the present application, the particle swarm algorithm is formulated according to the deviation amount, including:

[0136] determining the common control target of the power supply, power grid, load and energy storage system;

[0137] defining the variables of the power supply, power grid, load and energy storage system, and initializing the particle swarm;

[0138] setting the fitness function according to the common control target, and updating the particle position and velocity, and determining the control optimal solution through multiple iterations.

[0139] In some embodiments of the present application, the common control target of the power supply, power grid, load and energy storage system is determined, including:

[0140] determining all control targets, and recording the control targets with a correlation degree with response speed exceeding a first threshold as first echelon targets;

[0141] recording the control targets with a correlation degree with response speed exceeding a second threshold and not exceeding the first threshold as second echelon targets;

[0142] taking the first echelon targets and the second echelon targets as the common control target.

[0143] In some embodiments of the present application, the fitness function is set according to the common control target, including:

[0144] setting the constraint conditions of the power supply, power grid, load and energy storage system, and formulating the constraint penalty term of the first echelon targets and the second echelon targets according to the constraint conditions;

[0145] establishing the fitness function according to the constraint penalty term;

[0146]

[0147] wherein, F(x i , y j ) is the fitness function, β1 is the conversion coefficient corresponding to the first echelon target, n is the number of the first echelon targets, α i is the first weight corresponding to the i-th first echelon target, x i is the size of the i-th first echelon target, c1 is the constraint penalty term of the first echelon target, β2 is the conversion coefficient corresponding to the second echelon target, m is the number of the first echelon targets, γ j is the second weight corresponding to the j-th second echelon target, and y jA jth second echelon target size, c2 is a constraint penalty term of the second echelon target.

[0148] By applying the above technical solutions, real-time data of the power source, power grid, load and energy storage system are collected, and a correction value is determined according to error terms of the power source, power grid, load and energy storage system; a prediction state of the power source, power grid, load and energy storage system at a first time node is predicted through a plurality of prediction models, and an actual state of the power source, power grid, load and energy storage system at the first time node is monitored; whether the deviation of the actual state and the prediction state of the first time node is within a reasonable range is compared through the correction value, and a deviation amount is obtained; a particle swarm algorithm is formulated according to the deviation amount, and the optimal solution of the control of the power source, power grid, load and energy storage system is calculated based on the particle swarm algorithm, so as to control the power source, power grid, load and energy storage system at a second time node. In this way, the response speed of the power source, power grid, load and energy storage system is improved, and the safe and stable operation of the system is ensured.

[0149] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by hardware, or can be implemented by means of software and a necessary general hardware platform. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.), and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present application.

[0150] In order to further illustrate the technical idea of the present application, the technical solutions of the present application will be described in combination with specific application scenarios.

[0151] Correspondingly, the present application also provides a source-grid-load-storage fast response control system, as shown in Figure 2 , which comprises:

[0152] The first module 201 is configured to collect real-time data of the power source, power grid, load and energy storage system, and determine a correction value according to error terms of the power source, power grid, load and energy storage system;

[0153] The second module 202 is configured to predict a prediction state of the power source, power grid, load and energy storage system at a first time node through a plurality of prediction models, and monitor an actual state of the power source, power grid, load and energy storage system at the first time node;

[0154] The third module 203 is configured to compare whether the deviation of the actual state and the prediction state of the first time node is within a reasonable range through the correction value, and obtain a deviation amount;

[0155] The fourth module 204 is configured to formulate a particle swarm algorithm according to the deviation amount, calculate the optimal solution of the control of the power supply, the power grid, the load and the energy storage system based on the particle swarm algorithm, and control the power supply, the power grid, the load and the energy storage system at the second time node.

[0156] Those skilled in the art can understand that the modules in the system in the implementation scenario can be distributed in the system in the implementation scenario according to the description of the implementation scenario, or can be changed to be located in one or more systems different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0157] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art can understand that the technical solutions recorded in the foregoing examples can still be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not drive the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A fast response control method for source-grid-load-storage systems, characterized in that, include: Collect real-time data from power sources, power grids, loads, and energy storage systems, and determine calibration values ​​based on the error terms of power sources, power grids, loads, and energy storage systems; By using multiple prediction models to predict the predicted state of power sources, power grids, loads, and energy storage systems at the first time point, the actual state of power sources, power grids, loads, and energy storage systems at the first time point is monitored. The deviation is obtained by comparing the actual state and the predicted state at the first time point with the calibration value to see if the deviation is within a reasonable range. The particle swarm optimization algorithm is formulated based on the deviation. The optimal control solution for the power source, grid, load and energy storage system is calculated based on the particle swarm optimization algorithm, and then the power source, grid, load and energy storage system are controlled at the second time point. The calibration value is determined based on the error terms of the power source, power grid, load, and energy storage system, including: The error term includes a quality error term and a time delay error term; Identify each quality error term and time delay error term for the power source, grid, load, and energy storage system; Calculate the error impact of each quality error term and the error impact of the time delay error term, and sort the error impacts by magnitude; The size of the first interval is determined based on the range of the error impact of the quality error term, and the size of the second interval is determined based on the range of the error impact of the time delay error term. Based on the size of the first interval, the range of the error impact of the quality error term is divided into multiple intervals, and each interval is assigned a different weight. Based on the size of the second interval, the range of the error impact of the delay error term is divided into multiple intervals, and each interval is assigned a different weight. Determine the interval influence for each interval, and compare the interval influence with the corresponding ranking to determine whether it meets the interval influence threshold; The quality error items and time delay error items that meet the interval influence threshold are retained, and the total error influence of the retained quality error items and time delay error items is calculated. The calibration value is determined based on the sum of the error impact of the quality error term and the sum of the error impact of the time delay error term. The particle swarm optimization algorithm is formulated based on the deviation, including: Determine the common control objectives for power sources, power grids, loads, and energy storage systems; Define the variables for the power source, power grid, load, and energy storage system, and initialize the particle swarm optimization. The fitness function is set according to the common control objective, and the particle position and velocity are updated. The optimal control solution is determined through multiple iterations.

2. The source-grid-load-storage fast response control method as described in claim 1, characterized in that, Monitor the actual status of power sources, power grids, loads, and energy storage systems at the first point in time, including: Define power performance status based on real-time data from the power system; Define the power grid stability state and load state based on real-time data from the power grid system; Defining power demand status based on real-time data from the load system; The energy storage status and energy usage status are defined based on real-time data from the energy storage system.

3. The source-grid-load-storage fast response control method as described in claim 2, characterized in that, By comparing the calibration values ​​with the actual state at the first time point, the deviation is determined to be within a reasonable range, including: The power supply performance status, grid stability status and load status, power demand status, power storage status and power usage status are quantified, and the deviations between the actual status and the predicted status of these states are calculated respectively to obtain the first deviation, the second deviation, the third deviation and the fourth deviation. Determine the deviation ranges corresponding to the first, second, third, and fourth deviations based on the calibration values; If the first, second, third, and fourth deviations are all within their respective deviation ranges, then the deviation between the actual state and the predicted state is within a reasonable range; otherwise, the deviation between the actual state and the predicted state is not within a reasonable range.

4. The source-grid-load-storage fast response control method as described in claim 1, characterized in that, The deviation is obtained by comparing the actual state and the predicted state at the first time point with the calibration value to see if the deviation is within a reasonable range. This also includes: If the deviation between the actual state and the predicted state at the first time point is within a reasonable range, then the deviation is 0. Otherwise, the total deviation is determined based on the deviations of the first, second, third, and fourth deviations from their respective deviation intervals, and the deviation amount is obtained based on the total deviation and the conversion coefficient.

5. The source-grid-load-storage fast response control method as described in claim 1, characterized in that, Determine the common control objectives for power sources, power grids, loads, and energy storage systems, including: Identify all control objectives, and designate the control objectives whose correlation with response speed exceeds a first threshold as first-tier objectives; Control targets whose correlation with response speed exceeds the second threshold but does not exceed the first threshold are categorized as second-tier targets. The first-tier and second-tier targets are treated as common control targets.

6. The source-grid-load-storage fast response control method as described in claim 1, characterized in that, The fitness function is set according to the common control objective, including: Set constraints for power sources, power grids, loads, and energy storage systems, and formulate constraint penalties for first-tier and second-tier objectives based on these constraints; Establish a fitness function based on the constraint penalty terms; ； in, For the fitness function, Here, represents the conversion coefficient corresponding to the first-tier targets, and n represents the number of first-tier targets. Let the first weight be the first weight corresponding to the i-th first-tier target. Size of the i-th first-tier target Constraints and penalties for targets in the first tier. Let be the conversion coefficient corresponding to the second-tier targets, and m be the number of first-tier targets. The second weight corresponding to the j-th second-tier target Size of the j-th second-tier target Constraints and penalties for second-tier targets.

7. A fast response control system for power generation, grid, load, and storage, characterized in that, include: The first module is configured to collect real-time data from the power source, grid, load, and energy storage system, and determine the calibration value based on the error terms of the power source, grid, load, and energy storage system. The second module is configured to monitor the actual status of the power source, grid, load and energy storage system at the first time point by using multiple prediction models to predict the predicted status of the power source, grid, load and energy storage system at the first time point. The third module is configured to compare the deviation between the actual state and the predicted state at the first time point with the calibration value to determine whether the deviation is within a reasonable range, and to obtain the deviation amount. The fourth module is configured to formulate a particle swarm optimization algorithm based on the deviation, and calculate the optimal control solution for the power supply, grid, load and energy storage system based on the particle swarm optimization algorithm, so as to control the power supply, grid, load and energy storage system at the second time point. The calibration value is determined based on the error terms of the power source, power grid, load, and energy storage system, including: The error term includes a quality error term and a time delay error term; Identify each quality error term and time delay error term for the power source, grid, load, and energy storage system; Calculate the error impact of each quality error term and the error impact of the time delay error term, and sort the error impacts by magnitude; The size of the first interval is determined based on the range of the error impact of the quality error term, and the size of the second interval is determined based on the range of the error impact of the time delay error term. Based on the size of the first interval, the range of the error impact of the quality error term is divided into multiple intervals, and each interval is assigned a different weight. Based on the size of the second interval, the range of the error impact of the delay error term is divided into multiple intervals, and each interval is assigned a different weight. Determine the interval influence for each interval, and compare the interval influence with the corresponding ranking to determine whether it meets the interval influence threshold; The quality error items and time delay error items that meet the interval influence threshold are retained, and the total error influence of the retained quality error items and time delay error items is calculated. The calibration value is determined based on the sum of the error impact of the quality error term and the sum of the error impact of the time delay error term. The particle swarm optimization algorithm is formulated based on the deviation, including: Determine the common control objectives for power sources, power grids, loads, and energy storage systems; Define the variables for the power source, power grid, load, and energy storage system, and initialize the particle swarm optimization. The fitness function is set according to the common control objective, and the particle position and velocity are updated. The optimal control solution is determined through multiple iterations.

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