Reliability evaluation method and system for high-proportion new energy power grid considering operation risk of agc system

Abstract

The method and system for reliability evaluation of high-proportion new energy power grid considering AGC system operation risk, utilize the fault probability of AGC function, the fault probability of dispatching instruction, the fault probability of line, the fault probability of new energy unit and the fault probability of conventional unit to establish a power grid fault probability model; iteratively solve the power flow model, power flow constraint condition and operation risk constraint of AGC control unit of high-proportion new energy power grid under different fault scenarios to obtain the minimum load reduction of power grid under each fault scenario; based on the minimum load reduction of power grid under all random faults, calculate the variance coefficient of reliability index expectation of high-proportion new energy power grid; when the variance coefficient is not greater than 0.01, take the power shortage expectation value as the reliability evaluation result of high-proportion new energy power grid considering AGC system operation risk, and quantitatively evaluate the reliability of high-proportion new energy power grid under the superposition of AGC system operation risk and power grid equipment operation risk.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new power system, in particular, to a high-proportion new energy power grid reliability evaluation method and system considering automatic generation control (AGC) system operation risk. BACKGROUND

[0002] The control system of power dispatching is of great significance to the safe and stable operation of the power grid, and defects and risks of the control system will directly lead to events such as power shortage of the power grid, large fluctuation of tie-line power, frequency anomaly, DC blocking, voltage collapse, etc., which seriously affect the safe and stable operation of the physical power grid. In the actual operation of the power grid, there have been many accidents caused by the failure of AGC control link of automatic control system and human error leading to incorrect system command value. Firstly, due to the defects of AGC system software anti-misoperation strategy, the AGC of a regional power grid continuously reduces the unit output for more than one hour, which forces another connected regional power grid to increase the output, resulting in large fluctuation of ultra-high voltage line power flow and continuous frequency lower than 50 Hz, which brings great safety risk to the power grid operation; secondly, due to the defects of AGC anti-misoperation strategy and lack of necessary control means in software upgrading, the AGC sends incorrect control instructions to the new energy power station of the provincial power grid during the AGC software upgrading process, and the new energy power generation output drops by more than 1 million kilowatts, and the frequency of the regional AC power grid where the provincial power grid is located drops to 49.75 Hz; thirdly, due to operation errors and unreasonable AGC regulation limit setting, the new energy power generation output suddenly increases by more than 2.5 million kilowatts, and the power grid frequency is out of limit.

[0003] The above accidents show that AGC system not only supports the safe and stable operation of the power grid, but also introduces new uncertain factors. The power grid equipment and information system interact with each other, and the faults occurring in a single system will be coupled and spread, which seriously threatens the safety of the power system, so it is urgent to carry out reliability evaluation research of high-proportion new energy power grid considering the risk model of automatic power control.

[0004] In the prior art, the reliability evaluation of high-proportion new energy power grid mainly focuses on the operation risk of power grid equipment (such as power grid, new energy, energy storage, generator, etc.), and it is difficult to reveal the operation risk of various faults of AGC and the superimposed risk induced by the interaction between the operation risk of AGC and the operation risk of power grid equipment, especially the research on risk modeling of AGC and reliability evaluation of combined faults of AGC operation risk and power grid equipment operation risk is relatively less. SUMMARY

[0005] To solve the problems in the prior art, the present application provides a high-proportion new energy power grid reliability evaluation method and system considering AGC system operation risk, proposes an AGC system fault operation risk modeling method, considers the superimposed risk induced by the interaction of AGC system operation risk and power grid equipment operation risk, quantitatively evaluates the degree of reliability decline of a high-proportion new energy power grid possibly caused by the superimposed risk induced by the interaction of AGC system operation risk and power grid equipment operation risk, and provides a technical basis for evaluating the reliability of a high-proportion new energy power grid.

[0006] The present application adopts the following technical solutions.

[0007] The present application provides a high-proportion new energy power grid reliability evaluation method considering AGC system operation risk, which includes the following steps:

[0008] The failure rate and repair rate of AGC function in AGC control units are obtained, an AGC function risk model of AGC control units is established to determine the failure probability of AGC function, the deviation between the power regulation command issued value and the power regulation amount sent by the AGC system to the AGC control units is obtained, a scheduling instruction risk model of AGC control units is established based on the probability distribution of the deviation and the power regulation amount to determine the failure probability of scheduling instruction, the failure rate and repair rate of lines are obtained, a line operation risk model is established to determine the failure probability of lines, the failure rate and repair rate of new energy units are obtained, a new energy unit operation risk model is established to determine the failure probability of new energy units, the failure rate and repair rate of conventional units are obtained, a conventional unit operation risk model is established to determine the failure probability of conventional units, and a power grid failure probability model is established by using the failure probability of AGC function, the failure probability of scheduling instruction, the failure probability of lines, the failure probability of new energy units, and the failure probability of conventional units.

[0009] Different failure scenarios of a high-proportion new energy power grid are generated by using a sampling method, different failure scenarios generated are mapped into power grid failure events based on the power grid failure probability model to obtain state auxiliary variables of AGC control units, operation risk constraints of AGC control units under different failure scenarios are established based on the state auxiliary variables of AGC control units, different failure scenarios generated are mapped into power grid failure events to obtain state auxiliary variables of equipment, and a power flow model and power flow constraints of a high-proportion new energy power grid considering AGC system operation risk are constructed based on the state auxiliary variables of equipment.

[0010] The power flow model, power flow constraint condition and operation risk constraint of the AGC control unit under different fault scenarios are solved iteratively to obtain the minimum load reduction of the power grid under each fault scenario; based on the minimum load reduction of the power grid under all random faults, the variance coefficient of the reliability index expectation of the high-proportion new energy power grid is calculated; when the variance coefficient β≤0.01, the power shortage expectation is taken as the reliability evaluation result of the high-proportion new energy power grid considering the operation risk of the AGC system.

[0011] Preferably, the AGC function risk model of the AGC control unit comprises a failure probability and a normal probability of the AGC function, which satisfy the following relationships respectively:

[0012]

[0013]

[0014] In the formula, and respectively represent the failure probability and the normal probability of the AGC function of the AGC control unit k, and respectively represent the failure rate and the repair rate of the AGC function of the AGC control unit k, represents the state auxiliary variable of the AGC control unit k, 0 represents a failure state, and 1 represents an operation state, k∈n AGC , n AGC represents an AGC control unit set, and → represents a mapping relationship between probability and state;

[0015] Based on the AGC function risk model of the AGC control unit, the failure probability of the AGC function is determined as

[0016]

[0017] Preferably, the power regulation command value issued value sent by the AGC system to the AGC control unit and the power regulation amount of the AGC control unit are obtained, and based on the power regulation state of the AGC control unit, the deviation of the power regulation command value issued value and the power regulation amount is determined, which satisfies the following relationship:

[0018]

[0019] In the formula, represents the power regulation command value issued value sent by the AGC system to the AGC control unit k from time τ=1 to time τ=t, represents the power regulation amount of the AGC control unit k, represents the deviation of the power regulation command value issued value and the power regulation amount sent by the AGC system to the AGC control unit k, denotes the power linear adjustment rate of AGC controlled unit k at time τ, denotes the power adjustment state identification variable of AGC controlled unit k at time τ, 1 means the unit increases power output, 0 means the unit runs steadily, and -1 means the unit reduces power output.

[0020] Preferably, the deviation obeys a probability distribution, satisfying the following relationship:

[0021]

[0022] wherein, is the standard deviation of the normal distribution;

[0023] Using the probability distribution of the deviation and the power adjustment amount, a dispatch instruction risk model is established, including the failure probability and normal probability of the dispatch instruction, satisfying the following relationship respectively:

[0024]

[0025]

[0026] wherein, and respectively denote the failure probability and normal probability of the dispatch instruction of AGC controlled unit k;

[0027] Based on the dispatch instruction risk model of the AGC controlled unit, the failure probability of the dispatch instruction is determined as

[0028] Preferably, the line operation risk model includes the failure probability and normal probability of the line, satisfying the following relationship respectively:

[0029]

[0030]

[0031] wherein, and respectively denote the failure probability and normal probability of the line ll', and respectively denote the failure rate and repair rate of the line ll', denotes that the line ll' belongs to the line set l B , denotes the state auxiliary variable of the line ll', 0 means in the failure state, and 1 means in the running state, → denotes the mapping relationship between the probability and the state;

[0032] Based on the line operation risk model, the failure probability of the line is determined as

[0033] Preferably, the new energy unit operation risk model includes a failure probability and a normal probability of the new energy unit, and satisfies the following relationship respectively:

[0034]

[0035]

[0036] In the formula, and respectively represent the failure probability and the normal probability of the new energy unit i, and respectively represent the failure rate and the repair rate of the new energy unit i, represents that the new energy unit i belongs to the new energy unit set n RE , represents the state auxiliary variable of the new energy unit i, 0 represents a failure state, and 1 represents a running state, and → represents the mapping relationship between the probability and the state.

[0037] Based on the new energy unit operation risk model, the failure probability of the new energy unit is determined as

[0038] Preferably, the conventional unit operation risk model includes a failure probability and a normal probability of the conventional unit, and satisfies the following relationship respectively:

[0039]

[0040]

[0041] In the formula, and respectively represent the failure probability and the normal probability of the conventional unit j, and respectively represent the failure rate and the repair rate of the conventional unit j, represents that the conventional unit j belongs to the conventional unit set n G , represents the state auxiliary variable of the conventional unit j, 0 represents a failure state, and 1 represents a running state, and → represents the mapping relationship between the probability and the state.

[0042] Based on the conventional unit operation risk model, the failure probability of the conventional unit is determined as

[0043] Preferably, the power grid failure probability model satisfies the following relationship:

[0044]

[0045] In the formula, δsys represents the probability of grid failure, represents the probability of failure of the AGC function of the AGC controlled unit k, represents the probability of failure of the dispatching instruction of the AGC controlled unit k, represents the probability of failure of the transmission line ll', represents the probability of failure of the new energy unit i, represents the probability of failure of the conventional unit j, → represents the mapping relationship between probability and state, x sys is a grid failure event, represents the state auxiliary variable of the AGC controlled unit k, 0 represents a failure state, and 1 represents a running state, represents the deviation between the issued value of the power regulation command value sent by the AGC system to the AGC controlled unit k and the power regulation amount, represents the state auxiliary variable of the transmission line ll', 0 represents a failure state, and 1 represents a running state, represents the state auxiliary variable of the new energy unit i, 0 represents a failure state, and 1 represents a running state, represents the state auxiliary variable of the conventional unit j, 0 represents a failure state, and 1 represents a running state;

[0046] wherein k ∈ n AGC , n AGC represents the AGC controlled unit set, i ∈ n RE , n RE represents the new energy unit set, j ∈ n G , n G represents the conventional unit set, and satisfies n AGC = n RE ∪ n G .

[0047] Preferably, the operation risk constraint of the AGC controlled unit under a single failure scenario satisfies the following relationship:

[0048]

[0049]

[0050] In the formula, and respectively represent the lower limit and the upper limit of the adjustable capacity of the AGC controlled unit k, and respectively represent the minimum limit output and the maximum limit output of the AGC controlled unit k, represents the power linear regulation rate of the AGC controlled unit k at time τ, is the power adjustment state indicator variable of AGC controlled unit k at time τ, and is 1 for increasing the output of the unit, is 0 for steady operation of the unit, and is -1 for reducing the output of the unit, is the power adjustment command value sent by the AGC system to AGC controlled unit k from time τ = 1 to time τ = t.

[0051] Preferably, the power flow model of the high-proportion new energy power grid comprises:

[0052] 1) a system power balance model satisfying the following relationship:

[0053]

[0054] In the formula, is the power of the conventional unit j at time τ, is the power of the new energy unit i at time τ, is the power of the AGC controlled unit k at time τ, is the power of the load node m at time τ, is the load reduction of the load node m at time τ;

[0055] 2) a node power balance constraint satisfying the following relationship:

[0056]

[0057] In the formula, and respectively represent the power flowing from node l' to node l and the power flowing from node l to node l' at time τ, l' ∈ B l represents that node l' belongs to the node set directly connected with node l.

[0058] Preferably, the power flow constraint condition of the high-proportion new energy power grid comprises:

[0059] 1) a line power flow constraint satisfying the following relationship:

[0060]

[0061] In the formula, B ll′ represents the mutual admittance of line l', δ l,τ and δ l′,τ respectively represent the voltage phase angle of node l and node l' at time τ, represents the state auxiliary variable of line l', and is 0 for a fault state and is 1 for an operating state, and M represents an arbitrary large (but not infinite) positive number;

[0062] 2) a line transmission capacity constraint satisfying the following relationship:

[0063]

[0064] In the formula, This indicates the maximum transmission capacity of line ll′;

[0065] 3) The output constraints of conventional generating units satisfy the following relationship:

[0066]

[0067] In the formula, and These represent the maximum and minimum output of conventional unit j, respectively;

[0068] 4) Load reduction constraints satisfy the following relationship:

[0069]

[0070] 5) The output constraints of new energy units satisfy the following relationship:

[0071]

[0072] In the formula, This represents the maximum output of the new energy unit i at time τ;

[0073] 6) Node phase angle constraints satisfy the following relationship:

[0074] -θ max ≤θ τ ≤θ max

[0075]

[0076] In the formula, θ max and θ τ Let represent the maximum voltage phase angle of the grid node and the voltage phase angle at time τ, respectively. This represents the reference value of the voltage phase angle at time τ.

[0077] Preferably, the minimum load reduction of a high-proportion renewable energy power grid under a single fault scenario satisfies the following relationship:

[0078]

[0079] In the formula, For the power grid fault event corresponding to fault scenario e The minimum load reduction resulting from Let m be the load reduction at load node m at time τ. M represents the load reduction amount for all load nodes during the period from time τ=1 to time τ=t under fault scenario e.L is the total number of load nodes.

[0080] Preferably, the reliability index expected coefficient of variance satisfies the following relationship:

[0081]

[0082] wherein β is the coefficient of variance of the power shortage expectation value, E is the total number of fault scenarios, is the power shortage expectation value,

[0083] The application further provides a high-proportion new energy power grid reliability evaluation system considering AGC system operation risk, comprising:

[0084] The power grid fault probability model establishing module is used to acquire the failure rate and repair rate of the AGC function in the AGC control unit, establish an AGC function risk model of the AGC control unit to determine the failure probability of the AGC function, acquire the deviation between the power regulation command issued value and the power regulation amount sent by the AGC system to the AGC control unit, and establish a scheduling instruction risk model of the AGC control unit based on the probability distribution of the deviation and the power regulation amount to determine the failure probability of the scheduling instruction, acquire the failure rate and repair rate of the line, establish a line operation risk model to determine the failure probability of the line, acquire the failure rate and repair rate of the new energy unit, establish a new energy unit operation risk model to determine the failure probability of the new energy unit, acquire the failure rate and repair rate of the conventional unit, establish a conventional unit operation risk model to determine the failure probability of the conventional unit, and establish a power grid fault probability model by using the failure probability of the AGC function, the failure probability of the scheduling instruction, the failure probability of the line, the failure probability of the new energy unit and the failure probability of the conventional unit.

[0085] The power grid model and constraint generating module is used to generate different fault scenarios of the high-proportion new energy power grid by using a sampling method, map the generated different fault scenarios into power grid fault events to acquire the state auxiliary variables of the AGC control unit based on the power grid fault probability model, establish operation risk constraints of the AGC control unit under different fault scenarios based on the state auxiliary variables of the AGC control unit, map the generated different fault scenarios into power grid fault events to acquire the state auxiliary variables of the equipment, and construct a power flow model and power flow constraint condition of the high-proportion new energy power grid considering the AGC system operation risk based on the state auxiliary variables of the equipment.

[0086] A power grid reliability evaluation module is configured to iteratively solve a power flow model, power flow constraint conditions and operation risk constraints of AGC control units of the high-proportion new energy power grid under different fault scenarios to obtain minimum load reduction of the power grid under each fault scenario; based on the minimum load reduction of the power grid under all random faults, a variance coefficient of a reliability index expectation of the high-proportion new energy power grid is calculated; when the variance coefficient β is less than or equal to 0.01, the power shortage expectation value is taken as the reliability evaluation result of the high-proportion new energy power grid considering the operation risk of the AGC system.

[0087] A terminal comprises a processor and a storage medium; the storage medium is configured to store instructions; and the processor is configured to operate according to the instructions to perform the steps of the method.

[0088] A computer-readable storage medium has a computer program stored thereon, and the program is executed by a processor to implement the steps of the method.

[0089] The present application has the advantages that, compared with the prior art, at least the following advantages are included: the method proposed by the present application improves the traditional high-proportion new energy power grid reliability evaluation method, proposes a modeling method for various fault operation risks of the AGC system, considers the superimposed risk induced by the interaction between the operation risks of various faults of the AGC system and the operation risks of power grid equipment, quantitatively evaluates the degree of reliability reduction of the high-proportion new energy power grid caused by the superimposed operation risks of the AGC system and the power grid equipment, and provides a technical basis for evaluating the reliability of the high-proportion new energy power grid. BRIEF DESCRIPTION OF DRAWINGS

[0090] Figure 1 is a specific flowchart of the high-proportion new energy power grid reliability evaluation method considering the operation risk of the AGC system established by the present application;

[0091] Figure 2 is a schematic diagram of the actual example system grid structure in the embodiment of the present application. DETAILED DESCRIPTION

[0092] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. The embodiments described in the present application are only a part of the embodiments of the present application, but not all the embodiments. All other embodiments obtained by those skilled in the art without creative labor based on the spirit of the present application are within the protection scope of the present application.

[0093] The prior art only models the operation risk of grid equipment or generator set from a physical layer, and only focuses on the operation risk from a physical layer.

[0094] The present application provides a high-proportion new energy power grid reliability evaluation method considering AGC system operation risk. Figure 1 As shown in the formula (1), the method comprises the following steps:

[0095] Step 1, obtain the failure rate and repair rate of the AGC function in the AGC control unit, and establish an AGC function risk model of the AGC control unit to determine the failure probability of the AGC function.

[0096] Since the AGC control unit includes AGC controlled conventional generator sets and AGC controlled new energy generator sets, when performing high-proportion new energy power grid reliability evaluation considering AGC system operation risk, a separate operation risk model representing the AGC control function in the AGC control unit is established to decouple the unit operation risk and the AGC control function operation risk.

[0097] Specifically, the AGC function risk model of the AGC control unit includes the failure probability and the normal probability of the AGC function, which satisfy the following relationships respectively:

[0098]

[0099]

[0100] In the formula, f k and g k respectively represent the failure probability and the normal probability of the AGC function of the AGC control unit k, which constitute the AGC function risk model, and respectively represent the failure rate and the repair rate of the AGC function of the AGC control unit k, and ​denotes the state auxiliary variable of the AGC controlled unit k, 0 indicates a fault state, and 1 indicates a running state, k∈n AGC , n AGC denotes the AGC controlled unit set;

[0101] Based on the AGC function risk model of the AGC controlled unit, the fault probability of the AGC function is determined as

[0102]

[0103] Step 2, the deviation between the power adjustment command value sent by the AGC system to the AGC controlled unit and the power adjustment amount is obtained, and a scheduling instruction risk model of the AGC controlled unit is established based on the probability distribution of the deviation and the power adjustment amount to determine the fault probability of the scheduling instruction.

[0104] Specifically, the AGC system sends a power adjustment command to the AGC controlled unit within a control period (from time τ=1 to time τ=t), due to various unknown operation risks of the AGC system, an error exists between the power adjustment command value and the power adjustment command calculated by the controller, and the deviation between the power adjustment command value caused by the error and the actual power adjustment amount of the AGC controlled unit will affect the reliability of the power grid, and the fault probability of the scheduling instruction of the AGC system is determined through the deviation, which can accurately evaluate the load reduction amount caused by the scheduling instruction and is more suitable for the reliability evaluation scene of the AGC system high proportion power grid.

[0105] Specifically, step 2 includes:

[0106] Step 2.1, the power adjustment command value sent by the AGC system to the AGC controlled unit and the power adjustment amount of the AGC controlled unit are obtained, and the deviation between the power adjustment command value and the power adjustment amount is determined based on the power adjustment state of the AGC controlled unit, satisfying the following relationship:

[0107]

[0108] In the formula, denotes the power adjustment command value sent by the AGC system to the AGC controlled unit k from time τ=1 to time τ=t, denotes the power adjustment amount of the AGC controlled unit k, denotes the deviation between the power adjustment command value sent by the AGC system to the AGC controlled unit k and the power adjustment amount, denotes the power linear adjustment rate of the AGC controlled unit k at time τ, an indicator variable representing the power adjustment state of the AGC controlled unit k at time τ, being 1 for increasing output, 0 for steady operation, and -1 for decreasing output;

[0109] Step 2.2, establishing the deviation obeys the following relationship:

[0110]

[0111] wherein, is the standard deviation of the normal distribution;

[0112] Step 2.3, using the probability distribution of the deviation and the power adjustment amount, a dispatch instruction risk model is established, including the failure probability and normal probability of the dispatch instruction, satisfying the following relationship respectively:

[0113]

[0114]

[0115] wherein, and are respectively the failure probability and normal probability of the dispatch instruction of the AGC controlled unit k;

[0116] Based on the dispatch instruction risk model of the AGC controlled unit, the failure probability of the dispatch instruction is determined as

[0117] Step 3, obtaining the failure rate and repair rate of the line, a line operation risk model is established to determine the failure probability of the line.

[0118] Specifically, the line operation risk model includes the failure probability and normal probability of the line, satisfying the following relationship respectively:

[0119]

[0120]

[0121] wherein, and are respectively the failure probability and normal probability of the line ll', and are respectively the failure rate and repair rate of the line ll', represents that the line ll' belongs to the line set l B , represents the state auxiliary variable of the transmission line ll', being 0 for the failure state and 1 for the operation state;

[0122] ​Based on the line operation risk model, the fault probability of the line is determined as

[0123] Step 4, the failure rate and repair rate of the new energy unit are obtained, and a new energy unit operation risk model is established to determine the failure probability of the new energy unit.

[0124] Specifically, the new energy unit operation risk model includes the failure probability and the normal probability of the new energy unit, which satisfy the following relationships respectively:

[0125]

[0126]

[0127] In the formula, and respectively represent the failure probability and the normal probability of the new energy unit i, and respectively represent the failure rate and the repair rate of the new energy unit i, represents that the new energy unit i belongs to the new energy unit set n RE , represents the state auxiliary variable of the new energy unit i, and is 0 indicating a failure state and 1 indicating a running state;

[0128] Based on the new energy unit operation risk model, the failure probability of the new energy unit is determined as

[0129] Step 5, the failure rate and repair rate of the conventional unit are obtained, and a conventional unit operation risk model is established to determine the failure probability of the conventional unit.

[0130] Specifically, the conventional unit operation risk model includes the failure probability and the normal probability of the conventional unit, which satisfy the following relationships respectively:

[0131]

[0132]

[0133] In the formula, and respectively represent the failure probability and the normal probability of the conventional unit j, and respectively represent the failure rate and the repair rate of the conventional unit j, represents that the conventional unit j belongs to the conventional unit set n G , represents the state auxiliary variable of the conventional unit j, and is 0 indicating a failure state and 1 indicating a running state;

[0134] Based on the conventional unit operation risk model, the failure probability of the conventional unit is determined as

[0135] In the present application, for the new energy unit and the conventional unit controlled by AGC, the failure probability of AGC function related to AGC system and the failure probability of dispatching instruction are separated, so that the failure probability of new energy unit and conventional unit highlights the influence of AGC system operation risk on power grid reliability on the basis of using related data of non-AGC unit.

[0136] Step 6, using the failure probability of AGC function, the failure probability of dispatching instruction, the failure probability of line, the failure probability of new energy unit and the failure probability of conventional unit, a power grid failure probability model is established;

[0137] The power grid failure probability model satisfies the following relationship:

[0138]

[0139] In the formula, δ sys represents the power grid failure probability, represents the mapping relationship between probability and state, x sys represents the power grid failure event, represents the state auxiliary variable of AGC controlled unit k, 0 represents the failure state, and 1 represents the running state, represents the deviation between the power regulation command issued value and the power regulation amount sent by AGC system to AGC controlled unit k,

[0140] Wherein, k∈n AGC , n AGC represents the AGC controlled unit set, i∈n RE , n RE represents the new energy unit set, j∈n G , n G represents the conventional unit set, and satisfies n AGC =n RE ∪n G ;

[0141] At present, all new energy units and conventional thermal power units are included in the provincial AGC control system, through the provincial AGC control system, the dispatching can realize the AGC precise control of all new energy units and thermal power units, and with the increase of energy storage devices, the energy storage devices are also gradually included in the provincial AGC control system.

[0142] This invention builds upon the traditional physical-level random failure probability model of power grid equipment or generator set operation risk by constructing a random failure probability model of AGC control system operation risk. It introduces the operation risk of AGC control system into the traditional power grid physical equipment reliability assessment problem, taking the impact of AGC control system on the output and start-up / shutdown of traditional power grid generator sets as the interaction point at the physical-information level. It analyzes the impact of AGC control system operation risk on the reliability of the original physical-level power grid system, and promotes the continuous improvement of the reliability of the power grid's information and control systems.

[0143] Step 7: Use sampling to generate different fault scenarios for a high-proportion renewable energy power grid; based on the power grid fault probability model, map the generated different fault scenarios to power grid fault events to obtain the state auxiliary variables of the AGC control unit; based on the state auxiliary variables of the AGC control unit, establish the operational risk constraints of the AGC control unit under different fault scenarios.

[0144] The operational risk constraints of the AGC control unit under a single failure scenario satisfy the following relationship:

[0145]

[0146]

[0147] In the formula, and These represent the lower and upper limits of the adjustable capacity of AGC control unit k, respectively. and Let represent the minimum and maximum limit output of AGC-controlled unit k, respectively. This represents the auxiliary variable indicating the state of AGC control unit k. A value of 0 indicates a fault state, while a value of 1 indicates a running state.

[0148] This represents the linear power regulation rate of unit k under AGC control at time τ. This variable represents the power regulation status of unit k under AGC control at time τ. A value of 1 indicates that the unit is increasing its output, a value of 0 indicates that the unit is operating smoothly, and a value of -1 indicates that the unit is decreasing its output.

[0149] In the embodiment, after sampling and generating a single fault scenario of a high-proportion renewable energy power grid based on the power grid fault probability model, the operational risk constraint of the AGC control unit under the single fault scenario is an operational constraint condition that considers the superposition of the operational risk of the AGC system and the risk of the power grid equipment after a single random fault. Through the operational risk constraint of the AGC control unit under the single fault scenario, the operational risk of the AGC system (the failure probability of the AGC function and the failure probability of the scheduling command) is introduced into the subsequent power flow calculation.

[0150] Step 8, based on the power grid failure probability model, the generated different failure scenarios are mapped into power grid failure events to obtain the state auxiliary variables of the equipment; based on the state auxiliary variables of the equipment, a power flow model and power flow constraint conditions of the high-proportion new energy power grid considering the operation risk of the AGC system are constructed;

[0151] The power flow model of the high-proportion new energy power grid includes:

[0152] 1) System power balance model, satisfying the following relationship:

[0153]

[0154] In the formula, is the power of the conventional unit j at time τ, is the power of the new energy unit i at time τ, is the power of the AGC control unit k at time τ, is the power of the load node m at time τ, is the load reduction of the load node m at time τ;

[0155] 2) Node power balance constraint, satisfying the following relationship:

[0156]

[0157] In the formula, and respectively represent the power flowing from node l' to node l and the power flowing from node l to node l' at time τ, l' ∈ B l represents that node l' belongs to the node set directly connected with node l;

[0158] The power flow constraint conditions of the high-proportion new energy power grid include:

[0159] 1) Line power flow constraint, satisfying the following relationship:

[0160]

[0161] In the formula, B ll′ represents the mutual admittance of line l', δ l,τ and δ l′,τ respectively represent the voltage phase angle of node l and node l' at time τ, represents the state auxiliary variable of line l', 0 represents the failure state, 1 represents the running state, and M represents an arbitrary large (not infinite) positive number;

[0162] 2) Line transmission capacity constraint, satisfying the following relationship:

[0163]

[0164] wherein, represents the maximum transmission capacity of line ll';

[0165] In the iterative optimization of the objective function, the state auxiliary variable achieves the mapping of the line flow constraint, the line transmission capacity constraint and the line operation risk model;

[0166] 3) The conventional unit output constraint satisfies the following relationship:

[0167]

[0168] wherein, and respectively represent the maximum output and the minimum output of the conventional unit j;

[0169] In the iterative optimization of the objective function, the state auxiliary variable achieves the mapping of the conventional unit output constraint and the conventional unit operation risk model;

[0170] 4) The load reduction constraint satisfies the following relationship:

[0171]

[0172] 5) The new energy unit output constraint satisfies the following relationship:

[0173]

[0174] wherein, represents the maximum output of the new energy unit i at time τ;

[0175] In the iterative optimization of the objective function, the state auxiliary variable achieves the mapping of the new energy unit output constraint and the new energy unit operation risk model;

[0176] 6) The node phase angle constraint satisfies the following relationship:

[0177] -θ max ≤θ τ ≤θ max

[0178]

[0179] wherein, θ max and θ τ respectively represent the maximum voltage phase angle of the power grid node and the voltage phase angle at time τ, represents the voltage phase angle reference value at time τ.

[0180] Step 9, iteratively solve the power flow model, power flow constraint condition and operation risk constraint of AGC control unit under different fault scenarios of high proportion new energy power grid to obtain the minimum load reduction of power grid under each fault scenario;

[0181] The minimum load reduction of high proportion new energy power grid under single fault scenario meets the following relationship:

[0182]

[0183] In the formula, is the minimum load reduction caused by the power grid fault event corresponding to fault scenario e is the minimum load reduction caused by the power grid fault event corresponding to fault scenario e is the load reduction of load node m at time τ, is the load reduction of all load nodes from time τ = 1 to time τ = t during fault scenario e, M L is the total number of load nodes.

[0184] Step 10, based on the minimum load reduction of power grid under all random faults, calculate the reliability index expected variance coefficient of high proportion new energy power grid, meet the following relationship:

[0185]

[0186] In the formula, β is the variance coefficient of power shortage expectation, E is the total number of fault scenarios, is the power shortage expectation,

[0187] When the variance coefficient β ≤ 0.01, take the power shortage expectation as the reliability evaluation result of high proportion new energy power grid considering the operation risk of AGC system.

[0188] Taking the actual simulation system shown in Figure 2 as an example, the implementation process of the reliability evaluation method of high proportion new energy power grid considering the operation risk of AGC system is as follows:

[0189] 1. Random fault sampling generation of high proportion new energy power grid

[0190] For a certain actual high proportion new energy power grid, its structure diagram is as shown in Figure 2The Bus 17 load in the IEEE-RTS24 node system is modified to be a tie-line node connected to the external network, and the conventional generator units in the IEEE-RTS24 node system located at Bus 1 and Bus 2 are modified to be new energy nodes. Based on the fault rates of the elements in the system, Monte Carlo module of Matlab software is used to realize 10000 times of random fault sampling generation of the high-proportion new energy power grid, to determine the elements and corresponding fault rates of each random fault in the high-proportion new energy power grid. Here, the operation risk model of AGC system control link failure, the operation risk model of AGC system command value error caused by human error, the risk of single first-order to third-order fault modes such as line fault and unit fault, and the risk of two-order to three-order fault superposition modes of various types of faults are considered.

[0191] 2. Constructing and solving the optimal power flow model for reliability evaluation of high-proportion new energy power grid under single random fault

[0192] For a certain actual high-proportion new energy power grid, based on the single fault mode obtained by sampling, the Matlab software YALMIP tool package is used to construct the optimal power flow model program of high-proportion new energy power grid reliability evaluation under single random fault, and the IPOPT solver is called to solve the high-proportion new energy power grid optimal power flow model considering the AGC system operation risk after the e-th random fault, to obtain the minimum load reduction under the e-th random fault.

[0193] 3. Obtaining the reliability evaluation results of high-proportion new energy power grid considering AGC system operation risk

[0194] Based on the minimum load reduction under all random faults, the estimated value of the reliability index expectation of the high-proportion new energy power grid is calculated, and the above steps are repeated until the estimated value of the reliability comprehensive cost expectation of the high-proportion new energy power grid meets the convergence condition, to obtain the reliability evaluation results of high-proportion new energy power grid considering the AGC system operation risk.

[0195] For convenient comparison, the operation risk model considering failure of the control link of the AGC system, the operation risk model caused by human error of the AGC system command value, and the random fault of the high-proportion new energy power grid considering multiple risks such as the operation risk of the AGC system and the operation risk of the grid equipment are regarded as scenario 1, and the reliability result is obtained based on the method provided in the embodiment of the application; the operation risk model considering failure of the control link of the AGC system and the random fault of the high-proportion new energy power grid considering multiple risks such as the operation risk of the grid equipment are regarded as scenario 2. The operation risk model caused by human error of the AGC system command value and the random fault of the high-proportion new energy power grid considering multiple risks such as the operation risk of the grid equipment are regarded as scenario 3, and the reliability evaluation result is obtained by using the traditional reliability evaluation method. The random fault of the high-proportion new energy power grid considering only the operation risk of the grid equipment is regarded as scenario 4, and the reliability evaluation result is obtained by using the traditional reliability evaluation method. The results of the four scenarios are shown in Table 1:

[0196] Table 1: Calculation results in each scenario

[0197] Scenario EENS (MWh / 24h) 1 7.3146 x 10 3 ]] 2 6.6546 x 10 3 ]] 3 6.4534 x 10 3 ]] 4 3.3423 x 10 3 ]]

[0198] As can be seen from Table 1, the more risk categories are considered, the higher the reliability index EENS is, which verifies the effectiveness of the method; at the same time, some references and information are provided for subsequent power grid operation.

[0199] The application provides a modeling method for various faults of the AGC system, considers the superimposed risk induced by the interaction between the operation risk of the AGC system and the operation risk of the grid equipment, quantitatively evaluates the reliability reduction of the high-proportion new energy power grid caused by the superimposed risk induced by the interaction between the operation risk of the AGC system and the operation risk of the grid equipment, and provides a technical basis for evaluating the reliability of the high-proportion new energy power grid, so as to accurately guide the reliable operation of the high-proportion new energy power grid in the future.

[0200] The application further provides a high-proportion new energy power grid reliability evaluation system considering the operation risk of the AGC system, which comprises:

[0201] The power grid failure probability model establishing module is configured to obtain a failure rate and a repair rate of AGC functions of AGC control units, establish an AGC function risk model of the AGC control units to determine a failure probability of the AGC functions, obtain a deviation between a power regulation command value and a power regulation amount sent by an AGC system to the AGC control units, establish a scheduling instruction risk model of the AGC control units based on a probability distribution of the deviation and the power regulation amount to determine a failure probability of the scheduling instruction, obtain a failure rate and a repair rate of lines, establish a line operation risk model to determine a failure probability of the lines, obtain a failure rate and a repair rate of new energy units, establish a new energy unit operation risk model to determine a failure probability of the new energy units, obtain a failure rate and a repair rate of conventional units, establish a conventional unit operation risk model to determine a failure probability of the conventional units, and establish a power grid failure probability model by using the failure probability of the AGC functions, the failure probability of the scheduling instruction, the failure probability of the lines, the failure probability of the new energy units, and the failure probability of the conventional units.

[0202] The power grid model and constraint generating module is configured to generate different failure scenarios of the high-proportion new energy power grid by using a sampling method, map the generated different failure scenarios to power grid failure events to obtain state auxiliary variables of the AGC control units based on the power grid failure probability model, establish operation risk constraints of the AGC control units under different failure scenarios based on the state auxiliary variables of the AGC control units, map the generated different failure scenarios to power grid failure events to obtain state auxiliary variables of devices, and construct a power flow model and power flow constraint conditions of the high-proportion new energy power grid considering operation risks of the AGC system based on the state auxiliary variables of the devices.

[0203] The power grid reliability evaluation module is configured to iteratively solve the power flow model, the power flow constraint conditions, and the operation risk constraints of the AGC control units of the high-proportion new energy power grid under different failure scenarios to obtain minimum load reduction amounts of the power grid under each failure scenario, calculate a variance coefficient of a reliability index expectation of the high-proportion new energy power grid based on the minimum load reduction amounts of the power grid under all random failures, and take a power deficiency expectation value as a reliability evaluation result of the high-proportion new energy power grid considering operation risks of the AGC system when the variance coefficient β is less than or equal to 0.01.

[0204] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium having computer readable program instructions embodied therewith, wherein the computer readable program instructions are used to cause a processor to implement various aspects of the present disclosure.

[0205] Computer readable storage media can be tangible storage media which can retain and store instructions for use by an instruction execution device. Computer readable storage media can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer readable storage media include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0206] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0207] Computer readable program instructions for carrying out operations of the present disclosure can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0208] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, but not to limit it. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced, and any modification or replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A reliability assessment method for high-proportion renewable energy power grids considering the operational risks of AGC systems, characterized in that, include: Obtain the failure rate and repair rate of AGC function in the AGC control unit, and establish an AGC function risk model of the AGC control unit to determine the failure probability of AGC function. The system obtains the deviation between the power regulation command issued by the AGC system and the power regulation amount, and establishes a scheduling command risk model for the AGC control unit based on the probability distribution of the deviation and the power regulation amount to determine the failure probability of the scheduling command; it obtains the failure rate and repair rate of the line, and establishes a line operation risk model to determine the failure probability of the line; it obtains the failure rate and repair rate of the new energy unit, and establishes a new energy unit operation risk model to determine the failure probability of the new energy unit; it obtains the failure rate and repair rate of the conventional unit, and establishes a conventional unit operation risk model to determine the failure probability of the conventional unit; using the failure probability of the AGC function, the failure probability of the scheduling command, the failure probability of the line, the failure probability of the new energy unit, and the failure probability of the conventional unit, it establishes a power grid failure probability model. A sampling method is used to generate different fault scenarios for a high-proportion renewable energy power grid; based on the power grid fault probability model, the generated different fault scenarios are mapped to power grid fault events to obtain the state auxiliary variables of the AGC control unit; Based on the state auxiliary variables of the AGC control unit, operational risk constraints of the AGC control unit under different fault scenarios are established; the generated different fault scenarios are mapped to power grid fault events to obtain the state auxiliary variables of the equipment. Based on the state auxiliary variables of the equipment, a power flow model and power flow constraints for a high-proportion renewable energy power grid that considers the operational risks of the AGC system are constructed. Iteratively solve the power flow model, power flow constraints, and operational risk constraints of AGC-controlled units for high-proportion renewable energy power grids under different fault scenarios to obtain the minimum load reduction of the power grid under each fault scenario; based on the minimum load reduction of the power grid under all random faults, calculate the expected variance coefficient of the reliability index of the high-proportion renewable energy power grid. When the variance coefficient is no greater than 0.01, the expected value of power shortage is used as the reliability assessment result of high-proportion new energy power grid considering the operational risk of AGC system.

2. The high-proportion renewable energy grid reliability assessment method considering the operational risks of AGC systems according to claim 1, characterized in that, The risk model for the AGC function of the AGC control unit includes the failure probability and the normal operation probability of the AGC function, which respectively satisfy the following relationships: In the formula, and Let represent the failure probability and normal operation probability of the AGC function in AGC control unit k, respectively. and These represent the failure rate and repair rate of the AGC function in AGC control unit k, respectively. This represents the state auxiliary variable of AGC control unit k, where 0 indicates a fault state and 1 indicates an operating state, k∈n. AGC n AGC This represents the set of AGC control units, and → represents the mapping relationship between probability and state. Based on the AGC function risk model of the AGC control unit, the failure probability of the AGC function is determined as follows:

3. The high-proportion renewable energy grid reliability assessment method considering the operational risks of the AGC system according to claim 2, characterized in that, The power regulation command value sent by the AGC system to the AGC control unit and the power regulation amount of the AGC control unit are obtained. Based on the power regulation status of the AGC control unit, the deviation between the power regulation command value and the power regulation amount is determined, satisfying the following relationship: In the formula, This represents the power regulation command value sent by the AGC system to AGC control unit k during the period from time τ=1 to time τ=t. This represents the power adjustment amount of unit k controlled by the AGC. This indicates the deviation between the power adjustment command value sent by the AGC system to the AGC control unit k and the actual power adjustment amount. This represents the linear power regulation rate of unit k under AGC control at time τ. This variable represents the power regulation status of unit k under AGC control at time τ. A value of 1 indicates that the unit is increasing its output, a value of 0 indicates that the unit is operating smoothly, and a value of -1 indicates that the unit is decreasing its output.

4. The high-proportion renewable energy grid reliability assessment method considering the operational risks of the AGC system according to claim 3, characterized in that, Establishing deviation obey The probability distribution of satisfies the following relationship: In the formula, The standard deviation of the normal distribution; Using the probability distribution of deviations and power regulation, a risk model for dispatching instructions is established, including the failure probability and normal probability of dispatching instructions, which respectively satisfy the following relationships: In the formula, and The failure probability and normal probability of the scheduling command of AGC control unit k are respectively determined; Based on the risk model of scheduling instructions for AGC control units, the failure probability of scheduling instructions is determined to be:

5. The high-proportion renewable energy grid reliability assessment method considering the operational risks of the AGC system according to claim 4, characterized in that, The line operation risk model includes the line's failure probability and normal operation probability, which respectively satisfy the following relationships: In the formula, and Let represent the fault probability and normal probability of line ll′, respectively. and These represent the failure rate and repair rate of line ll′, respectively. This indicates that line ll′ belongs to the set of lines l. B , The auxiliary variable representing the state of line ll′ is 0, which indicates a fault state, and 1 indicates an operating state. → indicates the mapping relationship between probability and state. Based on the line operation risk model, the line failure probability is determined to be:

6. The high-proportion renewable energy grid reliability assessment method considering the operational risks of the AGC system according to claim 5, characterized in that, The risk model for the operation of new energy generating units includes the probability of failure and the probability of normal operation of the new energy generating units, which respectively satisfy the following relationships: In the formula, and Let represent the failure probability and normal operation probability of new energy unit i, respectively. and These represent the failure rate and repair rate of new energy unit i, respectively. This indicates that new energy unit i belongs to the set of new energy units n. RE , The auxiliary variable representing the state of new energy unit i is denoted by 0, which indicates a fault state, and 1, which indicates an operating state. → indicates the mapping relationship between probability and state. Based on the risk model for the operation of new energy generating units, the failure probability of the new energy generating units is determined to be:

7. The high-proportion renewable energy grid reliability assessment method considering the operational risks of the AGC system according to claim 6, characterized in that, The operational risk model for conventional generating units includes the probability of failure and the probability of normal operation, which respectively satisfy the following relationships: In the formula, and Let represent the failure probability and normal operation probability of conventional unit j, respectively. and These represent the failure rate and repair rate of conventional unit j, respectively. This indicates that conventional unit j belongs to the set of conventional units n. G , The auxiliary variable representing the state of conventional unit j is 0, which indicates a fault state, and 1 indicates an operating state. → represents the mapping relationship between probability and state. Based on the conventional unit operation risk model, the failure probability of the conventional unit is determined to be:

8. The high-proportion renewable energy grid reliability assessment method considering the operational risks of the AGC system according to claim 7, characterized in that, The power grid fault probability model satisfies the following relationship: In the formula, δ sys Indicates the probability of power grid failure. This represents the failure probability of the AGC function in AGC control unit k. This represents the failure probability of the scheduling command for AGC control unit k. This represents the failure probability of transmission line ll′. This represents the failure probability of new energy unit i. Let x represent the failure probability of conventional unit j, and → represent the mapping relationship between probability and state. sys This is a power grid fault event. This represents the auxiliary variable indicating the state of AGC control unit k; a value of 0 indicates a fault state, and a value of 1 indicates an operating state. This indicates the deviation between the power adjustment command value sent by the AGC system to the AGC control unit k and the actual power adjustment amount. The auxiliary variable represents the state of the transmission line ll′; a value of 0 indicates a fault state, and a value of 1 indicates an operating state. The auxiliary variable representing the state of new energy unit i is 0, indicating a fault state, and 1, indicating an operating state. This represents the auxiliary variable indicating the state of conventional unit j; a value of 0 indicates a fault state, and a value of 1 indicates an operating state. Where, k∈n AGC n AGC Let i ∈ n be the set of AGC control units. RE n RE Let j∈n be the set of new energy generating units. G n G Let n represent the set of conventional generating units. AGC =n RE ∪n G .

9. The high-proportion renewable energy grid reliability assessment method considering the operational risks of the AGC system according to claim 8, characterized in that, The operational risk constraints of the AGC control unit under a single failure scenario satisfy the following relationship: In the formula, and These represent the lower and upper limits of the adjustable capacity of AGC control unit k, respectively. and Let represent the minimum and maximum limit output of AGC-controlled unit k, respectively. This represents the linear power regulation rate of unit k under AGC control at time τ. This variable represents the power regulation status of unit k under AGC control at time τ. A value of 1 indicates that the unit is increasing its output, a value of 0 indicates that the unit is operating smoothly, and a value of -1 indicates that the unit is decreasing its output. This represents the power adjustment command value sent by the AGC system to AGC control unit k during the period from time τ=1 to time τ=t.

10. The high-proportion renewable energy grid reliability assessment method considering the operational risks of the AGC system according to claim 8, characterized in that, Power flow models for high-proportion renewable energy power grids include: 1) The system power balance model satisfies the following relationship: In the formula, Let j be the power of a conventional unit at time τ. Let be the power of the new energy unit i at time τ. Let be the power of AGC-controlled unit k at time τ. Let be the power of load node m at time τ. Let m be the load reduction amount at load node m at time τ; 2) Node power balance constraints satisfy the following relationship: In the formula, and Let B represent the power flowing from node l′ to node l and from node l to node l′ at time τ, respectively, where l′∈B. l This indicates that node l′ belongs to the set of nodes that are directly connected to node l.

11. The high-proportion renewable energy grid reliability assessment method considering the operational risks of the AGC system according to claim 10, characterized in that, Power flow constraints for high-proportion renewable energy power grids include: 1) Line power flow constraints satisfy the following relationship: In the formula, B ll′ δ represents the mutual admittance of line ll′. l,τ and δ l′,τ Let represent the voltage phase angles of node l and node l′ at time τ, respectively. The auxiliary variable representing the state of line ll′ is 0, which indicates a fault state, and 1 indicates an operating state. M represents an arbitrarily large (but not infinite) positive number. 2) Line transmission capacity constraints satisfy the following relationship: In the formula, This indicates the maximum transmission capacity of line ll′; 3) The output constraints of conventional generating units satisfy the following relationship: In the formula, and These represent the maximum and minimum output of conventional unit j, respectively; 4) Load reduction constraints satisfy the following relationship: 5) The output constraints of new energy units satisfy the following relationship: In the formula, This represents the maximum output of the new energy unit i at time τ; 6) Node phase angle constraints satisfy the following relationship: -θ max ≤θ τ ≤θ max In the formula, θ max and θ τ Let represent the maximum voltage phase angle of the grid node and the voltage phase angle at time τ, respectively. This represents the reference value of the voltage phase angle at time τ.

12. The high-proportion renewable energy grid reliability assessment method considering the operational risks of the AGC system according to claim 10, characterized in that, The minimum load reduction for a high-proportion renewable energy power grid under a single fault scenario satisfies the following relationship: In the formula, For fault scenario e, the corresponding power grid fault event The minimum load reduction resulting from Let be the load reduction amount at load node m at time τ. M represents the load reduction amount for all load nodes during the period from time τ=1 to time τ=t under fault scenario e. L This represents the total number of load nodes.

13. The high-proportion renewable energy grid reliability assessment method considering the operational risks of the AGC system according to claim 12, characterized in that, The expected variance coefficient of the reliability index satisfies the following relationship: In the formula, β is the variance coefficient of the expected power shortage value, and E is the total number of failure scenarios. As the expected value of insufficient power, 14. A high-proportion renewable energy grid reliability assessment system considering the operational risks of AGC systems, characterized in that, include: The power grid fault probability model establishment module is used to obtain the failure rate and repair rate of the AGC function in the AGC control unit, establish an AGC function risk model of the AGC control unit to determine the failure probability of the AGC function; obtain the deviation between the power regulation command issued by the AGC system to the AGC control unit and the power regulation amount, and establish a dispatch command risk model of the AGC control unit based on the probability distribution of the deviation and the power regulation amount to determine the failure probability of the dispatch command; obtain the failure rate and repair rate of the line, establish a line operation risk model to determine the failure probability of the line; obtain the failure rate and repair rate of the new energy unit, establish a new energy unit operation risk model to determine the failure probability of the new energy unit; obtain the failure rate and repair rate of the conventional unit, establish a conventional unit operation risk model to determine the failure probability of the conventional unit; and use the failure probabilities of the AGC function, dispatch command, line, new energy unit, and conventional unit to establish a power grid fault probability model. The power grid model and constraint generation module is used to generate different fault scenarios for a high-proportion renewable energy power grid using a sampling method; based on the power grid fault probability model, the generated different fault scenarios are mapped to power grid fault events to obtain the state auxiliary variables of the AGC control unit; Based on the state auxiliary variables of the AGC control unit, operational risk constraints of the AGC control unit under different fault scenarios are established; the generated different fault scenarios are mapped to power grid fault events to obtain the state auxiliary variables of the equipment. Based on the state auxiliary variables of the equipment, a power flow model and power flow constraints for a high-proportion renewable energy power grid that considers the operational risks of the AGC system are constructed. The power grid reliability assessment module is used to iteratively solve the power flow model, power flow constraints, and AGC control unit operation risk constraints of the high-proportion renewable energy power grid under different fault scenarios, and obtain the minimum load reduction of the power grid under each fault scenario. Based on the minimum load reduction of the power grid under all random faults, the variance coefficient of the expected reliability index of the high-proportion renewable energy power grid is calculated. When the variance coefficient β≤0.01, the expected power shortage value is used as the reliability assessment result of the high-proportion renewable energy power grid considering the operation risk of the AGC system.

15. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-13.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-13.

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