A method and device for displaying power grid operation risk indicators and a storage medium

Abstract

The application discloses a power grid operation risk index display method and device and a storage medium. The method comprises the following steps: acquiring a plurality of risk influence factors after power grid system operation, respectively performing quantitative calculation on a plurality of power grid risk indexes of the power grid system according to the risk influence factors and power grid operation parameters, and obtaining a plurality of risk index values; solving the weight of each index according to the relative importance value of each index; calculating the severity value of each power grid risk according to the plurality of risk index values and the weight; calculating the overall risk assessment index value of the power grid system according to the severity value of the plurality of power grid risks; and acquiring the risk index value and the overall risk assessment index value of a preset time after the power grid system operation to perform display, so that the operation risk of the power system can be comprehensively quantitatively evaluated, and the risk of the massive adjustable resources participating in the power grid operation is more clear and intuitive.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method, device and storage medium for displaying power grid operation risk indicators. Background Technology

[0002] Faced with increasingly severe environmental problems and the need to achieve future carbon dioxide emission targets, vigorously promoting the development of renewable energy and transforming the utilization and consumption patterns of various adjustable resources have become important means to support sustainable development. In recent years, with the large-scale integration of massive adjustable resources on both the source and load sides, the structure and operation of traditional power grids have undergone tremendous changes. The intermittency and volatility of distributed generation and flexible load resources make the power grid prone to risks such as frequency and voltage exceeding limits, branch power flow exceeding limits, and even grid disconnection, posing significant challenges to the safe and stable operation of the power grid. Therefore, risk assessment of the power system with the participation of massive adjustable resources is an urgent problem to be solved.

[0003] The presentation of risk assessments for power systems mainly involves two aspects: demonstrating the probability of risk occurrence and demonstrating the severity of risk. The probability of power system risk occurrence is influenced by the uncertainties of its internal components and the external environment. For systems with a large number of adjustable resources, the probability of risk occurrence is also affected by the uncertainties of distributed generation on the source side and flexible load resources on the load side. The severity of risk requires quantitative assessment of various risk indicators. Currently, many studies lack clear standards for power system risk assessment systems, fail to comprehensively demonstrate various risks in power systems, and do not consider the risks arising from the intermittency and uncertainty of adjustable resources, especially flexible load resources. Summary of the Invention

[0004] This invention provides a method, device, and storage medium for displaying power grid operation risk indicators, so as to achieve a comprehensive quantitative assessment and display of the operation risk of the power grid system.

[0005] To make the risks of massive adjustable resources participating in power grid operation clearer and more intuitive, and to make the risks of the power grid system more observable, this invention provides a method for displaying power grid operation risk indicators, including: obtaining several risk influencing factors after the power grid system is in operation; and quantitatively calculating several power grid risk indicators of the power grid system based on the risk influencing factors and power grid operation parameters to obtain several risk indicator values.

[0006] Based on the relative importance values ​​of each of the power grid risk indicators, the weights of each of the power grid risk indicators are determined; based on the risk indicator values ​​and the weights, the severity values ​​of each of the power grid risks are calculated; based on the severity values ​​of the power grid risks, the overall risk assessment index value of the power grid system is calculated.

[0007] The risk index values ​​and overall risk assessment index values ​​at preset time points after the power grid system has been in operation are obtained and displayed.

[0008] As a preferred embodiment, this invention quantifies and calculates several power grid risk indicators for power grid systems involving massive adjustable resources, accurately determining the severity of each risk. By assigning weights to each risk indicator, the severity value of each power grid risk is calculated, making the risks clearer and more intuitive. This invention establishes a risk assessment indicator system for power grid operation involving massive adjustable resources. Based on the severity values ​​of each risk, a comprehensive assessment of the overall system risk is conducted, calculating the overall risk assessment indicator value of the power grid system and deriving the comprehensive risk score. The quantification of several power grid risk indicators makes the various risks associated with the operation of the power grid involving massive adjustable resources clearer and more intuitive, with more specific details displayed for each risk. The calculation of the overall risk assessment indicator value enables a comprehensive and quantitative assessment and display of the operational risks of the power grid system.

[0009] As a preferred approach, several risk factors after the power grid system is in operation are obtained. Based on these risk factors and power grid operating parameters, several indicators of power grid risk are quantitatively calculated, specifically as follows:

[0010] Based on the risk influencing factors, power grid risks are divided into several categories, resulting in the first risk and the second risk.

[0011] Based on the power grid operating parameters, the indicators of the first risk are quantitatively calculated; the first risk includes structural risk, operational risk, and benefit risk.

[0012] The indicators of the second risk are quantified based on the power grid operating parameters; the second risk includes source-side risk and load-side risk.

[0013] As a preferred embodiment, this invention considers the impact of grid risk, source-side risk, and load-side risk factors on the grid system in a system with massive adjustable resources participating in grid operation. It also quantifies and calculates the indicators of each grid risk, accurately calculates the severity of each risk, and achieves a comprehensive and quantitative assessment and display of the operational risks of the grid system.

[0014] As a preferred option, the indicators of the first risk are quantitatively calculated based on the power grid operating parameters, specifically as follows:

[0015] Indicators for quantifying structural risk:

[0016] The structural risks include: grid disconnection;

[0017] The quantitative calculation formula for the power grid disconnection index is as follows:

[0018]

[0019] Where α is the severity coefficient of branch line disconnection in grid disconnection, l is the number of branches disconnected from the system, L is the total number of branches in the system, and ΔP G P represents the lost active power of the generator. N The power generation required by the system;

[0020] Indicators for quantifying operational risks:

[0021] The operational risks include: frequency exceeding limits, insufficient backup reserves, voltage exceeding limits, and branch power flow exceeding limits;

[0022] The quantitative calculation formula for the frequency exceeding the limit index is as follows:

[0023] S FRE =32.4×(max(|Δf|-Δf) max ,0)) 3 ;

[0024] Where |Δf| is the system frequency deviation, Δf max This represents the maximum frequency deviation that the system can tolerate during normal operation.

[0025] The quantitative calculation formula for insufficient reserve indicators is as follows:

[0026]

[0027] Among them, R X R represents the actual spinning reserve capacity of the system. C R represents the actual energy storage reserve capacity of the system. N Reserve capacity for the system's rated standby.

[0028] The quantitative calculation formula for voltage over-limit index is as follows:

[0029] S V =0.9×(max(V) i -V i,max V i,min -V i ,0)) 2 ;

[0030] Among them, Vi Let V be the voltage value at node i. i,max and V i，min These are the upper and lower limits of the node voltage, respectively;

[0031] The quantitative calculation formula for the branch power flow exceeding the limit index is as follows:

[0032] S LPF =0.76×max(P) l -P l,max ,0);

[0033] Among them, P l P represents the per-unit value of the actual active power of line l. l，max This is the per-unit value of the maximum active power allowed to be transmitted by line l;

[0034] Indicators for quantifying benefit and risk:

[0035] The aforementioned benefits and risks include: environmental benefits risks and network damage benefits risks;

[0036] The formula for quantifying environmental benefit risk indicators is as follows:

[0037]

[0038] Where, α CO2 β is the carbon dioxide coefficient converted from coal consumption. SO2 γ is the sulfur dioxide coefficient converted from coal consumption. Nox C is the nitrogen oxide coefficient converted from coal consumption. coal C represents the actual coal consumption. E-coal The expected coal consumption;

[0039] The formula for quantifying the network loss benefit risk index is as follows:

[0040]

[0041] Where, ΔP l For the actual loss of line l, ΔP N-l Let r be the expected loss of line l. l r is the actual network loss rate of line l. N-l Let P be the expected network loss rate of line l. l This refers to the active power transmitted at the beginning of line l.

[0042] As a preferred option, the first risk includes structural risk, operational risk, and benefit risk; among which, structural risk is grid disconnection; operational risk includes: frequency exceeding limits, insufficient reserve, voltage exceeding limits, and branch power flow exceeding limits; benefit risk includes environmental benefit risk and grid loss benefit risk; the indicators of the first risk are quantitatively calculated to accurately calculate the severity of each grid risk, so as to achieve a comprehensive and quantitative assessment and display of the operational risk of the power grid system.

[0043] As a preferred option, the indicators of the second risk are quantitatively calculated based on the power grid operating parameters, specifically as follows:

[0044] Indicators for quantifying source-side risks:

[0045] The source-side risks include the risk of high penetration rate of distributed power generation, the risk of fluctuation in the output of distributed power generation, and the risk of wind and solar curtailment.

[0046] The formula for quantifying the severity of high penetration rate of distributed power sources is as follows:

[0047]

[0048] Among them, P DG (t) represents the output power of the distributed power source at time t, P max (t) represents the maximum power supply capacity of the power grid at time t;

[0049] Indicators for quantifying the output fluctuation risk of distributed power sources:

[0050] The formula for quantifying the severity of distributed power generation output fluctuation is as follows:

[0051] S CL =1.53×(max(|P DG (t)-P DG (t-1)|,0)) 2 ;

[0052] Among them, P DG (t) represents the per-unit value of the output power of the distributed power source at time t, P DG (t-1) represents the per-unit value of the output power of the distributed power source at time t-1;

[0053] Indicators for quantifying the risk of wind and solar power curtailment:

[0054] The formula for quantifying the severity of wind and solar power curtailment is as follows:

[0055]

[0056] ΔP W-L (t) represents the power of wind and solar power curtailment at time t, P W-L(t) represents the actual power generation of wind and solar power at time t, where t1 is the start time of the evaluation and t2 is the end time of the evaluation.

[0057] Indicators for quantifying load-side risk:

[0058] The load-side risks include forecast qualification rate, scheduling capacity failure risk, and load abandonment risk.

[0059] The quantitative calculation formula for the qualification rate of flexible load resource power prediction is as follows:

[0060]

[0061] Where, ΔP MAE ΔP represents the mean absolute error of the prediction. MAPE ΔP represents the average percentage error of the prediction. MSE P represents the prediction mean square error, δ1 is the percentage of prediction error, δ2 is the percentage of prediction mean square error, and P is the percentage of prediction mean square error. k This represents the actual power value. Here, N represents the predicted power value, and N is the sequence length.

[0062] The formula for quantifying the severity index of scheduling capability failure is as follows:

[0063]

[0064] Where, η k The degree of incomplete response scheduling is represented by t3~t4, which represents the time of incomplete response scheduling, and t5~t6, which represents the time of unresponsive scheduling. P F To predict output;

[0065] The formula for quantifying the severity of load abandonment is as follows:

[0066]

[0067] Where, ΔP DRl (t) represents the power consumed by the load shedding at time t, P DRl (t) represents the total power consumed by the load at time t, t7 is the start time of the evaluation, and t8 is the end time of the evaluation.

[0068] As a preferred option, the second risk is the risk of adjustable resources participating in grid operation, including: the severity of high penetration rate of distributed generation, the severity of output fluctuation of distributed generation and the severity of wind and solar curtailment (source-side risks); and the load-side risks of power prediction qualification rate of flexible load resources, the severity of dispatch capability failure and the severity of load curtailment (load-side risks). The indicators of the second risk are quantitatively calculated to accurately calculate the severity of the risk of each adjustable resource participating in grid operation, so as to achieve a comprehensive and quantitative assessment and display of the operation risk of the grid system.

[0069] As a preferred embodiment, the overall risk assessment index value of the power grid system is calculated based on several severity values ​​of the power grid risks, specifically as follows:

[0070] Based on the severity and probability values ​​of several power grid risks, calculate the overall risk assessment index value:

[0071]

[0072] Where E represents the overall risk assessment index value of the entire system after massive adjustable resources participate in the operation of the power grid, Ω is the weighting coefficient, and S C With P C These represent the severity and probability of occurrence of power grid structural risks, S. O With P O These represent the severity and probability of occurrence of power grid operation risks, respectively, S. E With P E S represents the severity and probability of occurrence of power grid benefit risk, respectively. P With P P These represent the severity and probability of source-side risks after adjustable resources participate in grid operation, S DR With P DR These represent the severity and probability of load-side risks after adjustable resources participate in grid operation.

[0073] As a preferred embodiment, this invention constructs quantitative calculation formulas for various risk indicators, calculating the grid risk, source-side risk, and load-side risk indicators for the participation of massive adjustable resources in the power grid, making the risks of massive adjustable resources participating in power grid operation clearer and more intuitive. This invention establishes a risk assessment indicator system for the participation of massive adjustable resources in power grid operation, constructing risk assessment indicator calculation formulas for the entire system based on the severity and probability values ​​of grid risks; solving for indicator weights and calculating the overall risk assessment indicator value of the power grid system, comprehensively quantifying and scoring the risks of the power grid system, making the displayed content of each risk more specific, and achieving a comprehensive and quantitative assessment and display of the operational risks of the power grid system.

[0074] Accordingly, the present invention also provides a display device for power grid operation risk indicators, including: a risk indicator value calculation module, an overall risk assessment indicator value calculation module, and a display module;

[0075] The risk index value calculation module is used to obtain several risk influencing factors after the power grid system is in operation, and to quantify several power grid risk indicators of the power grid system based on the risk influencing factors and power grid operation parameters to obtain several risk index values.

[0076] The overall risk assessment index value calculation module is used to solve the weight of each power grid risk index based on the relative importance value of each power grid risk index; calculate the severity value of each power grid risk based on several risk index values ​​and the weights; and calculate the overall risk assessment index value of the power grid system based on several power grid risk severity values.

[0077] The display module is used to obtain and display the risk indicator values ​​and overall risk assessment indicator values ​​at preset time points after the power grid system has been in operation.

[0078] As a preferred embodiment, the power grid operation risk indicator display device of the present invention quantifies and calculates several power grid risk indicators of a power grid system with massive adjustable resources participating through a risk indicator value calculation module, accurately calculating the severity of each risk. Quantifying these indicators makes the various risks associated with the participation of massive adjustable resources in power grid operation clearer and more intuitive, and the displayed content of each risk is more specific. Furthermore, the overall risk assessment indicator value calculation module establishes a risk assessment indicator system for the participation of massive adjustable resources in power grid operation. Based on the severity and probability values ​​of each risk, a comprehensive assessment of the overall system risk is performed, calculating the overall risk assessment indicator value of the power grid system and obtaining a comprehensive risk score for the power grid system. The calculation of the overall risk assessment indicator value enables a comprehensive and quantitative assessment and display of the operation risks of the power grid system, and the display module shows the risk data, making the displayed content of each risk associated with the participation of massive adjustable resources in power grid operation more specific. The calculation of the overall risk assessment indicator value enables a comprehensive and quantitative assessment and display of the operation risks of the power grid system.

[0079] As a preferred embodiment, the risk indicator value calculation module includes: a first risk indicator calculation unit and a second risk indicator calculation unit;

[0080] The first risk indicator calculation unit is used to quantify and calculate the indicators of structural risk:

[0081] The structural risks include: grid disconnection;

[0082] The quantitative calculation formula for the power grid disconnection index is as follows:

[0083]

[0084] Where α is the severity coefficient of branch line disconnection in grid disconnection, l is the number of branches disconnected from the system, L is the total number of branches in the system, and ΔP G P represents the lost active power of the generator. N The power generation required by the system;

[0085] Indicators for quantifying operational risks:

[0086] The operational risks include: frequency exceeding limits, insufficient backup reserves, voltage exceeding limits, and branch power flow exceeding limits;

[0087] The quantitative calculation formula for the frequency exceeding the limit index is as follows:

[0088] S FRE =32.4×(max(|Δf|-Δf) max ,0)) 3 ;

[0089] Where |Δf| is the system frequency deviation, Δf max This represents the maximum frequency deviation that the system can tolerate during normal operation.

[0090] The quantitative calculation formula for insufficient reserve indicators is as follows:

[0091]

[0092] Among them, R X R represents the actual spinning reserve capacity of the system. C R represents the actual energy storage reserve capacity of the system. N Reserve capacity for the system's rated standby.

[0093] The quantitative calculation formula for voltage over-limit index is as follows:

[0094] S V =0.9×(max(V) i -V i,max V i,min -V i ,0)) 2 ;

[0095] Among them, V i Let V be the voltage value at node i. i,max and V i，min These are the upper and lower limits of the node voltage, respectively;

[0096] The quantitative calculation formula for the branch power flow exceeding the limit index is as follows:

[0097] S LPF =0.76×max(P) l -P l,max ,0);

[0098] Among them, P l P represents the per-unit value of the actual active power of line l. l，max This is the per-unit value of the maximum active power allowed to be transmitted by line l;

[0099] Indicators for quantifying benefit and risk:

[0100] The aforementioned benefits and risks include: environmental benefits risks and network damage benefits risks;

[0101] The formula for quantifying environmental benefit risk indicators is as follows:

[0102]

[0103] Where, α CO2 β is the carbon dioxide coefficient converted from coal consumption. SO2 γ is the sulfur dioxide coefficient converted from coal consumption. Nox C is the nitrogen oxide coefficient converted from coal consumption. coal C represents the actual coal consumption. E-coal The expected coal consumption;

[0104] The formula for quantifying the network loss benefit risk index is as follows:

[0105]

[0106] Where, ΔP l For the actual loss of line l, ΔP N-l Let r be the expected loss of line l. l r is the actual network loss rate of line l. N-l Let P be the expected network loss rate of line l. l This refers to the active power transmitted at the beginning of line l.

[0107] The second risk indicator calculation unit is used to quantify the indicators of source-side risk:

[0108] The source-side risks include the risk of high penetration rate of distributed power generation, the risk of fluctuation in the output of distributed power generation, and the risk of wind and solar curtailment.

[0109] The formula for quantifying the severity of high penetration rate of distributed power sources is as follows:

[0110]

[0111] Among them, P DG (t) represents the output power of the distributed power source at time t, P max (t) represents the maximum power supply capacity of the power grid at time t;

[0112] Indicators for quantifying the output fluctuation risk of distributed power sources:

[0113] The formula for quantifying the severity of distributed power generation output fluctuation is as follows:

[0114] S CL =1.53×(max(|P DG (t)-P DG (t-1)|,0))2 ;

[0115] Among them, P DG (t) represents the per-unit value of the output power of the distributed power source at time t, P DG (t-1) represents the per-unit value of the output power of the distributed power source at time t-1;

[0116] Indicators for quantifying the risk of wind and solar power curtailment:

[0117] The formula for quantifying the severity of wind and solar power curtailment is as follows:

[0118]

[0119] ΔP W-L (t) represents the power of wind and solar power curtailment at time t, P W-L (t) represents the actual power generation of wind and solar power at time t, where t1 is the start time of the evaluation and t2 is the end time of the evaluation.

[0120] Indicators for quantifying load-side risk:

[0121] The load-side risks include forecast qualification rate, scheduling capacity failure risk, and load abandonment risk.

[0122] The quantitative calculation formula for the qualification rate of flexible load resource power prediction is as follows:

[0123]

[0124] Where, ΔP MAE ΔP represents the mean absolute error of the prediction. MAPE ΔP represents the average percentage error of the prediction. MSE P represents the prediction mean square error, δ1 is the percentage of prediction error, δ2 is the percentage of prediction mean square error, and P is the percentage of prediction mean square error. k This represents the actual power value. Here, N represents the predicted power value, and N is the sequence length.

[0125] The formula for quantifying the severity index of scheduling capability failure is as follows:

[0126]

[0127] Where, η k The degree of incomplete response scheduling is represented by t3~t4, which represents the time of incomplete response scheduling, and t5~t6, which represents the time of unresponsive scheduling. P F To predict output;

[0128] The formula for quantifying the severity of load abandonment is as follows:

[0129]

[0130] Where, ΔP DRl (t) represents the power consumed by the load shedding at time t, P DRl (t) represents the total power consumed by the load at time t, t7 is the start time of the evaluation, and t8 is the end time of the evaluation.

[0131] As a preferred option, the first risk includes structural risk, operational risk, and benefit risk. Structural risk refers to grid disconnection; operational risks include frequency exceeding limits, insufficient reserve, voltage exceeding limits, and branch power flow exceeding limits; benefit risks include environmental benefit risks and grid loss benefit risks. The second risk is the risk of adjustable resources participating in grid operation, including source-side risks such as the severity of high distributed generation penetration, the severity of distributed generation output fluctuations, and the severity of wind and solar power curtailment; and load-side risks such as the accuracy of flexible load resource power prediction, the severity of dispatch capability failure, and the severity of load curtailment. The first risk indicator calculation unit and the second risk indicator calculation unit respectively quantify the indicators of the first and second risks, accurately calculating the severity of each grid risk and the risk of adjustable resources participating in grid operation, making each grid risk clearer and more intuitive, and achieving a comprehensive quantitative assessment and display of the operational risks of the grid system.

[0132] As a preferred option, the overall risk assessment index value calculation module includes: an overall risk assessment index value calculation unit;

[0133] The overall risk assessment index value calculation unit is used to calculate the overall risk assessment index value based on the severity values ​​and probability values ​​of several power grid risks:

[0134]

[0135] Where E represents the overall risk assessment index value of the entire system after massive adjustable resources participate in the operation of the power grid, Ω is the weighting coefficient, and S C With P C These represent the severity and probability of occurrence of power grid structural risks, S. O With P O These represent the severity and probability of occurrence of power grid operation risks, respectively, S. E With P E S represents the severity and probability of occurrence of power grid benefit risk, respectively. P With P P These represent the severity and probability of source-side risks after adjustable resources participate in grid operation, S DR With P DR These represent the severity and probability of load-side risks after adjustable resources participate in grid operation.

[0136] As a preferred embodiment, this invention constructs quantitative calculation formulas for various risk indicators, calculating the grid risk, source-side risk, and load-side risk indicators for the participation of massive adjustable resources in the power grid. A risk assessment indicator system for the participation of massive adjustable resources in power grid operation is established in the overall risk assessment indicator value calculation unit. Based on the severity and probability of occurrence of grid risks, a risk assessment indicator calculation formula for the entire system is constructed. The indicator weights are solved, and the overall risk assessment indicator value of the power grid system is calculated. A comprehensive quantitative score is given for the risks of the power grid system, making the risks of the participation of massive adjustable resources in power grid operation clearer and more intuitive, and the content displayed for each risk more specific, thus achieving a comprehensive quantitative assessment and display of the operational risks of the power grid system.

[0137] Accordingly, the present invention also provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program; wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform a method for displaying power grid operation risk indicators as described in the present invention. Attached Figure Description

[0138] Figure 1 This is a flowchart illustrating an embodiment of a method for displaying power grid operation risk indicators provided by the present invention.

[0139] Figure 2 This is a schematic diagram of an embodiment of an improved IEEE 14 node system provided by the present invention; wherein, 1-14 are nodes of the improved IEEE 14 node system;

[0140] Figure 3 This is a schematic diagram of an embodiment of a power grid operation risk indicator display device provided by the present invention. Detailed Implementation

[0141] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0142] Example 1

[0143] Please refer to Figure 1 The present invention provides a method for displaying power grid operation risk indicators, comprising steps S101-S103:

[0144] Step S101: Obtain several risk influencing factors after the power grid system is in operation. Based on the risk influencing factors and power grid operation parameters, quantify and calculate several power grid risk indicators of the power grid system to obtain several risk indicator values.

[0145] In this embodiment, the risk factors affecting the participation of massive adjustable resources in power grid operation are as follows:

[0146] Factors affecting grid risks include: extreme weather, uncertainty of renewable energy output, insufficient active support capacity of renewable energy, renewable energy ramping phenomenon, large fluctuation of flexible load resources, insufficient support capacity of flexible load resources, and poor efficiency of adjustable resources.

[0147] Factors affecting source-side risks include excessively high renewable energy penetration, large fluctuations in renewable energy output, and the ramp-up phenomenon of renewable energy.

[0148] Factors influencing load-side risks include inaccurate power tracking predictions for flexible load resources, poor dispatchability, and ramp-up phenomena of flexible load resources.

[0149] In this embodiment, extreme weather events, exemplarily, include typhoons, total solar eclipses, heavy rain, and thunderstorms. Among these, typhoons and total solar eclipses significantly impact renewable energy generation, thereby affecting grid power quality, causing branch power flow exceeding limits, and excessive renewable energy penetration, leading to serious consequences such as wind and solar power curtailment. When the current time is identified as being during a typhoon or total solar eclipse, the risks to consider include: grid power quality risk, branch power flow exceeding limits risk, excessive renewable energy penetration risk, and wind and solar power curtailment risk.

[0150] Extreme weather events such as torrential rain and lightning make power generation and electrical equipment prone to failure and malfunction, posing a risk to the power grid. These issues affect the accuracy of flexible load resource power forecasting, severely impacting user comfort and easily leading to load shedding. When the current weather forecast indicates torrential rain and lightning, the risks to consider include: structural risk, operational risk, profitability risk, flexible load resource power forecast accuracy risk, and load shedding risk.

[0151] The vast amount of adjustable resources is characterized by uneven distribution, diverse types, and significant differences in characteristics. Furthermore, due to the varying geographical environments of these resources, their output is uncertain. This uncertainty in new energy power generation output can easily cause fluctuations in grid frequency and voltage, leading to problems such as voltage and current exceeding limits and branch power flow exceeding limits, and in severe cases, grid disconnection. The volatility of flexible load resources also adversely affects the grid's operating voltage, frequency, and power flow.

[0152] Regarding the interaction between massive adjustable resources and the power grid system, adjustable resources can provide active support capabilities for grid operation, such as inertia, primary frequency regulation, and grid adaptability. However, due to insufficient active support capabilities of some resources, adverse effects on grid operation can occur. For example, insufficient active support capabilities of new energy sources prevent them from effectively supporting grid peak shaving, thus affecting power quality, causing voltage and current exceedances, branch power flow exceedances, and even grid disconnection. Furthermore, insufficient support capabilities for flexible load resources lead to excessive penetration of load-related resources, thereby impacting user comfort.

[0153] Due to weather conditions, the power output of adjustable resources can fluctuate dramatically within a short timescale, leading to high-risk ramp-up events. With the increasing penetration rate of massive adjustable resources, the adverse effects of wind power ramp-up on the safe and stable operation of the power grid cannot be ignored. Wind power ramp-up easily causes power imbalances in the power system, resulting in voltage and frequency exceeding limits, branch power flow exceeding limits, and ultimately, wind and solar power curtailment. Similarly, ramp-up of flexible load resources can easily cause load curtailment events.

[0154] Currently, in addition to transmitting conventional energy, the power grid also undertakes the task of connecting and transmitting clean new energy sources. The addition of clean energy can reduce carbon emissions. However, due to the intermittent and fluctuating nature of new energy sources, it has an adverse impact on the operating efficiency of the power grid. Therefore, with the participation of massive adjustable resources, the power grid faces uncertainties in terms of whether it can meet the environmental benefits of carbon reduction standards, as well as the benefits of transmission and grid loss.

[0155] Therefore, in order to comprehensively quantify and demonstrate the operational risks of the power grid system, it is necessary to consider structural risks, operational risks, and benefit risks. Among them, structural risks include grid disconnection; operational risks include frequency exceeding limits, insufficient reserve, voltage exceeding limits, and branch power flow exceeding limits; benefit risks include environmental benefit risks and grid loss benefit risks.

[0156] Other risks to consider include: the severity of high distributed power penetration, the severity of distributed power output fluctuations and the severity of wind and solar curtailment; and the load-side risks of the accuracy of power forecasting for flexible load resources, the severity of scheduling capability failures and the severity of load curtailment.

[0157] In this embodiment, based on the risk influencing factors, the power grid risks are divided into several categories to obtain the first risk and the second risk;

[0158] Based on the power grid operating parameters, the indicators of the first risk are quantitatively calculated; the first risk includes structural risk, operational risk, and benefit risk.

[0159] The indicators of the second risk are quantified based on the power grid operating parameters; the second risk includes source-side risk and load-side risk.

[0160] In this embodiment, the indicators of the first risk are quantitatively calculated based on the power grid operating parameters, specifically as follows:

[0161] Indicators for quantifying structural risk:

[0162] The structural risks include grid disconnection. For grid disconnection risks, two aspects are considered: branch line disconnection and generator disconnection. The severity of grid disconnection risks is determined by considering the ratio of disconnected branches to the total number of branches and the change in system active power after generator disconnection.

[0163] The quantitative calculation formula for the power grid disconnection index is as follows:

[0164]

[0165] Where α is the severity coefficient of branch line disconnection in power grid disconnection, α is taken as 0.6, l is the number of branches disconnected from the system, L is the total number of branches in the system, and ΔP G P represents the lost active power of the generator. N The power generation required by the system;

[0166] Indicators for quantifying operational risks:

[0167] The operational risks include: frequency exceeding limits, insufficient backup reserves, voltage exceeding limits, and branch power flow exceeding limits;

[0168] Considering the steady-state frequency characteristics of the system, the severity of this index is measured by the degree to which the steady-state frequency deviation exceeds the allowable range. The quantitative calculation formula for the frequency exceedance index is as follows:

[0169] S FRE =32.4×(max(|Δf|-Δf) max ,0)) 3 ;

[0170] Where |Δf| is the system frequency deviation, Δf max This represents the maximum frequency deviation that the system can tolerate during normal operation.

[0171] The severity of insufficient system reserve is measured by the ratio of the sum of the system's actual spinning reserve and energy storage reserve to the system's rated reserve capacity. The quantitative calculation formula for the reserve reserve insufficiency index is as follows:

[0172]

[0173] Among them, R X R represents the actual spinning reserve capacity of the system.C R represents the actual energy storage reserve capacity of the system. N Reserve capacity for the system's rated standby.

[0174] The severity of voltage exceedance is measured by the degree to which the voltage amplitude at the node deviates from the normal operating range. The quantitative calculation formula for the voltage exceedance index is as follows:

[0175] S V =0.9×(max(V) i -V i,max V i,min -V i ,0)) 2 ;

[0176] Among them, V i Let V be the voltage value at node i. i,max and V i，min These are the upper and lower limits of the node voltage, respectively;

[0177] Similar to voltage overruns, the severity of branch power flow overruns is measured by the degree to which the line's active power exceeds its maximum carrying capacity. The quantitative calculation formula for the branch power flow overrun index is as follows:

[0178] S LPF =0.76×max(P) l -P l,max ,0);

[0179] Among them, P l P represents the per-unit value of the actual active power of line l. l，max This is the per-unit value of the maximum active power allowed to be transmitted by line l;

[0180] Indicators for quantifying benefit and risk:

[0181] The aforementioned benefits and risks include: environmental benefits risks and network damage benefits risks;

[0182] Coal combustion produces pollutants such as carbon dioxide, sulfur dioxide, and nitrogen oxides. While the integration of new energy sources can reduce coal consumption, its impact on environmental benefits is limited. Therefore, the severity of environmental risk is measured by multiplying the difference between actual and expected coal consumption with the pollutant emission coefficients converted from coal consumption. The quantitative calculation formula for the environmental risk index is as follows:

[0183]

[0184] Where, α CO2 The carbon dioxide conversion coefficient for coal consumption is taken as 0.008, β SO2 The sulfur dioxide conversion factor for coal consumption is taken as 0.007, γ NoxThe nitrogen oxide coefficient for coal consumption is taken as 0.006, C coal C represents the actual coal consumption. E-coal The expected coal consumption;

[0185] In the economic operation of the power grid, network losses have a significant impact. The severity of network losses is mainly measured by the line loss rate. Therefore, the actual line loss rate and the rated line loss rate are calculated separately, and the maximum difference between the two is used to describe the severity of the network loss benefit risk. The quantitative calculation formula for the network loss benefit risk index is as follows:

[0186]

[0187] Where, ΔP l For the actual loss of line l, ΔP N-l Let r be the expected loss of line l. l r is the actual network loss rate of line l. N-l Let P be the expected network loss rate of line l. l This refers to the active power transmitted at the beginning of line l.

[0188] In this embodiment, the indicators of the second risk are quantitatively calculated based on the power grid operating parameters, specifically as follows:

[0189] Indicators for quantifying source-side risks:

[0190] The source-side risks include the risk of high penetration rate of distributed power generation, the risk of fluctuation in the output of distributed power generation, and the risk of wind and solar curtailment.

[0191] The severity of high distributed generation penetration is measured by the ratio of distributed generation output power to the maximum grid supply capacity. The quantitative calculation formula for the severity index of high distributed generation penetration is as follows:

[0192]

[0193] Among them, P DG (t) represents the output power of the distributed power source at time t, P max (t) represents the maximum power supply capacity of the power grid at time t;

[0194] Indicators for quantifying the output fluctuation risk of distributed power sources:

[0195] The severity of distributed power output fluctuation is measured by the degree of fluctuation in the output power of the distributed power source at time t relative to the output power at the previous time. The quantitative calculation formula for the severity index of distributed power output fluctuation is as follows:

[0196] S CL =1.53×(max(|P DG (t)-P DG (t-1)|,0))2 ;

[0197] Among them, P DG (t) represents the per-unit value of the output power of the distributed power source at time t, P DG (t-1) represents the per-unit value of the output power of the distributed power source at time t-1;

[0198] Indicators for quantifying the risk of wind and solar power curtailment:

[0199] The severity of wind and solar curtailment is measured by the ratio of the energy curtailed from renewable energy sources to the total energy over a given period. The quantitative calculation formula for the severity of wind and solar curtailment is as follows:

[0200]

[0201] ΔP W-L (t) represents the power of wind and solar power curtailment at time t, P W-L (t) represents the actual power generation of wind and solar power at time t, where t1 is the start time of the evaluation and t2 is the end time of the evaluation.

[0202] Indicators for quantifying load-side risk:

[0203] The load-side risks include forecast qualification rate, scheduling capacity failure risk, and load abandonment risk.

[0204] Because flexible load resources are intermittent and fluctuating, there are many errors in power forecasting for them. This study primarily considers the mean absolute error, mean percentage error, and root mean square error of the forecast. The relative impact of these three errors is used to measure the success rate risk index of flexible load resource power forecasting. The quantitative calculation formula for the success rate index of flexible load resource power forecasting is as follows:

[0205]

[0206] Where, ΔP MAE ΔP represents the mean absolute error of the prediction. MAPE ΔP represents the average percentage error of the prediction. MSE The mean squared error of the prediction is represented by δ1, which is the percentage of prediction error, taken as 0.3, and δ2, which is the percentage of prediction mean squared error, taken as 0.4. k This represents the actual power value. Here, N represents the predicted power value, and N is the sequence length.

[0207] Incomplete or unresponsive scheduling of flexible load resources leads to scheduling capacity failure. Therefore, the severity of scheduling capacity failure can be measured by the degree of incomplete or unresponsive scheduling. The formula for quantifying the severity index of scheduling capacity failure is as follows:

[0208]

[0209] Where, η k The degree of incomplete response scheduling is represented by t3~t4, which represents the time of incomplete response scheduling, and t5~t6, which represents the time of unresponsive scheduling. P F To predict output;

[0210] The severity of load shedding is measured by the ratio of the energy abandoned by flexible load resources over a period of time to the total energy consumed by all loads. The quantitative calculation formula for the load shedding severity index is as follows:

[0211]

[0212] Where, ΔP DRl (t) represents the power consumed by the load shedding at time t, P DRl (t) represents the total power consumed by the load at time t, t7 is the start time of the evaluation, and t8 is the end time of the evaluation.

[0213] In this embodiment, in a system where a large number of adjustable resources participate in grid operation, the impact of grid risk, source-side risk, and load-side risk factors on the grid system are considered respectively. The indicators of each grid risk are quantitatively calculated to accurately determine the severity of each risk. The first risk includes structural risk, operational risk, and benefit risk; structural risk is grid disconnection; operational risks include frequency exceeding limits, insufficient reserve, voltage exceeding limits, and branch power flow exceeding limits; benefit risks include environmental benefit risks and grid loss benefit risks. The second risk is the risk of adjustable resources participating in grid operation, including source-side risks such as the severity of high distributed generation penetration, the severity of distributed generation output fluctuations, and the severity of wind and solar curtailment; and load-side risks such as the power prediction qualification rate of flexible load resources, the severity of dispatch capability failure, and the severity of load curtailment. The quantitative calculation of the first and second risks enables a comprehensive quantitative assessment of the operational risks of the grid system.

[0214] Step S102: Based on the relative importance values ​​of each of the power grid risk indicators, calculate the weight of each of the power grid risk indicators; based on the risk indicator values ​​and the weights, calculate the severity value of each of the power grid risks; based on the severity values ​​of the power grid risks, calculate the overall risk assessment index value of the power grid system.

[0215] Based on the severity values ​​of several power grid risks, the overall risk assessment index value of the power grid system is calculated. Specifically, the overall risk assessment index value is calculated based on the severity values ​​and probability values ​​of several power grid risks.

[0216]

[0217] Where E represents the overall risk assessment index value of the entire system after massive adjustable resources participate in the operation of the power grid, and S C With P C These represent the severity and probability of occurrence of power grid structural risks, S. O With P O These represent the severity and probability of occurrence of power grid operation risks, respectively, S. E With P E S represents the severity and probability of occurrence of power grid benefit risk, respectively. P With P P These represent the severity and probability of source-side risks after adjustable resources participate in grid operation, S DR With P DR These represent the severity and probability of load-side risks after adjustable resources participate in grid operation, respectively. Ω is a weighting coefficient that considers the relative importance of the severity and probability among grid structure risks, grid operation risks, grid benefit risks, source-side risks, and load-side risks. Ω is set to 0.6.

[0218] The probability of occurrence of power grid structure risk, power grid operation risk, power grid benefit risk, source-side risk and load-side risk is obtained by directly taking values ​​based on engineering practice, expert experience and comprehensive consideration.

[0219] In this embodiment, the weight values ​​among the various power grid risk indicators reflect the percentage of each indicator in the comprehensive index calculation. This characterizes the relative importance of each indicator, directly affecting the comprehensive evaluation result and its reliability. The Analytic Hierarchy Process (AHP) is a method for assigning weights to indicators based on expert experience. It can qualitatively and quantitatively calculate the weight coefficients between various evaluation indicators. Therefore, the AHP is used to solve for the weights of each risk indicator. The steps are as follows:

[0220] For each power grid risk indicator, pairwise comparisons and assessments are performed. The relative importance value of each indicator is determined using a 1-9 scale, generating a judgment matrix for the power grid risk indicators.

[0221]

[0222] In the formula, ɑ xy This represents the ratio of the importance of the x-th indicator to that of the y-th indicator.

[0223] The relative importance value of each power grid risk indicator is determined using the 1-9 scale method, specifically as follows:

[0224] 1 Equally important Both factors are equally important. 3 Slightly important When comparing two elements, one element is slightly more important than the other. 5 Obviously important When comparing two elements, one element is significantly more important than the other. 7 Strongly important When comparing two elements, one element is much more important than the other. 9 Extremely important When comparing two elements, one element is extremely more important than the other. 2，4，6，8 Inverse comparison Inverse comparison of two elements

[0225] The consistency of the judgment matrix is ​​checked using the following formula:

[0226]

[0227] In the formula, CI is the consistency index; RI is the average random consistency index.

[0228] CI can be represented as:

[0229]

[0230] In the formula, λ max It is the largest eigenvalue of the matrix.

[0231] RI is the average consistency index value of the analytic hierarchy process (AHP).

[0232]

[0233]

[0234] If n < 3, the judgment matrix will always be consistent. If CR = 0, the judgment matrix will be completely consistent. The larger CR is, the worse the consistency of the judgment matrix will be. If CR < 0.1, the judgment matrix is ​​consistent; if CR ≥ 0.1, the judgment matrix needs to be modified until the condition is met.

[0235] After verifying the consistency of the judgment matrix, the weights of the data in the judgment matrix are calculated as follows:

[0236]

[0237] After normalization, the weights of each indicator are obtained as follows:

[0238]

[0239] Please refer to Figure 2 This is a schematic diagram of the improved IEEE 14-node system, which includes wind power, photovoltaic power, flexible load resources, and conventional thermal power units, with a base power of 100 MVA. Conventional thermal power units are connected at nodes 1, 2, 3, 6, and 8; flexible load resources are connected at nodes 4 and 5, with rated power consumption of 47.8 MW and 7.6 MW respectively; a 50 MW photovoltaic power plant is connected at node 9; a 50 MW wind power plant is connected at node 14; and conventional load resources are also connected at nodes 2, 3, 6, 9, 10, 11, 12, 13, and 14.

[0240] For example, the weights are calculated using the analytic hierarchy process (AHP) based on the relative importance of each indicator:

[0241] The weight of the power grid disconnection index in structural risk is 1;

[0242] The weights of the frequency over-limit indicator, insufficient reserve indicator, voltage over-limit indicator, and branch power flow over-limit indicator in the operational risk are 0.248, 0.224, 0.283, and 0.244, respectively.

[0243] The environmental benefit risk indicator has a weight of 0.522, while the network loss benefit risk indicator has a weight of 0.478.

[0244] The weights of the severity indicators for high penetration rate of distributed power sources, output fluctuation of distributed power sources, and curtailment of wind and solar power in the source-side risk assessment are 0.324, 0.290, and 0.386, respectively.

[0245] In the load-side risk assessment, the weights of the flexible load resource power prediction qualification rate, the dispatch capability failure severity, and the load abandonment severity are 0.306, 0.322, and 0.372, respectively.

[0246] In this embodiment, the severity value S of each risk is obtained by multiplying the weight of each indicator in each risk with the corresponding risk indicator value. For example, load-side risks include the flexibility load resource power prediction qualification rate indicator, the scheduling capacity failure severity indicator, and the load abandonment severity indicator. The weights of the solved flexibility load resource power prediction qualification rate indicator, scheduling capacity failure severity indicator, and load abandonment severity indicator are 0.306, 0.322, and 0.372, respectively, and are multiplied by the corresponding risk indicator value S. DRP S SX and S DRl Multiply and sum to obtain the severity value S of the load-side risk. DR ; respectively obtain the severity values ​​of structural risk, operational risk, benefit risk, source-side risk, and load-side risk, S C It is 0.35, S O S is 0.28. E S is 0.15. P It is 0.44, S DR It is 0.42;

[0247] Substituting the severity and probability of occurrence of structural risk, operational risk, benefit risk, source-side risk, and load-side risk into the formula for the overall risk assessment index, we obtain the overall risk assessment index value E:

[0248]

[0249] In this embodiment, quantitative calculation formulas for various risk indicators are constructed to calculate the grid risk, source-side risk, and load-side risk indicators for the participation of massive adjustable resources in the power grid, making the risks of massive adjustable resources participating in power grid operation clearer and more intuitive. This invention establishes a risk assessment indicator system for the participation of massive adjustable resources in power grid operation, and constructs the risk assessment indicator calculation formula for the entire system based on the severity and probability values ​​of grid risks. The indicator weights are solved and the overall risk assessment indicator value of the power grid system is calculated, and the risks of the power grid system are comprehensively quantitatively scored, making the displayed content of each risk more specific, and realizing a comprehensive and quantitative assessment and display of the operational risks of the power grid system.

[0250] Step S103: Obtain and display the risk index values ​​and overall risk assessment index values ​​at preset time points after the power grid system has been in operation.

[0251] In this embodiment, considering the power output fluctuations of new energy units and the uncertainty of flexible load resources, five time points T1-T5 are randomly selected. The system risk is comprehensively scored based on the overall risk assessment index values ​​at these five time points, resulting in the following risk index values, i.e., the severity of each risk, and E-value data:

[0252]

[0253]

[0254] Analysis shows that the overall risk value at time T1 is relatively low, with a risk score (overall risk assessment index) of 26.2085. At time T2, the severity of insufficient reserve, network loss benefit risk, and failure of flexible load resource dispatching capability are relatively high, with a risk score of 31.9887. At time T3, the risk level is significantly affected by environmental benefit risk, with a score of 53.4555. At time T4, the severity of grid disconnection risk, branch power flow exceeding limit risk, and flexible load resource power prediction qualification rate risk are all relatively high, resulting in a high risk score of 66.2658. At time T5, the risk severity is mainly affected by the severity of failure of flexible load resource dispatching capability, with a score of 68.7608.

[0255] The above analysis shows that the risk assessment index system and method constructed in this invention can accurately calculate the severity of each risk and comprehensively assess the overall risk of the system with a large number of adjustable resources participating in the operation of the power grid, thereby obtaining a system risk score and achieving the goal of system risk observability, thus verifying the effectiveness of the results of this invention.

[0256] Implementing the embodiments of the present invention has the following effects:

[0257] This invention quantifies and calculates several power grid risk indicators for power grid systems involving massive adjustable resources, accurately determining the severity of each risk. By assigning weights to each risk indicator, the severity value of each power grid risk is calculated, making the risks clearer and more intuitive. This invention establishes a risk assessment indicator system for power grid operation involving massive adjustable resources. Based on the severity and probability values ​​of each risk, a comprehensive assessment of the overall system risk is conducted, calculating the overall risk assessment indicator value of the power grid system and deriving a comprehensive risk score. The quantification of several power grid risk indicators makes the various risks associated with the operation of the power grid involving massive adjustable resources clearer and more intuitive, with more specific details displayed for each risk. The calculation of the overall risk assessment indicator value enables a comprehensive and quantitative assessment and display of the operational risks of the power grid system.

[0258] Example 2

[0259] Please refer to Figure 3 The present invention provides a power grid operation risk indicator display device, comprising: a risk indicator value calculation module 201, an overall risk assessment indicator value calculation module 202, and a display module 203.

[0260] The risk index value calculation module 201 is used to obtain several risk influencing factors after the power grid system is in operation, and to quantify and calculate several power grid risk indicators of the power grid system based on the risk influencing factors and power grid operation parameters to obtain several risk index values.

[0261] The overall risk assessment index value calculation module 202 is used to solve the weight of each of the power grid risk indicators based on the relative importance value of each of the power grid risk indicators; calculate the severity value of each of the power grid risks based on several risk indicator values ​​and the weights; and calculate the overall risk assessment index value of the power grid system based on several severity values ​​of the power grid risks.

[0262] The display module 203 is used to obtain and display the risk index values ​​and overall risk assessment index values ​​at preset times after the power grid system has been in operation.

[0263] The risk indicator value calculation module 201 includes: a first risk indicator calculation unit and a second risk indicator calculation unit;

[0264] The first risk indicator calculation unit is used to quantify and calculate the indicators of structural risk:

[0265] The structural risks include: grid disconnection;

[0266] The quantitative calculation formula for the power grid disconnection index is as follows:

[0267]

[0268] Where α is the severity coefficient of branch line disconnection in grid disconnection, l is the number of branches disconnected from the system, L is the total number of branches in the system, and ΔP G P represents the lost active power of the generator. N The power generation required by the system;

[0269] Indicators for quantifying operational risks:

[0270] The operational risks include: frequency exceeding limits, insufficient backup reserves, voltage exceeding limits, and branch power flow exceeding limits;

[0271] The quantitative calculation formula for the frequency exceeding the limit index is as follows:

[0272] S FRE =32.4×(max(|Δf|-Δf) max ,0)) 3 ;

[0273] Where |Δf| is the system frequency deviation, Δf max This represents the maximum frequency deviation that the system can tolerate during normal operation.

[0274] The quantitative calculation formula for insufficient reserve indicators is as follows:

[0275]

[0276] Among them, R X R represents the actual spinning reserve capacity of the system. C R represents the actual energy storage reserve capacity of the system. N Reserve capacity for the system's rated standby.

[0277] The quantitative calculation formula for voltage over-limit index is as follows:

[0278] S V =0.9×(max(V) i -V i,max V i,min -V i ,0)) 2 ;

[0279] Among them, V i Let V be the voltage value at node i. i,max and V i，min These are the upper and lower limits of the node voltage, respectively;

[0280] The quantitative calculation formula for the branch power flow exceeding the limit index is as follows:

[0281] S LPF =0.76×max(P)l -P l,max ,0);

[0282] Among them, P l P represents the per-unit value of the actual active power of line l. l，max This is the per-unit value of the maximum active power allowed to be transmitted by line l;

[0283] Indicators for quantifying benefit and risk:

[0284] The aforementioned benefits and risks include: environmental benefits risks and network damage benefits risks;

[0285] The formula for quantifying environmental benefit risk indicators is as follows:

[0286]

[0287] Where, α CO2 β is the carbon dioxide coefficient converted from coal consumption. SO2 γ is the sulfur dioxide coefficient converted from coal consumption. Nox C is the nitrogen oxide coefficient converted from coal consumption. coal C represents the actual coal consumption. E-coal The expected coal consumption;

[0288] The formula for quantifying the network loss benefit risk index is as follows:

[0289]

[0290] Where, ΔP l For the actual loss of line l, ΔP N-l Let r be the expected loss of line l. l r is the actual network loss rate of line l. N-l Let P be the expected network loss rate of line l. l This refers to the active power transmitted at the beginning of line l.

[0291] The second risk indicator calculation unit is used to quantify the indicators of source-side risk:

[0292] The source-side risks include the risk of high penetration rate of distributed power generation, the risk of fluctuation in the output of distributed power generation, and the risk of wind and solar curtailment.

[0293] The formula for quantifying the severity of high penetration rate of distributed power sources is as follows:

[0294]

[0295] Among them, P DG (t) represents the output power of the distributed power source at time t, P max (t) represents the maximum power supply capacity of the power grid at time t;

[0296] Indicators for quantifying the output fluctuation risk of distributed power sources:

[0297] The formula for quantifying the severity of distributed power generation output fluctuation is as follows:

[0298] S CL =1.53×(max(|P DG (t)-P DG (t-1)|,0)) 2 ;

[0299] Among them, P DG (t) represents the per-unit value of the output power of the distributed power source at time t, P DG (t-1) represents the per-unit value of the output power of the distributed power source at time t-1;

[0300] Indicators for quantifying the risk of wind and solar power curtailment:

[0301] The formula for quantifying the severity of wind and solar power curtailment is as follows:

[0302]

[0303] ΔP W-L (t) represents the power of wind and solar power curtailment at time t, P W-L (t) represents the actual power generation of wind and solar power at time t, where t1 is the start time of the evaluation and t2 is the end time of the evaluation.

[0304] Indicators for quantifying load-side risk:

[0305] The load-side risks include forecast qualification rate, scheduling capacity failure risk, and load abandonment risk.

[0306] The quantitative calculation formula for the qualification rate of flexible load resource power prediction is as follows:

[0307]

[0308] Where, ΔP MAE ΔP represents the mean absolute error of the prediction. MAPE ΔP represents the average percentage error of the prediction. MSE P represents the prediction mean square error, δ1 is the percentage of prediction error, δ2 is the percentage of prediction mean square error, and P is the percentage of prediction mean square error. k This represents the actual power value. Here, N represents the predicted power value, and N is the sequence length.

[0309] The formula for quantifying the severity index of scheduling capability failure is as follows:

[0310]

[0311] Where, η kThe degree of incomplete response scheduling is represented by t3~t4, which represents the time of incomplete response scheduling, and t5~t6, which represents the time of unresponsive scheduling. P F To predict output;

[0312] The formula for quantifying the severity of load abandonment is as follows:

[0313]

[0314] Where, ΔP DRl (t) represents the power consumed by the load shedding at time t, P DRl (t) represents the total power consumed by the load at time t, t7 is the start time of the evaluation, and t8 is the end time of the evaluation.

[0315] The overall risk assessment index value calculation module 202 includes: an overall risk assessment index value calculation unit;

[0316] The overall risk assessment index value calculation unit is used to calculate the overall risk assessment index value based on the severity values ​​and probability values ​​of several power grid risks:

[0317]

[0318] Where E represents the overall risk assessment index value of the entire system after massive adjustable resources participate in the operation of the power grid, Ω is the weighting coefficient, and S C With P C These represent the severity and probability of occurrence of power grid structural risks, S. O With P O These represent the severity and probability of occurrence of power grid operation risks, respectively, S. E With P E S represents the severity and probability of occurrence of power grid benefit risk, respectively. P With P P These represent the severity and probability of source-side risks after adjustable resources participate in grid operation, S DR With P DR These represent the severity and probability of load-side risks after adjustable resources participate in grid operation.

[0319] The aforementioned device for displaying power grid operation risk indicators can implement the method for displaying power grid operation risk indicators described in the above-described method embodiments. The options described in the above method embodiments are also applicable to this embodiment and will not be detailed here. The remaining content of this application embodiment can be referred to the content of the above method embodiments, and will not be repeated in this embodiment.

[0320] Implementing the embodiments of the present invention has the following effects:

[0321] The power grid operation risk indicator display device of the present invention quantifies and calculates several power grid risk indicators of a power grid system with massive adjustable resources through a risk indicator value calculation module, accurately calculating the severity of each risk. This quantification of several power grid risk indicators makes the various risks of power grid operation with massive adjustable resources clearer and more intuitive, and the displayed content of each risk is more specific. The overall risk assessment indicator value calculation module establishes a risk assessment indicator system for power grid operation with massive adjustable resources. Based on the severity and probability values ​​of each risk, it comprehensively assesses the overall risk of the system with massive adjustable resources, calculates the overall risk assessment indicator value of the power grid system, and obtains the comprehensive risk score of the power grid system. The calculation of the overall risk assessment indicator value realizes a comprehensive and quantitative assessment and display of the operation risk of the power grid system, and the display module shows the risk data, making the displayed content of each risk of power grid operation with massive adjustable resources more specific. The calculation of the overall risk assessment indicator value realizes a comprehensive and quantitative assessment and display of the operation risk of the power grid system.

[0322] Example 3

[0323] Accordingly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the method for displaying power grid operation risk indicators as described in any of the above embodiments.

[0324] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0325] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0326] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0327] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile terminal, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0328] Wherein, if the modules / units integrated into the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0329] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for displaying power grid operation risk indicators, characterized in that, include: Several risk influencing factors after the power grid system is put into operation are obtained. Based on the risk influencing factors and power grid operating parameters, several power grid risk indicators are quantitatively calculated to obtain several risk indicator values. Specifically, based on the risk influencing factors, power grid risks are divided into several categories to obtain the first risk and the second risk. Based on the power grid operating parameters, the indicators of the first risk are quantitatively calculated. The first risk includes structural risk, operational risk, and benefit risk. Based on the power grid operating parameters, the indicators of the second risk are quantitatively calculated; the second risk includes source-side risk and load-side risk. The method of quantifying and calculating the indicators of the first risk based on power grid operating parameters is as follows: Indicators for quantifying structural risk: The structural risks include: grid disconnection; The quantitative calculation formula for the power grid disconnection index is as follows: ； Where α is the severity coefficient of branch line disconnection in power grid disconnection, l is the number of branches disconnected from the system, and L is the total number of branches in the system. P G P represents the lost active power of the generator. N The power generation required by the system; Indicators for quantifying operational risks: The operational risks include: frequency exceeding limits, insufficient backup reserves, voltage exceeding limits, and branch power flow exceeding limits; The quantitative calculation formula for the frequency exceeding the limit index is as follows: ； in, f is the system frequency deviation. f max This represents the maximum frequency deviation that the system can tolerate during normal operation. The quantitative calculation formula for insufficient reserve indicators is as follows: ； Among them, R X R represents the actual spinning reserve capacity of the system. C R represents the actual energy storage reserve capacity of the system. N Reserve capacity for the system's rated standby. The quantitative calculation formula for voltage over-limit index is as follows: ； Among them, V i Let V be the voltage value at node i. i,max and V i，min These are the upper and lower limits of the node voltage, respectively; The quantitative calculation formula for the branch power flow exceeding the limit index is as follows: ； Among them, P l P represents the per-unit value of the actual active power of line l. l，max This is the per-unit value of the maximum active power allowed to be transmitted by line l; Indicators for quantifying benefit and risk: The aforementioned benefits and risks include: environmental benefits risks and network damage benefits risks; The formula for quantifying environmental benefit risk indicators is as follows: ； in, This is the carbon dioxide coefficient converted from coal consumption. The sulfur dioxide coefficient converted from coal consumption. C is the nitrogen oxide coefficient converted from coal consumption. coal C represents the actual coal consumption. E-coal The expected coal consumption; The formula for quantifying the network loss benefit risk index is as follows: ； in, P l This represents the actual loss of line l. P N-l Let r be the expected loss of line l. l r is the actual network loss rate of line l. N-l Let P be the expected network loss rate of line l. l This refers to the active power transmitted at the beginning of line l; The method of quantifying and calculating the indicators of the second risk based on power grid operating parameters is as follows: Indicators for quantifying source-side risks: The source-side risks include the risk of high penetration rate of distributed power generation, the risk of fluctuation in the output of distributed power generation, and the risk of wind and solar curtailment. The formula for quantifying the severity of high penetration rate of distributed power sources is as follows: ； Among them, P DG (t) represents the output power of the distributed power source at time t, P max (t) represents the maximum power supply capacity of the power grid at time t; Indicators for quantifying the output fluctuation risk of distributed power sources: The formula for quantifying the severity of distributed power generation output fluctuation is as follows: ； Among them, P DG (t) represents the per-unit value of the output power of the distributed power source at time t, P DG (t-1) represents the per-unit value of the output power of the distributed power source at time t-1; Indicators for quantifying the risk of wind and solar power curtailment: The formula for quantifying the severity of wind and solar power curtailment is as follows: ； P W-L (t) represents the power of wind and solar power curtailment at time t, P W-L (t) represents the actual wind and solar power generation at time t, where t1 is the start time of the evaluation and t2 is the end time of the evaluation; Indicators for quantifying load-side risk: The load-side risks include forecast qualification rate, scheduling capacity failure risk, and load abandonment risk. The quantitative calculation formula for the qualification rate of flexible load resource power prediction is as follows: ； in, P MAE Indicates the mean absolute error of the prediction. P MAPE This indicates the average percentage error in prediction. P MSE P represents the prediction mean square error, δ1 is the percentage of prediction error, δ2 is the percentage of prediction mean square error, and P is the percentage of prediction mean square error. k This represents the actual power value. Here, N represents the predicted power value, and N is the sequence length. The formula for quantifying the severity index of scheduling capability failure is as follows: ； Where, η k The degree of incomplete response scheduling is represented by t3~t4, which represents the time of incomplete response scheduling, and t5~t6, which represents the time of unresponsive scheduling. P F To predict output; The formula for quantifying the severity of load abandonment is as follows: ； in, P DRl (t) represents the power consumed by the load shedding at time t, P DRl (t) represents the total power consumed by the load at time t, t7 is the start time of the evaluation, and t8 is the end time of the evaluation; Based on the relative importance values ​​of each of the power grid risk indicators, the weights of each of the power grid risk indicators are determined; based on the risk indicator values ​​and the weights, the severity values ​​of each of the power grid risks are calculated; based on the severity values ​​of the power grid risks, the overall risk assessment index value of the power grid system is calculated. The risk index values ​​and overall risk assessment index values ​​at preset time points after the power grid system has been in operation are obtained and displayed.

2. A device for displaying power grid operation risk indicators, characterized in that, include: The module includes a risk indicator value calculation module, an overall risk assessment indicator value calculation module, and a display module. The risk index value calculation module is used to acquire several risk influencing factors after the power grid system is in operation, and to quantify several power grid risk indicators based on the risk influencing factors and power grid operating parameters to obtain several risk index values. Specifically, based on the risk influencing factors, the power grid risks are divided into several categories to obtain a first risk and a second risk; based on the power grid operating parameters, the index of the first risk is quantified; the first risk includes structural risk, operational risk, and benefit risk; based on the power grid operating parameters, the index of the second risk is quantified; the second risk includes source-side risk and load-side risk. The risk indicator value calculation module includes: a first risk indicator calculation unit and a second risk indicator calculation unit; The first risk indicator calculation unit is used to quantify and calculate the indicators of structural risk: The structural risks include: grid disconnection; The quantitative calculation formula for the power grid disconnection index is as follows: ； Where α is the severity coefficient of branch line disconnection in power grid disconnection, l is the number of branches disconnected from the system, and L is the total number of branches in the system. P G P represents the lost active power of the generator. N The power generation required by the system; Indicators for quantifying operational risks: The operational risks include: frequency exceeding limits, insufficient backup reserves, voltage exceeding limits, and branch power flow exceeding limits; The quantitative calculation formula for the frequency exceeding the limit index is as follows: ； in, f is the system frequency deviation. f max This represents the maximum frequency deviation that the system can tolerate during normal operation. The quantitative calculation formula for insufficient reserve indicators is as follows: ； Among them, R X R represents the actual spinning reserve capacity of the system. C R represents the actual energy storage reserve capacity of the system. N Reserve capacity for the system's rated standby. The quantitative calculation formula for voltage over-limit index is as follows: ； Among them, V i Let V be the voltage value at node i. i,max and V i，min These are the upper and lower limits of the node voltage, respectively; The quantitative calculation formula for the branch power flow exceeding the limit index is as follows: ； Among them, P l P represents the per-unit value of the actual active power of line l. l，max This is the per-unit value of the maximum active power allowed to be transmitted by line l; Indicators for quantifying benefit and risk: The aforementioned benefits and risks include: environmental benefits risks and network damage benefits risks; The formula for quantifying environmental benefit risk indicators is as follows: ； in, This is the carbon dioxide coefficient converted from coal consumption. The sulfur dioxide coefficient converted from coal consumption. C is the nitrogen oxide coefficient converted from coal consumption. coal C represents the actual coal consumption. E-coal The expected coal consumption; The formula for quantifying the network loss benefit risk index is as follows: ； in, P l This represents the actual loss of line l. P N-l Let r be the expected loss of line l. l r is the actual network loss rate of line l. N-l Let P be the expected network loss rate of line l. l This refers to the active power transmitted at the beginning of line l; The second risk indicator calculation unit is used to quantify the indicators of source-side risk: The source-side risks include the risk of high penetration rate of distributed power generation, the risk of fluctuation in the output of distributed power generation, and the risk of wind and solar curtailment. The formula for quantifying the severity of high penetration rate of distributed power sources is as follows: ； Among them, P DG (t) represents the output power of the distributed power source at time t, P max (t) represents the maximum power supply capacity of the power grid at time t; Indicators for quantifying the output fluctuation risk of distributed power sources: The formula for quantifying the severity of distributed power generation output fluctuation is as follows: ； Among them, P DG (t) represents the per-unit value of the output power of the distributed power source at time t, P DG (t-1) represents the per-unit value of the output power of the distributed power source at time t-1; Indicators for quantifying the risk of wind and solar power curtailment: The formula for quantifying the severity of wind and solar power curtailment is as follows: ； P W-L (t) represents the power of wind and solar power curtailment at time t, P W-L (t) represents the actual wind and solar power generation at time t, where t1 is the start time of the evaluation and t2 is the end time of the evaluation; Indicators for quantifying load-side risk: The load-side risks include forecast qualification rate, scheduling capacity failure risk, and load abandonment risk. The quantitative calculation formula for the qualification rate of flexible load resource power prediction is as follows: ； in, P MAE Indicates the mean absolute error of the prediction. P MAPE This indicates the average percentage error in prediction. P MSE P represents the prediction mean square error, δ1 is the percentage of prediction error, δ2 is the percentage of prediction mean square error, and P is the percentage of prediction mean square error. k This represents the actual power value. Here, N represents the predicted power value, and N is the sequence length. The formula for quantifying the severity index of scheduling capability failure is as follows: ； Where, η k The degree of incomplete response scheduling is represented by t3~t4, which represents the time of incomplete response scheduling, and t5~t6, which represents the time of unresponsive scheduling. P F To predict output; The formula for quantifying the severity of load abandonment is as follows: ； in, P DRl (t) represents the power consumed by the load shedding at time t, P DRl (t) represents the total power consumed by the load at time t, t7 is the start time of the evaluation, and t8 is the end time of the evaluation; The overall risk assessment index value calculation module is used to solve the weight of each power grid risk index based on the relative importance value of each power grid risk index; calculate the severity value of each power grid risk based on several risk index values ​​and the weights; and calculate the overall risk assessment index value of the power grid system based on several power grid risk severity values. The display module is used to obtain and display the risk indicator values ​​and overall risk assessment indicator values ​​at preset time points after the power grid system has been in operation.

3. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program; wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform a method for displaying power grid operation risk indicators as described in claim 1.

Images