A main and auxiliary micro-time and space hierarchical early warning method considering risk transmission effect

Abstract

The application relates to the technical field of power system operation optimization, and discloses a main-distribution-micro time-space hierarchical early warning method considering risk conduction effect, which comprises the following steps: based on an uncertainty risk propagation effect analysis method of each level, a risk conduction model based on a sensitivity matrix is established, and three typical conduction paths of downward conduction of a main grid, upward conduction of a micro grid and upward and downward conduction of a distribution grid are identified; based on an N-1 fault risk bidirectional conduction analysis method considering a transfer path, a cross-level risk diffusion range is quantified; based on a risk diffusion range identification method considering aggravation / buffering effect, a region boundary where risk conduction is likely to occur is identified; a risk intensity probability evaluation model considering accumulation effect is constructed by comprehensively considering operation risk types, duration, occurrence frequency and influence degree; a dynamic hierarchical rolling early warning mechanism based on risk space-time confidence is constructed, and accurate risk intensity evaluation and multi-level early warning of different regions in a future period are realized. The application has the advantage of effectively improving the operation safety level of a main-distribution-micro system.

Description

Technical Field

[0001] This invention relates to the field of power system operation optimization and safety early warning technology, and in particular to a primary and secondary micro-spatiotemporal hierarchical early warning method that takes into account the risk transmission effect. Background Technology

[0002] With a high proportion of distributed resources integrated into the power system, the operational risks of the main grid, distribution network, and microgrids exhibit complex characteristics of multi-point dispersion and spatiotemporal coupling. Traditional security analysis methods are mostly static assessments of single-level, typical scenarios, which are difficult to adapt to multi-level dynamic risk analysis under the influence of multi-point dispersion, uncertainty, and spatiotemporal coupling in the main grid, distribution network, and microgrids. A complex two-way risk transmission mechanism exists between the various levels of the main grid, distribution network, and microgrid: risks in the main grid may be transmitted downwards to the distribution network, and risks in the distribution network may also be transmitted upwards to affect the main grid; distribution network risks may also be transmitted simultaneously in both directions. This multi-level risk interaction and coupling may produce amplification, offsetting, or buffering effects, posing a severe challenge to system operational safety.

[0003] In existing technologies, there is a lack of systematic analysis of the bidirectional transmission mechanism of multi-level operational risks in main and distribution microsystems. It is difficult to accurately identify the scope of risk diffusion and quantify the risk intensity, and it is impossible to achieve accurate dynamic hierarchical early warning. This leads to a reduction in the safety margin of system operation and difficulty in ensuring power supply reliability. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a primary and secondary micro-spatial hierarchical early warning method that takes into account the risk transmission effect.

[0005] The objective of this invention is achieved through the following technical solution: a primary-secondary spatiotemporal hierarchical early warning method considering risk transmission effects, the method comprising,

[0006] S1. Based on the analysis method of uncertainty risk propagation effect at each level, establish a risk transmission model based on the sensitivity matrix, and identify three typical transmission paths: downward transmission from the main grid, upward transmission from distribution microgrids, and vertical transmission from distribution grid to distribution network.

[0007] S2. An N-1 fault risk bidirectional transmission analysis method based on the transfer path is used to quantify the cross-level risk diffusion range;

[0008] S3. A risk diffusion range identification method based on aggravation / buffering effects is used to identify the boundaries of areas where risk transmission may occur.

[0009] S4. Taking into account the type, duration, frequency and impact of operational risks, construct a multi-dimensional coupled risk intensity probability assessment model that takes into account the cumulative effect;

[0010] S5. Construct a dynamic hierarchical rolling early warning mechanism based on risk spatiotemporal confidence to achieve accurate assessment and multi-level early warning of risk intensity in different regions in the future.

[0011] Specifically, the specific steps of S1 include:

[0012] S11. Establish a system state sensitivity model: Based on the Jacobian matrix calculated from the power grid flow, establish a sensitivity relationship model between system state changes and disturbances.

[0013] ;

[0014] In the formula, The change in the system state variable; This is the sensitivity matrix; For state variables; For control or disturbance variables; This is the steady-state operating equation of the power grid;

[0015] S12. Identify risk propagation paths: Based on graph theory and complex network theory, construct a risk propagation model; using key nodes at each level as risk carriers and electrical connections between levels as channels, identify vertical, horizontal, and reverse propagation paths;

[0016] S13. Analyze three typical risk transmission mechanisms: quantify the voltage transmission effect and power transmission effect of the main grid risk transmission downward, the power flow reverse transmission effect and voltage superposition effect of the distribution network risk transmission upward, and the impact of the distribution network risk transmission upward and downward on the main grid and the lower-level microgrid / load.

[0017] S14. Extract typical conduction modes: Through simulation analysis and historical data mining, extract typical risk conduction modes, including attenuation conduction, amplification conduction and blocking conduction.

[0018] Specifically, the steps of S2 are as follows:

[0019] S21. Establish a comprehensive risk index: Define a weighted measure of the deviation of key system operating indicators from their safety thresholds under specific disturbance scenarios as a comprehensive risk index.

[0020] S22. Analyze the multi-level interactive coupling effect of risks: Quantitatively analyze the amplification effect, offsetting effect, and buffering effect of risks under the intervention of automatic adjustment measures;

[0021] S23. Analysis of the bidirectional transmission of N-1 risk considering the transfer path: By reconstructing the transfer path and analyzing the power flow under equipment failure conditions, the scope and intensity of the transmission of N-1 risk from the main grid to the downstream and the feedback of N-1 risk from the downstream to the upstream are identified and quantified.

[0022] S24. Identify the direction and scope of risk transmission: Based on the power flow tracing algorithm and complex network theory, construct a risk transmission path map to quantify the range of affected equipment and load loss caused by N-1 faults.

[0023] Specifically, the specific steps of S3 include:

[0024] S31. Establish a spatial quantitative model for the scope of risk diffusion: Based on the comprehensive risk index, combined with the physical importance weight of nodes and time dynamics, calculate the risk impact measure of nodes, and define the set of all nodes whose risk impact measure exceeds the preset critical threshold as the scope of risk diffusion.

[0025] S32. Boundary dynamic adjustment method considering coupling effect: Introduce spatial amplification correction factor and spatial buffer correction factor to dynamically adjust the boundary of the initial risk diffusion range, so as to reflect the actual impact of amplification effect and offset / buffering effect on risk propagation.

[0026] S33. Cross-level boundary identification: Based on the risk blocking index at the key connection point of the boundary, determine whether the risk has successfully crossed the boundaries of each level of the main, auxiliary, and micro-level systems.

[0027] Specifically, the specific steps of S4 include:

[0028] S41. Quantification of risk accumulation effect: Quantify the accumulation effect of operational risks from four dimensions: risk type weight, time accumulation factor, frequency impact coefficient, and impact degree coefficient.

[0029] S42. Comprehensive Risk Intensity Probability Model: Construct a comprehensive risk intensity probability assessment model that considers cumulative effects, and calculate the risk intensity probability of a specific region within a given time period.

[0030] Specifically, the steps of S5 include:

[0031] S51. Quantification of multi-dimensional operational risk indicators: Standardize and quantify four types of operational risk indicators: power imbalance risk, equipment overload risk, voltage limit violation risk, and N-1 non-compliance risk.

[0032] S52. Construct a risk confidence assessment function: comprehensively consider the risk probability intensity, risk evolution trend, and system controllability to calculate the risk confidence of each region within a future time window. The function takes into account the risk transmission impact of adjacent regions in the spatial dimension and the accumulation and evolution trend of risk in the time dimension.

[0033] S53. Dynamic Grading and Rolling Early Warning: Based on the calculated risk confidence level, multiple early warning level thresholds are set for multi-level early warning; and the early warning information is updated on a rolling basis based on the forward sliding time window.

[0034] The present invention has the following advantages:

[0035] This invention establishes a risk transmission model based on a sensitivity matrix and complex networks, systematically revealing the mechanism of bidirectional risk transmission between the main grid, distribution network, and microgrids. It achieves accurate quantification of the cross-level risk propagation path, intensity, and coupling amplification / buffering effects. Furthermore, it constructs a dynamic probability assessment model that considers spatiotemporal cumulative effects and a hierarchical rolling early warning mechanism. Ultimately, it solves the problem that traditional methods are unable to cope with the multi-level, multi-directional, and spatiotemporally coupled risks of the main grid, distribution network, and microgrids under high-proportion renewable energy access, significantly improving the safety pre-control capability and power supply reliability of the power grid operation. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the early warning method of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described embodiments are merely some embodiments of the invention, and not all embodiments. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0038] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0039] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0040] The present invention will be further described below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0041] like Figure 1 As shown, a primary-secondary spatiotemporal hierarchical early warning method considering risk transmission effects is proposed. This method includes:

[0042] S1. Based on the analysis method of uncertainty risk propagation effect at each level, establish a risk transmission model based on the sensitivity matrix, and identify three typical transmission paths: downward transmission from the main grid, upward transmission from distribution microgrids, and vertical transmission from distribution grid to distribution network.

[0043] S11. Establishing a system state sensitivity model: To quantify the propagation effect of uncertainty risks, based on the Jacobian matrix calculated from the power flow of the power grid, the steady-state operating equations of the power grid can be summarized as follows: ,in For state variables, To introduce control or disturbance variables such as power, a sensitivity relationship model is established between the system state change and the disturbance:

[0044] ;

[0045] In the formula, The change in the system state variable; For state variables; For control or disturbance variables; This is the steady-state operating equation of the power grid; The sensitivity matrix has the following elements. The disturbance was quantified. State The extent of the impact becomes the theoretical basis for analyzing risk transmission.

[0046] S12. Identifying Risk Transmission Paths: Considering the electrical coupling relationships between the main, distribution, and microsystems, a risk propagation model is constructed based on graph theory and complex network theory. This model is a comprehensive analytical framework integrating electrical physical relationships and network topology. Its core purpose is to quantitatively characterize the path, intensity, and dynamic process of risk (disturbance) propagation in the three-tiered power grid (main, distribution, and microsystems). The theoretical basis is the system state sensitivity model, which is the model's quantification engine. It derives the sensitivity matrix by linearizing the Jacobian matrix of the power system's steady-state power flow equations. Structural framework: A topology model based on graph theory and complex networks. It abstracts the main grid, distribution network, and microgrid into a hierarchical complex network consisting of nodes (substations, buses, critical load points, and microgrid access points) and edges (transmission lines, transformers, and tie switches). In this model, the critical nodes of each level of the power system are risk carriers. By analyzing the electrical connection characteristics between the three levels of the main grid, distribution network, and microgrid, it identifies the following: the vertical propagation path (220kV / 110kV substations in the main grid → 10kV feeders in the distribution network → microgrid access points), the horizontal propagation path (the risk diffusion mechanism within the same level network), and the reverse propagation path (the influence channel of the lower-level network on the upper-level network).

[0047] S13. Analyze three typical risk transmission mechanisms: quantify the voltage transmission effect and power transmission effect of the main grid risk transmission downward, the power flow reverse transmission effect and voltage superposition effect of the distribution network risk transmission upward, and the impact of the distribution network risk transmission upward and downward on the main grid and the lower-level microgrid / load.

[0048] Based on the operational characteristics and risk transmission direction of the main and auxiliary microsystems, the two-way risk transmission mechanism can be summarized into the following three typical cases:

[0049] 1. Main grid risks propagate downwards. Uncertainty risks at the main grid level mainly stem from factors such as fluctuations in renewable energy output, load forecasting deviations, and equipment failures. The transmission of main grid disturbances to distribution microgrids exhibits significant voltage and power transmission effects. The voltage transmission effect manifests as main grid bus voltage fluctuations being transmitted to the distribution network through the main transformer, affecting the distribution network voltage quality. If the main grid nodes... Voltage fluctuations Then it affects the distribution network nodes. Voltage influence It can be represented as:

[0050] ;

[0051] In the formula, The voltage sensitivity factor is closely related to the transformer turns ratio, impedance parameters, and network topology. The larger its absolute value, the stronger the downward transmission effect of voltage risk.

[0052] The power transmission effect manifests as the main grid power gap being transmitted to the distribution microgrid through optimized scheduling strategies, and can be characterized by the power transmission distribution factor (PTDF). If the main grid experiences a power gap due to disturbances... This gap will be allocated to each lower-level network. The flow will be directed to the distribution network feeder. power change It can be represented as:

[0053] ;

[0054] In the formula, For mainnet nodes 1MW of active power is injected at the point and supplied by the system balancing node. When absorbing the same power, the distribution network feeder The change in active power flow generated on the surface.

[0055] 2. Upward transmission of risks from microgrids: Uncertainty risks at the microgrid level mainly stem from multiple factors, including the randomness of distributed power generation output, load fluctuation characteristics, changes in the operating status of energy storage systems, and the random charging and discharging behavior of electric vehicles. A typical manifestation is the backflow phenomenon during the midday peak. When the output of distributed power sources in the microgrid exceeds local load demand, the surplus power is collected by the distribution network and fed back to the main grid. Changes in net injected power of distributed power sources at the location For the main network line Trend influence It can be quantified as:

[0056] ;

[0057] In the formula: For the trend from the distribution network node The route or meeting point To the mainnet node The benchmark quantity for current flow.

[0058] Voltage fluctuations in distribution microgrids also exhibit a significant upward transmission characteristic. If multiple distribution microgrids simultaneously experience overvoltage issues, the fluctuations can be propagated upwards through cascading distribution transformers and main distribution transformers, potentially causing the main grid voltage to exceed limits due to the cumulative effect. This can be assessed through voltage-power sensitivity. To evaluate distribution network nodes Active injection For mainnet nodes Voltage Impact:

[0059] ;

[0060] In scenarios with high penetration of distributed power sources, voltage disturbances generated simultaneously by multiple spatially dispersed microgrids will superimpose upwards, affecting the main grid nodes. The cumulative effect is:

[0061] .

[0062] 3. Distribution network risks are transmitted vertically and horizontally. As the connecting hub between the main grid and microgrids, the distribution network exhibits a two-way risk transmission characteristic. When a distribution network failure occurs, the transmission risk can be assessed through the following indicators:

[0063] Upward transmission risk index This is used to measure the impact on the mainnet:

[0064] ;

[0065] In the formula, and These are the main network lines after the fault. Trends and key moments voltage, and The upper limit allowed for it; For nodes Rated voltage during normal operation; and The weighting coefficient is used to comprehensively assess the severity of power flow and voltage exceedances in the main grid caused by distribution network faults.

[0066] Downward transmission risk index Used to measure the impact on downstream microgrids and loads, it is often measured by load shedding:

[0067] ;

[0068] In the formula, For fault This can lead to microgrids or load nodes. Expected load loss; Weights that characterize the importance of the load.

[0069] S14. Extract typical transmission modes: Through extensive simulation analysis and historical data mining, extract typical transmission chains and state change patterns of risk upward transmission and downward diffusion, mainly including: attenuation transmission (risk gradually weakens with the distance of propagation), amplification transmission (risk is amplified and transmitted under specific conditions), and blocking transmission (risk transmission is blocked by protection or control mechanisms).

[0070] S2. Comprehensively analyze the amplification, offsetting and buffering effects of multi-level interactive coupling of risks, and quantify the cross-level risk diffusion range based on the N-1 fault risk bidirectional transmission analysis method that takes into account the transfer path.

[0071] S21. Establish a comprehensive risk index: Define a comprehensive risk index as the weighted measure of the deviation of key system operating indicators from their safety thresholds under specific disturbance scenarios; define the comprehensive risk index. In specific disturbance scenarios The following is a weighted measure of the deviation of key system operating indicators from their safety thresholds:

[0072] ;

[0073] In the formula, As an indicator Weighting coefficients; For disturbance Lowering the target The value; This is a safe value; This is the limit boundary; This is the penalty function.

[0074] S22. Analyze the multi-level interactive coupling effect of risks: Quantitatively analyze the amplification effect, offsetting effect, and buffering effect of risks under the intervention of automatic adjustment measures;

[0075] Risk multi-level interactive coupling amplification effect:

[0076] When two or more disturbances , When they occur simultaneously, their combined risks Compared to the sum of the risks of each disturbance occurring individually, the amplification effect can be mathematically expressed as:

[0077] ;

[0078] This indicates that the nonlinear gain generated by the coupling effect of the disturbance causes the system state to deteriorate to a much greater extent than expected.

[0079] Multiple risks at the same level coupled and amplified,

[0080] Within each level of the main grid, distribution network, and microgrid, when multiple disturbance sources act simultaneously within a short period, the system's operating state will face significant fluctuations. The risk not only manifests as increased amplitude but also often exhibits a nonlinear amplification trend. This invention analyzes the coupling amplification and cross-level transmission effects caused by multiple disturbances within the main grid, distribution network, and microgrid. In the main grid system, once risk coupling amplification occurs, its impact will rapidly propagate downwards to the distribution network and microgrid. When a sudden drop in renewable energy (disturbance) occurs... ) and conventional unit regulation lag (disturbance) When superimposed, the instantaneous power gap of the system Represented as:

[0081] ;

[0082] In the formula, It is the power drop of new energy sources; This represents the actual response power of a conventional unit under limited regulation capacity; this gap will directly lead to a frequency drop, and if accompanied by insufficient reactive power regulation capacity (disturbance)... Voltage stability margin It will narrow dramatically, and the overall risks will be significant. It exhibits a highly nonlinear amplification relationship.

[0083] In a power distribution system, when multiple branch distributed generation (DG) sources simultaneously send back power to the main transformer... The total reverse power is collected at the point of convergence. The sum of the reverse power of each branch:

[0084] ;

[0085] In the formula, For nodes The load power.

[0086] When multiple Both reach their peak values ​​simultaneously, and During periods of low power, the total reverse power may far exceed the impact of a single branch, resulting in a cumulative impact on the main transformer and quantifying the amplified risk of power flow aggregation.

[0087] In microgrid systems, if multiple microgrids experience concentrated backfeeding, it will further increase the main transformer load rate, significantly increasing the burden on the main grid. Its local power balance margin... It can be represented as:

[0088] ;

[0089] In the formula, Available output for distributed power sources; The dischargeable power of the energy storage system; This represents the peak power of the user-side load.

[0090] Multi-level and multi-risk coupling amplification effect

[0091] Unlike the local amplification of multiple disturbance factors within a single level, the multi-level, multi-risk coupling amplification effect refers to the fact that the main grid level and the distribution network / microgrid level are subjected to different types of disturbances in the same time period, and generate cross-level interactive amplification through physical paths, ultimately evolving into a serious systemic risk.

[0092] 1. The superposition effect of multiple risks in the main grid and distribution network refers to the situation where the main grid and distribution network are subjected to different disturbance sources at the same time, and due to path mechanisms such as power boundary linkage, voltage reference constraints, and reactive power support capacity transmission, the risks are coupled and amplified at the main grid-distribution network boundary. If there is a power gap in the main grid... At the same time, the load on the distribution network surged. The power increment flowing through the principal-distributor boundary for:

[0093] ;

[0094] In the formula, and It is the power allocation factor. Since the two increments are in the same direction, Rapid amplification can lead to tie line overload and accelerated breakdown of boundary voltages.

[0095] 2. Multiple risks superimposed on distribution and microgrids refer to the fact that the distribution network and microgrid levels face operational disturbances on the basis of interdependence, and due to technical paths such as topology connection, energy mutual feedback, and frequency and voltage joint regulation, a risk amplification closed loop is formed from bottom to top or from top to bottom.

[0096] Multi-level interactive coupling and offsetting effects of risks:

[0097] When disturbances at different levels or within the same level naturally cancel each other out due to inconsistencies in time, space, or direction, this is called the interactive coupling cancellation effect.

[0098] ;

[0099] Based on typical scenarios, the offsetting effect can be categorized into the following two main types:

[0100] Single-level coupling cancellation

[0101] At the same system level, multiple disturbances, due to their opposite direction, misaligned location, or complementary resources, cancel each other out during physical transmission, failing to create an external superposition effect, and even achieving proactive correction of the system's operating state locally. At a certain node in the distribution network... If the power increment of the load is The power output increment of distributed photovoltaic systems near this node is Then the net power impact of this node on the upstream power grid for:

[0102] ;

[0103] Multi-level misalignment coupling cancellation,

[0104] Some disturbance propagation paths exhibit spatial asynchrony due to the influence of the main grid and distribution microgrid structure, thus achieving partial risk hedging or buffering, ultimately resulting in a more stable overall system operation. During a certain operating period, if the output of wind power on the main grid drops sharply due to "low wind" weather, while the distributed photovoltaic power output on the distribution network is high, the specific expression is:

[0105] ;

[0106] Risk buffering effect under automatic adjustment measures:

[0107] In the early stages or propagation phase of a risk, regulatory resources quickly identify abnormal signals and actively participate in regulation. Through power support, power flow guidance, voltage / frequency support, and other means, they effectively block or weaken the outward expansion of the risk chain, preventing the system from falling into cascading failures or unsteady operating ranges.

[0108] S23. Analysis of the bidirectional transmission of N-1 risk considering the transfer path: By reconstructing the transfer path and analyzing the power flow under equipment failure conditions, the scope and intensity of the transmission of N-1 risk from the main grid to the downstream and the feedback of N-1 risk from the downstream to the upstream are identified and quantified.

[0109] Identification and quantification of the transmission of N-1 risk from the mainnet to downstream applications:

[0110] When the main grid adjusts its power supply path to the distribution network due to an N-1 fault or dispatching instructions, collaborative power flow calculation will accurately reveal the impact on specific operating conditions such as changes in distribution network voltage distribution, power flow redistribution of lines or transformers, and even forced disconnection of local loads. By quantifying indicators such as the voltage deviation rate of each bus in the distribution network, the load rate of lines / transformers, and potential load losses, the magnitude and depth of the risk are assessed. The expression is as follows:

[0111] ;

[0112] In the formula, For distribution network bus Voltage deviation rate; For fault Post-distribution bus The voltage; For distribution network bus The rated voltage; For equipment Load rate; For equipment after failure Apparent power; For equipment Maximum permissible apparent power; For fault Potential load loss afterward; Busbars in the power outage area Potential load shedding power.

[0113] Identification and quantification of the impact of downstream N-1 risk feedback to upstream:

[0114] When a distribution network or microgrid undergoes islanding reconfiguration or load transfer due to its own faults, these risks will be fed back to the main grid through the grid connection point, affecting its boundary conditions. By calculating the coordinated power flow between the main grid and the distribution / microgrid, the specific impact of this feedback on the main grid tie-line power flow, voltage fluctuations, peak shaving, and frequency regulation pressures is revealed. Furthermore, by quantifying indicators such as the active / reactive power deviation at the main grid connection point and the voltage change on the main grid side bus, the strength and breadth of the feedback are assessed, providing a decision-making basis for risk warning and control in the upper-level power grid. The specific expression is as follows:

[0115] .

[0116] In the formula, , These are the active power deviation and reactive power deviation at the grid connection point, respectively. , The faults are respectively After the event, the actual active and reactive power of the grid connection point; , The active and reactive power of the grid connection point before the fault occurred; Main grid side bus The change in voltage; For fault After the incident, the main grid side busbar The actual voltage; Before the fault occurred, the main grid side bus... The voltage.

[0117] S24. Identify the direction and scope of risk transmission: Based on the power flow tracing algorithm and complex network theory, construct a risk transmission path map to quantify the range of affected equipment and load loss caused by N-1 faults.

[0118] S3. Based on the risk diffusion range identification method that takes into account the aggravation / buffering effect, identify the regional boundaries where risk transmission may occur; based on the changes in the operating status of the main and auxiliary microsystems under different operational risk scenarios, and combined with control measures, analyze the impact range of each type of operational risk on the upper and lower levels and surrounding areas.

[0119] S31. Establish a spatial quantitative model for the scope of risk diffusion: Based on the comprehensive risk index, combined with the physical importance weight of nodes and time dynamics, calculate the risk impact measure of nodes, and define the set of all nodes whose risk impact measure exceeds the preset critical threshold as the scope of risk diffusion.

[0120] The core of identifying the scope of risk diffusion lies in determining whether the impact of a risk event on each node exceeds a certain critical threshold. The scope of risk diffusion is defined as the set of all nodes whose system state is affected by disturbances and whose comprehensive operating indicators deviate from the safety threshold to the level of an early warning.

[0121] First, based on the comprehensive risk index Calculate a specific disturbance Below, any node within the system Operational risk indicators Considering the spatiotemporal nature of risk, it is necessary to incorporate time dynamics into the analysis, and combine this with the physical importance weight of the nodes. The node risk impact metric is:

[0122] ;

[0123] Risk diffusion range This refers to disturbance Measurement of node risk impact under its influence Exceeding the preset critical impact threshold The set of all nodes:

[0124] ;

[0125] By solving the above equation, the initial diffusion region of risk in the three-level topology of primary, secondary, and micro-level can be obtained.

[0126] S32. Dynamic boundary adjustment method considering coupling effects: Introducing spatial amplification correction factors and spatial buffer correction factors to dynamically adjust the initial risk diffusion range boundary, reflecting the actual impact of amplification and offset / buffering effects on risk propagation. The actual diffusion range of risk does not follow a simple physical distance attenuation law, but is significantly affected by amplification and offset / buffering effects. Therefore, a dynamic correction factor is needed to adjust the initial range.

[0127] The risk diffusion range expands under the coupling amplification effect.

[0128] When two or more disturbances When coupling occurs and produces an amplification effect (synergistic risk index) Its transmission intensity to the external region is nonlinearly enhanced. This enhancement makes risk impact measurement... It can still maintain its position at more distant nodes. This causes the diffusion boundary to expand outward.

[0129] Spatial magnification correction factor :

[0130] ;

[0131] Risk diffusion range shrinkage under coupling cancellation / buffering effect

[0132] When the system's internal automatic adjustment mechanisms (such as energy storage participating in reactive power support) or different disturbances form a cancellation effect ( The risk is absorbed or weakened during the transmission process. This effect makes the measurement of risk impact more difficult. rapidly descending to This causes the diffusion boundary to contract inward.

[0133] Space buffer correction factor :

[0134] ;

[0135] Determining the scope of dynamic risk diffusion

[0136] Taking into account the coupling effect, the final dynamic risk diffusion range Through the Boundary corrections are performed to determine this. This correction process relies on real-time calculations of the power / voltage sensitivity matrix under different operating conditions to obtain... The real-time value.

[0137] S33. Cross-level boundary identification: Based on the risk blocking index at the key connection point of the boundary, determine whether the risk has successfully crossed the boundaries of each level of the main, auxiliary, and micro-level systems.

[0138] Identifying the risk diffusion boundary across levels depends not only on coupling effects but also on the intervention of protection and control mechanisms. This boundary is typically the key connection point between the primary, secondary, and tertiary levels. Whether a risk crosses this boundary depends on the risk-blocking index at that connection point. :

[0139] ;

[0140] In the formula, The setting value for the protection device; The response speed and amplitude of automatic adjustment methods; This represents the margin of boundary power flow or voltage.

[0141] If the system is at the boundary Monitoring Successfully suppressed risk indicators at The following is If the risk is cut off at the boundary, it is determined that the risk has not spread to the next level. Conversely, if... Failure or If the effect is too large and causes the indicator to exceed the limit, then the risk is determined to have crossed the boundary and expanded the scope of diffusion. .

[0142] S4. Taking into account the type, duration, frequency and impact of operational risks, construct a multi-dimensional coupled risk intensity probability assessment model that takes into account the cumulative effect;

[0143] S41. Quantification of risk accumulation effect: Quantify the accumulation effect of operational risks from four dimensions: risk type weight, time accumulation factor, frequency impact coefficient, and impact degree coefficient.

[0144] Among them, risk type weight This reflects the varying degrees of impact of different types of operational risks on system safety and stability. Based on the severity of risk types such as power imbalance, heavy overload, voltage exceeding limits, and N-1 non-compliance, weighting coefficients are determined using expert evaluation combined with historical statistical data. Power imbalance risk has the highest weight, set at 1.0, while the relative weights for other risk types range from 0.6 to 0.9.

[0145] Time accumulation factor This describes the nonlinear growth characteristic of the cumulative damage to the system due to the duration of risk. Considering that the power system has a certain tolerance for short-term risks, while long-term risks lead to cumulative effects such as accelerated equipment aging and decreased system stability margin, a time accumulation function is established:

[0146] ;

[0147] In the formula, The system risk tolerance threshold time is typically set to 15-30 minutes. This is the cumulative effect intensity coefficient, with a value ranging from 0.1 to 0.3. This is a cumulative effect index, with values ​​ranging from 1.2 to 2.0 depending on the type of risk. Higher values ​​are used for risks related to voltage exceeding limits, while lower values ​​are used for risks related to heavy overload.

[0148] Frequency influence coefficient The impact of the frequency of risk occurrence on system reliability is quantified. Based on the Poisson distribution model, the number of risk occurrences per unit time is calculated. Contributing factors that translate into systemic risk intensity:

[0149] ;

[0150] In the formula, For the evaluation time window, use 24 hours or 168 hours.

[0151] S42. Comprehensive Risk Intensity Probability Model: Construct a comprehensive risk intensity probability assessment model that considers cumulative effects, and calculate the risk intensity probability of a specific region within a given time period.

[0152] Influence coefficient Characterize the direct impact of risk events on the system's operational state. Establish corresponding intensity evaluation functions for different risk types:

[0153] Regarding the risk of power imbalance:

[0154]

[0155] In the formula, , These are the rated capacities of active and reactive power, respectively.

[0156] Regarding the risk of severe overload:

[0157] ;

[0158] Regarding the risk of voltage exceeding limits:

[0159] ;

[0160] In the formula, , These are the active and reactive power deviations, respectively. , These are the actual current and rated current of the line, respectively. , These are the actual voltage and rated voltage of the node, respectively; The allowable range for voltage deviation is ±5%.

[0161] Based on the aforementioned multi-dimensional quantitative indicators, a comprehensive risk intensity probability assessment model considering cumulative effects is established. For the region... In time period Risk intensity probability within The calculation formula is:

[0162] ;

[0163] In the formula, The number of types of risk events that may occur in the region; For the first Time weighting coefficient for risk events;

[0164] In order to operate under current conditions and time Below, risk events In the region The conditional probability of occurrence.

[0165] Conditional probability Based on historical operational data and real-time status information, a Bayesian network method is used for dynamic updates:

[0166] ;

[0167] In the formula, The prior probability of a risk event is obtained through historical statistics; Given the risk event, this represents the likelihood probability of the operating conditions. This represents the marginal probability under the current operating conditions.

[0168] S5. Construct a dynamic hierarchical rolling early warning mechanism based on risk spatiotemporal confidence to achieve accurate assessment and multi-level early warning of risk intensity in different regions in the future; effectively improve the operational safety level of main and auxiliary microsystems.

[0169] S51. Quantification of multi-dimensional operational risk indicators: Standardize and quantify four types of operational risk indicators: power imbalance risk, equipment overload risk, voltage limit violation risk, and N-1 non-compliance risk.

[0170] First, it is necessary to standardize and quantify various operational risk indicators. (In time...) For the region A certain risk indicator Its real-time quantization value is .

[0171] Electricity imbalance risk: This indicator aims to quantify the risk of power balance disruption caused by uncertainties in source and load within a region, and is calculated as follows:

[0172] ;

[0173] In the formula, For the region exist Predicted power generation at any given time; Power for planned connecting lines with neighboring areas; For load forecasting; This serves as the baseline load for the region.

[0174] Equipment overload risk: This indicator is used to assess the safety and system stability risks faced by critical power transmission and transformation equipment due to overload operation, and is defined as the severe equipment overload level in the region.

[0175] ;

[0176] In the formula, For the region The set of critical lines or transformers within the system; For equipment exist Predicted current at any given time; This is the maximum allowable current over a long period.

[0177] Voltage exceedance risk: This indicator focuses on the voltage stability of the system and quantifies the severity of voltage deviations from normal operating range at critical nodes. It is defined as follows:

[0178] ;

[0179] In the formula, Let i be the set of key nodes within region i; For nodes The predicted voltage; Rated voltage; This represents the permissible percentage of voltage deviation.

[0180] N-1 does not meet the risk requirement. This indicator follows the power system safety and stability criteria, and its risk value is jointly determined by the probability of an accident occurring and the severity of its consequences. It is a typical probability × consequence risk measurement model.

[0181] ;

[0182] In the formula, Key Contingency Planned Incident Probability of occurrence; The accident caused the area Quantitative indicators of the severity of operational violations.

[0183] S52. Construct a risk confidence assessment function: comprehensively consider the risk probability intensity, risk evolution trend, and system controllability to calculate the risk confidence of each region within a future time window. The function takes into account the risk transmission impact of adjacent regions in the spatial dimension and the accumulation and evolution trend of risk in the time dimension.

[0184] Risk confidence level For future time window within, area The confidence level of the risk. It is determined by the probability strength of the risk and the trend of its evolution, and is modified by the controllability.

[0185] ;

[0186] In the formula, For the region The spatial distribution characteristics of risk; For the region The time-dependent evolution trend of risk; The corrected weighting coefficient for controllability; For the region At any moment The ability to control risks.

[0187] Spatial dimension aggregation, in order to reflect the transmission characteristics of risks on geographic networks, not only considers the superposition of multidimensional risks within a region, but also takes into account the transmission impact of risks in adjacent regions:

[0188] ;

[0189] In the formula, For different risk types The weights; For the region At any moment Next The intensity value of the risk class; For the region A set of adjacent regions where there is a possibility of risk transmission; Risk from the region Transmission to the region The influence coefficient.

[0190] Evolution over time, considering risks in future time windows Internal development trends:

[0191] ;

[0192] In the formula, For the future The mean risk integral within the range represents the cumulative effect of risk; It is the rate of change of risk at the current moment, representing the evolution trend of risk.

[0193] Controllable capability correction: The system's ability to control and buffer against risks.

[0194] ;

[0195] In the formula, Upward adjustment resources available within the region; For interruptible or schedulable load resources; This represents the predicted power deficit.

[0196] S53. Dynamic Grading and Rolling Early Warning: Based on the calculated risk confidence level, multiple early warning level thresholds are set for multi-level early warning; and the early warning information is updated on a rolling basis based on the forward sliding time window.

[0197] Dynamic grading, based on calculated risk confidence levels. Set different warning level thresholds The alert levels are blue (attention level), yellow (caution level), and orange (warning level):

[0198] ;

[0199] For the "Attention" level, although potential risk factors have been detected and there is some uncertainty in the future, the system is currently stable and has sufficient controllability, prompting dispatchers to pay attention and prepare contingency plans. For the "Caution" level, the probability and intensity of the risk have increased significantly, and the evolution trend is obvious, which may require the use of some reserve resources and the initiation of initial preventive measures. For the "Warning" level, the risk is very likely to occur in the short term and is of great intensity, which may exceed the normal controllable range and pose a threat to system security. Emergency control strategies need to be implemented immediately, and load control and other measures may be taken if necessary.

[0200] The aforementioned early warning mechanism is not calculated all at once, but rather updated on a rolling basis within a sliding time window. As new data is continuously input, the system recalculates all regions. This allows for dynamic updates of the warning levels for each region. This rolling mechanism ensures the timeliness of warnings and the system's ability to adapt to dynamic changes.

[0201] The main distribution microsystem spatiotemporal hierarchical early warning technology proposed in this invention, which takes into account the risk transmission effect, can systematically analyze the bidirectional transmission mechanism of risks at each level of the main distribution microsystem, accurately identify the risk diffusion range, dynamically quantify the risk intensity, and realize hierarchical rolling early warning based on spatiotemporal confidence, thereby effectively improving the operational safety level and power supply reliability of the main distribution microsystem.

[0202] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any person skilled in the art can make many possible variations and modifications to the technical solution of the present invention, or modify it into equivalent embodiments, without departing from the scope of the present invention. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technology of the present invention without departing from the scope of the present invention are within the protection scope of the present invention.

Claims

1. A primary-secondary spatiotemporal hierarchical early warning method considering risk transmission effects, characterized in that: The method includes the following steps: S1. Based on the analysis method of uncertainty risk propagation effect at each level, establish a risk transmission model based on the sensitivity matrix, and identify three typical transmission paths: downward transmission from the main grid, upward transmission from distribution microgrids, and vertical transmission from distribution grid to distribution network. S2. An N-1 fault risk bidirectional transmission analysis method based on the transfer path is used to quantify the cross-level risk diffusion range; S3. A risk diffusion range identification method based on aggravation / buffering effects is used to identify the boundaries of areas where risk transmission may occur. S4. Taking into account the type, duration, frequency and impact of operational risks, construct a multi-dimensional coupled risk intensity probability assessment model that takes into account the cumulative effect; S5. Construct a dynamic hierarchical rolling early warning mechanism based on risk spatiotemporal confidence to achieve accurate assessment and multi-level early warning of risk intensity in different regions in the future.

2. The primary-secondary spatiotemporal hierarchical early warning method considering risk transmission effects according to claim 1, characterized in that: The specific steps of S1 include: S11. Establish a system state sensitivity model: Based on the Jacobian matrix calculated by power grid flow, establish a sensitivity relationship model between system state changes and disturbances. S12. Identify risk propagation paths: Based on graph theory and complex network theory, construct a risk propagation model; using key nodes at each level as risk carriers and electrical connections between levels as channels, identify vertical, horizontal, and reverse propagation paths; S13. Analyze three typical risk transmission mechanisms: quantify the voltage transmission effect and power transmission effect of the main grid risk transmission downward, the power flow reverse transmission effect and voltage superposition effect of the distribution network risk transmission upward, and the impact of the distribution network risk transmission upward and downward on the main grid and the lower-level microgrid / load. S14. Extract typical conduction modes: Through simulation analysis and historical data mining, extract typical risk conduction modes, including attenuation conduction, amplification conduction and blocking conduction.

3. The primary and secondary spatiotemporal hierarchical early warning method considering risk transmission effects according to claim 1, characterized in that: The specific steps of S2 are as follows: S21. Establish a comprehensive risk index: Define a weighted measure of the deviation of key system operating indicators from their safety thresholds under specific disturbance scenarios as a comprehensive risk index. S22. Analyze the multi-level interactive coupling effect of risks: Quantitatively analyze the amplification effect, offsetting effect, and buffering effect of risks under the intervention of automatic adjustment measures; S23. Analysis of the bidirectional transmission of N-1 risk considering the transfer path: By reconstructing the transfer path and analyzing the power flow under equipment failure conditions, the scope and intensity of the transmission of N-1 risk from the main grid to the downstream and the feedback of N-1 risk from the downstream to the upstream are identified and quantified. S24. Identify the direction and scope of risk transmission: Based on the power flow tracing algorithm and complex network theory, construct a risk transmission path map to quantify the range of affected equipment and load loss caused by N-1 faults.

4. The primary and secondary spatiotemporal hierarchical early warning method considering risk transmission effects according to claim 3, characterized in that: The specific steps of S3 include: S31. Establish a spatial quantitative model for the scope of risk diffusion: Based on the comprehensive risk index, combined with the physical importance weight of nodes and time dynamics, calculate the risk impact measure of nodes, and define the set of all nodes whose risk impact measure exceeds the preset critical threshold as the scope of risk diffusion. S32. Boundary dynamic adjustment method considering coupling effect: Introduce spatial amplification correction factor and spatial buffer correction factor to dynamically adjust the boundary of the initial risk diffusion range, so as to reflect the actual impact of amplification effect and offset / buffering effect on risk propagation. S33. Cross-level boundary identification: Based on the risk blocking index at the key connection point of the boundary, determine whether the risk has successfully crossed the boundaries of each level of the main, auxiliary, and micro-level systems.

5. The primary and secondary spatiotemporal hierarchical early warning method considering risk transmission effects according to claim 1, characterized in that: The specific steps of S4 include: S41. Quantification of risk accumulation effect: Quantify the accumulation effect of operational risks from four dimensions: risk type weight, time accumulation factor, frequency impact coefficient, and impact degree coefficient. S42. Comprehensive Risk Intensity Probability Model: Construct a comprehensive risk intensity probability assessment model that considers cumulative effects, and calculate the risk intensity probability of a specific region within a given time period.

6. The primary and secondary spatiotemporal hierarchical early warning method considering risk transmission effects according to claim 1, characterized in that: The specific steps of S5 include: S51. Quantification of multi-dimensional operational risk indicators: Standardize and quantify four types of operational risk indicators: power imbalance risk, equipment overload risk, voltage limit violation risk, and N-1 non-compliance risk. S52. Construct a risk confidence assessment function: comprehensively consider the risk probability intensity, risk evolution trend, and system controllability to calculate the risk confidence of each region within a future time window. The function takes into account the risk transmission impact of adjacent regions in the spatial dimension and the accumulation and evolution trend of risk in the time dimension. S53. Dynamic Grading and Rolling Early Warning: Based on the calculated risk confidence level, multiple early warning level thresholds are set for multi-level early warning; and the early warning information is updated on a rolling basis based on the forward sliding time window.

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