WO2023035499A1 - Procédé et système d'évaluation exhaustive de résilience de réseau de distribution d'énergie - Google Patents

Procédé et système d'évaluation exhaustive de résilience de réseau de distribution d'énergie Download PDF

Info

Publication number
WO2023035499A1
WO2023035499A1 PCT/CN2021/142298 CN2021142298W WO2023035499A1 WO 2023035499 A1 WO2023035499 A1 WO 2023035499A1 CN 2021142298 W CN2021142298 W CN 2021142298W WO 2023035499 A1 WO2023035499 A1 WO 2023035499A1
Authority
WO
WIPO (PCT)
Prior art keywords
index
formula
resilience
distribution network
calculation expression
Prior art date
Application number
PCT/CN2021/142298
Other languages
English (en)
Chinese (zh)
Inventor
魏新迟
何维国
黄晨宏
周健
宋平
时珊珊
张琪祁
郑真
黄鑫
张开宇
Original Assignee
国网上海市电力公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 国网上海市电力公司 filed Critical 国网上海市电力公司
Priority to AU2021335236A priority Critical patent/AU2021335236A1/en
Publication of WO2023035499A1 publication Critical patent/WO2023035499A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring

Definitions

  • the invention relates to the technical field of resilience evaluation of distribution networks, in particular to a comprehensive evaluation method and system for resilience of distribution networks.
  • Resilient grid is one of the main goals of power system development in the next stage. Its huge complexity and high coupling with other systems in resilient cities make it more difficult to accurately evaluate grid resilience. In order to quickly and accurately grasp the current resilience level of the distribution network and provide reliable data support for the subsequent operation decision-making, it is of great research significance to analyze the distribution network resilience assessment method and establish a comprehensive evaluation system.
  • the purpose of the present invention is to overcome the relatively limited evaluation research on the three generalized resilience characteristics of perception, synergy, and learning ability in the above-mentioned prior art.
  • perception there are only some discussions on perception, but most of them focus on combining situational awareness technology Evaluating the specific operational performance of the power grid, and the evaluation of the respective realization effects of the generalized characteristics under the resilience demand are still in the initial stage of development, so as to provide a comprehensive evaluation method and system for the resilience of the distribution network.
  • a method for comprehensively evaluating the resilience of distribution networks comprising the following steps:
  • the distribution network resilience comprehensive evaluation system includes first-level evaluation indicators and second-level evaluation indicators, and each of the first-level evaluation indicators is There are corresponding secondary evaluation indicators;
  • the first-level evaluation indicators of the distribution network resilience comprehensive evaluation system include perception, response, defense, resilience, synergy and learning;
  • the secondary evaluation index corresponding to the perception includes one or more of smart meter coverage, weak node observability, power grid measurement redundancy, average transmission delay, situation visibility and distribution automation system operation index ;
  • the secondary evaluation index corresponding to the synergy includes one or more of distribution line connection rate, coordinated flexible load ratio, distribution network transfer rate and local clean energy consumption rate;
  • the secondary evaluation index corresponding to the learning ability includes one or more of the error expectation between the situation prediction data and the actual data and the ratio of repairable loopholes in the post-disaster perception system;
  • the calculation expression of the smart meter coverage A1 is:
  • n sm represents the number of smart meters in the grid area
  • n m represents the total number of meters in the grid area
  • n wm represents the number of considerable weak nodes, and n w represents the total number of weak nodes;
  • n pm represents the number of considerable nodes
  • n p represents the total number of grid nodes
  • t mi represents the time when meter i collects and measures
  • t ui represents the time when the measurement data of electric meter i is updated to the database
  • n m represents the total number of electric meters in the grid area
  • n is the number of blocks divided by the situation graph
  • N i is the number of nodes in block i, is the arithmetic mean of N i ;
  • the calculation expression of the operation index A6 of the distribution automation system is:
  • a 6 ⁇ aor ⁇ P aor + ⁇ rs ⁇ P rs + ⁇ rc ⁇ P rc + ⁇ fac ⁇ P fac
  • P aor represents the average online rate of distribution automation terminals
  • P rs represents the success rate of remote control
  • P rc represents the correct rate of remote signaling action
  • P fac represents the success rate of feeder automation
  • connection ratio E1 of the distribution line is:
  • n l,H is the total number of 35-110kV high-voltage lines in the area
  • n tl,H is the number of 35-110kV high-voltage connection lines in the area
  • n l,L is the total number of 10(20)kV low-voltage lines in the area
  • the number of lines, n tl,L is the number of 10(20)kV low-voltage connection lines in the area
  • n l, tl are the number of transferable lines within the scope of the distribution network, and n l is the total number of lines.
  • S FL is the peak value of the coordinated flexible load
  • S L,max is the maximum load of the annual network supply
  • P oi is the net electricity received outside the region
  • P oa is the agreed electricity outside the region
  • P co is the on-grid electricity of local clean energy
  • P cg is the power generation of local clean energy
  • T is the total duration of measurement
  • N z,t is the total number of measurements predicted by the sensing system at time t, is the predicted value of the i-th measurement obtained by the perception system at time t, Measure the true value of the corresponding system state for the i-th item at time t;
  • the calculation expression of the repairable vulnerability ratio F2 of the post-disaster awareness system is:
  • n vf is the total number of vulnerabilities found in the perception system after the disaster
  • n vr is the number of repairable vulnerabilities in the perception system after the disaster.
  • the secondary evaluation indicators corresponding to the strain force include voltage transient rate, power flow overrun rate, voltage harmonic distortion rate, frequency deviation rate, power distribution demand rate, N-1 verification pass rate, active power reserve rate One or more of and topological integrity.
  • n p is the number of voltage transients, is the voltage transient variable at the current transient moment of node i
  • V i,max is the transient upper limit voltage of node i
  • V i,min is the transient lower limit voltage of node i
  • V i (t) refers to the current transient moment of node i voltage
  • T is the statistical period
  • the calculation expression of the power flow limit rate B2 is:
  • V 1,i is the effective value of the fundamental wave voltage of node i
  • V k,i is the effective value of the kth harmonic voltage of node i
  • n p is the total number of nodes
  • f is the current frequency of the system
  • f N is the rated frequency
  • ⁇ f th is the frequency deviation limit
  • P a is the total power actually consumed by users of the resilient grid
  • P N is the total rated frequency of users of the resilient grid
  • n t is the total number of substations within the power grid
  • n t(N-1) is the number of substations passing N-1 verification
  • n l is the total number of lines within the power grid
  • n l(N-1) is the number of substations passing N-1 1
  • P r is the active power reserve capacity of the power grid
  • P r.lim is the limit value of the power grid active power reserve capacity
  • the calculation expression of the topological integrity B8 is:
  • the secondary evaluation index corresponding to the defense force includes one or more of performance index, distribution capacity-to-load ratio, and grid failure rate.
  • P o is the original grid capacity before the disaster
  • P d is the grid capacity after taking active defense measures
  • P low is the capacity when the grid performance drops to the lowest level
  • t low is the time when the grid performance drops to the lowest level
  • t d is the time for taking active defense measures
  • S T is the total capacity of distribution network substation equipment
  • S L,max is the maximum load of annual network supply
  • n pe is the total number of power equipment in the grid
  • p fault,i is the failure probability of equipment i.
  • the secondary evaluation index corresponding to the resilience includes one or more of the average power outage time of users, the fault self-healing rate, the success rate of black start, and the average pre-arranged power outage time of the system.
  • t cut is the power outage duration of the i-th fault
  • n ucut,i is the number of users of the i-th fault power outage
  • n u is the total number of users of the power grid
  • n ush,i is the number of self-healing users of the i-th fault
  • n ufault,i is the total number of users affected by the i-th fault
  • n ubs,i is the number of users whose power supply is restored through black start for the i-th fault
  • n ufault,i is the total number of users affected by the i-th fault.
  • t pc,i is the i-th pre-scheduled power outage time
  • n upc,i is the number of pre-scheduled power outage users for the i-th time
  • n u is the total number of power grid users.
  • the setting process of the weight of the secondary evaluation index and the primary evaluation index includes the following steps:
  • Subjective weighting step carry out subjective weighting on each indicator
  • Objective empowerment step objectively empower each indicator
  • M is the total number of weighting methods
  • u m (u m1 ,u m2 ,...,u mN ) is the weight vector of the mth weighting method
  • u mn is the nth weighting method obtained by the mth weighting method
  • the weight of indicators, N is the total number of indicators, d n is the set weight;
  • h(u m ,d) is the relative entropy between the weight vector of the mth weighting method and the set weight
  • the calculation expression of the preference coefficient is:
  • a m is the preference coefficient of the mth weighting method
  • the comprehensive index weight coefficient of each index is calculated according to the preference coefficient, and the calculation expression of the comprehensive index weight coefficient is:
  • w n is the comprehensive index weight coefficient of the nth index.
  • the subjective weighting step includes: using the binomial coefficient method to subjectively weight each index, and the binomial coefficient method includes the following steps:
  • O(x n ) is the average importance ranking of the nth index
  • O m (n) is the importance ranking of the mth index to the nth index
  • x 1 , x 2 ,..., x N are the sorted evaluation indicators
  • u i is the subjective weight of the indicator whose index number is i, Computational results for indicator permutations.
  • the execution of the objective weighting step it also includes: performing normalization processing on each index, and the normalization processing is specifically:
  • x mn is the actual calculated value of the nth index of the mth candidate
  • y mn ′ is the normalized value of the benefit index of the nth index of the mth candidate
  • x n,min is The minimum value of the nth item index
  • x n, max is the maximum value of the nth item index
  • the described candidate scheme is each actual index value obtained by adopting the described index
  • y mn ′′ is the normalized value of the cost index of the nth index of the mth objective weighting scheme.
  • the objective weighting step includes: using the anti-entropy weight method to carry out objective weighting on various indicators, and the calculation process of the anti-entropy weight method includes:
  • y mn is the index normalization value of the nth item index of the mth objective weighting scheme, and M is the total number of alternative schemes;
  • index weight is further determined according to the anti-entropy value, and the calculation expression of the index weight is:
  • u n is the indicator weight of the nth indicator
  • N is the total number of indicators.
  • the objective weighting step includes: adopting the CRITIC (Criteria Importance Through Intercriteria Correlation) method to carry out objective weighting to each index, and the calculation process of the CRITIC method includes:
  • p n is the redundant information entropy of the nth item index
  • y mn is the index normalization value of the nth item index of the mth objective weighting scheme
  • M is the total number of alternative schemes
  • n * th index is the normalized value of the nth index
  • s n is the variation coefficient of the nth index
  • N is the total number of indexes
  • correlation coefficient between the nth item and the n * th item is the index value of the n * th item
  • the covariance between is the coefficient of variation of the n * th item
  • i n is the amount of information of the nth index
  • the indicator weight is determined according to the amount of information, and the calculation expression of the indicator weight is:
  • u n is the index weight of the nth index.
  • the present invention also provides a comprehensive evaluation system for the resilience of distribution networks, including a memory and a processor, the memory stores a computer program, and the processor invokes the computer program to execute the steps of the above-mentioned method.
  • the present invention has the following advantages:
  • the present invention constructs a comprehensive evaluation of the resilience of the distribution network including 27 micro indicators from the six macroscopic resilience measurement dimensions of perception, strain, defense, resilience, coordination, and learning
  • the indicator system especially the setting of the secondary indicators of perception, synergy, and learning ability, is set and proposed for the innovation of the present invention, which can realize the situation awareness, disturbance response, and self-improvement of the distribution network under the demand of resilience to be more comprehensive.
  • comprehensive evaluation the present invention focuses on the six categories of key characteristics of the resilient power grid, focusing on the three functions of distribution network situation awareness, disturbance response and self-improvement capabilities, and can establish a more comprehensive and refined comprehensive evaluation system under the demand for resilience to improve the evaluation Accuracy and reliability of results.
  • the present invention adopts a comprehensive weight optimization method based on relative entropy, combined with the binomial coefficient method, anti-entropy weight method, and correlation weight method to carry out comprehensive weighting on the index system, which can realize subjective and objective weight calculations and improve evaluation results accuracy and reliability.
  • the present invention mainly considers its hardware configuration and operating level and the grasping degree to the system state for the assessment of perception, and the specific indicators of perception have the following technical effects respectively:
  • the smart meter is the basic terminal equipment that realizes the measurement, storage, and calculation of power measurement by the situation awareness system and the two-way communication with the data center. Its coverage reflects the basic perception of the resilient grid. level;
  • weak nodes When high-risk events occur, weak nodes have a greater probability of power performance degradation.
  • the degree of grasp of the real-time status of weak nodes can reflect the ability of the resilient power grid perception system to deal with potential disasters, remove hidden dangers in time, and prevent further expansion of faults;
  • Grid measurement redundancy Different from the observability of weak nodes, which mainly reflects the response ability of the power grid sensing system under disaster conditions, the power grid measurement redundancy reflects whether the sensing system can fully grasp the operating status of the power grid and Assist other systems to make decisions, which can be used as a supplement to the observability of weak nodes;
  • the average transmission delay is defined as the average time required for the resilient grid operation data to be transmitted in the sensing system and updated to the database, which reflects the real-time requirements of the resilient grid sensing system;
  • Situational visibility reflects the visualization level of the perception system. Convenient and clear situational information is helpful for operation managers to quickly identify and analyze power events and make corresponding decisions when responding to disasters; evaluate the situational map through the uniformity of node distribution Visualization level, the smaller the uniformity of node distribution, the more uniform the distribution of sample data and the higher the degree of visualization;
  • Operation index of distribution automation system as a collection of power grid data collection and monitoring system, geographic information system, demand side management and other systems, distribution automation system integrates functions such as power data monitoring analysis and remote control; it is different from previous system configuration indicators , the operation index of the distribution automation system mainly reflects the perceived results of the decision-making execution accuracy and execution efficiency of the resilient power grid, which is a real-time dynamic index.
  • the synergy index set by the present invention represents the ability of the resilient power grid to use internal and external resources reasonably and efficiently, and jointly concentrate forces to resist disturbance events.
  • the synergy-related indicators can be used to improve and reflect the internal and external defense resources and multi-dimensional system information of the distribution network and other control capabilities; the specific indicators of synergy have the following technical effects:
  • Distribution line contact rate As the most intuitive and basic contact method, the direct connection of distribution lines provides flexibility for the dispatching and cooperation of physical systems inside and outside the region, and the increase of its coupling elements also provides higher measurement redundancy;
  • Coordinated flexible load ratio As an important component of the active distribution network, flexible loads are responsible for maintaining power balance and coordinated recovery from disasters; the coordinated flexible load ratio determines the peak capacity of the resilient grid’s collaborative anti-disturbance, reflecting the flexible load Synergies for grid management
  • Distribution network transfer rate line transfer can effectively reduce the number of lost loads in the resilient power grid under partial fault conditions, and exert a greater degree of coordinated disaster response capabilities of the system. It also provides a basis for distribution network risk situation assessment and decision-making effect simulation Greater flexibility;
  • the amount of clean energy consumption can comprehensively reflect the synergistic effect of the resilient grid, which is different from the coordinated flexible load ratio, which dynamically represents the coordinated balance level of power and electricity inside and outside the region and the flexible anti-disturbance capability.
  • the learning power set by the present invention serves as a common support for the rest of the features, which represents the ability of the resilient power grid to self-correct and improve from historical experience and combine new technologies to improve innovation, and can be used to reflect the ability of the resilient distribution network to self-correct and continuously improve ;
  • the specific indicators of learning ability have the following technical effects respectively:
  • the proportion of vulnerabilities that can be repaired by the post-disaster perception system directly reflects the vulnerability identification, error correction and self-learning capabilities of the resilient grid system.
  • Fig. 1 is the schematic flow chart of a kind of binomial coefficient method provided in the embodiment of the present invention
  • Fig. 2 is a schematic flow chart of an anti-entropy weight method provided in an embodiment of the present invention
  • Fig. 3 is a schematic flow chart of a CRITIC method provided in an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of a comprehensive weight optimization process based on relative entropy provided in an embodiment of the present invention
  • Fig. 5 is a schematic flow diagram of a comprehensive resilience assessment process of a distribution network provided in an embodiment of the present invention.
  • This embodiment provides a method for comprehensively evaluating resilience of a distribution network, including the following steps:
  • distribution network resilience comprehensive evaluation system includes first-level evaluation indicators and second-level evaluation indicators, and each first-level evaluation index has a corresponding Secondary evaluation index;
  • the first-level evaluation indicators of the distribution network resilience comprehensive evaluation system include perception, response, defense, resilience, coordination and learning;
  • the secondary evaluation indicators corresponding to perception include one or more of smart meter coverage, weak node observability, power grid measurement redundancy, average transmission delay, situation visibility and distribution automation system operation indicators; , smart meter coverage, weak node observability and power grid measurement redundancy are used to reflect the perception level of the edge side of the distribution network; average transmission delay, situation visibility and distribution automation system operation indicators are used to reflect the distribution network control center perception level;
  • the secondary evaluation indicators corresponding to strain stress include voltage transient rate, power flow exceeding rate, voltage harmonic distortion rate, frequency deviation rate, power distribution demand rate, N-1 verification pass rate, active power reserve rate and topology integrity One or more of them; Among them, the voltage transient rate, the power flow limit rate, the voltage harmonic distortion rate and the frequency deviation rate are used to reflect the electrical quantity parameter indicators of the distribution network operation process; the distribution demand rate, N-1 verification Through rate, active power reserve rate and topology integrity are used to reflect the macro planning indicators of distribution network;
  • the secondary evaluation indicators corresponding to the defense force include one or more of performance index, distribution capacity load ratio and grid failure rate;
  • the secondary evaluation indicators corresponding to the resilience include one or more of the user average power outage time, fault self-healing rate, black start success rate, and system average pre-scheduled power outage time; among them, the system’s average pre-scheduled power outage time is used to reflect the power distribution
  • the pre-accident warning and maintenance level of the distribution network; the average power outage time of users, the fault self-healing rate and the black start success rate are used to reflect the post-accident recovery level of the distribution network;
  • the secondary evaluation indicators corresponding to synergy include one or more of distribution line connection rate, coordinated flexible load ratio, distribution network transfer rate and local clean energy consumption rate; among them, distribution line connection rate and distribution network
  • the transfer rate is used to reflect the physical line coordination level of the distribution network; the coordinated flexible load ratio and the local clean energy consumption rate are used to reflect the flexible resource coordination level of the distribution network.
  • the secondary evaluation indicators corresponding to the learning ability include one or more of the error expectation between the situation prediction data and the actual data and the ratio of repairable loopholes in the post-disaster perception system;
  • the number of secondary evaluation indicators corresponding to the above-mentioned perception, strain, defense, resilience, synergy and learning ability is not limited, and can be arbitrarily selected from the above-mentioned secondary evaluation indicators, preferably using the one provided by this embodiment. For all the corresponding secondary evaluation indicators, an optimal implementation manner will be described in detail below.
  • the actual resilience level of the distribution network is comprehensively evaluated from the six dimensions of perception, strain, defense, resilience, coordination, and learning, and the power distribution system is established.
  • the comprehensive evaluation index system of network toughness is shown in Table 1.
  • the evaluation of perception mainly considers its hardware configuration and operation level and the degree of grasp of the system state. Algorithmic efficiency, risk situation prediction, and other related indicators of the resilience level of the power grid that are difficult to evaluate directly will be indirectly reflected by subsequent indicators of other dimensions.
  • the smart meter is the basic terminal equipment that realizes the measurement, storage and calculation of the power measurement of the situation awareness system and the two-way communication with the data center. Its coverage reflects the basic perception level of the resilient grid.
  • the calculation formula is as follows :
  • n sm represents the number of smart meters in the grid area
  • n m represents the total number of meters in the grid area
  • the calculation formula of the load-system short-circuit capacity ratio is as follows:
  • E Pi and Z Pi are the Thevenin equivalent circuit parameters of the power grid at the node
  • U Li and Z Li are the Thevenin equivalent circuit parameters of the load at the node.
  • the node is considered to be a weak node.
  • a node is substantial when its voltage phasor can be measured directly or calculated.
  • the formula for calculating the observability of weak nodes is as follows:
  • n wm represents the number of considerable weak nodes
  • n w represents the total number of weak nodes
  • the grid measurement redundancy reflects whether the sensing system can fully grasp the operating status of the grid and assist other systems to make decisions under the normal state of the resilient grid. Can be used as a supplement to the observability of weak nodes.
  • the grid measurement redundancy calculation formula is as follows:
  • n pm represents the number of considerable nodes
  • n p represents the total number of grid nodes
  • the average transmission delay is defined as the average time required for the resilient grid operation data to be transmitted in the sensing system and updated to the database, reflecting the real-time requirements of the resilient grid sensing system. Calculated as follows:
  • t mi represents the time when the meter i collects and measures
  • t ui represents the time when the measurement data of the electric meter i is updated to the database.
  • n is the number of blocks divided by the situation graph
  • N i is the number of nodes in block i
  • the distribution automation system integrates functions such as power data monitoring, analysis, and remote control.
  • the distribution automation system operation indicators mainly reflect the perception results of the decision-making execution accuracy and execution efficiency of the resilient power grid. They are real-time dynamic indicators, including the average online rate of distribution automation terminals, remote control success rate, The four sub-items are the correct rate of remote signaling action and the success rate of feeder automation.
  • a 6 ⁇ aor ⁇ P aor + ⁇ rs ⁇ P rs + ⁇ rc ⁇ P rc + ⁇ fac ⁇ P fac
  • P aor represents the average online rate of distribution automation terminals
  • P rs represents the success rate of remote control
  • P rc represents the correct rate of remote signaling action
  • P fac represents the success rate of feeder automation
  • this embodiment provides the specific calculation formula of the optimal weight configuration of the distribution automation system operation index A6 as follows:
  • a 6 0.25 ⁇ P aor +0.25 ⁇ P rs +0.2 ⁇ P rc +0.3 ⁇ P fac (7)
  • P aor represents the average online rate of distribution automation terminals
  • P rs represents the success rate of remote control
  • P rc represents the correct rate of remote signaling actions
  • P fac represents the success rate of feeder automation.
  • the coefficients in the distribution automation system operation index A6 can be adjusted according to the actual situation and are not specifically limited. This embodiment provides the optimal coefficient of the distribution automation system operation index A6 .
  • Resilience reflects the ability of a resilient grid to resist disturbances, maintain its own performance, and formulate risk plans before extreme events occur. Resilience-related indicators can be used to reflect the reliability, efficiency, risk prediction and pre-accident deployment effectiveness of the power grid in daily operation decisions.
  • the voltage transient rate is mainly used to reflect the frequency and magnitude of voltage sags (or swells) in the normal operation of the power grid.
  • the lower voltage transient rate reflects the strong transient stability and anti-interference ability of the system, and also reflects the It improves the monitoring and early warning capabilities of the power grid for transient disturbances.
  • the calculation formula of voltage transient rate is as follows:
  • n p is the number of voltage transients, is the voltage transient variable at the current transient moment of node i
  • V i,max is the transient upper limit voltage of node i
  • V i,min is the transient lower limit voltage of node i
  • V i (t) refers to the current transient moment of node i
  • the voltage, T is the statistical period.
  • V 1,i is the effective value of the fundamental wave voltage of node i
  • V k,i is the effective value of the kth harmonic voltage of node i
  • n p is the total number of nodes.
  • the frequency deviation rate belongs to the steady-state power index of the power grid, which reflects the active safety performance of the system. Technologies such as power flow monitoring and load forecasting and deployment are conducive to reducing the frequency deviation of the resilient power grid.
  • the calculation formula of frequency deviation rate is as follows:
  • f is the current frequency of the system
  • f N is the rated frequency
  • ⁇ f th is the frequency deviation limit of 0.2 Hz.
  • the power distribution demand rate is defined as the ratio of the actual operating power of the user to the rated power in the resilient grid. This value reflects the rationality and economy of power resource allocation in the resilient grid, and also reflects the grid's ability in flexible load scheduling and resource allocation. .
  • the formula for calculating the power distribution demand rate is as follows:
  • P a is the actual total power consumption of the resilient grid users
  • P N is the total rated frequency of the resilient grid users
  • the N-1 pass rate includes the N-1 pass rate of substations and transmission lines within the power grid. As the most intuitive indicator to characterize the reliability and risk warning capabilities of the power grid, it also reflects the perceived risk situation of the resilient power grid and Ability to simulate anticipated accidents.
  • N-1 verification pass rate B 6 The calculation expression of N-1 verification pass rate B 6 is:
  • n t is the total number of substations within the power grid
  • n t(N-1) is the number of substations passing N-1 verification
  • n l is the total number of lines within the power grid
  • n l(N-1) is the number of substations passing N-1 1
  • this embodiment provides the calculation formula for the optimal weight configuration of the N-1 verification pass rate as follows:
  • n t is the total number of substations within the power grid
  • n t(N-1) is the number of substations passing N-1 verification
  • n l is the total number of lines within the power grid
  • n l(N-1) is the number of substations passing N-1 1 The number of lines to check.
  • the active power reserve rate pays more attention to the pre-arrangement of emergency resources.
  • An appropriate amount of active power reserve is conducive to maintaining system performance in risky accidents, reducing losses caused by disasters, and meeting the economy of normal system operation.
  • the specific calculation formula is as follows:
  • P r is the active power reserve capacity of the grid
  • P r.lim is the limit value of the active power reserve capacity of the power grid, which is generally taken as 0.1 times the maximum load of the system.
  • topological integrity greatly affects its vulnerability to disasters. Reducing the number of outage lines and the outage time is conducive to building a stronger and more reliable resilient power grid.
  • the calculation formula of topological integrity is as follows:
  • Defensive power focuses on the ability of the resilient power grid to use internal and external defense resources to actively resist disaster damage and reduce the overall impact of events during the occurrence of extreme events.
  • the defense-related indicators can be used to reflect the operational efficiency of the power grid under extreme conditions and the response rate under variable conditions.
  • the availability performance curve describes the dynamic changes of the resilient grid during extreme events, and the grid capacity is selected as the performance index.
  • the performance index is defined as the product of factors such as active defense speed and active defense effect. This index simultaneously reflects the real-time response ability of perception during the disaster occurrence process, the ability to predict the development of disaster situation, and the ability to evaluate defense resources.
  • the specific calculation formula is as follows:
  • P o is the original grid capacity before the disaster
  • P d is the grid capacity after taking active defense measures
  • P low is the capacity when the grid performance drops to the lowest level
  • t low is the time when the grid performance drops to the lowest level
  • t d is the time to take active defense measures.
  • the distribution network capacity-load ratio is defined as the ratio of the total capacity of substation equipment in the distribution network area to the annual maximum load.
  • a certain transformer reserve capacity is conducive to improving the adaptability of the power grid in the process of disasters and bringing greater flexibility to the deployment of defense resources; at the same time, an excessive capacity-to-load ratio not only increases the investment cost and construction period of the resilient power grid, It also reduces the economics of grid operation. How to reduce the capacity-load ratio of the distribution network while satisfying reliability depends on more accurate load forecasting and operation control technology of the distribution network.
  • the calculation formula of distribution network capacity load ratio is as follows:
  • S T is the total capacity of distribution network substation equipment
  • S L,max is the maximum load of annual network supply.
  • n pe is the total number of power equipment in the power grid
  • p fault,i is the failure probability of equipment i, which can be obtained from historical data combined with situation prediction technology.
  • Resilience considers the emergency guarantee and gradual restoration of power grid performance after an accident, and timeliness is the most important consideration for resilience.
  • Resilience-related indicators can be used to reflect the power grid's capabilities in fault location, maintenance and repair, remote control, and decision-making simulation evaluation.
  • Power outage time is the most intuitive indicator to reflect the resilience level of the resilient grid after a disturbance. Technologies such as fault location, fault data recording, and disturbance factor analysis can effectively reduce the average power outage time.
  • the formula for calculating the average outage time of users is as follows:
  • t cut is the power outage duration of the i-th fault
  • n ucut,i is the number of users in the i-th fault outage
  • n u is the total number of users in the power grid.
  • Fault self-healing is mainly reflected in the self-healing ability of the power grid in the face of small disturbances or the initial stage of extreme events, and its cycle includes three levels of monitoring and early warning, diagnosis and analysis, and automatic repair.
  • a high-level resilient power grid can give an alarm and diagnose its triggering factors when the fault is still in the "potential" stage, and use remote control to achieve fault self-healing attempts.
  • the formula for calculating the fault self-healing rate is as follows:
  • n ush,i is the number of self-healing users of the i-th fault
  • n ufault,i is the total number of users affected by the i-th fault
  • Black start is a key measure for emergency protection after extreme events.
  • the formulation of its optimal scheme largely depends on the estimation of the system state under the fault set by the distribution network and the operation effect of the scheme in the simulation environment such as the twin system.
  • the black start success rate can effectively reflect the initial post-disaster recovery ability of the resilient grid, and its calculation formula is as follows:
  • n ubs,i is the number of users whose power supply is restored through black start for the i-th fault.
  • Pre-arranged power outages refer to the planned outage maintenance or capacity expansion of relevant areas by the power department, which is suitable for delayable fault repairs that do not cause damage in a short period of time. Pre-blackout is mainly judged based on indicators such as failure rate and failure rate.
  • the formula for calculating the average pre-scheduled outage time of the system is as follows:
  • t pc,i is the i-th pre-scheduled power outage time
  • n upc,i is the number of i-th pre-scheduled power outage users.
  • Synergy represents the ability of the resilient grid to rationally and efficiently utilize internal and external resources, and jointly concentrate forces to resist disturbance events. Like perception, it serves as a basic feature for the three core features.
  • the indicators related to synergy can be used to improve and reflect the ability of the distribution network to control internal and external defense resources and multi-dimensional system information.
  • the direct connection of distribution lines provides flexibility for the scheduling and cooperation of physical systems inside and outside the region, and the increase of its coupling elements also provides higher measurement redundancy for the distribution network .
  • n l,H is the total number of 35-110kV high-voltage lines in the area
  • n tl,H is the number of 35-110kV high-voltage connection lines in the area
  • n l,L is the total number of 10(20)kV low-voltage lines in the area
  • the number of lines, n tl,L is the number of 10(20)kV low-voltage connection lines in the area
  • this embodiment provides the calculation formula for the optimal weight configuration of the distribution network connection ratio as follows:
  • n l,H is the total number of 35-110kV high-voltage lines in the area
  • n tl,H is the number of 35-110kV high-voltage connection lines in the area
  • n l,L is the total number of 10(20)kV low-voltage lines in the area
  • the number of lines, n tl,L is the number of 10(20)kV low-voltage contact lines in the area.
  • the coefficients in the formula for calculating the connection ratio of the distribution network can be adjusted according to actual conditions, and are not specifically limited. This embodiment provides the optimal coefficient of the formula for calculating the connection ratio of the distribution network.
  • Line transfer can effectively reduce the number of lost loads in the case of partial faults in resilient power grids, give play to a greater degree of coordinated disaster response capabilities of the system, and also provide greater flexibility for distribution network risk situation assessment and decision-making effect simulation.
  • the formula for calculating the distribution network transfer rate is as follows:
  • S FL is the peak value of adjustable flexible load.
  • n l, tl are the number of transferable lines within the distribution network range.
  • the amount of clean energy consumed can comprehensively reflect the synergistic effect of the resilient power grid. Different from the proportion of flexible loads that can be coordinated, it dynamically represents the coordinated balance level of power and electricity inside and outside the region and the flexible anti-disturbance capability.
  • the specific calculation formula is as follows:
  • P oi is the net electricity received outside the region
  • P oa is the agreed electricity outside the region
  • P co is the on-grid electricity of local clean energy
  • P cg is the power generation of local clean energy
  • Learning ability represents the ability of the resilient grid to self-correct and improve from historical experience and combine new technologies to promote innovation.
  • Indicators related to learning ability can be used to reflect the ability of autonomous error correction and continuous improvement of resilient distribution network.
  • the error between the forecast data and the actual data of the resilient power grid can be obtained.
  • the error expectation and change trend can effectively reflect the self-correction and self-improvement capabilities of the distribution network.
  • T is the total duration of measurement
  • N z,t is the total number of measurements predicted by the sensing system at time t
  • N z,t is the predicted value of the i-th measurement obtained by the perception system at time t
  • the proportion of vulnerabilities that can be repaired by the post-disaster perception system directly reflects the vulnerability identification, error correction and self-learning capabilities of the resilient grid system.
  • the calculation formula is as follows:
  • n vf is the total number of vulnerabilities found in the perception system after the disaster
  • n vr is the number of repairable vulnerabilities in the perception system after the disaster.
  • the weighting method can be divided into subjective weighting method and objective weighting method according to its basis, and there is a possibility of one-sided evaluation in the single use of any type of method, so the present invention utilizes the comprehensive weighting optimization method based on relative entropy to optimize the binomial
  • the subjective and objective weights obtained by the coefficient method, the anti-entropy weight method and the CRITIC method realize the weight calculation of the comprehensive evaluation of power grid resilience.
  • the subjective weighting method of this embodiment adopts the binomial coefficient method.
  • the binomial coefficient method is easier to calculate, and comprehensively considers the opinions of all parties to avoid ignoring minority opinions.
  • the flow chart of the binomial coefficient method is shown in Figure 1, and the weighting steps are as follows:
  • S104 sort the index set symmetrically and number each index again from left to right, denoted as i.
  • the binomial coefficients can be determined according to the serial number results to calculate the subjective weight of each indicator, the formula is as follows:
  • u i is the subjective weight of the indicator whose index number is i, Computational results for indicator permutations.
  • the subjective weighting method relies on the subjective cognition of evaluation experts on the evaluation index set, and there is a possibility of random deviation and one-sidedness. It must be used in combination with the objective weighting method that relies on objective data.
  • the objective weighting method selected in the present invention is the anti-entropy weighting method with the CRITIC Act.
  • the anti-entropy weight method is based on the concept of information entropy, which can effectively evaluate the quality of information provided by indicators for decision-making, and avoids the extreme situation of too small weighting in the entropy weight method.
  • the CRITIC method which is suitable for high correlation index weighting, is supplemented to improve the effectiveness of objective weighting.
  • x mn represents the actual calculated value of the nth indicator of the mth scheme. Since the actual calculation value range of each micro-indicator is quite different, and there are different dimensions, it is not conducive to determine its objective weight. Therefore, each indicator is first normalized, and the specific formula is as follows:
  • Benefit-type indicators that is, the index value is positively correlated with the evaluated ability.
  • Cost-type indicators that is, the indicator value is negatively correlated with the assessed ability.
  • each index value y mn is a dimensionless constant with a range of 0 to 1, and the larger the index value, the stronger the ability to reflect the scheme.
  • the specific weight calculation process of the anti-entropy weight method and the CRITIC method will be introduced below.
  • n * th is the normalized value of the nth index
  • s n is the variation coefficient of the nth index
  • N is the total number of indexes
  • s n is the variation coefficient of the nth index
  • N is the total number of indexes
  • s n is the variation coefficient of the nth index
  • N is the total number of indexes
  • index value of the n * th item is the index value y n
  • the covariance between is the coefficient of variation of the n * th term.
  • the specific optimization model can refer to the prior art, such as the following model formula:
  • S3 Calculate the subjective weight and objective weight of each first-level evaluation index and second-level evaluation index
  • This embodiment also provides a system for comprehensively evaluating resilience of a distribution network, including a memory and a processor, the memory stores a computer program, and the processor invokes the computer program to execute the method for comprehensively evaluating resilience of a distribution network as described above A step of.

Abstract

La présente invention concerne un procédé et un système d'évaluation exhaustive de la résilience d'un réseau de distribution d'énergie. Le procédé comporte les étapes consistant à: obtenir des paramètres d'un réseau de distribution d'énergie, et effectuer une évaluation selon un système prédéfini d'évaluation exhaustive de résilience de réseau de distribution d'énergie, le système d'évaluation exhaustive de résilience de réseau de distribution d'énergie comportant des indices d'évaluation de premier niveau et des indices d'évaluation de second niveau, et chaque indice d'évaluation de premier niveau étant muni d'un indice correspondant d'évaluation de second niveau; les indices d'évaluation de premier niveau comportant la perceptibilité, l'adaptabilité, les capacités de défense, les capacités de rétablissement, les capacités de synergie et les capacités d'apprentissage, et sur la base d'un résultat d'évaluation de chaque indice d'évaluation de second niveau, réaliser un calcul exhaustif selon le poids de chaque indice d'évaluation de second niveau et le poids de chaque indice d'évaluation de premier niveau pour obtenir un résultat d'évaluation exhaustive concernant la résilience du réseau de distribution d'énergie. Comparée à l'état antérieur de la technique, la présente invention se concentre sur les trois fonctions de perception de la situation du réseau de distribution, de réaction aux perturbations et de capacités d'autoamélioration par rapport à six catégories de caractéristiques-clés d'un réseau électrique résilient, qui peuvent établir un système plus exhaustif et raffiné d'évaluation exhaustive sous des exigences de résilience, et améliorer l'exactitude et la fiabilité du résultat d'évaluation.
PCT/CN2021/142298 2021-09-10 2021-12-29 Procédé et système d'évaluation exhaustive de résilience de réseau de distribution d'énergie WO2023035499A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2021335236A AU2021335236A1 (en) 2021-09-10 2021-12-29 Comprehensive resilience evaluation method and system for distribution network

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202111059384.0 2021-09-10
CN202111059384.0A CN113868585A (zh) 2021-09-10 2021-09-10 一种配电网的韧性综合评估方法和系统

Publications (1)

Publication Number Publication Date
WO2023035499A1 true WO2023035499A1 (fr) 2023-03-16

Family

ID=78995242

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/142298 WO2023035499A1 (fr) 2021-09-10 2021-12-29 Procédé et système d'évaluation exhaustive de résilience de réseau de distribution d'énergie

Country Status (3)

Country Link
CN (1) CN113868585A (fr)
AU (1) AU2021335236A1 (fr)
WO (1) WO2023035499A1 (fr)

Cited By (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113283698A (zh) * 2021-04-21 2021-08-20 华北电力大学 一种继电保护抗电磁干扰评估指标体系及评估方法
CN116136988A (zh) * 2023-04-19 2023-05-19 国网浙江省电力有限公司宁波供电公司 基于时空罗盘分布的配电网供电网格建设优选排序方法
CN116341992A (zh) * 2023-05-29 2023-06-27 广东电网有限责任公司佛山供电局 一种配电网综合评估方法、装置、电子设备及存储介质
CN116361925A (zh) * 2023-05-31 2023-06-30 西北工业大学 一种船舶传动构型的多方案评估方法及系统
CN116485286A (zh) * 2023-06-21 2023-07-25 中铁第四勘察设计院集团有限公司 基于层次分析法的区域客运专线规划评价方法及系统
CN116629455A (zh) * 2023-07-19 2023-08-22 山西阳风新能售电有限公司 一种基于电力供电的智能定向预测方法
CN116703368A (zh) * 2023-08-08 2023-09-05 国网信通亿力科技有限责任公司 基于数据挖掘的同期线损智能闭环监测方法
CN116742645A (zh) * 2023-08-15 2023-09-12 北京中电普华信息技术有限公司 一种电力负荷调控任务分配方法及装置
CN116739417A (zh) * 2023-05-24 2023-09-12 国家电网有限公司华东分部 关口电能表状态评价方法及装置、存储介质、计算机设备
CN116744321A (zh) * 2023-08-11 2023-09-12 中维建技术有限公司 一种用于5g通信智能运维一体化平台的数据调控方法
CN116901774A (zh) * 2023-09-11 2023-10-20 南京安充智能科技有限公司 基于全网通充电桩的柔性配电方法、系统及存储介质
CN116933981A (zh) * 2023-09-15 2023-10-24 安徽方能电气技术有限公司 基于停复电事件数据监测的区域电力稳定性分析评价方法
CN117114491A (zh) * 2023-09-06 2023-11-24 青岛民航凯亚系统集成有限公司 一种基于熵值法的机场信息系统运维能力评价方法及系统
CN117200460A (zh) * 2023-11-06 2023-12-08 国网湖北省电力有限公司经济技术研究院 一种有源配电网量测优化配置方法及系统
CN117235373A (zh) * 2023-11-14 2023-12-15 四川省计算机研究院 基于信息熵的科研热点推荐方法
CN117726079A (zh) * 2024-02-05 2024-03-19 肯拓（天津）工业自动化技术有限公司 基于机电一体化的自动化环形产线优化方法

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116433073A (zh) * 2023-02-23 2023-07-14 华北电力大学 风电场运营效能评价方法、装置、设备及介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200153273A1 (en) * 2018-11-13 2020-05-14 Mitsubishi Electric Research Laboratories, Inc. Methods and Systems for Post-Disaster Resilient Restoration of Power Distribution System
CN111179117A (zh) * 2019-12-27 2020-05-19 天津大学 一种智能配电网态势感知效果评定的计算方法及装置
CN112001626A (zh) * 2020-08-21 2020-11-27 广东电网有限责任公司广州供电局 一种台风天气下的配电网韧性评价方法、存储介质及设备
CN113033886A (zh) * 2021-03-18 2021-06-25 国网江苏省电力有限公司扬州供电分公司 一种配电网规划建设评估方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200153273A1 (en) * 2018-11-13 2020-05-14 Mitsubishi Electric Research Laboratories, Inc. Methods and Systems for Post-Disaster Resilient Restoration of Power Distribution System
CN111179117A (zh) * 2019-12-27 2020-05-19 天津大学 一种智能配电网态势感知效果评定的计算方法及装置
CN112001626A (zh) * 2020-08-21 2020-11-27 广东电网有限责任公司广州供电局 一种台风天气下的配电网韧性评价方法、存储介质及设备
CN113033886A (zh) * 2021-03-18 2021-06-25 国网江苏省电力有限公司扬州供电分公司 一种配电网规划建设评估方法

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
LIN TINGJUN, DONG KUN; ZHAO JIANFENG; LIU KANGLI; YU YUE; WANG JIN: "Research Status and Prospects of Internal and External Collaborative Optimization Scheduling Technology for Integrated Energy System", SHAANXI ELECTRONIC POWER, vol. 49, no. 6, 1 June 2021 (2021-06-01), pages 1 - 8, XP093044916, ISSN: 2096-4145 *
QIANTU RUAN, WEI XIE, YIN XU, BIN HUA, PING SONG, JINGHAN HE, QIQI ZHANG: "Concept and Key Features of Resilient Power Grids", PROCEEDINGS OF THE CSEE, ZHONGGUO DIANJI GONGCHENG XUEHUI, CN, vol. 40, no. 21, 5 November 2020 (2020-11-05), CN , pages 6773 - 6784, XP093044914, ISSN: 0258-8013, DOI: 10.13334/j.0258-8013.pcsee.201308 *
YANG ZHAO, HE MINGHAO; HAN JUN; FENG MINGYUE: "A New Evaluation Method for Radar Emitter Signal Recognition", XIANDAI-LEIDA = MODERN RADAR, NANJING DIANZI JISHU YANJIUSUO, CN, vol. 41, no. 10, 1 October 2019 (2019-10-01), CN , pages 68 - 73, XP093044918, ISSN: 1004-7859, DOI: 10.16592/j.cnki.1004-7859.2019.10.013 *
ZHOU JIANGXIN; ZHAO WANJIAN; ZHANG SHIWEI; LIU YANGYANG; GONG JINXIA; YE YAO: "Resilience Enhancement of Power Grid Considering Optimal Operation of Regional Integrated Energy System Under Typhoon Disaster", 2021 3RD ASIA ENERGY AND ELECTRICAL ENGINEERING SYMPOSIUM (AEEES), IEEE, 26 March 2021 (2021-03-26), pages 546 - 551, XP033904967, DOI: 10.1109/AEEES51875.2021.9403154 *

Cited By (28)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113283698A (zh) * 2021-04-21 2021-08-20 华北电力大学 一种继电保护抗电磁干扰评估指标体系及评估方法
CN116136988A (zh) * 2023-04-19 2023-05-19 国网浙江省电力有限公司宁波供电公司 基于时空罗盘分布的配电网供电网格建设优选排序方法
CN116136988B (zh) * 2023-04-19 2023-07-07 国网浙江省电力有限公司宁波供电公司 基于时空罗盘分布的配电网供电网格建设优选排序方法
CN116739417A (zh) * 2023-05-24 2023-09-12 国家电网有限公司华东分部 关口电能表状态评价方法及装置、存储介质、计算机设备
CN116341992A (zh) * 2023-05-29 2023-06-27 广东电网有限责任公司佛山供电局 一种配电网综合评估方法、装置、电子设备及存储介质
CN116361925A (zh) * 2023-05-31 2023-06-30 西北工业大学 一种船舶传动构型的多方案评估方法及系统
CN116361925B (zh) * 2023-05-31 2023-11-03 西北工业大学 一种船舶传动构型的多方案评估方法及系统
CN116485286A (zh) * 2023-06-21 2023-07-25 中铁第四勘察设计院集团有限公司 基于层次分析法的区域客运专线规划评价方法及系统
CN116485286B (zh) * 2023-06-21 2023-10-03 中铁第四勘察设计院集团有限公司 基于层次分析法的区域客运专线规划评价方法及系统
CN116629455B (zh) * 2023-07-19 2023-10-31 山西阳风新能售电有限公司 一种基于电力供电的智能定向预测方法
CN116629455A (zh) * 2023-07-19 2023-08-22 山西阳风新能售电有限公司 一种基于电力供电的智能定向预测方法
CN116703368B (zh) * 2023-08-08 2023-10-13 国网信通亿力科技有限责任公司 基于数据挖掘的同期线损智能闭环监测方法
CN116703368A (zh) * 2023-08-08 2023-09-05 国网信通亿力科技有限责任公司 基于数据挖掘的同期线损智能闭环监测方法
CN116744321A (zh) * 2023-08-11 2023-09-12 中维建技术有限公司 一种用于5g通信智能运维一体化平台的数据调控方法
CN116744321B (zh) * 2023-08-11 2023-11-14 中维建技术有限公司 一种用于5g通信智能运维一体化平台的数据调控方法
CN116742645A (zh) * 2023-08-15 2023-09-12 北京中电普华信息技术有限公司 一种电力负荷调控任务分配方法及装置
CN116742645B (zh) * 2023-08-15 2024-02-27 北京中电普华信息技术有限公司 一种电力负荷调控任务分配方法及装置
CN117114491A (zh) * 2023-09-06 2023-11-24 青岛民航凯亚系统集成有限公司 一种基于熵值法的机场信息系统运维能力评价方法及系统
CN116901774A (zh) * 2023-09-11 2023-10-20 南京安充智能科技有限公司 基于全网通充电桩的柔性配电方法、系统及存储介质
CN116901774B (zh) * 2023-09-11 2023-11-14 南京安充智能科技有限公司 基于全网通充电桩的柔性配电方法、系统及存储介质
CN116933981A (zh) * 2023-09-15 2023-10-24 安徽方能电气技术有限公司 基于停复电事件数据监测的区域电力稳定性分析评价方法
CN116933981B (zh) * 2023-09-15 2023-12-08 安徽方能电气技术有限公司 基于停复电事件数据监测的区域电力稳定性分析评价方法
CN117200460B (zh) * 2023-11-06 2024-01-23 国网湖北省电力有限公司经济技术研究院 一种有源配电网量测优化配置方法及系统
CN117200460A (zh) * 2023-11-06 2023-12-08 国网湖北省电力有限公司经济技术研究院 一种有源配电网量测优化配置方法及系统
CN117235373A (zh) * 2023-11-14 2023-12-15 四川省计算机研究院 基于信息熵的科研热点推荐方法
CN117235373B (zh) * 2023-11-14 2024-03-15 四川省计算机研究院 基于信息熵的科研热点推荐方法
CN117726079A (zh) * 2024-02-05 2024-03-19 肯拓（天津）工业自动化技术有限公司 基于机电一体化的自动化环形产线优化方法
CN117726079B (zh) * 2024-02-05 2024-04-16 肯拓（天津）工业自动化技术有限公司 基于机电一体化的自动化环形产线优化方法

Also Published As

Publication number Publication date
CN113868585A (zh) 2021-12-31
AU2021335236A1 (en) 2023-03-30

Similar Documents

Publication Publication Date Title
WO2023035499A1 (fr) Procédé et système d'évaluation exhaustive de résilience de réseau de distribution d'énergie
Afzal et al. State‐of‐the‐art review on power system resilience and assessment techniques
Chuansheng et al. Safety evaluation of smart grid based on AHP-entropy method
CN107909253B (zh) 基于区间层次分析法的智能配电网调度控制效果评估方法
CN102013085A (zh) 配电网可靠性评价方法
Setreus et al. Identifying critical components for transmission system reliability
Bai et al. Improved resilience measure for component recovery priority in power grids
Kong et al. Power supply reliability evaluation based on big data analysis for distribution networks considering uncertain factors
Ge et al. Evaluation of the situational awareness effects for smart distribution networks under the novel design of indicator framework and hybrid weighting method
Liu et al. Seismic resilience evaluation and retrofitting strategy for substation system
CN110991816A (zh) 一流配电网的建设水平监测方法和装置
Bie et al. Evaluation of power system cascading outages
Chen et al. Evaluation for the resilience of distribution network
Cai et al. Method for Evaluation on Power Grid Operation Status for Intra-day Tie-line Scheduling
CN108711845A (zh) 一种基于配电网网架的分析方法
Wu et al. Study on risk assessment system of power failure in distribution network of large cities
Chu et al. Self-healing control method in abnormal state of distribution network
Liu et al. Power grid risk assessment method based on risk probability engineering and its application
Zhang et al. Enhancing power grid resilience against typhoon disasters by scheduling of generators along with optimal transmission switching
Yang et al. Comprehensive evaluation of distribution network reliability for power consumer based on AHP and entropy combination method
Zhang et al. Research on smart grid evaluation index system based on the analytic hierarchy process
Hao et al. Data-driven lean management for distribution network
Liu et al. Evaluation Index System of Power Grid Corporation's Operation Benefits Based on Future Low-Carbon Energy Framework
Chen et al. Risk management for electrifying off-grid island using renewable energy microgrid
Pan et al. A Survivability Assessment Model for the Weak-Link Power Grid under Extreme Events

Legal Events

Date Code Title Description
ENP Entry into the national phase

Ref document number: 2021335236

Country of ref document: AU

Date of ref document: 20211229

Kind code of ref document: A

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21956668

Country of ref document: EP

Kind code of ref document: A1