CN117318020A - Medium voltage distribution network weakness identification method considering user blackout risk value - Google Patents
Medium voltage distribution network weakness identification method considering user blackout risk value Download PDFInfo
- Publication number
- CN117318020A CN117318020A CN202311160104.4A CN202311160104A CN117318020A CN 117318020 A CN117318020 A CN 117318020A CN 202311160104 A CN202311160104 A CN 202311160104A CN 117318020 A CN117318020 A CN 117318020A
- Authority
- CN
- China
- Prior art keywords
- node
- fault
- network
- power failure
- power
- Prior art date
- Legal status (The legal status 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 status listed.)
- Pending
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 76
- 208000003443 Unconsciousness Diseases 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 38
- 230000009471 action Effects 0.000 claims abstract description 58
- 238000010586 diagram Methods 0.000 claims abstract description 29
- 238000010248 power generation Methods 0.000 claims description 24
- 238000003860 storage Methods 0.000 claims description 13
- 238000004458 analytical method Methods 0.000 claims description 11
- 238000012546 transfer Methods 0.000 claims description 11
- 238000002955 isolation Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000012163 sequencing technique Methods 0.000 claims description 7
- 238000005520 cutting process Methods 0.000 claims description 6
- 230000005611 electricity Effects 0.000 claims description 6
- 239000013598 vector Substances 0.000 claims description 6
- 241001246638 Gentian ovary ringspot virus Species 0.000 claims description 3
- 230000001502 supplementing effect Effects 0.000 claims description 3
- 238000004590 computer program Methods 0.000 description 11
- 230000006870 function Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 3
- 238000011084 recovery Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000000903 blocking effect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000004913 activation Effects 0.000 description 1
- 238000012271 agricultural production Methods 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- 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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- 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
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy 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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Power Engineering (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a medium-voltage distribution network weakness identification method considering the blackout risk value of a user, which comprises the following steps: generating a network topology map based on the power distribution network information; selecting a certain fault element to be simulated, and analyzing a node range of the power failure influence of the fault element based on a network topology diagram; analyzing the action logic and the time sequence of the switching equipment related to the fault element, executing the action logic of the switching equipment, and determining the power failure range of the fault element based on the node range of the power failure influence of the fault element; calculating the power failure risk value of the network power failure node in the power failure range affected by the fault element; and analyzing the weak links of the power grid based on the power failure risk value of the power failure nodes of the network until all fault elements in the network topology graph are calculated, and outputting a result. The method can simulate faults of main elements such as lines and switches in the medium-voltage distribution network, analyze the power failure risk caused by the faults, and further identify weak elements such as the branch circuits and the tie switches in the medium-voltage distribution network.
Description
Technical Field
The invention relates to a medium-voltage distribution network weakness identification method, system, equipment and medium which comprise a distributed power supply access power distribution network and consider the blackout risk value of a user, and relates to the field of power distribution network planning and design.
Background
The power distribution network is a bridge between a power transmission network and end users, and with the development of economy and society and the improvement of living standard of people, the continuous power supply capability of the power distribution network by the end users is increasingly high. The risk information such as the number of households and the loss of the power failure load electric quantity directly reflects the satisfaction degree of the power grid on the electric energy demand, and is an important index for measuring the power supply quality of the power distribution network. The medium voltage distribution network is an important component of the distribution network, and identifying weak points inside the network is important for improving the power supply reliability level of the distribution network.
The current traditional mode is to evaluate the structural characteristics and the operation level of the feeder line through the data such as the interconnection level, the load rate level, the power failure frequency statistics value, the power failure time statistics value and the like of the feeder line, and to screen the fragile feeder line. The above-mentioned mode can not reflect the influence of feeder line internal branch or component faults of sectionalized branch, branched branch, sectionalized (tie) switch, distribution transformer, etc. on end user and on power grid operation enterprise.
Meanwhile, with the access of a distributed power supply, the characteristics of power distribution network activation, active power bidirectional transmission and the like are displayed, the form and the characteristics of the power distribution network are also deeply changed, the influence of power failure of internal elements or branches of a feeder line is scientifically evaluated and prejudged, and the thinning and improving of the medium-voltage feeder line weakness recognition capability is an important subject of the reliability research of the power distribution network.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention aims to provide a medium voltage distribution network weakness identification method, system, equipment and medium containing the user outage risk value of a distributed power supply accessing to a distribution network, which can simulate faults of main elements such as lines and switches in the medium voltage distribution network, analyze outage risk caused by the faults, identify weak elements such as segmented branches and segmented contact switches in the medium voltage distribution network and improve the power supply reliability of the medium voltage distribution network.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, the present invention provides a method for identifying weakness of a medium voltage distribution network in consideration of a blackout risk value of a user, including:
generating a network topology map based on the power distribution network information;
selecting a certain fault element to be simulated, and analyzing a node range of the power failure influence of the fault element based on a network topology diagram; analyzing the action logic and the time sequence of the switching equipment related to the fault element, executing the action logic of the switching equipment, and determining the power failure range of the fault element based on the node range of the power failure influence of the fault element; calculating the power failure risk value of the network power failure node in the power failure range affected by the fault element;
and analyzing the weak links of the power grid based on the power failure risk value of the power failure nodes of the network until all fault elements in the network topology graph are calculated, and outputting a result.
Further, the power distribution network information includes: network node number and type information, network element connection relationship and parameter information thereof, and power generation and load connection relationship and parameter information.
Further, selecting a node range simulating a fault element, analyzing the influence of power failure of the fault element based on a network topology diagram, and comprising:
removing fault elements in the network topological graph, and searching by applying a depth-first strategy to obtain discovery sequence ordering associated node vectors at two sides of the fault elements through nodes at two sides of the fault elements;
marking all the related nodes as outage events, and determining the node range affected by the outage element.
Further, analyzing the switching device action logic and timing associated with the failed component, comprising:
analyzing all switching equipment in the associated branch by means of node association vectors at two sides of the fault element, wherein all switching equipment comprises a source side feeder switch, an isolating fault switch and a contact transfer switch;
analyzing the node attribute and the switch running state to obtain a source side feeder switch, an isolation fault switch and a contact transfer switch;
cutting off a fault branch according to the fact that a source side feeder switch is firstly disconnected; then the isolating fault switch is disconnected, isolating fault and locking the switch state; closing the source side feeder switch and the interconnection transfer switch, recovering the power supply of the non-fault branch, and setting the action logic and action time sequence of the switch.
Further, executing the switchgear action logic to determine a blackout range for the failed component based on the node range affected by the failed component blackout, comprising:
executing a switch-off action logic, and sequentially realizing the disconnection of the source side feeder line switch equipment and the disconnection of the fault isolation switch through the operation state of a switch branch in a network topological diagram; analyzing the connectivity of the topological structure in the current state, marking nodes which are not related to the balance node and the power generation node as isolated nodes, and determining the power outage range;
executing a switch closing action logic, and closing a source side feeder switch and a contact transfer switch through the operation state of a switch branch in a network topological diagram; and (3) carrying out network power flow calculation, checking whether branch current and node voltage are out of limit, supplementing segment switch action logic if the out-of-limit condition exists, cutting off part of distribution transformer load, ensuring that the branch current and the node voltage meet requirements, analyzing topological structure connectivity in the current state, marking nodes which are not related to balance nodes and power generation nodes as isolated nodes, and determining the power failure range.
Further, calculating a network outage node outage risk value for the outage range affected by the failed component, comprising:
calculating expected values of power failure quantity of a certain network power failure node in the power failure range affected by the fault element:
wherein CENS j A user shortage power expected value caused by the j-th element fault; lambda (lambda) j The probability of failure for the jth component year; u (U) ji The j-th elementThe power failure time of the ith node affected by the part year fault; pav ji The average load of the ith node affected by the jth element fault is represented by m, and the m is the total number of the power failure nodes;
calculating the power failure risk value of a network power failure node based on the expected value of the power failure quantity of the network power failure node:
CORV i ＝VI i *CENS j
wherein, CORV i The power failure risk value of the ith node; CENS (CENS) j A user shortage power expected value caused by the j-th element fault; VI (VI) i The power supply value coefficient of the ith node;
calculating the blackout risk value of all network blackout nodes in the blackout range affected by the fault element based on the blackout risk value of the network blackout nodes:
wherein GORV j A power failure loss risk value of the power generation node caused by the j-th element fault; lambda (lambda) j The probability of failure for the jth component year; u (U) ji The power failure time of the ith node affected by the jth element year fault; pgav ji Average load lost to the ith power generation node, vg, affected by the jth element fault i And k is the total number of power failure power generation nodes, wherein the power price is the electricity price of the ith power generation node.
Further, analyzing the weak link of the power grid, and outputting a result, including:
calculating expected value of power shortage quantity of the medium voltage load node and power failure risk value of the medium voltage load node caused by faults of all lines, transformers and switching elements in a network;
and sequencing the comprehensive medium-voltage load node outage risk values, determining the vulnerability of the network element according to the sequencing result, and determining the weak links in the network.
In a second aspect, the present invention provides a medium voltage distribution network weakness identification system considering a user blackout risk value, including:
a topology generation unit configured to read power distribution network information and generate a network topology map;
a first analysis unit configured to select a node range simulating a certain fault element, and analyze the influence of the fault element power outage based on a network topology map;
a second analysis unit configured to analyze switching device action logic and timing associated with the failed component and to execute the switching device action logic, the blackout range of the failed component being determined based on the node range affected by the blackout of the failed component;
a computing unit configured to compute a network outage node outage risk value within an impact outage range of the failed component;
and the output unit is configured to analyze the weak links of the power grid based on the power failure risk value of the power failure node of the network and output a result until all fault elements in the network topological graph are calculated.
In a third aspect, the present invention also provides an electronic device, including: one or more processors, memory, and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods.
In a fourth aspect, the present invention also provides a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods.
The invention adopts the technical proposal and has the following characteristics: the invention can simulate faults of main elements such as lines and switches in the medium-voltage distribution network, analyze the power failure risk caused by the faults, identify weak elements such as the branch circuits and the tie switches in the medium-voltage distribution network, and lay a foundation for improving the power supply reliability of the medium-voltage distribution network. In conclusion, the method and the device can be widely applied to planning and designing the power distribution network.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Like parts are designated with like reference numerals throughout the drawings. In the drawings:
FIG. 1 is a flowchart of a medium voltage distribution network vulnerability identification based on user outage risk value according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a topology connection of a 10kV distribution network according to an embodiment of the present invention;
fig. 3 is a schematic diagram of topology analysis of a 10kV distribution network according to an embodiment of the present invention;
fig. 4 is a schematic diagram of power grid topology analysis after a branch fault feeder outlet switch acts according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating topology analysis of a power grid after branch fault isolation according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating a topology analysis of a power grid after a non-faulty branch is recovered or diverted after a branch fault in an embodiment of the present invention;
FIG. 7 is a schematic diagram showing the ranking of power outage risk results after traversing all branch faults of a network according to an embodiment of the present invention;
fig. 8 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
It is to be understood that the terminology used herein is for the purpose of describing particular example embodiments only, and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "comprising," "includes," "including," and "having" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order described or illustrated, unless an order of performance is explicitly stated. It should also be appreciated that additional or alternative steps may be used.
Although the terms first, second, third, etc. may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms may be only used to distinguish one element, component, region, layer or section from another region, layer or section. Terms such as "first," "second," and other numerical terms when used herein do not imply a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the example embodiments.
For ease of description, spatially relative terms, such as "inner," "outer," "lower," "upper," and the like, may be used herein to describe one element or feature's relationship to another element or feature as illustrated in the figures. Such spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures.
Since medium voltage distribution networks are an important component of the distribution network, identifying weak points inside the network is crucial for improving the power supply reliability level of the distribution network. The medium-voltage distribution network weakness identification method considering the user blackout risk value provided by the invention comprises the following steps: generating a network topology map based on the power distribution network information; selecting a certain fault element to be simulated, and analyzing a node range of the power failure influence of the fault element based on a network topology diagram; analyzing the action logic and the time sequence of the switching equipment related to the fault element, executing the action logic of the switching equipment, and determining the power failure range of the fault element based on the node range of the power failure influence of the fault element; calculating the power failure risk value of the network power failure node in the power failure range affected by the fault element; and analyzing the weak links of the power grid based on the power failure risk value of the power failure nodes of the network until all fault elements in the network topology graph are calculated, and outputting a result. Therefore, the invention can simulate faults of main elements such as lines, switches and the like in the medium-voltage distribution network, analyze the power failure risk caused by the faults, identify weak elements such as the branch circuits and the tie switches in the medium-voltage distribution network, and lay a foundation for improving the power supply reliability of the medium-voltage distribution network.
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Embodiment one: as shown in fig. 1, the medium voltage distribution network weakness identification method considering the blackout risk value of the user provided in this embodiment includes:
s1, reading distribution network information, and generating a network topology diagram according to a distribution network geographical wiring diagram.
In this embodiment, reading the data information of the power distribution network to be optimized includes:
network node number and type information, wherein the type information is respectively balance node, power generation, load and the like.
The network element connection relation and parameter information thereof comprise lines, transformers, switches, parallel compensators and the like, and the parameter information comprises line lengths, line models, transformer capacities and the like.
Power generation and load connection relationship and parameter (active, reactive) information.
S2, selecting a certain fault element to be simulated, and analyzing the node range of the fault element affected by the power failure based on the network topology diagram.
In this embodiment, setting a simulated fault element, analyzing a power outage range based on a network topology, includes:
s21, removing the fault element in the network, and obtaining the discovery sequence ordering associated node vectors at the two sides of the element by applying depth-first strategy search through the nodes at the two sides of the fault element.
S22, marking all the related nodes as outage events, and determining the node range affected by the outage element.
S3, analyzing the action logic and the time sequence of the switching equipment related to the fault element.
In this embodiment, analyzing the switching device action logic and timing associated with the failed component includes:
s31, analyzing all the switching devices in the associated branch through the node association vectors at the two sides of the fault element.
S32, analyzing the node attribute and the switch operation state (on or off) to obtain the source side feeder switch, the isolation fault switch and the interconnection transfer switch.
S33, cutting off a fault branch according to the fact that a source side feeder switch is firstly disconnected; then the isolating fault switch is disconnected, isolating fault and locking the switch state; closing the source side feeder switch and the interconnection transfer switch, recovering the power supply of the non-fault branch, and setting the action logic and action time sequence of the switch.
And S4, executing the switching equipment action logic, and determining the power failure range of the fault element based on the node range influenced by the power failure of the fault element.
In this embodiment, executing the switching device action logic to determine the power outage scope includes:
s41, executing a switch-off action logic, and sequentially realizing the disconnection of the source side feeder line switch equipment and the disconnection of the fault isolation switch by setting the operation state of a switch branch in the topological diagram.
S42, analyzing the connectivity of the topological structure in the current state, marking nodes which are not associated with the balance node and the power generation node as isolated nodes, and determining the power outage range.
S43, executing a switch closing action logic, and closing the source side feeder switch and the contact transfer switch through the operation state of a switch branch in the equipment topological diagram.
S44, carrying out network power flow calculation, checking whether the branch current and the node voltage are out of limit, and if the out-of-limit condition exists, supplementing the sectionalizing switch action logic, cutting off part of the distribution transformer load, and ensuring that the branch current and the node voltage meet the requirements.
S45, analyzing the connectivity of the topological structure in the current state, marking nodes which are not related to the balance node and the power generation node as isolated nodes, and determining the power outage range.
S5, calculating the power failure risk.
Information such as fault branch power failure frequency, fault isolation time, branch repair time, power failure user load and the like is considered, the network power failure node power failure risk value in the influence range of a fault element is calculated, and the power failure risk is calculated, and the method comprises the following steps:
s51, calculating a desired value of the power shortage quantity of the medium-voltage load node, wherein the calculation formula is as follows:
wherein CENS j Expected value of user's power shortage caused by the j-th element fault, unit: kWh; lambda (lambda) j The unit is the failure probability of the jth element year: times/year; u (U) ji Power outage time of the ith node affected by the jth element year fault, unit: hours; pav ji Average load in units of the ith node affected by the jth element fault: kW, m is the total number of power failure nodes, unit: and each.
S52, calculating a medium-voltage load node value coefficient, wherein the calculation formula is as follows:
wherein VI i The power supply value coefficient for the ith node, unit: meta/kWh; kp ki The power duty cycle of the kth class of load powering the ith node, in units of: the%; price k Average electricity selling price of class k load, unit: units: meta/kWh. n is the total number of power failure load types, unit: and each. In general, the power load types are classified into 4 major categories, respectively: residents, large industrial electricity, general industry, commercial and agricultural production.
S53, calculating the power failure risk value of the medium-voltage load node, wherein the calculation formula is as follows:
CORV i ＝VI i *CENS j (3)
wherein, CORV i Power outage risk value for the i-th node, units: a meta-element; CENS (CENS) j Expected value of user's power shortage caused by the j-th element fault, unit: kWh; VI (VI) i The power supply value coefficient for the ith node, unit: meta/kWh.
S54, calculating the power failure risk value of the power generation node, wherein the calculation formula is as follows:
wherein: GORV (gate driver enhanced RV) j The power failure loss risk value of the power generation node caused by the j-th element fault is as follows: a meta-element; lambda (lambda) j For the j-th element year failure probability, unit: times/year; u (U) ji Power outage time of the ith node affected by the jth element year fault, unit: hours; pgav ji Average load in units of loss of the ith power generation node affected by the jth element fault: kW. Vg (Vg) i Electricity price for the i-th power generation node, unit: meta/kWh. k is the total number of power failure power generation nodes, and the unit is: and each.
S6, judging whether all elements in the power distribution network are calculated, if yes, entering a step S7, and if not, entering a step S2.
S7, analyzing the weak links of the power grid, and outputting a result.
In this embodiment, the analyzing the weak link of the power grid, outputting the result includes:
s71, calculating expected value of power failure quantity of a medium-voltage load node and power failure risk value of the medium-voltage load node caused by faults of all lines, transformers and switching elements in a network;
s72, sequencing the blackout risk values of the comprehensive medium-voltage load nodes, determining the vulnerability of the network elements according to the sequencing result, and determining weak links in the network.
The following describes in detail, by means of specific embodiments, specific applications of the medium voltage distribution network weakness identification method taking into account the blackout risk value of the user. In the embodiment, a 10kV power distribution network in a certain area is taken as an example to describe the network structure and the characteristics of the embodiment in detail.
1. And acquiring power distribution network information.
As shown in fig. 2, the topology structure of the 10kV power distribution network is as follows, and the feeder 1 and the feeder 2 are interconnected through a tie switch, where the feeder 1 and the feeder 2 each include 4 loads and 1 photovoltaic power station. Wherein: node 11 in feeder 1 is a substation 10kV bus, and nodes 12-17 and 100 are intermediate nodes; the node 21 in the feeder line 2 is a 10kV bus of a transformer substation, and the intermediate nodes of the nodes 22-27 and 200 are arranged. Two feeders are interconnected between node 15 and node 25. The information conditions of load access nodes, load levels and load types in the network are shown in Table 1, and the voltage levels of various loads in the area are shown in Table 2.
Table 1 power load access, load level and load characteristics
Table 2 list of electricity prices for various loads
| Load class | Resident | Commercial business | Industrial process | Agricultural use |
| Price (Yuan/kWh) | 0.512 | 0.806 | 0.853 | 0.554 |
2. According to step 1, the corresponding power grid information of fig. 2 is read to form network topology information, meanwhile, an operation topology diagram (see fig. 3) is formed according to the operation state of the equipment, and the node connection, the element type and the reliability basic data of the branch element are shown in table 3 in detail.
TABLE 3 bypass element types and reliability parameters
3. And (2) setting a medium-voltage fault element and analyzing the power outage range according to the step (2).
Taking the branch of the node {206, 23} as an example, the range of outage nodes after the bus-bar outlet breaker of the transformer substation 21 is opened is considered as shown in fig. 4, wherein the outage nodes comprise 13 nodes in total, namely 22, 203, 23, 206, 26, 204, 24, 207, 27, 200, 25 and 15, and 4 load nodes in total
4. And according to the step 3, analyzing and obtaining the action logic and time of the switching equipment related to the fault element.
Taking node {206, 23} branches as an example, through connectivity and switch position analysis of the network, 4 groups of switch branches and three attributes of states, actions and locking of the switch branches are obtained, wherein: a state of 1 indicates closed, 0 indicates open; action 1 represents the next time period switch action, change the switch state, 0 represents no action, keep the current state; latch 1 indicates that the next time period switch is not active, and 0 indicates that it is active.
1) Fault phase of fault removal after fault
The operation state of the switch branch {202, 21} is 0 through the substation outlet and the switch closest to the fault point is {23,203} because of the existence of another switch in the same connecting branch chain, and the follow-up consideration and recovery are considered; closing the switch locking state of the branch {202, 21} switch to be 0 by combining a power distribution automation strategy; before the fault branch is effectively isolated, the switch branch {202, 21} cannot be closed, and the switch action state of the next period is 0. Wherein the switch branches {15, 105} are tie-line branches, and the action logic is similar to the switch branches {202, 21 }. Table 4 shows the current state of the 4 groups of switching branches and the next stage of operation and blocking.
Table 4 switch and action logic list 1 at branch {23, 206} fault removal
| Head node | End node | Status of | Action | Locking device |
| 23 | 203 | 1 | 1 | 0 |
| 202 | 21 | 0 | 0 | 0 |
| 204 | 24 | 1 | 1 | 0 |
| 15 | 105 | 0 | 0 | 0 |
2) Fault section isolation stage
After the 4 groups of switches execute the action logic of the table 4, the state of the switch branches {23,203} and {204, 24} is changed from 1 to 0 after the switch branches {23,203} and {204, 24} execute the switch action, the switch action 0 is set, and the switch is locked 1; while the source side switching leg 202, 21 and the tie switching leg 15, 105 lower stage switching actions are 1. Table 5 shows the current state of the 4 groups of switching branches and the next stage of operation and blocking.
TABLE 5 switch and action logic List 2 for Branch {23, 206} fault isolation
| Head node | End node | Status of | Action | Locking device |
| 23 | 203 | 0 | 0 | 1 |
| 202 | 21 | 0 | 1 | 0 |
| 204 | 24 | 0 | 0 | 1 |
| 15 | 105 | 0 | 1 | 0 |
3) Non-failure recovery phase
After the 4 groups of switches execute the action logic of the table 5, the state is unchanged because the switch branches {23,203} and {204, 24} are locked and have no action; after the source side switching legs 202, 21 and the tie switching legs 15, 105 perform switching actions, the state is changed from 0 to 1, and the latch is set to 1. Table 6 shows the current state of the 4 groups of switch branches and the next stage of operation and locking.
TABLE 6 switch and action logic List 3 for Branch {23, 206} Fault non-Fault recovery
| Head node | End node | Status of | Action | Locking device |
| 23 | 203 | 0 | 0 | 1 |
| 202 | 21 | 1 | 0 | 1 |
| 204 | 24 | 0 | 0 | 1 |
| 15 | 105 | 1 | 0 | 1 |
5. And 4, executing the action logic of the switch equipment to determine the power failure range.
1) After table 4 is executed, the outage nodes comprise 13 nodes in total of 202, 22, 203, 23, 206, 26, 204, 24, 207, 27, 200, 25 and 15, and the total of 15, 22, 23, 24, 25 and 26 is 6 load nodes, and fig. 5 shows the network topology connection condition in the current stage;
2) After table 5 is executed, the outage nodes comprise 4 nodes, namely, nodes 23 and 26, and 2 load nodes, namely, nodes 23 and 26, and fig. 6 shows the network topology connection condition of the current stage. Table 7 gives the failed power down load node information for the victim leg {23, 206 }. The power failure time data of the load nodes in the comparison table can be seen, wherein the number of the short-time power failure nodes is 4, and the number of the long-time power failure nodes is 2.
Table 7 list of fault outage load node information for the branch {23, 206}
| Head node | End node | Power failure node | Frequency of power failure | Power outage time | Power failure number of households |
| 23 | 206 | 15 | 0.0075 | 0.025 | 18 |
| 23 | 206 | 22 | 0.0075 | 0.025 | 12 |
| 23 | 206 | 23 | 0.0075 | 4.025 | 19 |
| 23 | 206 | 24 | 0.0075 | 0.025 | 18 |
| 23 | 206 | 25 | 0.0075 | 0.025 | 3 |
| 23 | 206 | 26 | 0.0075 | 4.025 | 8 |
6. And 5, calculating the power failure risk according to the step 5.
According to formulas (1) to (3), after the branch {23, 206} faults are calculated by combining the data of the tables 1 to 3 and the data of the table 7, 78 users supplied by 6 load nodes are affected, the expected value of the power shortage quantity of the load nodes is 24.94kWh, the power failure risk value of the load nodes is 14.89 yuan, the expected value of the power loss network of the power generation node is 0.14 yuan, and the total power failure loss risk is 15.03 yuan.
7. And traversing 25 element branches in the network to obtain the power failure value condition of the load nodes affected by the branch faults in the network, wherein the power failure value condition is shown in figure 7.
The power failure risk information of all the branch faults after sequencing is shown in Table 8 in detail. According to data analysis, the average value of the outage values of the load nodes corresponding to 26 branches is 95.04 yuan, wherein the outage values of 6 branches with the serial numbers of 22, 24, 15, 25, 1 and 16 exceed the average value, the influence caused by outage is far greater than that of the other 20 branches, and the load nodes belong to medium-voltage weak branches relative to other branches.
Table 8 Power failure risk information list for all branch faults
Embodiment two: in the first embodiment, the method for identifying the weakness of the medium voltage distribution network in consideration of the blackout risk value of the user is provided, and in response thereto, the embodiment provides a device for identifying the weakness of the medium voltage distribution network in consideration of the blackout risk value of the user. The device provided in this embodiment may implement the medium voltage distribution network weakness identification method considering the user power outage risk value in the first embodiment, where the device may be implemented by software, hardware or a combination of software and hardware. For convenience of description, the present embodiment is described while being functionally divided into various units. Of course, the functions of the units may be implemented in the same piece or pieces of software and/or hardware. For example, the apparatus may comprise integrated or separate functional modules or functional units to perform the corresponding steps in the methods of the first embodiment. Since the apparatus of this embodiment is substantially similar to the method embodiment, the description of this embodiment is relatively simple, and the relevant points may be referred to the description of the first embodiment, and the embodiment of the medium voltage distribution network weakness identification apparatus provided by the present invention considering the power outage risk value of the user is merely illustrative.
The invention provides a medium-voltage distribution network weakness identification system considering the power failure risk value of a user, which comprises the following components:
a topology generation unit configured to read power distribution network information and generate a network topology map;
a first analysis unit configured to select a node range simulating a certain fault element, and analyze the influence of the fault element power outage based on a network topology map;
a second analysis unit configured to analyze switching device action logic and timing associated with the failed component and to execute the switching device action logic, the blackout range of the failed component being determined based on the node range affected by the blackout of the failed component;
a computing unit configured to compute a network outage node outage risk value within an impact outage range of the failed component;
and the output unit is configured to analyze the weak links of the power grid based on the power failure risk value of the power failure node of the network and output a result until all fault elements in the network topological graph are calculated.
Embodiment III: the present embodiment provides an electronic device corresponding to the medium voltage distribution network weakness identification method provided in the first embodiment, in which the medium voltage distribution network weakness identification method considers the risk value of a power outage of a user, and the electronic device may be an electronic device for a client, for example, a mobile phone, a notebook computer, a tablet computer, a desktop computer, etc., so as to execute the method in the first embodiment.
As shown in fig. 8, the electronic device includes a processor, a memory, a communication interface, and a bus, where the processor, the memory, and the communication interface are connected by the bus to complete communication with each other. The bus may be an industry standard architecture (ISA, industry Standard Architecture) bus, a peripheral component interconnect (PCI, peripheral Component) bus, or an extended industry standard architecture (EISA, extended Industry Standard Component) bus, among others. The memory stores a computer program that can be executed on the processor, and when the processor executes the computer program, the processor executes the method of the first embodiment, so that the principle and technical effects are similar to those of the first embodiment, and are not described herein again. Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the architecture relevant to the present application and is not limiting of the computing devices to which the present application may be applied, and that a particular computing device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In a preferred embodiment, the logic instructions in the memory described above may be implemented in the form of software functional units and stored in a computer readable storage medium when sold or used as a stand alone product. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an optical disk, or other various media capable of storing program codes.
In a preferred embodiment, the processor may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), or other general purpose processor, which is not limited herein.
Embodiment four: the present embodiment provides a computer program product, which may be a computer program stored on a computer readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is capable of executing the method provided in the above embodiment, and its implementation principles and technical effects are similar to those of the embodiment and are not repeated herein.
In a preferred embodiment, the computer-readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device, such as, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the foregoing. The computer-readable storage medium stores computer program instructions that cause a computer to perform the method provided by the first embodiment described above.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In the description of the present specification, reference to the terms "one preferred embodiment," "further," "specifically," "in the present embodiment," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the embodiments of the present specification. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A medium voltage power distribution network weakness identification method taking into account user blackout risk value, comprising:
generating a network topology map based on the power distribution network information;
selecting a certain fault element to be simulated, and analyzing a node range of the power failure influence of the fault element based on a network topology diagram; analyzing the action logic and the time sequence of the switching equipment related to the fault element, executing the action logic of the switching equipment, and determining the power failure range of the fault element based on the node range of the power failure influence of the fault element; calculating the power failure risk value of the network power failure node in the power failure range affected by the fault element;
and analyzing the weak links of the power grid based on the power failure risk value of the power failure nodes of the network until all fault elements in the network topology graph are calculated, and outputting a result.
2. The medium voltage power distribution network weakness identification method taking into account user blackout risk value according to claim 1, wherein the power distribution network information comprises: network node number and type information, network element connection relationship and parameter information thereof, and power generation and load connection relationship and parameter information.
3. The method for identifying the weakness of a medium voltage distribution network taking into account the risk value of a customer outage according to claim 1, wherein selecting a node range simulating a certain faulty element and analyzing the impact of the faulty element outage based on a network topology map comprises:
removing fault elements in the network topological graph, and searching by applying a depth-first strategy to obtain discovery sequence ordering associated node vectors at two sides of the fault elements through nodes at two sides of the fault elements;
marking all the related nodes as outage events, and determining the node range affected by the outage element.
4. A medium voltage power distribution network weakness identification method in consideration of customer blackout risk value as claimed in claim 3 wherein analysing the switchgear action logic and timing associated with the faulty element comprises:
analyzing all switching equipment in the associated branch by means of node association vectors at two sides of the fault element, wherein all switching equipment comprises a source side feeder switch, an isolating fault switch and a contact transfer switch;
analyzing the node attribute and the switch running state to obtain a source side feeder switch, an isolation fault switch and a contact transfer switch;
cutting off a fault branch according to the fact that a source side feeder switch is firstly disconnected; then the isolating fault switch is disconnected, isolating fault and locking the switch state; closing the source side feeder switch and the interconnection transfer switch, recovering the power supply of the non-fault branch, and setting the action logic and action time sequence of the switch.
5. The medium voltage distribution network vulnerability identification method considering customer outage risk value of claim 4, wherein executing the switchgear action logic, determining the outage scope of the faulty element based on the node scope of the outage influence of the faulty element comprises:
executing a switch-off action logic, and sequentially realizing the disconnection of the source side feeder line switch equipment and the disconnection of the fault isolation switch through the operation state of a switch branch in a network topological diagram; analyzing the connectivity of the topological structure in the current state, marking nodes which are not related to the balance node and the power generation node as isolated nodes, and determining the power outage range;
executing a switch closing action logic, and closing a source side feeder switch and a contact transfer switch through the operation state of a switch branch in a network topological diagram; and (3) carrying out network power flow calculation, checking whether branch current and node voltage are out of limit, supplementing segment switch action logic if the out-of-limit condition exists, cutting off part of distribution transformer load, ensuring that the branch current and the node voltage meet requirements, analyzing topological structure connectivity in the current state, marking nodes which are not related to balance nodes and power generation nodes as isolated nodes, and determining the power failure range.
6. The medium voltage power distribution network weakness identification method taking into account customer blackout risk value according to claim 1, wherein calculating network blackout node blackout risk value for the fault element to affect blackout range comprises:
calculating expected values of power failure quantity of a certain network power failure node in the power failure range affected by the fault element:
wherein CENS j A user shortage power expected value caused by the j-th element fault; lambda (lambda) j The probability of failure for the jth component year; u (U) ji The power failure time of the ith node affected by the jth element year fault; pav ji The average load of the ith node affected by the jth element fault is represented by m, and the m is the total number of the power failure nodes;
calculating the power failure risk value of a network power failure node based on the expected value of the power failure quantity of the network power failure node:
CORV i ＝VI i *CENS j
wherein, CORV i The power failure risk value of the ith node; CENS (CENS) j A user shortage power expected value caused by the j-th element fault; VI (VI) i The power supply value coefficient of the ith node;
calculating the blackout risk value of all network blackout nodes in the blackout range affected by the fault element based on the blackout risk value of the network blackout nodes:
wherein GORV j A power failure loss risk value of the power generation node caused by the j-th element fault; lambda (lambda) j The probability of failure for the jth component year; u (U) ji The power failure time of the ith node affected by the jth element year fault; pgav ji Average load lost to the ith power generation node, vg, affected by the jth element fault i And k is the total number of power failure power generation nodes, wherein the power price is the electricity price of the ith power generation node.
7. The medium voltage power distribution network weakness identification method considering the blackout risk value of a user according to claim 1, wherein analyzing the network weakness and outputting the result comprises:
calculating expected value of power shortage quantity of the medium voltage load node and power failure risk value of the medium voltage load node caused by faults of all lines, transformers and switching elements in a network;
and sequencing the comprehensive medium-voltage load node outage risk values, determining the vulnerability of the network element according to the sequencing result, and determining the weak links in the network.
8. A medium voltage power distribution network weakness identification system that considers a user's blackout risk value, comprising:
a topology generation unit configured to read power distribution network information and generate a network topology map;
a first analysis unit configured to select a node range simulating a certain fault element, and analyze the influence of the fault element power outage based on a network topology map;
a second analysis unit configured to analyze switching device action logic and timing associated with the failed component and to execute the switching device action logic, the blackout range of the failed component being determined based on the node range affected by the blackout of the failed component;
a computing unit configured to compute a network outage node outage risk value within an impact outage range of the failed component;
and the output unit is configured to analyze the weak links of the power grid based on the power failure risk value of the power failure node of the network and output a result until all fault elements in the network topological graph are calculated.
9. An electronic device, comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-7.
10. A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311160104.4A CN117318020A (en) | 2023-09-08 | 2023-09-08 | Medium voltage distribution network weakness identification method considering user blackout risk value |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311160104.4A CN117318020A (en) | 2023-09-08 | 2023-09-08 | Medium voltage distribution network weakness identification method considering user blackout risk value |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117318020A true CN117318020A (en) | 2023-12-29 |
Family
ID=89254468
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311160104.4A Pending CN117318020A (en) | 2023-09-08 | 2023-09-08 | Medium voltage distribution network weakness identification method considering user blackout risk value |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117318020A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117574786A (en) * | 2024-01-17 | 2024-02-20 | 国网经济技术研究院有限公司 | Active medium voltage overhead power distribution network segment optimization method, system and storage medium |
-
2023
- 2023-09-08 CN CN202311160104.4A patent/CN117318020A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117574786A (en) * | 2024-01-17 | 2024-02-20 | 国网经济技术研究院有限公司 | Active medium voltage overhead power distribution network segment optimization method, system and storage medium |
| CN117574786B (en) * | 2024-01-17 | 2024-03-19 | 国网经济技术研究院有限公司 | Active medium voltage overhead power distribution network segment optimization method, system and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | A reliability-constrained expansion planning model for mesh distribution networks | |
| Aziz et al. | Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience | |
| Zou et al. | Distribution system restoration with renewable resources for reliability improvement under system uncertainties | |
| Lin et al. | Heuristic rule-based phase balancing of distribution systems by considering customer load patterns | |
| Razi-Kazemi et al. | Priority assessment of online monitoring investment for power system circuit breakers—Part I: Qualitative-quantitative approach | |
| Xie et al. | Fast algorithm for the reliability evaluation of large-scale electrical distribution networks using the section technique | |
| CN113328437B (en) | Intelligent power distribution network CPS topology construction method and fault recovery method | |
| CN117318020A (en) | Medium voltage distribution network weakness identification method considering user blackout risk value | |
| CN111143764A (en) | Distribution network reliability assessment method with complex structure based on diffusion theory | |
| Zhai et al. | Modeling and identification of worst-case cascading failures in power systems | |
| CN110414810A (en) | Meter and the multiterminal intelligence Sofe Switch Optimal Configuration Method and system for losing load risk | |
| Coelho et al. | Distribution network reconfiguration with reliability constraints | |
| Akhavein et al. | Review of reduction techniques in the determination of composite system adequacy equivalents | |
| Zand et al. | Optimal coordination of DOCRs in microgrid via contingency reduction mechanism and clustering algorithms considering various network topologies based on N-2 contingencies | |
| Kalinowski et al. | A new look at component maintenance practices and their effect on customer, station and system reliability | |
| Souza Junior et al. | Adaptive overcurrent protection applied to power systems with distributed generation and active network management | |
| Hosseini et al. | Improving adaptive protection to reduce sensitivity to uncertainties which affect protection coordination of microgrids | |
| Sepehry et al. | A new algorithm for reliability assessment of distribution system based on reliability network equivalent technique | |
| Yongli et al. | An AI-based automatic power network topology processor | |
| Nighot | Incorporating substation and switching station related outages in composite system reliability evaluation | |
| Ângelos et al. | External topology tracking based on state estimation | |
| Qu et al. | Electric power cyber-physical systems vulnerability assessment under cyber attack | |
| Kashtanov et al. | Application of graph theory as a tool for reconfiguration of the distribution network | |
| Lourenco et al. | Fast decoupled steady-state solution for power networks modeled at the bus section level | |
| Hoshyarzadeh et al. | The impact of CLOD load model parameters on dynamic simulation of large power systems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |