CN112350320B - Method for improving dynamic reconfiguration of power distribution network - Google Patents
Method for improving dynamic reconfiguration of power distribution network Download PDFInfo
- Publication number
- CN112350320B CN112350320B CN202011325635.0A CN202011325635A CN112350320B CN 112350320 B CN112350320 B CN 112350320B CN 202011325635 A CN202011325635 A CN 202011325635A CN 112350320 B CN112350320 B CN 112350320B
- Authority
- CN
- China
- Prior art keywords
- period
- switch
- power distribution
- distribution network
- voltage
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000005457 optimization Methods 0.000 claims abstract description 18
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 6
- 230000009471 action Effects 0.000 claims description 22
- 125000004122 cyclic group Chemical group 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 4
- 230000000704 physical effect Effects 0.000 claims description 2
- 230000008521 reorganization Effects 0.000 claims description 2
- 230000006872 improvement Effects 0.000 abstract description 3
- 238000004458 analytical method Methods 0.000 abstract description 2
- 230000008859 change Effects 0.000 abstract description 2
- 230000009466 transformation Effects 0.000 abstract description 2
- 230000007246 mechanism Effects 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005312 nonlinear dynamic Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
Images
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
-
- 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
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
-
- 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
-
- 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
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
On the premise of realizing the dynamic reconstruction of the basic power distribution network, the method further integrates the number of optimal topologies to realize the division of dynamic time periods according to the numerical analysis of the improvement condition of the node voltage before and after the reconstruction, and achieves the aim of improving the electric energy quality by changing the network frame topology for effective times; the method provided by the invention is based on an active power distribution network scene after the distributed power supply is connected, realizes power distribution network planning under the consideration of the dynamic behavior of the distributed power supply, improves the algorithm on the basis of the traditional dynamic network reconstruction, overcomes the problem of frequent network frame topology transformation in the dynamic reconstruction, and simultaneously keeps the optimization capability of node voltage under the condition of reducing network frame topology change, so that the practical engineering application significance of the traditional power distribution network reconstruction is enhanced on the premise of improving the power quality.
Description
Technical Field
The invention relates to the technical field of power system power distribution network planning, in particular to an active power distribution network technology after high-permeability access to a distributed power supply, and discloses a method for improving dynamic reconstruction of a power distribution network.
Background
With the rapid development of new energy power generation technology in China, china forms a distributed power system with the highest standard mode and the highest permeability in the world. The distributed power generation capacity is influenced by weather factors, has strong randomness, has large day and night variation, is influenced by factors such as random access, three-phase unbalanced access and the like which lack planning, has the potential for the power quality condition of the high-permeability distributed active power distribution network, particularly the voltage quality is often out of limit, brings a plurality of difficulties for the interaction mechanism analysis of a power distribution system, and prevents the development of the active power distribution network with the distributed power supply.
In order to reduce the influence of the dynamic behavior of the active power distribution network with the distributed power supply, the industry proposes a steady state response mechanism for fundamentally solving the problem of the steady state response mechanism of the active power distribution system in the planning period of the power distribution network, namely, the multi-period dynamic reconstruction of the power distribution network is realized by establishing a planning mathematical model, the reconstruction problem is essentially a multi-objective optimized nonlinear dynamic planning problem, but the planning method has the problem of separating dynamic period division from topology optimization, the situation that a switch cannot be closed during network reconstruction occurs, each action of a sectionalizing switch and a connecting switch has safety risks and reliable power supply risks, and the inherent defect of the method for reconstructing the power distribution network is insufficient. How to better solve the dynamic planning problem of an active power distribution network with a distributed power supply is still a technical problem which is suspended but not solved.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a method for improving dynamic reconfiguration of a power distribution network, which solves the problem of frequent topology transformation of a grid frame in dynamic reconfiguration, effectively improves the problem of separation of dynamic time interval division and topology optimization in the traditional planning method, and is an improvement of the traditional power distribution network reconfiguration method.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
a method of improving dynamic reconfiguration of a power distribution network, comprising the steps of:
(1) On the basis of calculating power flow of a power distribution network by using a Newton Laportson method, calculating the power flow condition of the power distribution network containing DGs by referring to a semi-invariant method, describing uncertainty brought by dynamic behavior of the DGs to the power distribution network by using Gram-Charlier series, simulating to obtain node voltage and branch power flow state of a certain topology, further realizing power distribution network reconstruction by using an NSGA-II algorithm, obtaining optimal topological structures of each period of 24 hours in one day, and taking the obtained 24 optimal topological structures as initial reconstruction sets;
(2) Combining the 24 topological structures according to the step (1) to reduce the action times of the network switch, primarily combining the topologies with similar structures, and selecting one of the topologies to replace other topologies with similar shapes, wherein the output curve of the distributed power supply is a continuous function, so that the situation that the topologies are similar across a large time period does not exist;
(3) For integrating topologies with adjacent time but dissimilar structures, the concepts of the optimal pressure rate ρ and the optimal pressure parameter α are introduced first:
maxα t ＝E ρ E ΔZ (2)
wherein ρ is t For the planning front and back voltage optimization rate, alpha, of each time period t of the day t For the period t voltage optimization level expectations, Z opt.t’ Z is the voltage deviation index before planning t time period opt.t” For the voltage offset after t period planning, ΔZ t For the degree of voltage optimisation E ρ And E is ΔZ P respectively t And DeltaZ t Average value of (2);
the optimal voltage rate rho and the optimal voltage parameter alpha are obtained by formulas (1) and (2), then the voltage optimization degree of each topology is compared according to the optimal voltage parameter alpha, and if the voltage optimization capacities of different topologies are similar, the essential function capacities of the two topologies are similar, and the two topologies can be combined into one topology;
(4) Alpha in adjacent time periods within 24 time periods t Less phase difference case |alpha t -α t-1 |＜Δα ε When the default prescribed t period is combined with the t-1 period, delta alpha ε Is the maximum setting value; adjacent time periods alpha within 24 time periods t Case of large difference |α t -α t-1 |≥Δα ε When combined, the alpha is compared according to the following principle t ，α t+1 ，α t-1 If |alpha t -α t-1 |≤|α t -α t+1 T period and t-1, merging the time periods, otherwise merging the t time periods with the t+1 time periods;
(5) The condition of merging termination is the limitation of the total number of actions of the sectionalizing switch and the tie switch, so that the number of integrated topological structures is smaller than the upper limit of the number of actions of the switch:
wherein S is ∑ To plan the sum of the action times of all branch switches and tie switches in the period t of time, S Max S is the limitation of the physical action times of the branch switch and the tie switch ζ S is the number of times of zeta action switch in planning period t ζ,Max S is the limitation of the physical operation times of the zeta-action switch switch Is a collection of actionable switches;
if the limitation of the switch action requirement is not met, the step (4) is skipped to continue to merge topology, after the merging of one round is completed, the updating of the time period information continues to process the cyclic reorganization until the cyclic condition is met, and if the switch constraint is met, the cycle is skipped;
(6) And (3) if the cyclic condition of the switch action constraint of the formula (3) is met, the cyclic output dynamic reconstruction execution mode is jumped out, and the final typical topological structure is obtained.
The invention has the advantages that:
the difference of the dynamic behavior of the distributed power supply in different time periods of 24 hours of a day ultimately affects the reconstructed optimal topological structure, so that 24 hours of the day can be divided into a plurality of time periods, if the thought of limit is used, the time of day can be divided into n time periods, the n optimal topological structure is corresponding to the n optimal topological structure, in the actual engineering, the frequent change of the dynamic topological structure is considered, unnecessary operation risks are caused to the sectionalizing switch and the interconnecting switch, the action times of the remote control switch are required to be maintained at a lower level as much as possible, the requirement is placed on the action times of the movable switch, the method of planning the 24 hours into the less time period is feasible because the distribution power supply output curve changes less in the adjacent time period, the time period is planned based on the thought, the corresponding topological structure is reduced, and the action times of the switch are reduced. However, the requirement that the voltage improvement degree and the theoretical value are not greatly different in one day before and after the reduction of the time period must be met, so the method applies a semi-invariant random power flow algorithm to simulate the random power flow of the topology of each time period, and the topology power quality condition before and after the time period division is evaluated based on the obtained simulation data of the power distribution network.
Drawings
FIG. 1 is a detailed flow chart of the dynamic reconfiguration strategy according to the present invention.
Fig. 2 is a block diagram of an IEEE33 node power distribution system.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
The proposed theory is verified by taking an IEEE33 node power distribution network as an embodiment. The example head end reference voltage shown in fig. 2 is 35kV, the three-phase power reference value is 15MVA, the total deterministic load of the network is 12mw+j6mvar, and the balance node of the system is node No. 0. The system is provided with 33 nodes, 0 node, 14 node and 24 node which are connected into a distributed photovoltaic power supply, 7 node is an EV charging station, the rest 29 nodes are deterministic load nodes, K1 to K32 are branch sectional switches, T1 to T5 are grid interconnection switches, and the confidence interval of probability constraint conditions of line capacity and node voltage is 0.85.
Referring to fig. 1, a method for improving dynamic reconfiguration of a power distribution network includes the steps of:
step (1): on the basis of calculating power flow of a power distribution network by using a Newton Lapherson method, calculating the power flow condition of the power distribution network containing DG by referring to a semi-invariant method, describing uncertainty brought by dynamic behavior of DG to the power distribution network by using Gram-Charlier series, obtaining node voltage and branch power flow state of a certain topology by simulation, further completing static reconstruction work of each unit time of the IEEE-33 power distribution network by using NSGA-II algorithm, obtaining optimal topological structures of each time period of 24 hours a day, and taking the obtained 24 optimal topological structures as initial reconstruction sets.
Step (2): the unit time periods with the same topological structure in the 24 topological graphs obtained in the step (1) are preliminarily combined, the action times of the network switch are reduced, in the step, topologies with similar structures are preliminarily combined, one topology is selected to replace the topology with similar shapes, and as the output curve of the distributed power supply is a continuous function, the situation that the topologies of the two topological structures are similar across a large time period does not exist; and then, the voltage optimization conditions of the respective time periods after the preliminary merging are counted.
Step (3): in the step (2), 24 topologies are initially combined, and the adjacent topologies with dissimilar structures are integrated, wherein the concepts of the optimal voltage rate ρ and the optimal voltage parameter α are introduced first: and calculating according to the formula (1) and the formula (2) to obtain the optimal pressure rate rho and the optimal pressure parameter alpha of each period after preliminary combination
maxα t ＝E ρ E ΔZ (2)
Wherein ρ is t Reflecting the optimization rate of the voltage before and after planning of each period t of the day, alpha t Reflecting the expected degree of voltage optimization for the t period, Z opt.t’ Z is the voltage deviation index before planning t time period opt.t” For the voltage offset after t period planning, ΔZ t For the degree of voltage optimisation E ρ And E is ΔZ P respectively t And DeltaZ t Average value of (2).
The optimal pressure rate ρ and the optimal pressure parameter α are obtained by substituting the result of the power flow calculation into the equation (1) and the equation (2).
Then comparing the voltage optimization degree of each topology according to the optimal voltage parameter alpha, and if the voltage optimization capacities of different topologies are similar, the essential function capacities of the two topologies are similar, and the two topologies can be combined into one topology;
the results are shown in Table 1.
Table 1 period after preliminary reconstitution
As shown in the results of table 1, 24 time periods are initially combined into 10 time periods, the 10 time periods correspond to 10 typical topologies, 2 to 5 early morning points have similar topological structures, 6, 7 and 8 early morning points have larger structural differences of the optimal topologies of the three time periods due to load increase and uncertainty increase of distributed photovoltaic output, the load in daytime tends to be stable, the distributed photovoltaic power supply output tends to be stable, the topological structures are similar, and the topological structures of the time periods in the evening, especially 19, 20 and 21 to 22 points, are different due to the influence of charging power of an electric automobile. And (3) the action times of the tie switch and the sectionalizer in the topology after the primary merging are still too many, and the step (4) is carried out for further merging time period.
Step (4): comparing the voltage optimization degree (optimal voltage parameter) alpha of each period t ，α t+1 ，α t-1 If |alpha t -α t-1 |≤|α t -α t+1 The t period is combined with the t-1 period, otherwise, the t period is combined with the t+1 period; but if |alpha t -α t-1 |＜Δα ε In the present embodiment Δα ε =0.0030/p.u., the default prescribed t period is combined with the t-1 period, i.e., directly backward.
Step (5): the condition of merging termination is that the total number of actions of the sectionalizing switch and the tie switch is limited, the total number of actions of the remote control switch in the embodiment is less than 15 times in one day, and the IEEE33 node power distribution network has 5 remote control switches, namely the number of typical topology networks required to be output is less than 3, and 24h time periods in one day are merged into three large time periods. The iterative algorithm process of step (4) and step (5) is shown in table 2.
Table 2 iterative procedure for dynamic reconfiguration period
As shown in table 2, the entire merge requires 5 iterations to complete. The difference value between the optimal pressure parameter of the time period of 2 points to 5 points and the optimal pressure parameter of 6 points of the first iteration is smaller than a threshold value, and the time period is directly combined backwards to form 2 points to 6 points; if the parameter difference between 7 points and 8 points in the second iteration is larger than the threshold, whether the optimal pressure parameter of the time period is similar to 6 points or 8 points is needed to be considered, the time period is calculated to be similar to 6 points, and the time periods are combined forwards and integrated into a 2-7-point time period; in the third iteration process, the parameter difference between the 8-point section and the 18-point section is smaller than the threshold value, so that the 8-point section and the 18-point section are directly combined; the remaining two iterations and so on.
Step (6): and finally outputting a dynamic reconstruction scheme to obtain a final plurality of typical topological structures. Dynamic reconstruction is divided into 3 reconstruction periods (23:00-07:00, 07:00-19:00 and 19:00-23:00) under conditions that meet the switch constraints. On the premise of ensuring lower switching times, the method enables the dynamic reconstruction to be similar to the optimal reconstruction of all-day voltage values in each period, but has relatively higher voltage optimization effect compared with the non-reconstruction and static reconstruction strategies.
Claims (1)
1. A method for improving dynamic reconfiguration of a power distribution network, comprising the steps of:
(1) On the basis of calculating power flow of a power distribution network by using a Newton Laportson method, calculating the power flow condition of the power distribution network containing DGs by referring to a semi-invariant method, describing uncertainty brought by dynamic behavior of the DGs to the power distribution network by using Gram-Charlier series, simulating to obtain node voltage and branch power flow state of a certain topology, further realizing power distribution network reconstruction by using an NSGA-II algorithm, obtaining optimal topological structures of each period of 24 hours in one day, and taking the obtained 24 optimal topological structures as initial reconstruction sets;
(2) Combining the 24 topological structures according to the step (1) to reduce the action times of the network switch, primarily combining the topologies with similar structures, and selecting one of the topologies to replace other topologies with similar shapes, wherein the output curve of the distributed power supply is a continuous function, so that the situation that the topologies are similar across a large time period does not exist;
(3) For integrating topologies with adjacent time but dissimilar structures, the concepts of the optimal pressure rate ρ and the optimal pressure parameter α are introduced first:
maxα t ＝E ρ E ΔZ (2)
wherein ρ is t For the planning front and back voltage optimization rate, alpha, of each time period t of the day t For the period t voltage optimization level expectations, Z opt.t’ Z is the voltage deviation index before planning t time period opt.t” For the voltage offset after t period planning, ΔZ t For the degree of voltage optimisation E ρ And E is ΔZ P respectively t And DeltaZ t Average value of (2);
the optimal voltage rate rho and the optimal voltage parameter alpha are obtained by formulas (1) and (2), then the voltage optimization degree of each topology is compared according to the optimal voltage parameter alpha, and if the voltage optimization capacities of different topologies are similar, the essential function capacities of the two topologies are similar, and the two topologies can be combined into one topology;
(4) Alpha in adjacent time periods within 24 time periods t Less phase difference case |alpha t -α t-1 |＜Δα ε When the default prescribed t period is combined with the t-1 period, delta alpha ε Is the maximum setting value; adjacent time periods alpha within 24 time periods t Case of large difference |α t -α t-1 |≥Δα ε When combined, the alpha is compared according to the following principle t ，α t+1 ，α t-1 If |alpha t -α t-1 |≤|α t -α t+1 The t period is combined with the t-1 period, otherwise, the t period is combined with the t+1 period;
(5) The condition of merging termination is the limitation of the total number of actions of the sectionalizing switch and the tie switch, so that the number of integrated topological structures is smaller than the upper limit of the number of actions of the switch:
wherein S is ∑ To plan the sum of the action times of all branch switches and tie switches in the period t of time, S Max S is the limitation of the physical action times of the branch switch and the tie switch ζ S is the number of times of zeta action switch in planning period t ζ,Max S is the limitation of the physical operation times of the zeta-action switch switch Is a collection of actionable switches;
if the limitation of the switch action requirement is not met, the step (4) is skipped to continue to merge topology, after the merging of one round is completed, the updating of the time period information continues to process the cyclic reorganization until the cyclic condition is met, and if the switch constraint is met, the cycle is skipped;
(6) And (3) if the cyclic condition of the switch action constraint of the formula (3) is met, the cyclic output dynamic reconstruction execution mode is jumped out, and the final typical topological structure is obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011325635.0A CN112350320B (en) | 2020-11-24 | 2020-11-24 | Method for improving dynamic reconfiguration of power distribution network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011325635.0A CN112350320B (en) | 2020-11-24 | 2020-11-24 | Method for improving dynamic reconfiguration of power distribution network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112350320A CN112350320A (en) | 2021-02-09 |
| CN112350320B true CN112350320B (en) | 2023-05-23 |
Family
ID=74365540
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011325635.0A Active CN112350320B (en) | 2020-11-24 | 2020-11-24 | Method for improving dynamic reconfiguration of power distribution network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112350320B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116520019B (en) * | 2023-06-30 | 2023-09-12 | 中国电力科学研究院有限公司 | Intelligent electric energy metering device and circuit topology identification method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015027484A1 (en) * | 2013-08-30 | 2015-03-05 | Accenture Global Services Limited | System, method and apparatuses for distribution network reconfiguration and a tangible computer readable medium |
| CN104734153A (en) * | 2014-12-18 | 2015-06-24 | 国家电网公司 | Method of reconstructing power distribution network containing distributed power supply |
| CN105071437A (en) * | 2015-08-13 | 2015-11-18 | 同济大学 | Island dividing method considering distributed power output and load uncertainty |
| WO2018237340A1 (en) * | 2017-06-23 | 2018-12-27 | Johnson Controls Technology Company | Building equipment with predictive control |
-
2020
- 2020-11-24 CN CN202011325635.0A patent/CN112350320B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015027484A1 (en) * | 2013-08-30 | 2015-03-05 | Accenture Global Services Limited | System, method and apparatuses for distribution network reconfiguration and a tangible computer readable medium |
| CN104734153A (en) * | 2014-12-18 | 2015-06-24 | 国家电网公司 | Method of reconstructing power distribution network containing distributed power supply |
| CN105071437A (en) * | 2015-08-13 | 2015-11-18 | 同济大学 | Island dividing method considering distributed power output and load uncertainty |
| WO2018237340A1 (en) * | 2017-06-23 | 2018-12-27 | Johnson Controls Technology Company | Building equipment with predictive control |
Non-Patent Citations (2)
| Title |
|---|
| 含分布式电源的配电网多时段重构研究;徐礼舟;程若发;贺志华;肖济邦;LUO Hua-zhu;;计算机仿真(第04期);全文 * |
| 基于重构有效性和最优网架选择的配电网动态重构研究;李顺昕;张姚;岳云力;李赛;武冰清;曹昉;;电网与清洁能源(第07期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112350320A (en) | 2021-02-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111817345A (en) | Reconstruction method for power distribution network with distributed power supply after serious fault | |
| CN112910012B (en) | Power distribution system elasticity improvement evaluation method, storage medium and computing equipment | |
| CN102945296A (en) | Method for reconstructing and modeling uncertainty of distribution network in demand response viewing angle | |
| CN105337275A (en) | Medium-voltage power distribution network power supply capacity evaluation method based on reliability of power distribution system | |
| CN101895116A (en) | Online available transmission capacity computing method based on distribution factor | |
| CN112350320B (en) | Method for improving dynamic reconfiguration of power distribution network | |
| CN111641205A (en) | Active power distribution network fault management method based on random optimization | |
| CN104881708A (en) | Method for reconstructing power distribution network based on topology correction | |
| CN113344280A (en) | Power failure plan optimization model constraint construction method and device considering load transfer | |
| CN107359614B (en) | Dynamic reconfiguration method for safe operation of power distribution network under high-permeability renewable energy condition | |
| Sánchez-Pérez et al. | Effect of time resolution on capacity expansion modeling to quantify value of long-duration energy storage | |
| CN117543561A (en) | Flexible power distribution system power supply protection method for collaborative virtual power plant | |
| CN112103950A (en) | Power grid partitioning method based on improved GN splitting algorithm | |
| CN116316607A (en) | Combined optimization scheduling method, system, equipment and medium | |
| Ustun et al. | Modeling and simulation of a microgrid protection system with central protection unit | |
| CN108596475B (en) | PMU data recovery method based on dynamic change of interpolation interval | |
| Wang et al. | Early warning of distribution transformer based on bp neural network considering the influence of extreme weather | |
| CN113241794B (en) | Island micro-grid self-adaptive control method based on multiple intelligent agents | |
| Ma et al. | A review of the development of resilient highway energy system coping with climate | |
| CN115292869A (en) | Diamond-type power distribution network reliability calculation method considering large-scale photovoltaic energy storage access | |
| Niu et al. | Power supply reliability evaluation method for DC distribution network with power energy router | |
| CN101860027B (en) | Method for establishing model by combining power supply analysis with running mode during running power grid | |
| Wei et al. | Coordinated Post-Disaster Recovery and Assessment Method for Integrated Electricity-Gas-Transportation System | |
| Zhang et al. | Resilience-oriented comparative study of SOP-based service restoration in distribution systems | |
| Wang et al. | Fault Recovery Strategy of Distribution Network with Distributed Generation Based on Island Division |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |