CN108964037A - Based on the reconstitution model of high voltage distribution network - Google Patents
Based on the reconstitution model of high voltage distribution network Download PDFInfo
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- 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
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- 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
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Abstract
The invention discloses the reconstitution model of high voltage distribution network is based on, the method for model construction includes the following steps: step S1: building dynamic load model；Step S2: the random factors such as intermittence energy power output, electric car charge/discharge, system loading excision for being occurred according to current city power grid calculate network congestion risk index NCRI；Step S3: according to dynamic load model, high voltage distribution network HVDN topological network is calculated；Step S4 assesses high voltage distribution network HVDN topological network according to calculated NCRI；It solves system loading degree of unbalancedness, causes existing Model Practical and stability poor, it is difficult to the problem of adapting to the development of current science and technology.
Description
Technical field
The present invention relates to high voltage distribution network reconstruction fields, are based particularly on the reconstitution model of high voltage distribution network.
Background technique
During novel urban Energy Load is grown rapidly, high voltage distribution network topology reconstruction technology is in large-scale photovoltaic
The random charge and discharge of electric car of the consumption problem and Thief zone of electric station grid connection cause urban distribution network locally to block asking for exacerbation etc.
There are single section static state moulds clearly disadvantageous, that current techniques are constructed only for region power load distributing unevenness for processing in topic
Type fails to consider that the random variation characteristic of the unordered charge and discharge behavior and photovoltaic generating system of electric car makes system load flow
Randomness enhancing, urban distribution network topology risk assessment difficulty in timing increase, non-convex model intelligent algorithm is solved and restrained
Property it is poor caused by switch frequent operation the problem of；If still investigating system safety using extreme static certainty appraisal procedure at this time
Property will so that scheme is overly conservative, can not it is grid-connected the new distribution type energy after do not know fluctuation caused by system and determine
Amount estimation analysis, system economy are poor, it is difficult to adapt to the power grid security analysis of new energy；And it is opened up at this stage based on high voltage distribution network
The research for flutterring reconfiguration technique, which is aimed to solve the problem that, is unevenly distributed caused network part obstructing problem by conventional load, stringent fastidious
Its strong fluctuation enhances system loading degree of unbalancedness after photo-voltaic power supply and electric car large-scale grid connection, leads to existing model
Practicability and stability are poor, it is difficult to adapt to the development of current science and technology.
Summary of the invention
To solve problems of the prior art, it the present invention provides the reconstitution model of high voltage distribution network is based on, solves
System loading degree of unbalancedness, causes existing Model Practical and the stability poor, it is difficult to adapt to asking for the development of current science and technology
Topic.
The technical solution adopted by the present invention is that: it is based on the reconstitution model of high voltage distribution network, which is characterized in that model construction
Method includes the following steps:
The method of model construction includes the following steps:
Step S1: building dynamic load model；
Step S2: it is contributed according to the intermittence energy that current city power grid occurs, electric car charge/discharge, system loading
The random factors such as excision calculate network congestion risk index NCRI；
Step S3: according to dynamic load model, high voltage distribution network HVDN topological network is calculated；
Step S4 assesses high voltage distribution network HVDN topological network according to calculated NCRI；
Step S5: when NCRI is without departing from fiducial range, it is believed that the obstruction of photovoltaic the consumption degree and system of network this moment
Situation within the allowable range, does not have operational motion behavior；When NCRI exceeds fiducial range, triggering HVDN reconstruct, based on double-deck excellent
Change model, under maximum with photovoltaic consumption degree, maximum to the system congestion alleviation degree and the smallest integration objective of removal of load amount,
Obtain the optimum topology state of network this moment.
The present invention is based on having the beneficial effect that for the reconstitution model of high voltage distribution network:
1. based on the reconstitution new energy consumption of Probabilistic Load Flow network topology dynamic risk assessment strategy and HVDN, environment
The multiple-objection optimization strategy of benefit and user satisfaction is mutually coordinated, that is, makes full use of the NCRI index of network topology risk assessment
Evaluation and test is made to the obstruction and consumption degree of network topology, realizes the real-time monitoring to network state, makes full use of HVDN's
The non-depth feature of the polycyclic state of network carries out reasonable turn to the load of the synthetic load comprising electric car and photo-voltaic power supply and supplies,
Locally block aggravation problem with the system due to caused by urban distribution network power load distributing is unbalanced.
2. urban distribution network consumption proposed by the invention is with obstruction control strategy in the case where eliminating part obstruction with light
Volt consumption maximum turns to target, it is contemplated that office caused by the otherness of clean energy resource photovoltaic and electric automobile load on space-time
Portion's obstruction aggravates and dissolves insufficient problem, takes full advantage of the NCRI index comprising consumption and blockage factor and is prejudged, tool
There is very high practicability.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow charts of the reconstitution model of high voltage distribution network.
Specific embodiment
A specific embodiment of the invention is described below, in order to facilitate understanding by those skilled in the art this hair
It is bright, it should be apparent that the present invention is not limited to the ranges of specific embodiment, for those skilled in the art,
As long as various change is in the spirit and scope of the present invention that the attached claims limit and determine, these variations are aobvious and easy
See, all are using the innovation and creation of present inventive concept in the column of protection.
As shown in Figure 1, being based on the reconstitution model of high voltage distribution network, which is characterized in that the method for model construction includes as follows
Step:
Step S1: building dynamic load model；
Step S2: it is contributed according to the intermittence energy that current city power grid occurs, electric car charge/discharge, system loading
The random factors such as excision calculate network congestion risk index NCRI；
Step S3: according to dynamic load model, high voltage distribution network HVDN topological network is calculated；
Step S4 assesses high voltage distribution network HVDN topological network according to calculated NCRI；
Step S5: when NCRI is without departing from fiducial range, it is believed that the obstruction of photovoltaic the consumption degree and system of network this moment
Situation within the allowable range, does not have operational motion behavior；When NCRI exceeds fiducial range, triggering HVDN reconstruct, based on double-deck excellent
Change model, under maximum with photovoltaic consumption degree, maximum to the system congestion alleviation degree and the smallest integration objective of removal of load amount,
Obtain the optimum topology state of network this moment.
The dynamic load model of the step S1 of this programme includes electric car charge/discharge load simulation model, electric car
The integrated load model of charge/discharge load, certain time access number of units distributed model, the certain time power grid of electric car
Access the probability density letter of the number of units model of electric car, the prediction power output model of photovoltaic generator, photovoltaic active power of output
Several and photovoltaic power generation system output power desired value.
Electric car charge/discharge load simulation model is
The integrated load model of electric car charge/discharge load:
In formula, λEVFor the desired value for accessing electric car in the period, nEVFor the electric car platform that may access power grid
Number, if the probability density function of known each period access system electric car number of units, can acquire period electric car charge/discharge
Mean value, standard deviation and the High order central moment of power；
The number of units distributed model of certain time access electric car are as follows:
The number of units model of certain time power grid access electric car are as follows:
In formula, n is electric car number of units total in system, μt0=1 electric car accesses the expectation event of electric system, σt
For standard deviation, refer to charge/discharge time range, enabling the Annual distribution range of charge/discharge is σt=2；If the expected time of charging
For μt0=1, refer to period 1, i.e. 00:00-01:00, discharge time μt0=13, refer to period 13, i.e. 12:00-13:00；Certain time
Section access power grid electric car number λEVFor nEVExpectation；
The prediction power output model of photovoltaic generator:
In formula, basic functionFor power output of the photovoltaic in desired value, stochastic variable θ (t) indicates atmosphere
To the inhibition of solar irradiation；
The probability density function of the photovoltaic active power of output are as follows:
In formula: Γ is Gamma function, and α, β are respectively the form parameter of beta distribution, PPVFor the output work of photovoltaic generator
Rate；For the peak power output of photovoltaic array.
The desired value of photovoltaic power generation system output power are as follows:
The network risks evaluation index NCRI of the step S2 of this programme is by GZNCharacterization:
In formula, α, beta, gamma is respectively the weight coefficient of whole obstruction with part obstruction and photovoltaic consumption degree,WithRespectively
Indicate network over loading risk vector sum photovoltaic penetration degree vector；
Risk overloads vector
In formula, M is the total circuitry number of system,For column vector, giCharacterize i-th branch overload risk；
Branch overloads risk gm:
In formula, wXi, kFor probability right, a is integer,For the overload quantity of branch m；
Branch overload quantity
In formula, LmFor the ratio between branch m actual transmission power and power allowances, L0For set branch safety threshold, m=1,
2 ..., M；
Photovoltaic penetration degree vector
In formula, r is substation's number containing photo-voltaic power supply, ytCharacterize t-th of substation photovoltaic containing photo-voltaic power supply
Consumption degree；
In formula, PactPower, P are actually sent out for photo-voltaic power supplyplanPower is issued for plan.
Information between optimization layer decision variable and state variable above and below during the dual-layer optimization of the step S5 of this programme
Transmitting the specific steps are
Step A1: initialization PSO algorithm parameter and particle initial position inputs network parameter, initialization sample and random
Variable dimension m；
Step A2: by Nataf inverse transformation by the sample matrix transform in standard normal space be input variable in sample
This matrix X, in the optimal topology TP in upper layeriUnder state, using photovoltaic consumption as target, kth column the being determined property tide of matrix X is utilized
Stream calculation determines the weight of load to find photovoltaic and dissolve optimal load combination ζi, and by optimum results with fitness function
Form pass to upper layer topological optimization model；
Step A3: in the case where lower layer's photovoltaic dissolves optimal objective, turning to model optimization target with economic and environmental benefit maximum,
To find the optimal topology status TP of networki；
Step A4: more new particle history optimal location and population optimal location；
Step A5: perhaps reaching maximum number of iterations convergence or reach maximum number of iterations if judging whether to restrain,
Enter step A6；If not restraining, return step A2；
Step A6: calculating NCRI whether in fiducial range, if exceeding fiducial range, otherwise return step A1 enters
Step A7；
Step A7: the output optimal topology status of network.
The present embodiment is based on the reconstitution model of high voltage distribution network, step 1: the structure of dynamic load model when implementing
It builds: for electric car charge/discharge load the case where different function area charging pile carries out concentration charge/discharge, being become based on Nataf
It changes and handles random correlation principle, the intrinsic load of electric automobile load and region is subjected to decorrelation processing, using partly not
Quantity method constitutes the integrated load model for considering new energy to the superposition processing of mutual two stochastic variables；Based on season
Section property climate state models unit time photo-voltaic power supply power output, with true meteorological earning in a day digital simulation Various Seasonal
Sunshine time, the amount of radiation for simulating each period from sun to sun account for total radiation ratio to characterize different time sections photovoltaic electric
The strong fluctuation and randomness of source power output.
Step 2: the network topology dynamic risk assessment strategy based on Probabilistic Load Flow: occur for current city power grid
The random factors such as intermittent energy source power output, electric car charge/discharge, system loading excision, propose network congestion risk index
(NCRI) for assessing the operation risk of city transmission system, from part to integrally quantitative consideration system risk, NCRI makees
Quantitative criteria for triggering HVDN reconstruct provides theoretical foundation for the action policy of power transmission network topology reconstruction technology.
Step 3: urban distribution network consumption and obstruction control strategy:
When NCRI exceeds fiducial range, by triggering HVDN reconstruct；Upper layer model turns to model with economic and environmental benefit maximum
Optimization aim improves system to the consumption of photovoltaic and under electric car Thief zone by the optimization to HVDN topological structure
Bearing capacity；
Lower layer's Optimized model is established on the basis of the initial optimal topology status of upper layer optimization aim, is mentioned to greatest extent
High system finds photovoltaic to the consumption degree of photovoltaic and dissolves optimal load combination, assessment in some uncertain scene because
Cost is controlled caused by the network constraints such as trend constraint condition, and upper layer Optimized model of feeding is returned in the form of fitness value.
The network topology dynamic risk assessment strategy of step 2 is according to the strong fluctuation pair of the higher system of new energy permeability
The fast steady requirement of network state assessment, building are the network topology risk assessment side instructed with network congestion risk index (NCRI)
Method；Fluctuation of the probabilistic loadflow as auxiliary tool characterization PVs and EVs, and the risk by directly reflecting each quantity of state of system
Property probability distribution information, quickly system topological state is made from part to whole assessment strategy；Network over loading risk vectorInfinite Norm and two norms characterize the dangerous maximum case of overall risk implementations and local wind of network topology, model respectively
Several linear combination is constituted to network risks evaluation index NCRI；
Network risks evaluation index NCRI is by GZNCharacterization:
In formula, α, beta, gamma is respectively the weight coefficient of whole obstruction with part obstruction and photovoltaic consumption degree,WithRespectively
Indicate network over loading risk vector sum photovoltaic penetration degree vector；
Risk overloads vector
In formula, M is the total circuitry number of system,For column vector, giCharacterize i-th branch overload risk；
Branch overloads risk gm:
In formula, wXi, kFor probability right, a is integer,For the overload quantity of branch m；
Branch overload quantity:
In formula, LmFor the ratio between branch m actual transmission power and power allowances, L0For set branch safety threshold, m=1,
2 ..., M；
Photovoltaic penetration degree vector
In formula, r is substation's number containing photo-voltaic power supply, and yt characterizes t-th of substation photovoltaic containing photo-voltaic power supply
Consumption degree；
In formula, PactPower, P are actually sent out for photo-voltaic power supplyplanPower is issued for plan.
Network dynamic risk based on network over loading risk indicator is evaluated as the movement plan of power transmission network topology reconstruction technology
Slightly provide theoretical foundation, can for the new distribution type energy it is grid-connected after caused by system do not know fluctuation quantitatively estimated
Meter analysis.
The urban distribution network consumption of step 3 is with obstruction control strategy to improve system to the consumption of photovoltaic and in electronic vapour
Bearing capacity under overall height infiltration is optimization aim, considers the group topological constraints of HVDN electric transforming unit, trend equation and inequality constraints,
Node voltage constraint, branch power constraint and second order cone relaxation switch condition constraint, formulate dual-layer optimization strategy；The bilayer
The upper layer Optimized model of optimisation strategy turns to model optimization target with economic and environmental benefit maximum to find the optimal topology of network
State；Lower layer's Optimized model of the dual-layer optimization strategy is target to seek using photovoltaic consumption under the optimal topology status in upper layer
Photovoltaic is looked for dissolve optimal load combination, to achieve the purpose that the slow resistance of delustring；
The objective function of upper layer optimal economic benefit:
In formula,For lower layer's optimization aim,For switch motion cost, M indicates the number of stochastic variable, δiIndicate i-th
Kind load combines corresponding probability right；
Switch motion cost:
In formula, τ indicates the economy cost of each switch motion, and χ indicates electric transforming unit group number,Indicate that the t period becomes
Electric unit group switch motion number；
The constraint of upper layer economic benefit largest optimization model is mainly HVDN electric transforming unit group topological constraints:
A) topological structure need to meet radial constraint in unit group, i.e., any electric transforming unit has and only one in unit group
Access is connected to power supply point；
B) variation of switch state changes the connection relationship of power supply point and electric transforming unit in unit group, and then changes power supply point
Rate of load condensate, and the variation of different units group switch state and the influence of power supply point rate of load condensate are independent from each other.
Lower layer's photovoltaic dissolves maximum objective function:
In formula, Pwg,i t, Pg tThe desired value of t period i-node photovoltaic power output and the consumption power of photovoltaic are respectively indicated, n refers to
110kV power transformation tiny node total number, ω indicate that abandoning the environmental benefit that light behavior generates influences；
By lower layer's Optimized model trend constraint of cone conversion:
In formula, equality constraint, the trend equality constraint of high voltage transmission line direct current method, node of trend respectively after cone conversion
Voltage constraint and the power constraints of branch,For the 110kV power transformation tiny node being connected with 110kV substation node i
Set；
It is a second order cone when feasible zone relaxes, forms convex feasible zone, the constraint after relaxation conversion:
The building of dynamic load model, integrated load model and photo-voltaic power supply fluctuation including electric car charge/discharge
Property model, the index that the network topology dynamic risk assessment strategy based on Probabilistic Load Flow is assessed using NCRI as risk,
The urban distribution network consumption and obstruction control strategy, convert non-convex trend constraint based on second order cone relaxation, formulate double-deck
Optimisation strategy.
During dual-layer optimization up and down between optimization layer decision variable and state variable information transmitting:
Step 1: initialization PSO algorithm parameter and particle initial position inputs network parameter, initialization sample and random
Variable dimension m；
Step 2: by Nataf inverse transformation by the sample matrix transform in standard normal space be input variable in sample
This matrix X, in the optimal topology TP in upper layeriUnder state, using photovoltaic consumption as target, kth column the being determined property tide of matrix X is utilized
Stream calculation determines the weight of load to find photovoltaic and dissolve optimal load combination ζi, and by optimum results with fitness function
Form pass to upper layer topological optimization model；
Step 3: in the case where lower layer's photovoltaic dissolves optimal objective, turning to model optimization target with economic and environmental benefit maximum,
To find the optimal topology status TP of networki；
Step 4: more new particle history optimal location and population optimal location；
Step 5: perhaps reaching maximum number of iterations convergence or reach maximum number of iterations if judging whether to restrain,
It carries out step 6 and enters step two if not restraining；
Step 6: calculating NCRI whether in fiducial range, if exceeding fiducial range, enter step one, conversely, output
Export the optimal topology status of network.
Claims (5)
1. being based on the reconstitution model of high voltage distribution network, which is characterized in that the method for model construction includes the following steps:
Step S1: building dynamic load model；
Step S2: it is cut off according to the intermittence energy power output of current city power grid appearance, electric car charge/discharge, system loading
Equal random factors, calculate network congestion risk index NCRI；
Step S3: according to dynamic load model, high voltage distribution network HVDN topological network is calculated；
Step S4 assesses high voltage distribution network HVDN topological network according to calculated NCRI；
Step S5: when NCRI is without departing from fiducial range, it is believed that the congestion situations of photovoltaic the consumption degree and system of network this moment
Within the allowable range, there is no operational motion behavior；When NCRI exceeds fiducial range, triggering HVDN reconstruct is based on dual-layer optimization mould
Type obtains under maximum with photovoltaic consumption degree, maximum to the system congestion alleviation degree and the smallest integration objective of removal of load amount
The optimum topology state of network this moment.
2. according to claim 1 be based on the reconstitution model of high voltage distribution network, which is characterized in that the dynamic of the step S1
Load model includes electric car charge/discharge load simulation model, the integrated load model of electric car charge/discharge load, one
Number of units distributed model, the certain time power grid of section period access electric car access number of units model, the photovoltaic of electric car
Prediction power output model, the probability density function of photovoltaic active power of output and the phase of photovoltaic power generation system output power of generator
Prestige value.
3. according to claim 2 be based on the reconstitution model of high voltage distribution network, which is characterized in that the electric car fills/
Electric discharge load simulation model be
The integrated load model of the electric car charge/discharge load:
In formula, λEVFor the desired value for accessing electric car in the period, nEVFor the electric car number of units that may access power grid, if
The probability density function of known each period access system electric car number of units, can acquire period electric car charge/discharge power
Mean value, standard deviation and High order central moment；
The number of units distributed model of the certain time access electric car are as follows:
The number of units model of the certain time power grid access electric car are as follows:
In formula, n is electric car number of units total in system, μt0=1 electric car accesses the expectation event of electric system, σtFor mark
It is quasi- poor, refer to charge/discharge time range, enabling the Annual distribution range of charge/discharge is σt=2；If the expected time of charging is μt0
=1, refer to period 1, i.e. 00:00-01:00, discharge time μt0=13, refer to period 13, i.e. 12:00-13:00；Certain period connects
Enter power grid electric car number λEVFor nEVExpectation；
The prediction power output model of the photovoltaic generator:
In formula, basic functionFor power output of the photovoltaic in desired value, stochastic variable θ (t) indicates atmosphere to too
The inhibition that sunlight shines；
The probability density function of the photovoltaic active power of output are as follows:
In formula: Γ is Gamma function, and α, β are respectively the form parameter of beta distribution, PPVFor the output power of photovoltaic generator；For the peak power output of photovoltaic array.
The desired value of the photovoltaic power generation system output power are as follows:
4. according to claim 1 be based on the reconstitution model of high voltage distribution network, which is characterized in that the network of the step S2
Risk evaluation index NCRI is by GzNCharacterization:
In formula, α, beta, gamma is respectively the weight coefficient of whole obstruction with part obstruction and photovoltaic consumption degree,WithTable respectively
Show network over loading risk vector sum photovoltaic penetration degree vector；
Risk overloads vector
In formula, M is the total circuitry number of system,For column vector, giCharacterize i-th branch overload risk；
Branch overloads risk gm:
In formula, wXi, kFor probability right, a is integer,For the overload quantity of branch m；
Branch overload quantity
In formula, LmFor the ratio between branch m actual transmission power and power allowances, L0For set branch safety threshold, m=1,
2 ..., M；
Photovoltaic penetration degree vector
In formula, r is substation's number containing photo-voltaic power supply, ytCharacterize the consumption of t-th of substation photovoltaic containing photo-voltaic power supply
Degree；
In formula, PactPower, P are actually sent out for photo-voltaic power supplyplanPower is issued for plan.
5. according to claim 1 be based on the reconstitution model of high voltage distribution network, which is characterized in that the bilayer of the step S5
In optimization process up and down between optimization layer decision variable and state variable information transmitting the specific steps are
Step A1: initialization PSO algorithm parameter and particle initial position input network parameter, initialization sample and stochastic variable
Dimension m；
Step A2: by Nataf inverse transformation by the sample matrix transform in standard normal space be input variable in sample moment
Battle array X, in the optimal topology TP in upper layeriUnder state, using photovoltaic consumption as target, being determined property trend meter is arranged using the kth of matrix X
It calculates, determines the weight of load to find photovoltaic and dissolve optimal load combination ζi, and by optimum results with the shape of fitness function
Formula passes to upper layer topological optimization model；
Step A3: in the case where lower layer's photovoltaic dissolves optimal objective, model optimization target is turned to economic and environmental benefit maximum, to seek
Look for the topology status TP that network is optimali；
Step A4: more new particle history optimal location and population optimal location；
Step A5: perhaps reach maximum number of iterations convergence if judging whether to restrain or reach maximum number of iterations, enter
Step A6；If not restraining, return step A2；
Step A6: calculating NCRI whether in fiducial range, if exceeding fiducial range, otherwise return step A1 is entered step
A7；
Step A7: the output optimal topology status of network.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111105025A (en) * | 2019-12-06 | 2020-05-05 | 国网四川省电力公司电力科学研究院 | Urban high-voltage distribution network blocking management method based on data-driven heuristic optimization |
| CN112491037A (en) * | 2020-11-09 | 2021-03-12 | 四川大学 | Multi-target multi-stage dynamic reconstruction method and system for urban power distribution network |
| CN112688365A (en) * | 2020-12-26 | 2021-04-20 | 四川大川云能科技有限公司 | Power distribution network topology robust identification method based on mutual information-Bayesian network |
| CN115597872A (en) * | 2022-11-25 | 2023-01-13 | 南方电网调峰调频发电有限公司检修试验分公司（Cn） | Load shedding test method, device, equipment and medium for pumped storage unit |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106451429A (en) * | 2016-10-19 | 2017-02-22 | 合肥工业大学 | Power distribution network reconstruction method containing electric automobile network access based on game theory |
| CN106712037A (en) * | 2016-11-28 | 2017-05-24 | 武汉大学 | Electric power system static voltage stability assessment method considering electric automobile charging characteristic and load fluctuation limit |
| CN107808256A (en) * | 2017-11-20 | 2018-03-16 | 四川大学 | A kind of regional high voltage distribution network based on chance constrained programming turns supplier's method |
-
2018
- 2018-07-19 CN CN201810798107.3A patent/CN108964037B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106451429A (en) * | 2016-10-19 | 2017-02-22 | 合肥工业大学 | Power distribution network reconstruction method containing electric automobile network access based on game theory |
| CN106712037A (en) * | 2016-11-28 | 2017-05-24 | 武汉大学 | Electric power system static voltage stability assessment method considering electric automobile charging characteristic and load fluctuation limit |
| CN107808256A (en) * | 2017-11-20 | 2018-03-16 | 四川大学 | A kind of regional high voltage distribution network based on chance constrained programming turns supplier's method |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111105025A (en) * | 2019-12-06 | 2020-05-05 | 国网四川省电力公司电力科学研究院 | Urban high-voltage distribution network blocking management method based on data-driven heuristic optimization |
| CN112491037A (en) * | 2020-11-09 | 2021-03-12 | 四川大学 | Multi-target multi-stage dynamic reconstruction method and system for urban power distribution network |
| CN112688365A (en) * | 2020-12-26 | 2021-04-20 | 四川大川云能科技有限公司 | Power distribution network topology robust identification method based on mutual information-Bayesian network |
| CN112688365B (en) * | 2020-12-26 | 2023-07-04 | 四川大川云能科技有限公司 | Mutual information-Bayesian network-based power distribution network topology robust identification method |
| CN115597872A (en) * | 2022-11-25 | 2023-01-13 | 南方电网调峰调频发电有限公司检修试验分公司（Cn） | Load shedding test method, device, equipment and medium for pumped storage unit |
| CN115597872B (en) * | 2022-11-25 | 2023-03-07 | 南方电网调峰调频发电有限公司检修试验分公司 | Load shedding test method, device, equipment and medium for pumped storage unit |
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