CN106485344A - Transmission facility data processing method - Google Patents
Transmission facility data processing method Download PDFInfo
- Publication number
- CN106485344A CN106485344A CN201610757791.1A CN201610757791A CN106485344A CN 106485344 A CN106485344 A CN 106485344A CN 201610757791 A CN201610757791 A CN 201610757791A CN 106485344 A CN106485344 A CN 106485344A
- Authority
- CN
- China
- Prior art keywords
- power
- node
- constraint
- particle
- active
- 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
- 230000005540 biological transmission Effects 0.000 title claims abstract description 9
- 238000003672 processing method Methods 0.000 title claims abstract description 9
- 239000002245 particle Substances 0.000 claims abstract description 21
- 238000009826 distribution Methods 0.000 claims abstract description 10
- 238000000034 method Methods 0.000 claims description 10
- 238000005457 optimization Methods 0.000 claims description 6
- 238000010248 power generation Methods 0.000 claims description 4
- 239000013256 coordination polymer Substances 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 3
- 239000004744 fabric Substances 0.000 claims description 2
- 230000005611 electricity Effects 0.000 abstract description 7
- 238000005516 engineering process Methods 0.000 description 5
- 238000010438 heat treatment Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000005315 distribution function Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000005431 greenhouse gas Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- 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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- General Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Biophysics (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Operations Research (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
- Quality & Reliability (AREA)
- Biomedical Technology (AREA)
- Entrepreneurship & Innovation (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention provides a kind of transmission facility data processing method, including：Smart electric grid system parameter is read, determines optimized variable and its feasible zone；The position for making each particle is optimized variable vector；The position of each particle of random initializtion and speed；For each particle, first against current scene, line loss is calculated by power flow algorithm；Judge whether meet the constraint, if meeting constraints above, loss value is i.e. as fitness value.Transmission facility data processing method proposed by the present invention, in the case of only obtaining the part probability parameter of wind-powered electricity generation distribution, it is ensured that circuit is not out-of-limit in each state constraint, and while the loss of Intelligent Optimal power network line, realizes the lifting of performance driving economy.
Description
Technical field
The present invention relates to intelligent power distribution, more particularly to a kind of transmission facility data processing method.
Background technology
Growing with intelligent power grid technology, countries in the world put into great effort research energy-saving distribution technology and plus
Big new forms of energy access the dynamics of electrical network, and its purpose is exactly the discharge capacity of the consumption and reduction greenhouse gases for reducing conventional energy resource,
This is of great immediate significance for energy-saving and emission-reduction.Power system optimal dispatch is in Power System Analysis and control
Very important problem.Under conditions of its main task is guarantee user power utilization demand and power system safety and stability, by peace
Row's power operating mode, makes the total power production cost of system minimum.But for this instable energy of wind-powered electricity generation, to power train
System Optimized Operation brings greatly challenge.Although wind-powered electricity generation power system warp has been applied to based on the random optimization technology of wind-powered electricity generation
In Ji scheduling, but these prior arts mainly fuzzy and probabilistic Modeling, have some limitations, from the point of view of actual effect
Not ideal enough.
Content of the invention
For solving the problems of above-mentioned prior art, the present invention proposes a kind of transmission facility data processing method,
Including：
Smart electric grid system parameter is read, determines optimized variable and its feasible zone；The simulation parameter of particle cluster algorithm is set,
The position for making each particle is optimized variable vector；
The position of each particle of random initializtion and speed in the optimized variable feasible zone；
Assessment fitness function, i.e., for each particle, first against current scene, calculate circuit by power flow algorithm and damage
Consumption；Then judge whether to meet the constraint of balance nodes active power and reactive power under given probability, if more than meeting about
Bundle, then loss value is i.e. as fitness value；Otherwise, deduction is carried out using the out-of-limit constraint of absolute value deduction function pair, and to subtract
Subitem and loss are used as fitness function；
If current iteration number of times exceedes default maximum iteration time, terminate the iterative optimization procedure of algorithm；Otherwise
Global and individual history optimal location is updated, then the speed of more new particle, final updating particle position；Update iterations mark
Note, then the step of iteration above-mentioned assessment fitness function.
Preferably, under the given probability balance nodes active power and reactive power constraint, further include：
By PsumAnd QsumIt is designated as active power summation and the reactive power summation of all node loads respectively, and meets normal state
Distribution：
The expectation of respectively node burden with power,The expectation of respectively node load or burden without work；
Given probability level λ, balance nodes active-power PswAnd reactive power QswAt least which is met about with probability level λ
Bundle condition：
Wherein FCPAnd FCQRespectively PsumAnd QsumCumulative probability density function, subscript-1Represent corresponding inverse function；PND iWith
QND iActive the exerting oneself of the respectively distributed wind-power generator unit of node i is exerted oneself with idle, PDD iAnd QDD iRespectively node i
Active the exerting oneself of distributed heating power generator unit is exerted oneself with idle；Active the exerting oneself of wherein wind power generation unit is exerted oneself completely with idle
Foot is constrained：
PND i,min≤PND i≤PND i,max
QND i,min≤QND i≤QND i,max
Preferably, in the calculating line loss according to power flow algorithm, the trend under current scene is constrained to：
Pin i-Vi∑Vj(Gijcosδij+Bijsinδij)=0
Qin i-Vi∑Vj(Gijcosδij-Bijsinδij)=0
Wherein, Pin iAnd Qin iIt is the active total power input of bus set interior nodes i and idle total power input respectively, Gij
For the transefer conductance between node i and node j, BijFor the transfer susceptance between node i and node j, ViAnd VjRespectively node i
With the voltage magnitude of node j, δijFor the phase difference of voltage between node i and j；
The constraint out-of-limit using absolute value deduction function pair carries out deduction, including calculating absolute value deduction function E (∑
τideci), it is specifically defined as
If hi＞ hi,min, then deci=hi-hi,max
If hi≤hi,min, then deci=hi,min-hi
hiFor i-th state variable relevant with optimized variable constraint, hi,minAnd hi,maxRespectively hiLower limit and the upper limit；
deciIt is the deduction item of the state variable relevant with i-th state constraint；τiFor the out-of-limit deduction factor of i-th state variable.
The present invention compared to existing technology, with advantages below：
Transmission facility data processing method proposed by the present invention, only obtains the situation of the part probability parameter of wind-powered electricity generation distribution
Under, it is ensured that circuit is not out-of-limit in each state constraint, and while the loss of Intelligent Optimal power network line, realizes carrying for performance driving economy
Rise.
Description of the drawings
Fig. 1 is the flow chart of transmission facility data processing method of the present invention.
Specific embodiment
The detailed description being provided below to one or more embodiment of the present invention.This is described in conjunction with such embodiment
Bright, but the invention is not restricted to any embodiment.The scope of the present invention is limited only by the appended claims, and the present invention cover all
Many replacements, modification and equivalent.Illustrate many details to provide thorough understanding of the present invention in the following description.Go out
Some in these details, and these details of nothing are provided in the purpose of example or all details can also be according to power
Sharp claim realizes the present invention.
The intelligent grid dispatching method of the present invention, the method ensure that node voltage amplitude, balance nodes active power
At least met with certain probability level with reactive power constraint, by not out-of-limit probability level, balance and take into account intelligent grid peace
Require of both full property and economy etc., therefore which has reasonable scalability.
By PND iAnd QND iActive the exerting oneself for being designated as the distributed wind-power generator unit of node i respectively is exerted oneself with idle, PDD iWith
QDD iActive the exerting oneself for being designated as the distributed heating power generator unit of node i respectively is exerted oneself with idle.PLD iAnd QLD iNode is designated as respectively
The active power and reactive power of i load.The active of wind power generation unit is exerted oneself and idle meet the constraint of exerting oneself：
PND i,min≤PND i≤PND i,max
QND i,min≤QND i≤QND i,max
Then balance nodes active-power PswAnd reactive power QswOut-of-limit constraint satisfaction：
∑(PLD i-PND i,min-PDD i)≤Psw,max
∑(QLD i-QND i,min-QDD i)≤Qsw,max
∑(PLD i-PND i,max-PDD i)≥Psw,min
∑(QLD i-QND i,max-QDD i)≥Psw,min
PsumAnd QsumIt is designated as active power summation and the reactive power summation of all node loads respectively, and meets normal state dividing
Cloth：
The expectation of respectively node burden with power,The expectation of respectively node load or burden without work；
Therefore, probability level λ, balance nodes active-power P are givenswAnd reactive power QswAt least met with probability level λ
Its constraints.
Wherein FCPAnd FCQRespectively PsumAnd QsumCumulative probability density function, subscript-1Represent corresponding inverse function.
By riAnd xiNode i and i-1 between the resistance value of distribution line and reactance value are designated as respectively；PLL iAnd QLL iRemember respectively
Active power and reactive power for distribution line between node i and i-1.
Calculate-the r in the range of j ∈ [1, n]j/xj；
Power factor adjustment angle when node k ∈ [j, n]When, node voltage amplitude and PND kJust
Correlation, now meets following constraint with the probability level λ for giving：
Wherein FLEForQLL kNormal state accumulated probability distribution function.
WhenWhen, node voltage amplitude and PND kNegative correlation, now meets following constraint：
For the distribution line between node i and node i -1, its distribution line active and reactive power expression formula is：
When circuit nonoverload, SiMaximum meet following constraint：
Intelligent grid dispatching method proposed by the present invention, based on aforementioned one or more constraints, wind-force in a distributed manner
It is optimization aim to generate electricity with line loss value during node load predicted value, effectively processes the load variations Liang He area of probabilistic type
Between type distributed wind-power generator variable quantity.
Trend under wind-power electricity generation and node load predicted value scene is constrained in a distributed manner：
Pin i-Vi∑Vj(Gijcosδij+Bijsinδij)=0
Qin i-Vi∑Vj(Gijcosδij-Bijsinδij)=0
Wherein, Pin iAnd Qin iIt is the active total power input of bus set interior nodes i and idle total power input respectively, Gij
For the transefer conductance between node i and node j, BijFor the transfer susceptance between node i and node j, ViAnd VjRespectively node i
With the voltage magnitude of node j, δijFor the phase difference of voltage between node i and j；
The scheduling model of the present invention is a non-linear mixed integer optimization problem, and therefore the present invention adopts particle cluster algorithm
Solved.Specific algorithm flow process is as follows：
Smart electric grid system data and Uncertainty parameter is read, determines optimized variable and its feasible zone.Population is set
The simulation parameter of algorithm, the position for making each particle are optimized variable vector；
The position of each particle of random initializtion and speed in the optimized variable feasible zone；
Assessment fitness function, including for each particle, first against current scene, calculating circuit by power flow algorithm
Loss；Then judge whether previously described one or more constraints meet.If above-mentioned constraint satisfaction is required, loss value is
For fitness value；Otherwise, using absolute value deduction function E (∑ τideci) deduction is carried out to out-of-limit constraint, it is specifically defined as
If hi＞ hi,min, then deci=hi-hi,max
If hi≤hi,min, then deci=hi,min-hi
hiFor i-th state variable relevant with optimized variable constraint, hi,minAnd hi,maxRespectively hiLower limit and the upper limit；
deciIt is the deduction item of the state variable relevant with i-th state constraint；τiFor the out-of-limit deduction factor of i-th state variable；
And using deduction item and loss as fitness function；
If current iteration number of times exceedes default maximum iteration time, terminate the iterative optimization procedure of algorithm；
Global and individual history optimal location is updated, then the speed of more new particle, final updating particle position；
Iterations mark is updated, then the step of iteration above-mentioned assessment fitness function.
It should be appreciated that the above-mentioned specific embodiment of the present invention is used only for exemplary illustration or explains the present invention's
Principle, and be not construed as limiting the invention.Therefore, that done in the case of without departing from the spirit and scope of the present invention is any
Modification, equivalent, improvement etc., should be included within the scope of the present invention.Additionally, claims purport of the present invention
In the whole changes that covers in the equivalents for falling into scope and border or this scope and border and repair
Change example.
Claims (3)
1. a kind of transmission facility data processing method, it is characterised in that：
Smart electric grid system parameter is read, determines optimized variable and its feasible zone；The simulation parameter of particle cluster algorithm is set, and order is every
The position of individual particle is optimized variable vector；
The position of each particle of random initializtion and speed in the optimized variable feasible zone；
Assessment fitness function, i.e., for each particle, first against current scene, calculate line loss by power flow algorithm；
Then judge whether to meet the constraint of balance nodes active power and reactive power under given probability, if meeting constraints above,
Then loss value is i.e. as fitness value；Otherwise, deduction is carried out using the out-of-limit constraint of absolute value deduction function pair, and with deduction item
With loss as fitness function；
If current iteration number of times exceedes default maximum iteration time, terminate the iterative optimization procedure of algorithm；Otherwise update
Global and individual history optimal location, the then speed of more new particle, final updating particle position；Iterations mark is updated,
Then the step of iteration above-mentioned assessment fitness function.
2. method according to claim 1, it is characterised in that balance nodes active power and idle under the given probability
The constraint of power, further includes：
By PsumAnd QsumIt is designated as active power summation and the reactive power summation of all node loads respectively, and meets normal state dividing
Cloth：
The expectation of respectively node burden with power,The expectation of respectively node load or burden without work；
Given probability level λ, balance nodes active-power PswAnd reactive power QswAt least its constraint bar is met with probability level λ
Part：
Wherein FCPAnd FCQRespectively PsumAnd QsumCumulative probability density function, subscript-1Represent corresponding inverse function；PND iAnd QND i
Active the exerting oneself of the respectively distributed wind-power generator unit of node i is exerted oneself with idle, PDD iAnd QDD iThe distribution of respectively node i
Active the exerting oneself of formula thermal power generation unit is exerted oneself with idle；Active the exerting oneself with idle of wherein wind power generation unit exerts oneself satisfaction about
Bundle：
PND i,min≤PND i≤PND i,max
QND i,min≤QND i≤QND i,max.
3. method according to claim 2, it is characterised in that described calculated in line loss, currently according to power flow algorithm
Trend under scene is constrained to：
Pin i-Vi∑Vj(Gijcosδij+Bijsinδij)=0
Qin i-Vi∑Vj(Gijcosδij-Bijsinδij)=0
Wherein, Pin iAnd Qin iIt is the active total power input of bus set interior nodes i and idle total power input respectively, GijFor section
Transefer conductance between point i and node j, BijFor the transfer susceptance between node i and node j, ViAnd VjRespectively node i and section
The voltage magnitude of point j, δijFor the phase difference of voltage between node i and j；
The constraint out-of-limit using absolute value deduction function pair carries out deduction, including calculating absolute value deduction function E (∑ τideci), it is specifically defined as
If hi＞ hi,min, then deci=hi-hi,max
If hi≤hi,min, then deci=hi,min-hi
hiFor i-th state variable relevant with optimized variable constraint, hi,minAnd hi,maxRespectively hiLower limit and the upper limit；deci
It is the deduction item of the state variable relevant with i-th state constraint；τiFor the out-of-limit deduction factor of i-th state variable.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610757791.1A CN106485344A (en) | 2016-08-29 | 2016-08-29 | Transmission facility data processing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610757791.1A CN106485344A (en) | 2016-08-29 | 2016-08-29 | Transmission facility data processing method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106485344A true CN106485344A (en) | 2017-03-08 |
Family
ID=58273259
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610757791.1A Pending CN106485344A (en) | 2016-08-29 | 2016-08-29 | Transmission facility data processing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106485344A (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102856918A (en) * | 2012-07-31 | 2013-01-02 | 上海交通大学 | Power distribution network reactive power optimization method based on ecological niche particle swarm algorithm |
| CN104659816A (en) * | 2015-03-13 | 2015-05-27 | 贵州电力试验研究院 | Improved particle swarm algorithm-based optimized configuration method of distributed electrical connection power distribution system |
-
2016
- 2016-08-29 CN CN201610757791.1A patent/CN106485344A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102856918A (en) * | 2012-07-31 | 2013-01-02 | 上海交通大学 | Power distribution network reactive power optimization method based on ecological niche particle swarm algorithm |
| CN104659816A (en) * | 2015-03-13 | 2015-05-27 | 贵州电力试验研究院 | Improved particle swarm algorithm-based optimized configuration method of distributed electrical connection power distribution system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Jayasekara et al. | Optimal operation of distributed energy storage systems to improve distribution network load and generation hosting capability | |
| Rajaram et al. | Power system reconfiguration in a radial distribution network for reducing losses and to improve voltage profile using modified plant growth simulation algorithm with Distributed Generation (DG) | |
| Zhang et al. | A fuzzy chance-constrained program for unit commitment problem considering demand response, electric vehicle and wind power | |
| Rajkumar et al. | Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy | |
| Syahputra et al. | Reconfiguration of distribution network with DG using fuzzy multi-objective method | |
| Shui et al. | A data-driven distributionally robust coordinated dispatch model for integrated power and heating systems considering wind power uncertainties | |
| Wu et al. | Source-network-storage joint planning considering energy storage systems and wind power integration | |
| CN106803130B (en) | Planning method for distributed power supply to be connected into power distribution network | |
| CN106600459A (en) | Optimization method for overcoming voltage deviation of photovoltaic access point | |
| Zhao et al. | Robust distributed coordination of parallel restored subsystems in wind power penetrated transmission system | |
| CN113378100B (en) | Power distribution network source network load storage collaborative optimization scheduling model and method considering carbon emission | |
| Liu et al. | Two-stage optimal economic scheduling for commercial building multi-energy system through internet of things | |
| CN104915788B (en) | A method of considering the Electrical Power System Dynamic economic load dispatching of windy field correlation | |
| CN116345466A (en) | Two-stage power flow optimization method of active power distribution network considering distribution network reconstruction | |
| Li et al. | Flexible scheduling of microgrid with uncertainties considering expectation and robustness | |
| CN108306346A (en) | A kind of distribution network var compensation power-economizing method | |
| Habib et al. | Optimized management of reactive power reserves of transmission grid-connected photovoltaic plants driven by an IoT solution | |
| CN105226649B (en) | One kind predicting improved provincial power network power generation dispatching optimization method based on bus load | |
| Saber et al. | Smart micro-grid optimization with controllable loads using particle swarm optimization | |
| Kumar et al. | Smart home energy management with integration of PV and storage facilities providing grid support | |
| Zhang et al. | Ensemble learning for optimal active power control of distributed energy resources and thermostatically controlled loads in an islanded microgrid | |
| CN116865270A (en) | Optimal scheduling method and system for flexible interconnection power distribution network containing embedded direct current | |
| CN107067122A (en) | Mass data dispatching method based on intelligent grid | |
| CN106485344A (en) | Transmission facility data processing method | |
| CN107046300B (en) | Power transmitting device data processing method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20170308 |