CN106779313A - Multiple target distributed power source addressing constant volume method based on mixed integer programming - Google Patents
Multiple target distributed power source addressing constant volume method based on mixed integer programming Download PDFInfo
- Publication number
- CN106779313A CN106779313A CN201611053304.XA CN201611053304A CN106779313A CN 106779313 A CN106779313 A CN 106779313A CN 201611053304 A CN201611053304 A CN 201611053304A CN 106779313 A CN106779313 A CN 106779313A
- Authority
- CN
- China
- Prior art keywords
- multiple target
- constant volume
- target
- pareto
- solution
- 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
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000012423 maintenance Methods 0.000 claims abstract description 8
- 230000002123 temporal effect Effects 0.000 claims abstract description 4
- 239000013598 vector Substances 0.000 claims description 10
- 238000006243 chemical reaction Methods 0.000 claims description 8
- 238000013507 mapping Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000000205 computational method Methods 0.000 abstract description 3
- 230000008878 coupling Effects 0.000 abstract description 2
- 238000010168 coupling process Methods 0.000 abstract description 2
- 238000005859 coupling reaction Methods 0.000 abstract description 2
- 230000005611 electricity Effects 0.000 description 5
- 238000009434 installation Methods 0.000 description 4
- 238000005457 optimization Methods 0.000 description 4
- 230000035699 permeability Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000002040 relaxant effect Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000012795 verification 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- Biodiversity & Conservation Biology (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Educational Administration (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The present invention relates to a kind of multiple target distributed power source addressing constant volume method based on mixed integer programming, based on DG temporal characteristicses, set up and consider DG investments, operation and maintenance cost, the multiple target DG addressing constant volume models of via net loss expense.For the Pareto front ends solution being evenly distributed, multi-objective problem is processed using orthogonal edges intersection point NBI.DG addressing constant volume plan models are converted into Second-order cone programming model for power flow equation and constraint Relaxation Strategy by being pushed back before use power distribution network, problem is solved using CVX platforms and GUROBI solution musical instruments used in a Buddhist or Taoist mass.SOCP models coupling orthogonal edges intersection point NBI methods solve power distribution network multiple objective programming and are equally applicable to other power distribution network relevant issues, can provide new thinking and can computational methods for distribution network planning, running optimizatin.
Description
Technical field
The present invention relates to a kind of distributed power supply management technology, more particularly to a kind of multiple target based on mixed integer programming
Distributed power source addressing constant volume method.
Background technology
Under the increasingly serious background of global energy crisis and environmental pollution, power supply (Distributed in a distributed manner
Generation, DG) it is that the renewable energy power generation of representative is increasingly taken seriously.DG can reduce the transimission power of circuit,
Reduce distribution system loss and improve quality of voltage；Improve the reliability of power distribution network.But the access of DG also brings one to power distribution network
The problem of series, such as harmonic pollution, the rising of access point voltage, system bidirectional trend, short circuit current are raised, three-phase imbalance, electricity
Pressure fluctuation etc., while the presence of these problems can also limit the access of DG.If DG on-positions and capacity can suitably be reduced
System network loss, improvement network reliability, quality of voltage；If but DG on-positions or capacity are unreasonable, can not only cause power distribution network to provide
Produce and leave unused, and the negative effect of some row can be brought.With the increase of DG permeabilities in power distribution network, DG is grid-connected to alleviating electricity
Power demand is nervous, improve energy resource structure etc. serves important function, but simultaneously also to including a series of power trains including planning
System optimization problem brings new challenge.Therefore, DG addressing constant volumes are studied using the method for science, to the warp of power distribution network
Ji, safe operation are significant.
The content of the invention
The present invention be directed to distributed power source development and with the problem for existing, it is proposed that one kind is based on mixed integer programming
Multiple target distributed power source addressing constant volume method, DG addressing constant volume model conversions are into Second-order cone programming model (Second-
Order Cone Programming, SOCP) solved, accelerated model solution can be carried out using ripe solution musical instruments used in a Buddhist or Taoist mass, obtain
Globally optimal solution.
The technical scheme is that：A kind of multiple target distributed power source addressing constant volume side based on mixed integer programming
Multiobjective programming models are converted into exercisable single goal Second-order cone programming model SOCP by method, specifically include following steps：
1) based on DG temporal characteristicses, set up and consider DG investments, operation and maintenance cost, the multiple target DG choosings of via net loss expense
Location constant volume model；
2) optimal solution is asked with single target minimum：By DG addressing constant volume model conversions into SOCP models, with reference to solution musical instruments used in a Buddhist or Taoist mass pair
Each target carries out accelerated model solution, obtains the optimal solution of each single target；
3) multiple target is processed using orthogonal edges intersection point NBI methods：It is feasible for one in Multiobjective Programming
Point x*, when a lifting for target must to sacrifice other targets as cost when, then claim point x*It is a Pareto optimal solution,
Mapping matrix Φ is formed by the mode of 1 Pareto optimal solutions correspondence multiple targetb, by mapping φb, each target of setting
Weight vectors βkIt is mapped in object space L, obtains Utopia's line, normal vector n on Utopia's linebBefore multiple target Pareto
End disaggregation constitutes the intercept D of the point of intersection of Pareto curved surfaceskWhen maximum, the point on corresponding Pareto curved surfaces is multiple target DG choosings
Location constant volume optimal solution.
The beneficial effects of the present invention are：Multiple target distributed power source addressing constant volume of the present invention based on mixed integer programming
Method, SOCP models coupling orthogonal edges intersection point NBI methods solve power distribution network multiple objective programming and are equally applicable to other power distribution network phases
Pass problem, can provide new thinking and can computational methods for distribution network planning, running optimizatin.
Brief description of the drawings
Fig. 1 is that NBI methods of the present invention solve schematic diagram；
Fig. 2 solves flow chart figure for the present invention；
Fig. 3 is the node example initial network figures of IEEE of the present invention 33；
Fig. 4 is WTG of the present invention, PVG, load typical case's day curve map；
Fig. 5 is the Pareto curve maps of present invention investment and via net loss.
Specific embodiment
The present invention is based on DG temporal characteristicses, sets up and considers DG investments, operation and maintenance cost, the multiple target of via net loss expense
DG addressing constant volume models.For the Pareto front ends solution being evenly distributed, using orthogonal edges intersection point (Normal
Boundary Intersection, NBI) multi-objective problem is processed.Pushed back for power flow equation before by using power distribution network
And DG addressing constant volume plan models are converted into Second-order cone programming model by constraint Relaxation Strategy, solved using CVX platforms and GUROBI
Musical instruments used in a Buddhist or Taoist mass is solved to problem.
The present invention is realized by following approach：
1) DG addressings constant volume is set up for Multiobjective programming models, and object function includes：Minimize total investment in project period,
Operation and maintenance cost and total grid loss in project period is minimized, it is specific as follows：
min f1=Cinv+Co+Cm+Cgrid (1)
min f2=Cploss (2)
DG investment costs
DG operating costs
DG maintenance costs
Power distribution network top-ranking's power supply power purchase expense
Via net loss expense
δy=1/ (1+r)y-1 (8)
Wherein：f1It is the total cost in project period, f2It is the total system via net loss in project period；ΨD, ΨjRespectively treat
Build DG set of types, the set of node to be selected of jth class DG；The unit DG quantity installed at k-th node is represented, if 0 represents
DG is not installed；It is the unit DG capacity at k-th node；Represent unit capacity jth class DG investment costs；For
Operating costs of the unit capacity jth class DG in t；It is the year maintenance cost of unit capacity jth class DG；Y is planning
Year, δyIt is the present value factor of y, r is discount rate；For typical day t purchases power from higher level's power network；It is typical case
Day t is from higher level's power network purchase electricity price；For typical day t grid is lost.
2) setting up constraints includes DG investment and recoveries and network operation security constraint, specific as follows：
First, DG investment and recoveries
Wherein, (9) are the constraint of node k unit capacity DG installation numbers,It is unit capacity DG at k-th node
Maximum allowable installation number；(10) it is the total installed capacity constraints of DG, i.e., DG permeabilities are less than β；ΦjFor jth class DG has installed section
Point set；To have installed the rated capacity of DG at g-th node；It is the load power at n-th node.
2nd, network operation security constraint
Wherein, Ψb, ΨnTo represent power distribution network branch road collection and set of node respectively；(11) for node voltage bound is constrained,
Vn,It is respectively node voltage, lower voltage limit and the upper limit；(12) for branch current is constrained, Iij,It is respectively
Branch road (i, j) electric current and the upper limit.
Formula (13-15) is to push back for power flow equation constraint before power distribution network, carries out solution to be converted to SOCP models and does standard
It is standby；It is node j loads active power and reactive power；At respectively node j DG active power outputs with
It is idle to exert oneself；For reactive-load compensation capacitor injects idle amount at node j.rij, xijResistance and reactance for branch road (i, j)；
Pij, QijFor the head end of branch road (i, j) is active and reactive power；Vi, VjIt is node i and j voltage magnitudes；Pjk, QjkFor branch road (j,
K) the active and reactive power of head end；Formula (16) is active power loss expression formula；Formula (17) is DG units limits,It is DG active power outputs lower limit and the upper limit at node j.Formula (18) is DG active reactive units limits,It is DG power-factor angles at node j.
3) by model conversion MIXED INTEGER Second-order cone programming model
SOCP problems are a Nonlinear Convex problems, and Feasible Solution Region is SOC, because SOCP problems are that polynomial time can be counted
Calculation problem, is applied in substantial amounts of practical problem.The present invention utilizes model conversion MIXED INTEGER Second-order cone programming model
CVX modeling tools bag and GUROBI solution musical instruments used in a Buddhist or Taoist mass are solved to model.
Needing can representative function into second order cone by Restriction condition treat.Introduce new variables Then constraint (13-16) can be exchanged into：
Increase new equality constraint simultaneously：
Formula (23) can be converted into following SOC forms by lax：
Wherein | | | |2It is euclideam norm.
Constraint (11-12) can be expressed as form using new variables：
4) Multi-Objective Electric Power Network Planning model is converted into single goal model solution using NBI methods.Pareto curves by
Pareto front ends disaggregation is constituted, and Pareto curves are constituted by the Pareto front ends disaggregation of two or more multi-objective problems.
It is under some constraintss, while considering the Electric Power Network Planning problem of two and two or more object function.Relative to single goal
Planning, multiple objective programming finds the global solution being optimal with season multiple target due to being difficult, and generally requires to object function
Processed, be a complex optimization problem.The present invention is used for the Pareto front ends solution being evenly distributed
NBI methods are processed multi-objective problem.
For a typical multi-objective optimization question：
Wherein, n is target number to be optimized；X is solution vector；fiX () is i-th object function to be optimized；h(x)
Equation and inequality constraints are respectively with g (x).
Generally, formula (27) individually solves x in the absence of one*, make all of object function fi(x*) while minimum.When and
Only when in the absence of such point x, for all of i=1 ..., n, there is fi(x)≤fi(x*), and at least one for it is strict not
Equation.I.e. when a lifting for target must to sacrifice other targets as cost when, then claim point x*It is that a Pareto is optimal
Solution.
For each Pareto optimal solution, a Pareto forward terminal is all correspond in object space.Obviously,
It is more uniform that Pareto forward terminals are distributed in object space, and the reference information provided to policymaker is also more comprehensive and accurate,
And NBI rules are a kind of better methods that generation is uniformly distributed Pareto front ends disaggregation.NBI methods are given birth to by the method for geometric projection
Into Pareto forward terminals, main solution procedure is divided into Coordinate Conversion, normal vector projection and intercept and optimizes three parts.
With two object function f1, f2As a example by, make the mapping matrix beWherein：x1Not examine
Consider object function f1, only consider object function f2When optimal solution；x2Not consider object function f2When, only consider object function f1
When optimal solution.
By mapping φb, the weight vectors β that will can be setkIt is mapped in object space L：
L (β)=Φbβk (28)
In formula, weight vectors βkMeetN is the quantity of Utopia's line decile.
It can be seen from definition according to formula (28), L1=[f1(x1),f2(x1)]TAnd L2=[f1(x2),f2(x2)]TBe respectively β=
[1,0]TWith β=[0,1]TUnder two points.Obviously, this 2 points is two end points in object space, the company between the two-end-point
Line is referred to as Utopia's line.Therefore the effect of Coordinate Conversion is exactly by weight relationship βkIt is mapped to point L (β) on Utopia's line
On.
In actual applications, different physical significances and dimension are there may be between different targets.To avoid in model
Solution procedure in produce numerical problem, different targets can be normalized.Two end points of Utopia's line after specification
L1b, L2bAs shown in formula (29).
The solution schematic diagram of NBI methods is as shown in Figure 1 after specification.According to Pareto optimality conditions, to make object function most
It is small, normal vector nbWith the intercept D of the point of intersection of Pareto curved surfaceskIt is inevitable maximum.Therefore the Multiobjective programming models that the present invention sets up turn
It is changed to as the intercept between the solution of formula (30) objective programming model, i.e. Utopia's line and Pareto curved surfaces is maximum.
H (x)=0, g (x)≤0
By formula (30), you can obtain given weight relationship βkUnder Pareto optimal solutions.NBI methods can not only be obtained
The Pareto front ends solution of even distribution, it is ensured that for multiple target SOCP problems, can be equally continuing with SOCP models carried out
Solve, specific solution flow is as shown in Figure 2.
Model and algorithm are verified using example in Fig. 3.DG types include wind-powered electricity generation (Wind Turbine
Generator, WTG), photovoltaic (Photovoltaic Generator, PVG) and miniature gas turbine (Micro Turbine
Generator, MTG), DG nodes to be installed, investment, operation and maintenance cost are as shown in table 1.DG and load typical case's day curve are as schemed
Shown in 4.
Power reference value takes 10MVA, and voltage reference value takes 12.66kV.Node voltage constraint perunit value be 0.95~
1.05p.u., branch power is constrained to 5MVA.DG permeabilities are constrained to 35%.Electricity price is for 0.06 ten thousand yuan/MWh discount rates
10%, planning is limited to 5 years in year.
Table 1
Such as table 1DG mount messages, to invest operating cost minimum (f1) it is the f of single goal1(x1) it is 6921.2 ten thousand yuan, together
When via net loss f2(x1) it is 145.4 ten thousand yuan；With via net loss expense minimum (f2) it is the f of single goal2(x2) it is 132.4 ten thousand yuan,
Operating cost f is invested simultaneously2(x1) it is 7831.8 ten thousand yuan.Corresponding decision variable x1And x2DG installation results as shown in table 2.
It is identical dimension because the multi-objective problem object function is all expense, is not normalized herein.Wu Tuo
Two end points of nation's line are (6921.2,145.4), (7831.8,132.4), its normal vector nbIt is (- 0.9714, -0.2376).
Utopia's line is carried out into 10 deciles, k-th Along ent is βk=[1-k/10, k/10]T.Being changed by object function to obtain
Following new object function：
H (x)=0, g (x)≤0
K takes 1 to 9, and solution formula is carried out to model using CVX, obtains the Pareto front ends disaggregation after specification as shown in table 3
Pareto front ends solve.
Table 2
Table 3
Table 3 gives totally 10 Pareto Along ents corresponding target function value.As seen from table, in the first half of table,
The increase of DG costs of investment can bring the reduction of via net loss；In the latter half of table, DG costs of investment are to reducing grid
The influence of loss diminishes, and this is, because the DG capacity of some node installations is more than load, to cause reversal tidal current.In DG investments
Above the economy of economy and system operation, the front end solution that table 3 is given can to planning personnel according to region actual conditions and
Expense preference of interest selects optimal DG capital projects.
In the Pareto front ends disaggregation point such as Fig. 5 that are obtained using NBI methods shown in " o ".As a comparison, many using NSGA-II
Target algorithm is solved to example, and algorithm population scale 20, maximum iteration is 200.In acquired results such as Fig. 5
Shown in " * " point.As seen from the figure, NBI methods can generate more equally distributed Pareto front ends solution, be obtained more than NSGA-II
Uniformly.
The present invention proposes power distribution network multiple target DG addressing constant volume plan models, and using being pushed back for trend side before power distribution network
Journey and constraint relaxing techniques have obtained the computable SOCP models of DG addressing constant volumes, and multi-objective problem is solved using NBI methods.Pass through
Example Verification is just like drawing a conclusion：DG addressing constant volume model conversions are solved into SOCP models, can be entered using ripe solution musical instruments used in a Buddhist or Taoist mass
Row accelerated model is solved, and obtains globally optimal solution.
With NSGA-II methods contrast the Pareto front ends solution for showing that NBI methods can be evenly distributed.SOCP models
Power distribution network multiple objective programming is solved with reference to NBI methods and be equally applicable to other power distribution network relevant issues, can be distribution network planning, fortune
Row optimization provides new thinking and can computational methods.
Claims (1)
1. a kind of multiple target distributed power source addressing constant volume method based on mixed integer programming, it is characterised in that by multiple target
Plan model is converted into exercisable single goal Second-order cone programming model SOCP, specifically includes following steps：
1）Based on DG temporal characteristicses, set up and consider DG investments, operation and maintenance cost, the multiple target DG addressings of via net loss expense are determined
Molar type；
2）Optimal solution is asked with single target minimum：By DG addressing constant volume model conversions into SOCP models, with reference to solution musical instruments used in a Buddhist or Taoist mass to each
Target carries out accelerated model solution, obtains the optimal solution of each single target；
3）Multiple target is processed using orthogonal edges intersection point NBI methods：For a feasible point in Multiobjective Programming,
When a lifting for target must to sacrifice other targets as cost when, then claim the pointIt is a Pareto optimal solution, by 1
The mode of individual Pareto optimal solutions correspondence multiple target forms mapping matrix Φ b , by mapping φ b , the power of each target of setting
Weight vectorβ k It is mapped to object spaceLIn, obtain Utopia's line, normal vector on Utopia's linen b Solved with multiple target Pareto front ends
Collection constitutes the intercept of the point of intersection of Pareto curved surfacesWhen maximum, the point on corresponding Pareto curved surfaces is multiple target DG addressings
Constant volume optimal solution.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611053304.XA CN106779313A (en) | 2016-11-25 | 2016-11-25 | Multiple target distributed power source addressing constant volume method based on mixed integer programming |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611053304.XA CN106779313A (en) | 2016-11-25 | 2016-11-25 | Multiple target distributed power source addressing constant volume method based on mixed integer programming |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106779313A true CN106779313A (en) | 2017-05-31 |
Family
ID=58912862
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611053304.XA Pending CN106779313A (en) | 2016-11-25 | 2016-11-25 | Multiple target distributed power source addressing constant volume method based on mixed integer programming |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106779313A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108258724A (en) * | 2018-01-22 | 2018-07-06 | 佛山科学技术学院 | A kind of wind power plant unit is laid out Multipurpose Optimal Method |
| CN108599235A (en) * | 2018-04-20 | 2018-09-28 | 国网湖北省电力有限公司宜昌供电公司 | A kind of constant volume method that distributed photovoltaic networks |
| CN108629499A (en) * | 2018-04-25 | 2018-10-09 | 国家电网公司 | A kind of power distribution network photovoltaic plant addressing constant volume method based on second order cone theazy |
| CN109255558A (en) * | 2018-10-31 | 2019-01-22 | 国家电网有限公司 | Site selection method and system for connecting heat accumulating type electric boiler to power distribution network |
| CN111598718A (en) * | 2020-04-03 | 2020-08-28 | 广东电网有限责任公司电力调度控制中心 | STATCOM configuration planning method, device and equipment |
| CN113762622A (en) * | 2021-09-09 | 2021-12-07 | 国网上海市电力公司 | Virtual power plant access point and capacity optimization planning method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120130556A1 (en) * | 2010-11-18 | 2012-05-24 | Marhoefer John J | Virtual power plant system and method incorporating renewal energy, storage and scalable value-based optimization |
| CN102983574A (en) * | 2012-09-27 | 2013-03-20 | 华南理工大学 | On-line weight-changeable control method for multipurpose mixed integer coordinating secondary voltage |
| CN103715701A (en) * | 2013-12-30 | 2014-04-09 | 国家电网公司 | Active power distribution network reactive power control method with limitation to number of operating times of capacitor taken into account |
| CN105719015A (en) * | 2016-01-19 | 2016-06-29 | 国网河北省电力公司电力科学研究院 | PEPSO-basedsiting and sizing method optimization method of distribution type power supply |
| CN105844348A (en) * | 2016-03-22 | 2016-08-10 | 国网宁夏电力公司石嘴山供电公司 | Distributed power supply optimization configuration method |
-
2016
- 2016-11-25 CN CN201611053304.XA patent/CN106779313A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120130556A1 (en) * | 2010-11-18 | 2012-05-24 | Marhoefer John J | Virtual power plant system and method incorporating renewal energy, storage and scalable value-based optimization |
| CN102983574A (en) * | 2012-09-27 | 2013-03-20 | 华南理工大学 | On-line weight-changeable control method for multipurpose mixed integer coordinating secondary voltage |
| CN103715701A (en) * | 2013-12-30 | 2014-04-09 | 国家电网公司 | Active power distribution network reactive power control method with limitation to number of operating times of capacitor taken into account |
| CN105719015A (en) * | 2016-01-19 | 2016-06-29 | 国网河北省电力公司电力科学研究院 | PEPSO-basedsiting and sizing method optimization method of distribution type power supply |
| CN105844348A (en) * | 2016-03-22 | 2016-08-10 | 国网宁夏电力公司石嘴山供电公司 | Distributed power supply optimization configuration method |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108258724A (en) * | 2018-01-22 | 2018-07-06 | 佛山科学技术学院 | A kind of wind power plant unit is laid out Multipurpose Optimal Method |
| CN108599235A (en) * | 2018-04-20 | 2018-09-28 | 国网湖北省电力有限公司宜昌供电公司 | A kind of constant volume method that distributed photovoltaic networks |
| CN108599235B (en) * | 2018-04-20 | 2021-03-09 | 国网湖北省电力有限公司宜昌供电公司 | Constant volume method for distributed photovoltaic network access |
| CN108629499A (en) * | 2018-04-25 | 2018-10-09 | 国家电网公司 | A kind of power distribution network photovoltaic plant addressing constant volume method based on second order cone theazy |
| CN109255558A (en) * | 2018-10-31 | 2019-01-22 | 国家电网有限公司 | Site selection method and system for connecting heat accumulating type electric boiler to power distribution network |
| CN109255558B (en) * | 2018-10-31 | 2021-08-17 | 国家电网有限公司 | Site selection method and system for connecting heat accumulating type electric boiler to power distribution network |
| CN111598718A (en) * | 2020-04-03 | 2020-08-28 | 广东电网有限责任公司电力调度控制中心 | STATCOM configuration planning method, device and equipment |
| CN111598718B (en) * | 2020-04-03 | 2021-11-30 | 广东电网有限责任公司电力调度控制中心 | STATCOM configuration planning method, device and equipment |
| CN113762622A (en) * | 2021-09-09 | 2021-12-07 | 国网上海市电力公司 | Virtual power plant access point and capacity optimization planning method |
| CN113762622B (en) * | 2021-09-09 | 2023-09-19 | 国网上海市电力公司 | Virtual power plant access point and capacity optimization planning method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Elbasuony et al. | A unified index for power quality evaluation in distributed generation systems | |
| CN106779313A (en) | Multiple target distributed power source addressing constant volume method based on mixed integer programming | |
| CN111027807B (en) | Distributed power generation site selection and volume determination method based on tide linearization | |
| CN106026113A (en) | Micro-grid system monitoring method having reactive automatic compensation function | |
| CN109659973B (en) | Distributed power supply planning method based on improved direct current power flow algorithm | |
| Sarrias-Mena et al. | Fuzzy logic based power management strategy of a multi-MW doubly-fed induction generator wind turbine with battery and ultracapacitor | |
| CN107947192A (en) | A kind of optimal reactive power allocation method of droop control type isolated island micro-capacitance sensor | |
| CN106130004B (en) | A kind of grid entry point new energy comprehensively considering stability characteristic (quality) receives the appraisal procedure of ability | |
| CN103593711B (en) | A kind of distributed power source Optimal Configuration Method | |
| CN109214713B (en) | Planning method for active power distribution network containing distributed power supply | |
| CN109038541A (en) | A kind of photovoltaic electric station grid connection electromagnetic transient modeling and Harmonic Evaluation Method | |
| Somefun et al. | Review of different methods for siting and sizing distributed generator | |
| CN103761574A (en) | Distributed power supply and region load matched feature matching method | |
| Yang et al. | A multi-period scheduling strategy for ADN considering the reactive power adjustment ability of DES | |
| CN105958530A (en) | Microgrid system with reactive power automatic compensation function | |
| Matevosyan et al. | Aggregated models of wind-based generation and active distribution network cells for power system studies-literature overview | |
| CN106786766A (en) | A kind of method for improving wind-powered electricity generation maximum grid connection capacity based on P2G technologies | |
| Zhang et al. | Day-ahead stochastic optimal dispatch of LCC-HVDC interconnected power system considering flexibility improvement measures of sending system | |
| Zhuo et al. | Optimal operation of hybrid AC/DC distribution network with high penetrated renewable energy | |
| Alnawafah et al. | Modeling and validation of jordanian power grid in DIgSILENT PowerFactory toward implementing a smart grid scenario | |
| Dan et al. | The research on the integration of energy storage and generating wind turbine system model | |
| Jin et al. | Coordinated voltage optimal control between active distribution network and microgrids considering the integration of PVs | |
| Liao et al. | Fast screening techniques and process for grid interconnection of wind-storage systems | |
| Tang et al. | A Distributionally Robust Energy Storage Planning Model for Wind Integrated Power System Based on Scenario Probability | |
| CN117424294B (en) | Efficient reactive power planning method and system for power distribution network |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20170531 |