CN106487025B - The distribution method saved for energy consumption - Google Patents
The distribution method saved for energy consumption Download PDFInfo
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- CN106487025B CN106487025B CN201610766551.8A CN201610766551A CN106487025B CN 106487025 B CN106487025 B CN 106487025B CN 201610766551 A CN201610766551 A CN 201610766551A CN 106487025 B CN106487025 B CN 106487025B
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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
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
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- 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
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
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- 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
- Y02E40/30—Reactive power compensation
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- General Engineering & Computer Science (AREA)
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- Supply And Distribution Of Alternating Current (AREA)
- Control Of Eletrric Generators (AREA)
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Abstract
The present invention provides a kind of distribution methods saved for energy consumption, comprising: reads distribution network data, distributed electrical source dates, wind speed probability parameter, determines decision variable and its feasible zone；The control parameter of population is set, and enabling the position of each particle is decision constrained vector；Decision constraint is reinforced using predefined factor, reduces the feasible zone of decision variable, then computed losses expectation and standard deviation；If current iteration number is more than preset maximum number of iterations, terminate distribution optimization process.The distribution method proposed by the present invention saved for energy consumption in the case where only obtaining the part probability parameter that wind-powered electricity generation is distributed, guarantees that route is not out-of-limit in each state constraint, and optimizes distribution network line loss simultaneously, realizes the promotion of performance driving economy.
Description
Technical field
The present invention relates to intelligent power distribution, in particular to a kind of distribution method saved for energy consumption.
Background technique
It is increasingly developed with intelligent power grid technology, countries in the world put into great effort research energy-saving distribution technology and plus
The dynamics of big new energy access power grid, purpose are exactly the consumption for reducing conventional energy resource and the discharge amount for reducing greenhouse gases,
This is of great immediate significance for energy-saving and emission-reduction.Power system optimal dispatch is one in Power System Analysis and control
Extremely important problem.Its main task is under conditions of guaranteeing that user power utilization demand and power system security are stablized, to pass through peace
Power operating mode is arranged, keeps the total power production cost of system minimum.However the instable energy this for wind-powered electricity generation, give power train
System Optimized Operation brings great challenge.Although the random optimization technology based on wind-powered electricity generation has been applied to wind-powered electricity generation electric system warp
In Ji scheduling, but these prior arts mainly obscure and probabilistic Modeling, have some limitations, from the point of view of actual effect
It is not ideal enough.
Summary of the invention
To solve the problems of above-mentioned prior art, the invention proposes a kind of distribution sides saved for energy consumption
Method, comprising:
Distribution network data, distributed electrical source dates, wind speed probability parameter are read, determines decision variable and its feasible zone；If
The control parameter for setting population, enabling the position of each particle is decision constrained vector；
Decision constraint is reinforced using predefined factor, reduces the feasible zone of decision variable, then the computed losses phase
Prestige and standard deviation；
If current iteration number is more than preset maximum number of iterations, terminate distribution optimization process.
Preferably, the target of the distribution method is set as following constrained optimization problem:
Min [Fobj+E (∑ τ ideci)+τ α max (α PL- σ Ploss/E (Ploss), 0)]
If hi > hi, max, then deci=hi-hi, max
If hi≤hi, min, then deci=hi, min-hi
Wherein hi is i-th and decision variable constrains related state variable, and hi, min and hi, max are respectively under hi
Limit and the upper limit；Deci is the deduction item of state variable related with i-th of state constraint；τ i is that i-th of state variable is out-of-limit
Deduction factor, τ α are the deduction factor of loss reduction property constraint；
Wherein objective function Fobj is loss expectation E (Ploss), and following cost depletions reduction constraint is arranged:
σPloss/E(Ploss)≤αPL
Wherein, σ Ploss is the standard deviation of loss distribution, and α PL is that cost reduces threshold value；
Also, decision variable includes the active power output of distributed power generation component, reactive compensation power, balance nodes voltage amplitude
Value；Wherein, active power output constrains are as follows:
PDDGi, min < PDDGi < PDDGi, max
PDDGi is active power output；PDDGi, min and PDDGi, max are the lower and upper limit of PDDGi respectively；
Reactive compensation amount is constrained to
QCi, min < QCi < QCi, max
QCi is reactive compensation amount；QCi, min and QCi, max are the lower and upper limit of QCi respectively；
The power output of wind-powered electricity generation and wind speed are closely related, give air speed value v, the active power output Pwi of wind-powered electricity generation with minor function by closing
System obtains:
Pwi=0, v<vci or v>vco
Pwi=Pw, ri (v-vci)/(vr-vci), vr >=v >=vci
Pwi=Pw, ri, v<vci, vco>=v>=vr
Wherein, vci and vco is respectively the incision wind speed and cut-out wind speed of Wind turbines, and vr is rated wind speed, and Pw, ri are
Maximum output.
Preferably, described that decision constraint is reinforced using predefined factor, the feasible zone of decision variable is reduced, into one
Step includes:
Use factor k reinforces decision constraint, specific as follows:
Hi, min+ (1-k) | hi, min |≤hi≤hi, max- (1-k) | hi, max |.
The present invention compared with prior art, has the advantage that
The distribution method proposed by the present invention saved for energy consumption, only the case where the part probability parameter of acquisition wind-powered electricity generation distribution
Under, guarantee that route is not out-of-limit in each state constraint, and optimize distribution network line loss simultaneously, realizes mentioning for performance driving economy
It rises.
Detailed description of the invention
Fig. 1 is the flow chart for the distribution method that the present invention is saved for energy consumption.
Specific embodiment
The detailed description to one or more embodiment of the invention is provided below.This hair is described in conjunction with such embodiment
It is bright, but the present invention is not limited to any embodiments.The scope of the present invention is limited only by the appended claims, and the present invention cover it is all
More substitutions, modification and equivalent.Illustrate many details in order to provide thorough understanding of the present invention in the following description.Out
These details are provided in exemplary purpose, and can also be according to power without some or all details in these details
Sharp claim realizes the present invention.
Power distribution network dispatching method of the invention, it is therefore an objective in the case where only obtaining the part probability parameter of wind-powered electricity generation distribution,
Guarantee that route is not out-of-limit in each state constraint, and optimize distribution network line loss simultaneously, realizes the promotion of performance driving economy.With
This guarantees that the ratio between loss standard difference and desired value is not too high, therefore can be to the drop of distribution performance driving economy simultaneously
Low property control effectively.
Model of the present invention is using the desired value of loss as objective function:
Fobj=E (Ploss)
Following cost depletions, which are arranged, reduces constraint:
σPloss/E(Ploss)≤αPL
Wherein, σ Ploss is the standard deviation of loss distribution, and α PL is that cost reduces threshold value.Be lost distribution standard deviation and
The ratio of desired value cannot be too high.
In power distribution network dispatching method of the invention, decision variable includes the active power output of distributed power generation component, idle benefit
Repay power, balance nodes voltage magnitude.Wherein, active power output constrains are as follows:
PDDGi, min < PDDGi < PDDGi, max
PDDGi is active power output；PDDGi, min and PDDGi, max are the lower and upper limit of PDDGi respectively.
Reactive compensation amount is constrained to
QCi, min < QCi < QCi, max
QCi is reactive compensation amount；QCi, min and QCi, max are the lower and upper limit of QCi respectively.
Balance nodes voltage is constrained to
Vsw, min < Vsw < Vsw, max
Vsw is the voltage magnitude of balance nodes；Vsw, min and Vsw, max are the lower and upper limit of Vsw respectively.
For trend constraint, the power flow equation used is specific as follows for the equality constraint of random schedule model:
Pini-Vi ∑ Vj (Gijcos δ ij+Bijsin δ ij)=0
Qini-Vi ∑ Vj (Gijcos δ ij-Bijsin δ ij)=0
Wherein, Pini and Qini is the active total power input and idle total power input of bus set interior nodes i respectively,
Transefer conductance of the Gij between node i and node j, transfer susceptance of the Bij between node i and node j, Vi and Vj are respectively
The voltage magnitude of node i and node j, phase difference of voltage of the δ ij between node i and j.
The power output of wind-powered electricity generation and wind speed are closely related, give air speed value v, the active power output Pwi of wind-powered electricity generation with minor function by closing
System obtains:
Pwi=0, v<vci or v>vco
Pwi=Pw, ri (v-vci)/(vr-vci), vr >=v >=vci
Pwi=Pw, ri, v<vci, vco>=v>=vr
Wherein, vci and vco is respectively the incision wind speed and cut-out wind speed of Wind turbines, and vr is rated wind speed, and Pw, ri are
Maximum output.
Random schedule model of the invention is substantially a constrained optimization mathematical problem.Using absolute value deduction function side
The above correlation formula is converted following equivalent model by method:
Min [Fobj+E (∑ τ ideci)+τ α max (α PL- σ Ploss/E (Ploss), 0)]
If hi > hi, max, then deci=hi-hi, max
If hi≤hi, min, then deci=hi, min-hi
Hi is to constrain related state variable with decision variable i-th, hi, min and hi, max be respectively hi lower limit and
The upper limit；Deci is the deduction item of state variable related with i-th of state constraint；τ i is the out-of-limit deduction of i-th of state variable
Factor, τ α are the deduction factor of loss reduction property constraint.
For the complex mathematical optimization problem of variable containing discrete optimization above and Filled function variable, the present invention uses particle
Group's algorithm is as Optimization Solution tool.Decision constraint is reinforced first with factor k, specific as follows:
Hi, min+ (1-k) | hi, min |≤hi≤hi, max- (1-k) | hi, max |
According to foregoing description, the present invention will combine the above-mentioned power distribution network random schedule problem that solves, tool by particle swarm algorithm
Body algorithm flow is as follows:
1. reading distribution network data, distributed electrical source dates, wind speed probability parameter, decision variable and its feasible zone are determined；
The control parameter of population is set, and enabling the position of each particle is decision constrained vector；
2. the position of each particle of random initializtion in decision variable feasible zone, and initialize the speed of particle；
3. constraining decision the formula reinforced according to above-mentioned use factor k, the feasible zone of decision variable is reduced, then
E (∑ τ ideci) is calculated according to two-point estimate algorithm, and the expected value and standard deviation of loss；
If terminating the optimization process of particle swarm algorithm 4. current iteration number is more than preset maximum number of iterations,
It exports E (∑ τ ideci)；Otherwise, 5 are entered step；
5. update global optimum position and personal best particle, the then inertia of more new particle kth time according to the following formula
Weight w k:
Wk=wmax- (wmax-wmin) × k/kmax；
Wmax and wmin is respectively the bound of wk, and kmax is the parameter for being associated with maximum number of iterations；
6. updating the number of iterations label, then return step 3.
It should be understood that above-mentioned specific embodiment of the invention is used only for exemplary illustration or explains of the invention
Principle, but not to limit the present invention.Therefore, that is done without departing from the spirit and scope of the present invention is any
Modification, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.In addition, appended claims purport of the present invention
Covering the whole variations fallen into attached claim scope and boundary or this range and the equivalent form on boundary and is repairing
Change example.
Claims (2)
1. a kind of distribution method saved for energy consumption, it is characterised in that:
Distribution network data, distributed electrical source dates, wind speed probability parameter are read, determines decision variable and its feasible zone；Grain is set
The control parameter of subgroup, enabling the position of each particle is decision constrained vector；
Using predefined factor to decision constraint reinforce, reduce the feasible zone of decision variable, then computed losses expectation and
Standard deviation；
If current iteration number is more than preset maximum number of iterations, terminate distribution optimization process；
Wherein, the target of the distribution method is set as following constrained optimization problem:
Min [Fobj+E (∑ τ ideci)+τ α max (α PL- σ Ploss/E (Ploss), 0)]
If hi > hi, max, then deci=hi-hi, max
If hi≤hi, min, then deci=hi, min-hi
Wherein hi is to constrain related state variable with decision variable i-th, hi, min and hi, max be respectively hi lower limit and
The upper limit；Deci is the deduction item of state variable related with i-th of state constraint；τ i is the out-of-limit deduction of i-th of state variable
Factor, τ α are the deduction factor of loss reduction property constraint；
Wherein objective function Fobj is loss expectation E (Ploss), and following cost depletions reduction constraint is arranged:
σPloss/E(Ploss)≤αPL
Wherein, σ Ploss is the standard deviation of loss distribution, and α PL is that cost reduces threshold value；
Also, decision variable includes the active power output of distributed power generation component, reactive compensation power, balance nodes voltage magnitude；
Wherein, active power output constrains are as follows:
PDDGi, min < PDDGi < PDDGi, max
PDDGi is active power output；PDDGi, min and PDDGi, max are the lower and upper limit of PDDGi respectively；
Reactive compensation amount is constrained to
QCi, min < QCi < QCi, max
QCi is reactive compensation amount；QCi, min and QCi, max are the lower and upper limit of QCi respectively；
The power output of wind-powered electricity generation and wind speed are closely related, give air speed value v, and the active power output Pwi of wind-powered electricity generation is obtained by following functional relation
:
Pwi=0, v<vci or v>vco
Pwi=Pw, ri (v-vci)/(vr-vci), vr >=v >=vci
Pwi=Pw, ri, v<vci, vco>=v>=vr
Wherein, vci and vco is respectively the incision wind speed and cut-out wind speed of Wind turbines, and vr is rated wind speed, and Pw, ri are maximum
Power output.
2. the method according to claim 1, wherein described add decision constraint using predefined factor
By force, the feasible zone for reducing decision variable further comprises:
Use factor k reinforces decision constraint, specific as follows:
Hi, min+ (1-k) | hi, min |≤hi≤hi, max- (1-k) | hi, max |.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103490433A (en) * | 2013-09-30 | 2014-01-01 | 国家电网公司 | Method for reactive power optimization of power distribution network |
| CN104392334A (en) * | 2014-12-12 | 2015-03-04 | 冶金自动化研究设计院 | Joint optimized scheduling method for multiple types of generating sets of self-supply power plant of iron and steel enterprise |
| CN105069704A (en) * | 2015-08-14 | 2015-11-18 | 中国电力科学研究院 | Quick traversing power distribution network reconstruction method for improving distributed power supply permeability |
| CN105117517A (en) * | 2015-07-28 | 2015-12-02 | 中国电力科学研究院 | Improved particle swarm algorithm based distribution network reconfiguration method |
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| JP5278414B2 (en) * | 2004-10-29 | 2013-09-04 | 東京電力株式会社 | Distributed power supply, distribution facility, and power supply method |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103490433A (en) * | 2013-09-30 | 2014-01-01 | 国家电网公司 | Method for reactive power optimization of power distribution network |
| CN104392334A (en) * | 2014-12-12 | 2015-03-04 | 冶金自动化研究设计院 | Joint optimized scheduling method for multiple types of generating sets of self-supply power plant of iron and steel enterprise |
| CN105117517A (en) * | 2015-07-28 | 2015-12-02 | 中国电力科学研究院 | Improved particle swarm algorithm based distribution network reconfiguration method |
| CN105069704A (en) * | 2015-08-14 | 2015-11-18 | 中国电力科学研究院 | Quick traversing power distribution network reconstruction method for improving distributed power supply permeability |
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