CN108808693A - Power distribution network wattles power economic equivalent control method - Google Patents
Power distribution network wattles power economic equivalent control method 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
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in 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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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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
Abstract
The invention discloses a kind of power distribution network wattles power economic equivalent control methods.Traditional optimal reactive power dispatch Optimized model does not account for uncertain factor therein based on determining system condition.The present invention solves power distribution network wattles power economic equivalent control problem using moth flame optimization algorithm, by the control variable mappings from each moth to the load flow date, then obtains transmission loss by matpower software computational load flows；In each iteration, each moth update position obtains the transmission loss of corresponding moth relative to flame after updating position；Whether newer control variable will be examined beyond limitation, if control variable is beyond limitation, they will be marked on lower and upper limit, to obtain accurate result；3) above-mentioned evaluation process is repeated, is terminated until being limited by greatest iteration number.The present invention does not need many control parameters when solving optimal reactive power dispatch problem, and optimization method is simple and practicable.
Description
Technical field
The present invention relates to reactive power optimization of power system fields, specifically a kind of to be solved using moth flame optimization algorithm
The method of power distribution network wattles power economic equivalent control problem.
Background technology
In recent decades, since electric power has become the development foundation stone of modern economy, the research of electric system has become
More and more important project.Electric system is a kind of to be supplied to what the users such as industry, house and communications and transportation used generate electricity, is defeated
Electricity and distribution system；Moreover, electric system is also the core of renewable energy system.With the increase of electricity needs, resource
Consumption also will be increasing.And Optimal Reactive Power Dispatch of Power (optimal reactive powerdispatch, ORPD)
Problem refers to passing through regulator generator set end voltage, adjustment transformer in the case where power system reactive power power supply is more abundant
Tap no-load voltage ratio changes the measures such as output of reactive power compensator to adjust reactive power flow so that system voltage value is met the requirements, together
When keep the whole network active loss minimum, it can be seen that the optimal reactive power dispatch in electric system is to electric network reliability, safety and warp
Ji operation problem has significant impact, therefore optimal reactive power dispatch plays important work in the operation and control of electric system
With.Optimal reactive power dispatch is an important nonlinear optimal problem in electric system, it had both included discrete and continuous control
Variable, while meeting the constraint of equation and inequality again.The minimum value of object function (i.e. ORPD Optimized models) in order to obtain, institute
The optimization process to be established is exactly that acquisition includes generator bus voltage, load tap changer setting and reactive-load compensator size
The optimal combination of variable is controlled inside.
For the Reactive Power Optimazation Problem of conventional electric power system, what is be initially used widely is mathematic programming methods, including
The methods of linear programming, Non-Linear Programming, Dynamic Programming, interior point method.This kind of optimization method calculates rapidly, but it believes derivative
The demand of breath brings some inherent shortcomings to algorithm, if you need to want object function and constraints can it is micro-, be theoretically easily trapped into office
Portion's minimal point needs progress consolidation, error larger etc. handling discrete control variable.Intelligent optimization method is to solve idle work optimization
Problem provides another scheme.Common intelligent algorithm includes genetic algorithm, simulated annealing, TABU search calculation
The hybrid algorithm that method, particle swarm optimization algorithm and above-mentioned several method are mutually learnt from other's strong points to offset one's weaknesses.The common drawback of such methods is
Overlong time is calculated, the on-line operation of real system is not suitable for.
Traditional ORPD Optimized models do not account for uncertain factor therein based on determining system condition；And with big
Scale new energy concentrates access power grid, the intermittence and fluctuation of output power to make to must take into consideration not in the solutions of ORPD problems
Insignificant uncertain problem.In view of the background that the importance and new energy of OPRD problems rapidly develop, need pair
OPRD problems propose new solution.
Invention content
The technical problem to be solved by the present invention is to overcome the problems of the above-mentioned prior art, provide it is a kind of utilize moth
The method that flame optimization algorithm solves power distribution network wattles power economic equivalent control problem, need not be many when solving ORPD problems
Control parameter, keep optimization method simple and practicable.
For this purpose, the present invention adopts the following technical scheme that：Power distribution network wattles power economic equivalent control method utilizes moth fire
Flame optimization algorithm solves power distribution network wattles power economic equivalent control problem, and the moth flame optimization algorithm is the calculation based on population
The quantity (moth quantity) of number and search agent is arranged in method, and overall candidate solution indicates in the matrix form；Matrix column is control
The quantity of variable, row are the quantity of search agent；Moth flame optimization algorithm is asked in solution power distribution network wattles power economic equivalent control
Evaluation process in topic includes：
1) it by the control variable mappings from each moth to load flow date, is then calculated by matpower softwares
Load flow obtains transmission loss；In each iteration, each moth update position is relative to flame, after updating position,
Obtain the transmission loss of corresponding moth；Adaptive value of the solution based on matrix form is ranked up, best solution party
Case is by positioned at the top of matrix, and worst solution will be positioned at the lower part of matrix；
2) whether newer control variable will be examined beyond limitation, if control variable is beyond limitation, they will be marked
Note is in lower and upper limit, to obtain accurate result；
3) above-mentioned evaluation process is repeated, is terminated until being limited by greatest iteration number；
4) for second target function, using on above-mentioned identical moth flame optimization algorithm optimization order load bus
Voltage deviation.
As the supplement of above-mentioned technical proposal, the voltage amplitude of each loading bus is in the range of ± 10%.
As the supplement of above-mentioned technical proposal, (MFO) algorithm is optimized for moth flame, first key component is moth
Setting, indicated with following matrix：
Wherein d representation dimensions, the i.e. quantity of variable, n are the quantity of moth；It is assumed that moth is candidate solution, fly
The position of moth in space is exactly the parameter of problem；
Second key component is the set of flame, is indicated with matrix, and the matrix of moth is similar to, as follows：
Wherein, d and n is dimension and quantity respectively, since the dimension of above-mentioned two matrix is identical, it is therefore assumed that there are one numbers
The value that group stores corresponding fitness is as follows：
Wherein, n is the quantity of moth, knows that moth and flame can act as solution from foregoing description, and between them
Difference be processing and update their mode；Flame is the optimum position that moth obtains so far, and moth is then search
The actual search person of surrounding space；Target designation when flame is moth search search space, every moth find flame periphery,
And its position is updated, to find better result；It is best as a result, as previously mentioned, every that this mechanism helps moth not lose
The position of moth is updated according to flame, and flame passes through following formula and carries out mathematical simulation：
Mi=S (Mi,Fj),
MiAnd FjI-th of moth and j-th of flame are indicated respectively, and s is Spirallike Functions, expression formula below is logarithmic spiral
Function indicates the main update mechanism of moth：
S(Mi,Fj)=Di·ebt·cos(2πt)+Fj,
Wherein, b indicates the constant for defining logarithmic spiral wire shaped, and t is the random number in [- 1,1] section, DiIt indicates
J-th of flame calculates as follows at a distance from i-th of moth：
Di=| Fj-Mi|,
Wherein, FjRepresent j-th of flame, MiI-th of moth is represented, since a upper formula indicates that moth surrounds flame spiral
Flight, rather than only fly in space between them, realize the complete exploration of region of search；When next position is located at
When except the region between flame and moth, this block region will correspondingly be explored；On the other hand, when next position
When within the region between flame and moth, it can also be explored；To avoid being trapped in local optimum state, profit
Using a flame location of a formula makes each moth forcibly update its position, according to the adaptation in each iteration
Value, flame are classified and sort out, and then moth updates their position according to corresponding flame；Therefore, the quantity of flame
Can gradually decrease with iteration, reductions of flame quantity balances the exploitation and exploration of region of search, following equation expression about
The flame quantity of this phenomenon：
Wherein, N is the maximum quantity of flame, and l indicates that current iterations, T are maximum iterations.
As the supplement of above-mentioned technical proposal, the mathematical modeling of optimal reactive power dispatch model is as follows：
The object function of optimal reactive power dispatch problem is to make the transmission be under the conditions of meeting equality constraint and inequality constraints
The power attenuation and voltage deviation of system are minimum, and optimal reactive power dispatch problem is expressed as the minimum of function f (x, u), as described below：
Function f (x, u) is object function, in addition, g (x, u)=0 and h (x, u)≤0 are equality constraint and inequality respectively
Constraint；In optimal reactive power dispatch, equality constraint is power balance equation, and unequal constraint is then generator voltage, transformation
Device tap is arranged and the size of reactive-load compensator；X and u is dependent variable and control variable respectively.
As the supplement of above-mentioned technical proposal, the overall system transmission loss F on loading bus1With voltage deviation F2Respectively
It is expressed as：
Wherein, Nl indicates that the quantity of transmission line, Nd indicate the quantity of loading bus, PLossFor the loss of active power, Vi
It is the voltage on load bus, Vi ispIt is designated value (being typically set to 1.0p.u).
As the supplement of above-mentioned technical proposal, equality constraint is that the power of load is equal, that is, sends out power and consumption power
Difference be equal to power attenuation, equality constraint equation indicate it is as follows：
Wherein, ViAnd VjIt is the voltage of loaded line i and circuit j, B respectivelyijAnd GijIt is between circuit i and circuit j respectively
Susceptance and conductance, PGiAnd PDiIt is the active power and actual load demand of actual power, Q respectivelyGiAnd QDiIt is idle respectively
Power and load or burden without work demand, θijIndicate the phase angle difference of loaded line i and circuit j, j ∈ NiIndicate No. Σ after circuit j all with
Circuit i is connected, and the case where include j=i.
As the supplement of above-mentioned technical proposal, in inequality constraints condition, the limitation of generator is as follows：The production of busbar voltage
Raw and active power and reactive power generation is limited as follows by its boundary：
NGIt is the number of generator, PGi、QGi、VGiActive power, reactive power and the line electricity of actual power are indicated respectively
Pressure,Respectively PGiMaximum value and minimum value；Respectively QGiMaximum value and minimum value；Respectively VGiMaximum value and minimum value.
As the supplement of above-mentioned technical proposal, in inequality constraints condition, transformer tapping ratio TiIn its minimum value Ti min
And maximum of Ti maxIn boundary, as follows：
Ti min≤Ti≤Ti max, i=1 ..., NT,
Wherein, NTIt is the number of transformer.
As the supplement of above-mentioned technical proposal, in inequality constraints condition, reactive-load compensator size QCiRestricted range
It is as follows：
Wherein, NCIt is the quantity of reactive-load compensator,For QCiMaximum value,For QCiMinimum value.
The present invention proposes to optimize (MFO) algorithm using nature as the heuristic technique of inspiration-moth flame using a kind of to solve
Certainly ORPD problems, compared with other methods, which has the following advantages that in solving optimization problem：
1.MFO algorithms are optimized using one group of moth, and each moth needs the position for updating them relative to flame；
This helps avoid local trap, improves the exploring ability of search space.
2. moth always can update the position of oneself according to the most promising flame of newest acquisition, this flame will be by
Best solution is considered, then for instructing the action of moth, to help to obtain best as a result, this makes becoming for MFO
The same sex is guaranteed.
3. since MFO algorithms do not need many control parameters when solving ORPD problems, implement simple easy
Row.
Description of the drawings
Fig. 1 is power distribution network wattles power economic equivalent control flow chart of the present invention；
Fig. 2 is main estimation flow figure of the MFO algorithms of the present invention in solving ORPD problems.
Specific implementation mode
The invention will be further described with specific implementation mode with reference to the accompanying drawings of the specification.
The overall flow figure of the present invention is asked as shown in Figure 1, solving OPTIMAL REACTIVE POWER power dispatching using the method for moth flame
Topic.
1.OPRD mathematical modelings
1.1 object function
In the present invention, the object function of OPRD problems is to make biography under the conditions of meeting equality constraint and inequality constraints
The power attenuation and voltage deviation of defeated system are minimum.OPRD problems can be expressed as the minimum of function f (x, u), as described below：
Function f (x, u) is object function.In addition, g (x, u)=0 and h (x, u)≤0 are equality constraint and inequality respectively
Constraint.In OPRD, equality constraint is power balance equation, and unequal constraint is then generator voltage, load tap changer
It is arranged and the size of reactive-load compensator.X and u is dependent variable and control variable respectively.As previously mentioned, an object of the present invention is
It reduces the total transmission loss of system to the greatest extent, actually namely reduces economic loss.Another object function, is reduced to the maximum extent
Voltage deviation, and improve the stability of whole system.Overall system transmission loss F on loading bus1With voltage deviation F2Respectively
It is expressed as formula [2] and formula [3]：
Wherein, PLossFor the loss of active power, Nl indicates that the quantity of transmission line, Nd indicate the quantity of loading bus.Vi
It is the voltage on load bus, Vi ispIt is designated value (being typically set to 1.0p.u).
1.2 equality constraint
Equality constraint is that the power of load is equal, that is, sends out power with the difference for consuming power and be equal to power attenuation.Equation
Constraint equation can indicate as follows：
Wherein, ViAnd VjIt is the voltage of loaded line i and circuit j, B respectivelyijAnd GijIt is between circuit i and circuit j respectively
Susceptance and conductance, PGiAnd PDiIt is the active power and actual load demand of actual power respectively, and QGiAnd QDiIt is nothing respectively
Work(power and load or burden without work demand.
The constraint of 1.3 inequality
The limitation of generator：The generation of busbar voltage and the generation of active power and reactive power must be by its boundaries
Limitation is as follows：
Wherein, NGIt is the number of generator.
Transformer tapping ratio must be in its minimum value and maximum value boundary, as follows：
Wherein, NTIt is the number of transformer.
The range of the size-constrained system of reactive-load compensator is as follows：
Wherein, NCIt is the quantity of reactive-load compensator.
The operation of objective function is realized with MATPOWER software packages in the present invention.By using this software package
Operation Load Flow Program can ensure to obtain the result of accurate operation.
2. moth flame optimizer (MFO)
Moth flame optimization (MFO) algorithm is initially developed by Seyedaliirjalili, with other well-known optimizations
Technology compares, and has the advantage of oneself.In nature, moth is the insect of very close butterfly family.In their all one's life
In, they substantially experienced two main milestones, and larval phase is evolving to the manhood.The inspiration of this algorithm is that moth exists
Night unique airmanship.A kind of mechanism being known as horizontal flight, this flight side have been used when moth walks in the dark
Method depends on moonlight, they keep the position of oneself by the angle being kept fixed with light source.In real life, the big portion of moth
Divide all is to be attracted by the artificial light sources of lamp etc, and fly around the route of light source spirally, and they are intended to keep
Similar angle relative to artificial light sources flies.However, the behavior of this spiral flight path of moth is for them
It is fatal, because compared with the moon, light source is closer also more dangerous.
The mathematical formulae of 2.1MFO
For MFO models, first key component is the setting of moth, and matrix that can be following indicates：
Wherein, d representation dimensions (quantity of variable), n are the quantity of moth.It is assumed that moth is candidate solution, fly
The position of moth in space is exactly the parameter of problem.Second key component is the set of flame, can be indicated with matrix, similar
In the matrix of moth, F is as follows：
Wherein, d and n is dimension and quantity respectively.Since the dimension of equation (11) and (12) is identical, it is therefore assumed that having
The value of one corresponding fitness of storage of array is as follows：
Wherein, n is the quantity of moth.Moth and flame all can serve as solution as can be seen from the above description.And they
Between difference be processing and update their mode.Flame is the optimum position that moth obtains so far, and moth is then
Search for the actual search person of surrounding space.Target designation when flame is moth search search space, every moth find flame
Around, and its position is updated, to find better result.This mechanism helps moth not lose best result.Such as preceding institute
It states, the position of every moth is updated according to flame, and flame can carry out mathematical simulation by following formula：
Mi=S (Mi,Fj) (15)
MiAnd FjI-th of moth and j-th of flame are indicated respectively, and s is Spirallike Functions.Following expression formula is logarithmic spiral
Function indicates the main update mechanism of moth：
S(Mi,Fj)=Di·ebt·cos(2πt)+Fj (16)
Wherein b indicates the constant for defining logarithmic spiral wire shaped, and t is the random number in [- 1,1] section, DiIt indicates
J-th of flame calculates as follows at a distance from i-th of moth：
Di=| Fj-Mi| (17)
FjRepresent j-th of flame, MiRepresent i-th of moth.Due to indicating that moth surrounds flame vrille in equation (16),
Rather than only fly in space between them, it ensure that realizing the complete exploration of region of search.When next position
When except the region between flame and moth, this block region will correspondingly be explored.On the other hand, when next
When position is within the region between flame and moth, it can also be explored.It is local best in order to avoid being trapped in
State makes each moth forcibly update its position using a flame location of formula (16).According in each iteration
Adaptive value, flame are classified and sort out, and then moth updates their position according to corresponding flame.Therefore, flame
Quantity can be gradually decreased with iteration.The reduction of flame quantity balances the exploitation and exploration of region of search, and following equation indicates
Flame quantity about this phenomenon：
Wherein, N is the maximum quantity of flame, and l indicates that current iterations, T are maximum iterations.
2.2MFO is used in ORPD problems
MFO looks for the optimal value of control variable, makes object function minimum while meeting equality constraint and inequality constraints
Change.
First, MFO algorithms are the algorithms based on population, need the quantity (moth quantity) that number and search agent is arranged.
Overall candidate solution indicates that matrix column is the quantity for controlling variable in equation (11), and row is search agent in the matrix form
Quantity.In evaluation process, the position of each moth is mapped in flow data.Then Load Flow Program is executed to be transmitted
Loss.In each iteration, each moth updates position relative to flame, such as equation (12)-(14).After updating position,
Obtain the transmission loss of corresponding moth.The solution is that the adaptive value based on matrix form is ranked up.Best solution
Certainly scheme is by positioned at the top of matrix, and worst solution will be positioned at the lower part of matrix.Then, newer control variable will
It whether is examined beyond limitation.If controlling variable beyond limitation, they will be marked on lower and upper limit, accurate to obtain
As a result.Then emulation continues to assess MFO processes (formula (11)-(18)).Evaluation process repeats, and is limited until by greatest iteration number
It terminates.For second target function, optimize the voltage deviation on load bus using identical MFO steps.In addition, each
The voltage amplitude of loading bus must be in the range of ± 10%.
Main evaluation process of the MFO algorithms in solving ORPD problems is as shown in Figure 2.
The above described is only a preferred embodiment of the present invention, being not intended to limit the present invention in any form.It is all
It is that this is each fallen within to any simple modification, equivalent change and modification made by above example according to the technical essence of the invention
In the protection domain of invention.
Claims (9)
1. power distribution network wattles power economic equivalent control method, which is characterized in that using moth flame optimization algorithm solve power distribution network without
Work(power optimization control problem, the moth flame optimization algorithm is the algorithm based on population, and number and search agent is arranged
Quantity, overall candidate solution indicates in the matrix form；Matrix column is the quantity for controlling variable, and row is the quantity of search agent；
Evaluation process of the moth flame optimization algorithm in solving power distribution network wattles power economic equivalent control problem include：
1) by the control variable mappings from each moth to load flow date, then pass through matpower software computational loads
Flow obtains transmission loss；In each iteration, each moth updates position relative to flame, after updating position, obtains
The transmission loss of corresponding moth；Adaptive value of the solution based on matrix form is ranked up, and best solution will
Positioned at the top of matrix, and worst solution will be positioned at the lower part of matrix；
2) whether newer control variable will be examined beyond limitation, if control variable is beyond limitation, they will be marked on
Lower and upper limit, to obtain accurate result；
3) above-mentioned evaluation process is repeated, is terminated until being limited by greatest iteration number；
4) for second target function, the electricity on above-mentioned identical moth flame optimization algorithm optimization order load bus is used
Press deviation.
2. power distribution network wattles power economic equivalent control method according to claim 1, which is characterized in that each loading bus
Voltage amplitude is in the range of ± 10%.
3. power distribution network wattles power economic equivalent control method according to claim 1 or 2, which is characterized in that for moth fire
Flame optimization algorithm, first key component are the settings of moth, are indicated with following matrix：
Wherein d representation dimensions, the i.e. quantity of variable, n are the quantity of moth；It is assumed that moth is candidate solution, moth exists
Position in space is exactly the parameter of problem；
Second key component is the set of flame, is indicated with matrix, and the matrix of moth is similar to, as follows：
Wherein, d and n is dimension and quantity respectively, since the dimension of above-mentioned two matrix is identical, it is therefore assumed that being deposited there are one array
The value for storing up corresponding fitness is as follows：
Wherein, n is the quantity of moth, knows that moth and flame can act as solution from foregoing description, and the difference between them
Different is to handle and update their mode；Flame is the optimum position that moth obtains so far, and moth is then around search
The actual search person in space；Target designation when flame is moth search search space, every moth find flame periphery, and more
Its new position, to find better result；This mechanism helps moth not lose best as a result, as previously mentioned, every flies
The position of moth is updated according to flame, and flame carries out mathematical simulation by following formula：
Mi=S (Mi,Fj),
MiAnd FjI-th of moth and j-th of flame are indicated respectively, and s is Spirallike Functions, expression formula below is logarithmic spiral function,
Indicate the main update mechanism of moth：
S(Mi,Fj)=Di·ebt·cos(2πt)+Fj,
Wherein, b indicates the constant for defining logarithmic spiral wire shaped, and t is the random number in [- 1,1] section, DiIt indicates j-th
Flame calculates as follows at a distance from i-th of moth：
Di=| Fj-Mi|,
Wherein, FjRepresent j-th of flame, MiI-th of moth is represented, since a upper formula indicates that moth surrounds flame vrille,
Rather than only fly in space between them, realize the complete exploration of region of search；When next position is located at flame
When except the region between moth, this block region will correspondingly be explored；On the other hand, when next position is located at
When within the region between flame and moth, it can also be explored；To avoid being trapped in local optimum state, in utilization
One flame location of one formula makes each moth forcibly update its position, according to the adaptive value in each iteration, fire
Flame is classified and sorts out, and then moth updates their position according to corresponding flame；Therefore, the quantity of flame can be with
Iteration gradually decreases, and the reduction of flame quantity balances the exploitation and exploration of region of search, and following equation is indicated about this existing
The flame quantity of elephant：
Wherein, N is the maximum quantity of flame, and l indicates that current iterations, T are maximum iterations.
4. power distribution network wattles power economic equivalent control method according to claim 1 or 2, which is characterized in that idle work optimization tune
The mathematical modeling for spending model is as follows：
The object function of optimal reactive power dispatch problem is to make Transmission system under the conditions of meeting equality constraint and inequality constraints
Power attenuation and voltage deviation are minimum, and optimal reactive power dispatch problem is expressed as the minimum of function f (x, u), as described below：
Function f (x, u) is object function, in addition, g (x, u)=0 and h (x, u)≤0 are equality constraint and inequality constraints respectively；
In optimal reactive power dispatch, equality constraint is power balance equation, and unequal constraint is then generator voltage, transformer tap
The size of head setting and reactive-load compensator；X and u is dependent variable and control variable respectively.
5. power distribution network wattles power economic equivalent control method according to claim 4, which is characterized in that total on loading bus
System system transmission loss F1With voltage deviation F2It is expressed as：
Wherein, Nl indicates that the quantity of transmission line, Nd indicate the quantity of loading bus, PLossIndicate the loss of active power, ViIt is
Voltage on load bus, Vi ispIt is designated value.
6. power distribution network wattles power economic equivalent control method according to claim 4, which is characterized in that equality constraint loads
Power it is equal, that is, send out power with consume power difference be equal to power attenuation, equality constraint equation indicate it is as follows：
Wherein, ViAnd VjIt is the voltage of loaded line i and circuit j, B respectivelyijAnd GijIt is the electricity between circuit i and circuit j respectively
It receives and conductance, PGiAnd PDiIt is the active power and actual load demand of actual power, Q respectivelyGiAnd QDiIt is reactive power respectively
With load or burden without work demand, θijIndicate the phase angle difference of loaded line i and circuit j, j ∈ NiIndicate No. Σ after circuit j all with circuit i
It is connected, and the case where include j=i.
7. power distribution network wattles power economic equivalent control method according to claim 4, which is characterized in that inequality constraints condition
In, the limitation of generator is as follows：The generation of busbar voltage and the generation of active power and reactive power are limited by its boundary
It is as follows：
Wherein, NGIt is the number of generator, PGi、QGi、VGiActive power, reactive power and the line electricity of actual power are indicated respectively
Pressure,Respectively PGiMaximum value and minimum value；Respectively QGiMaximum value and minimum value；Respectively VGiMaximum value and minimum value.
8. power distribution network wattles power economic equivalent control method according to claim 4, which is characterized in that inequality constraints condition
In, transformer tapping ratio TiIn its minimum value Ti minAnd maximum of Ti maxIn boundary, as follows：
Ti min≤Ti≤Ti max, i=1 ..., NT,
Wherein, NTIt is the number of transformer.
9. power distribution network wattles power economic equivalent control method according to claim 4, which is characterized in that inequality constraints condition
In, reactive-load compensator size QCiRestricted range is as follows：
Wherein, NCIt is the quantity of reactive-load compensator,For QCiMaximum value,For QCiMinimum value.
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| CN109670650A (en) * | 2018-12-27 | 2019-04-23 | 华中科技大学 | The method for solving of Cascade Reservoirs scheduling model based on multi-objective optimization algorithm |
| CN112580198A (en) * | 2020-12-03 | 2021-03-30 | 国网山西省电力公司晋城供电公司 | Improved optimization classification method for transformer state evaluation |
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| CN109670650B (en) * | 2018-12-27 | 2020-08-04 | 华中科技大学 | Multi-objective optimization algorithm-based solving method for cascade reservoir group scheduling model |
| CN112580198A (en) * | 2020-12-03 | 2021-03-30 | 国网山西省电力公司晋城供电公司 | Improved optimization classification method for transformer state evaluation |
| CN112966360A (en) * | 2021-04-06 | 2021-06-15 | 国网辽宁省电力有限公司经济技术研究院 | Joint planning method for distributed power supply and electric vehicle charging station |
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| CN113312780A (en) * | 2021-06-07 | 2021-08-27 | 杭州市电力设计院有限公司余杭分公司 | Energy storage station planning method, device and equipment |
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